title: Multilingual Sentiment and Subjectivity Analysis

abstract: Subjectivity and sentiment analysis focuses on the automatic identification of private states, such as opinions, emotions, sentiments, evaluations, beliefs, and speculations in natural language. While subjectivity classification labels text as either subjective or objective, sentiment classification adds an additional level of granularity, by further classifying subjective text as either positive, negative or neutral. To date, a large number of text processing applications have already used techniques for automatic sentiment and subjectivity analysis, including automatic expressive text-to-speech synthesis [1], tracking sentiment timelines in on-line forums and news [22, 2], and mining opinions from product reviews [11]. In many natural language processing tasks, subjectivity and sentiment classification have been used as a first phase filtering to generate more viable data. Research that benefitted from this additional layering ranges from question answering [48], to conversation summarization [7] and text semantic analysis [41, 8]. Much of the research work to date on sentiment and subjectivity analysis has been applied to English, but work on other languages is growing, including Japanese [19, 34, 35, 15], Chinese [12, 49], German [18], and Romanian [23, 4]. In addition, several participants in the Chinese and Japanese Opinion Extraction tasks of NTCIR-6 [17] performed subjectivity and sentiment analysis in languages other than English.1 As only 29.4% of Internet users speak English,2 the construction of resources and tools for subjectivity and sentiment analysis in languages other than English is a growing need. In this chapter, we review the main directions of research focusing on the development of resources and tools for multilingual subjectivity and sentiment analysis. Specifically, we identify and overview three main categories of methods: (1) those focusing on word and phrase level annotations, overviewed in Section 4; (2) methods targeting the labeling of sentences, described in Section 5; and finally (3) methods for document-level annotations, presented in Section 6. We address both multilingual and cross-lingual methods. For multilingual methods, we review work concerned with languages other than English, where the resources and tools have been specifically developed for a given target language. In this category, in Section 3 we also briefly overview the main directions of work on English data, highlighting the methods that can be easily


title: Towards autonomous collision avoidance in surgical robots using image segmentation and genetic algorithms

abstract: Surgical robots provide various advantages, like improved precision and dexterity, over conventional surgical procedures and have been extensively studied in the last decade. However, safety is still a major concern mainly due to the unreliable or no haptic feedback to the surgeon and the shaky movements (tremors) in the surgeons hands. These issues may result in unwanted collisions between the robotic arm tips and the soft tissues of the human body or between the robotic arm tips inside the human body causing non-repairable damage. In this paper, we propose to overcome this problem by using image segmentation techniques, on the real-time video of the robotic surgery, along with genetic algorithms to avoid possible collisions. The main idea is to introduce an automatic collision avoidance mechanism upon detecting a threat for a possible collision. For illustration, some preliminary results on using image segmentation on a video of the cholecystectomy surgical procedure are presented.


title: Framewise phoneme classification with bidirectional LSTM networks

abstract: In this paper, we apply bidirectional training to a long short term memory (LSTM) network for the first time. We also present a modified, full gradient version of the LSTM learning algorithm. We discuss the significance of framewise phoneme classification to continuous speech recognition, and the validity of using bidirectional networks for online causal tasks. On the TIMIT speech database, we measure the framewise phoneme classification scores of bidirectional and unidirectional variants of both LSTM and conventional recurrent neural networks (RNNs). We find that bidirectional LSTM outperforms both RNNs and unidirectional LSTM.


title: Occlusion-Aware Real-Time Object Tracking

abstract: The online learning methods are popular for visual tracking because of their robust performance for most video sequences. However, the drifting problem caused by noisy updates is still a challenge for most highly adaptive online classifiers. In visual tracking, target object appearance variation, such as deformation and long-term occlusion, easily causes noisy updates. To overcome this problem, a new real-time occlusion-aware visual tracking algorithm is introduced. First, we learn a novel two-stage classifier with circulant structure with kernel, named integrated circulant structure kernels (ICSK). The first stage is applied for transition estimation and the second is used for scale estimation. The circulant structure makes our algorithm realize fast learning and detection. Then, the ICSK is used to detect the target without occlusion and build a classifier pool to save these classifiers with noisy updates. When the target is in heavy occlusion or after long-term occlusion, we redetect it using an optimal classifier selected from the classifier-pool according to an entropy minimization criterion. Extensive experimental results on the full benchmark demonstrate our real-time algorithm achieves better performance than state-of-the-art methods.


title: Headline Generation Based On Statistical Translation

abstract: Extractive summarization techniques cannot generate document summaries shorter than a single sentence, something that is often required. An ideal summarization system would understand each document and generate an appropriate summary directly from the results of that understanding. A more practical approach to this problem results in the use of an approximation: viewing summarization as a problem analogous to statistical machine translation. The issue then becomes one of generating a target document in a more concise language from a source document in a more verbose language. This paper presents results on experiments using this approach, in which statistical models of the term selection and term ordering are jointly applied to produce summaries in a style learned from a training corpus.


Reducing gender bias in word embeddings


title: Prediction of Pavement Performance through Neuro-Fuzzy Reasoning

abstract: : Government agencies and consulting companies in charge of pavement management face the challenge of maintaining pavements in serviceable conditions throughout their life from the functional and structural standpoints. For this, the assessment and prediction of the pavement conditions are crucial. This study proposes a neuro-fuzzy model to predict the performance of flexible pavements using the parameters routinely collected by agencies to characterize the condition of an existing pavement. These parameters are generally obtained by performing falling weight deflectometer tests and monitoring the development of distresses on the pavement surface. The proposed hybrid model for predicting pavement performance was characterized by multilayer, feedforward neural networks that led the reasoning process of the IF-THEN fuzzy rules. The results of the neuro-fuzzy model were superior to those of the linear regression model in terms of accuracy in the approximation. The proposed neuro-fuzzy model showed good generalization capability, and the evaluation of the model performance produced satisfactory results, demonstrating the efficiency and potential of these new mathematical modeling techniques.


title: Text Steganography in chat

abstract: Invention of the Internet and its spread in the world changes various aspects of human life. In addition, Internet changed human relations. Chat is one of the new aspects which invented after the Internet and is welcomed by users, especially by young people. In chat rooms, people talk with each other using text messages. Because of the need for quick typing of the word and also because of the high amount of sentences which is exchanged between users, new abbreviations are invented for various words and phrases in chat rooms. This new language is known as SMS-texting. On the other hand, the issue of safety and security of information and especially secret relationships has led to the introduction of numerous methods for secret communication. Among these methods, steganography is a rather new method. The present paper offers a new method for secret exchange of information through chat by using and developing abbreviation text steganography with the use of the SMS-texting language. This paper has been implemented by Java programming language.


title: Multifactor-Dimensionality Reduction Reveals High-Order Interactions among Estrogen-Metabolism Genes in Sporadic Breast Cancer

abstract: One of the greatest challenges facing human geneticists is the identification and characterization of susceptibility genes for common complex multifactorial human diseases. This challenge is partly due to the limitations of parametric-statistical methods for detection of gene effects that are dependent solely or partially on interactions with other genes and with environmental exposures. We introduce multifactor-dimensionality reduction (MDR) as a method for reducing the dimensionality of multilocus information, to improve the identification of polymorphism combinations associated with disease risk. The MDR method is nonparametric (i.e., no hypothesis about the value of a statistical parameter is made), is model-free (i.e., it assumes no particular inheritance model), and is directly applicable to case-control and discordant-sib-pair studies. Using simulated case-control data, we demonstrate that MDR has reasonable power to identify interactions among two or more loci in relatively small samples. When it was applied to a sporadic breast cancer case-control data set, in the absence of any statistically significant independent main effects, MDR identified a statistically significant high-order interaction among four polymorphisms from three different estrogen-metabolism genes. To our knowledge, this is the first report of a four-locus interaction associated with a common complex multifactorial disease.


title: Remote data checking for network coding-based distributed storage systems

abstract: Remote Data Checking (RDC) is a technique by which clients can establish that data outsourced at untrusted servers remains intact over time. RDC is useful as a prevention tool, allowing clients to periodically check if data has been damaged, and as a repair tool whenever damage has been detected. Initially proposed in the context of a single server, RDC was later extended to verify data integrity in distributed storage systems that rely on replication and on erasure coding to store data redundantly at multiple servers. Recently, a technique was proposed to add redundancy based on network coding, which offers interesting tradeoffs because of its remarkably low communication overhead to repair corrupt servers. Unlike previous work on RDC which focused on minimizing the costs of the prevention phase, we take a holistic look and initiate the investigation of RDC schemes for distributed systems that rely on network coding to minimize the combined costs of both the prevention and repair phases. We propose RDC-NC, a novel secure and efficient RDC scheme for network coding-based distributed storage systems. RDC-NC mitigates new attacks that stem from the underlying principle of network coding. The scheme is able to preserve in an adversarial setting the minimal communication overhead of the repair component achieved by network coding in a benign setting. We implement our scheme and experimentally show that it is computationally inexpensive for both clients and servers.


title: A Robust and Fast Reconstruction Framework for Noisy and Large Point Cloud Data

abstract: In this paper we present a robust reconstruction framework on noisy and large point cloud data. Though Poisson reconstruction performs well in recovering the surface from noisy point cloud data, it's problematic to reconstruct underlying surface from large cloud data, especially on a general processor. An inaccurate estimation of point normal for noisy and large dataset would result in local distortion on the reconstructed mesh. We adopt a systematical combination of Poisson-disk sampling, normal estimation and Poisson reconstruction to avoid the inaccuracy of normal calculated from k-nearest neighbors. With the fewer dataset obtained by sampling on original points, the normal estimated is more reliable for subsequent Poisson reconstruction and the time spent in normal estimation and reconstruction is much less. We demonstrate the effectiveness of the framework in recovering topology and geometry information when dealing with point cloud data from real world. The experiment results indicate that the framework is superior to Poisson reconstruction directly on raw point dataset in the aspects of time consumption and visual fidelity.


title: A Serious Game for Eliciting Social Engineering Security Requirements

abstract: Social engineering is the acquisition of informationabout computer systems by methods that deeply include non-technical means. While technical security of most critical systemsis high, the systems remain vulnerable to attacks from socialengineers. Social engineering is a technique that: (i) does notrequire any (advanced) technical tools, (ii) can be used by anyone,(iii) is cheap. Traditional security requirements elicitation approaches oftenfocus on vulnerabilities in network or software systems. Fewapproaches even consider the exploitation of humans via socialengineering and none of them elicits personal behaviours of indi-vidual employees. While the amount of social engineering attacksand the damage they cause rise every year, the security awarenessof these attacks and their consideration during requirementselicitation remains negligible. We propose to use a card game to elicit these requirements, which all employees of a company can play to understand thethreat and document security requirements. The game considersthe individual context of a company and presents underlyingprinciples of human behaviour that social engineers exploit, aswell as concrete attack patterns. We evaluated our approachwith several groups of researchers, IT administrators, andprofessionals from industry.


title: Relations between Executive Function and Academic Achievement from Ages 5 to 17 in a Large, Representative National Sample.

abstract: Abstract This study examined age-related changes in complex executive function (EF) in a large, representative sample ( N = 2036) aged 5 to 17 using the Cognitive Assessment System (CAS; Naglieri & Das, 1997a). Relations between complex EF and academic achievement were examined on a sub-sample (N = 1395) given the Woodcock–Johnson Tests of Achievement–Revised (Woodcock & Johnson, 1989). Performance on the three complex EF tasks improved until at least age 15, although improvement slowed with increasing age and varied some across tasks. Moreover, the different developmental patterns in the correlations between completion time and accuracy provide clues to developmental processes. Examination of individual achievement subtests clarified the specific aspects of academic performance most related to complex EF. Finally, the correlation between complex EF and academic achievement varied across ages, but the developmental pattern of the strength of these correlations was remarkably similar for overall math and reading achievement, suggesting a domain-general relation between complex EF and academic achievement.


title: Interferometric estimation of three-dimensional ice-flow using ascending and descending passes

abstract: Satellite radar interferometry (SRI) provides an important new tool for determining ice-flow velocity. Interferometric measurements made from a single-track direction are sensitive only to a single component of the three-component velocity vector. Observations from along three different track directions would allow the full velocity vector to be determined. A north/south-looking synthetic aperture radar (SAR) could provide these observations over large portions of the globe, but not over large areas of the polar ice sheets. The authors develop and demonstrate a technique that allows the three-component velocity vector to be estimated from data acquired along two track directions (ascending and descending) under a surface-parallel flow assumption. This technique requires that there are accurate estimates of the surface slope, which are also determined interferometrically. To demonstrate the technique, the authors estimate the three-component velocity field for the Ryder Glacier, Greenland. Their results are promising, although they do not have yet ground-truth data with which to determine the accuracy of their estimates.


title: Semantics for the Internet of Things: Early Progress and Back to the Future

abstract: The Internet of Things IoT has recently received considerable interest from both academia and industry that are working on technologies to develop the future Internet. It is a joint and complex discipline that requires synergetic efforts from several communities such as telecommunication industry, device manufacturers, semantic Web, and informatics and engineering. Much of the IoT initiative is supported by the capabilities of manufacturing low-cost and energy-efficient hardware for devices with communication capacities, the maturity of wireless sensor network technologies, and the interests in integrating the physical and cyber worlds. However, the heterogeneity of the "Things" makes interoperability among them a challenging problem, which prevents generic solutions from being adopted on a global scale. Furthermore, the volume, velocity and volatility of the IoT data impose significant challenges to existing information systems. Semantic technologies based on machine-interpretable representation formalism have shown promise for describing objects, sharing and integrating information, and inferring new knowledge together with other intelligent processing techniques. However, the dynamic and resource-constrained nature of the IoT requires special design considerations to be taken into account to effectively apply the semantic technologies on the real world data. In this article the authors review some of the recent developments on applying the semantic technologies to IoT.


title: Return on Roller Coasters: A Model to Guide Investments in Theme Park Attractions

abstract: Despite the economic significance of the theme park industry and the huge investments needed to set up new attractions, no marketing models exist to guide these investment decisions. This study addresses this gap in the literature by estimating a response model for theme park attendance. The model not only determines the contribution of each attraction to attendance, but also how this contribution is distributed within and across years. The model accommodates saturation effects, which imply that the impact of a new attraction is smaller if similar attractions are already present. It also captures reinforcement effects, meaning that a new attraction may reinforce the drawing power of similar extant attractions, especially when these were introduced recently. The model is calibrated on 25 years of weekly attendance data from the Efteling, a leading European theme park. Our return on investment calculations show that it is more profitable to invest in multiple smaller attractions than in one big one. This finding is in remarkable contrast with the current “arms race” in the industry. Furthermore, even though thrill rides tend to be more effective than theme rides, there are conditions under which one should consider to switch to the latter.


title: Urban Air Pollution Monitoring System With Forecasting Models

abstract: A system for monitoring and forecasting urban air pollution is presented in this paper. The system uses low-cost air-quality monitoring motes that are equipped with an array of gaseous and meteorological sensors. These motes wirelessly communicate to an intelligent sensing platform that consists of several modules. The modules are responsible for receiving and storing the data, preprocessing and converting the data into useful information, forecasting the pollutants based on historical information, and finally presenting the acquired information through different channels, such as mobile application, Web portal, and short message service. The focus of this paper is on the monitoring system and its forecasting module. Three machine learning (ML) algorithms are investigated to build accurate forecasting models for one-step and multi-step ahead of concentrations of ground-level ozone (O3), nitrogen dioxide (NO2), and sulfur dioxide (SO2). These ML algorithms are support vector machines, M5P model trees, and artificial neural networks (ANN). Two types of modeling are pursued: 1) univariate and 2) multivariate. The performance evaluation measures used are prediction trend accuracy and root mean square error (RMSE). The results show that using different features in multivariate modeling with M5P algorithm yields the best forecasting performances. For example, using M5P, RMSE is at its lowest, reaching 31.4, when hydrogen sulfide (H2S) is used to predict SO2. Contrarily, the worst performance, i.e., RMSE of 62.4, for SO2 is when using ANN in univariate modeling. The outcome of this paper can be significantly useful for alarming applications in areas with high air pollution levels.


title: Qualitative Analysis of Cross-Border E-Commerce Based on Transaction Costs Theory

abstract: Cross-border e-commerce is a newly arising and burgeoning model of cross-border trade. This paper qualitatively analyzes the reasons of cross-border e-commerce's emerging based on transaction costs theory. First, we introduce cross-border e-commerce and describe the supply chains of B2C and B2B2C cross-border e-commerce models. Then we give an overview of the development of transaction costs theory and analyze the composition of transaction costs from four different perspectives. Finally, we compare the transaction costs of three main cross-border e-commerce models to traditional cross-border trade based on the work above and draw a conclusion that the decrease of transaction costs is a fundamental cause of its development.


title: Design science in information systems research

abstract: A silver halide photographic material which contains at least one blocked photographic reagent having in the molecule thereof at least one structure and capable of releasing a photogrpahically useful reagent by cleavage of the bond by a nucleophilic attack of a nucleophilic reagent on the structure and a subsequent intramolecular electron transfer reaction or an intramolecular nucleophilic reaction and which has at least one photosensitive silver halide emulsion layer. There is provided a photogrphic material which includes, in combination, a photographic reagent precursor capable of releasing a photographically useful reagent timely on photographic processing and a photosensitive silver halide emulsion layer.


title: An Effective Framework for Cloud Modeling, Rendering, and Morphing *

abstract: In many multimedia applications such as computer games, flight simulation, and spe- cial effects for movies, clouds play an important role. In this paper, we present a variety of techniques for cloud modeling, cloud rendering, and cloud morphing in order to make the visual appearance of clouds in these applications more effective and interesting. For cloud modeling, we propose a polygon sampling technique that enables users to accurately model various types of cloud shapes from a 2D image. Moreover, if a 3D model is available, we provide a tetrahedron sampling method to model the cloud. In cloud rendering, we present two line integral convolution (LIC) techniques. The first technique eliminates the Gaussian blobs caused by the splatting algorithm that is commonly used in conventional cloud ren- dering while the second one produces a sky plane which increases the visual appearance of the cloud rendering. Finally, we present a "greedy" morphing algorithm for the purpose of cloud morphing. This algorithm allows a gradual transition of smooth cloud shapes during the animation process with only a limited number of frames. Our techniques increase the visual realism of the appearance of clouds.


title: Privacy Leakage through Innocent Content Sharing in Online Social Networks

abstract: The increased popularity and ubiquitous availability of online social networks and globalised Internet access have affected the way in which people share content. The information that users willingly disclose on these platforms can be used for various purposes, from building consumer models for advertising, to inferring personal, potentially invasive, information. In this work, we use Twitter, Instagram and Foursquare data to convey the idea that the content shared by users, especially when aggregated across platforms, can potentially disclose more information than was originally intended. We perform two case studies: First, we perform user de-anonymization by mimicking the scenario of finding the identity of a user making anonymous posts within a group of users. Empirical evaluation on a sample of real-world social network profiles suggests that cross-platform aggregation introduces significant performance gains in user identification. In the second task, we show that it is possible to infer physical location visits of a user on the basis of shared Twitter and Instagram content. We present an informativeness scoring function which estimates the relevance and novelty of a shared piece of information with respect to an inference task. This measure is validated using an active learning framework which chooses the most informative content at each given point in time. Based on a large-scale data sample, we show that by doing this, we can attain an improved inference performance. In some cases this performance exceeds even the use of the user's full timeline.


title: Ultra-wideband bandpass filter with hybrid microstrip/CPW structure

abstract: A novel ultra-wideband (UWB) bandpass filter (BPF) is presented using the hybrid microstrip and coplanar waveguide (CPW) structure. A CPW nonuniform resonator or multiple-mode resonator (MMR) is constructed to produce its first three resonant modes occurring around the lower end, center, and higher end of the UWB band. Then, a microstrip/CPW surface-to-surface coupled line is formed and modeled to allocate the enhanced coupling peak around the center of this UWB band, i.e., 6.85GHz. As such, a five-pole UWB BPF is built up and realized with the passband covering the entire UWB band (3.1-10.6GHz). A predicted frequency response is finally verified by the experiment. In addition, the designed UWB filter, with a single resonator, only occupies one full-wavelength in length or 16.9mm.


title: Biologic basis for interleukin-1 in disease

abstract: To understand the role of the proinflammatory cytokine interleukin-1 (IL-1) in disease, investigators have studied how production of the different members of the IL-1 family is controlled, the various biologic activities of IL-1, the distinct and various functions of the IL-1 receptor (IL-1R) family, and the complexity of intracellular signaling. Mice deficient in IL-1Beta, IL-1Beta converting enzyme, and IL-1R type I have also been studied. Humans have been injected with IL- 1 (either IL-1alpha or IL-1beta) for enhancing bone marrow recovery and for cancer treatment. The IL-1-specific receptor antagonist (IL-1Ra) has also been tested in clinical trials. The topics discussed in this review include production and activities of IL-1 and IL-1Ra molecules, the effects of IL-1 on gene expression, functions of cell-bound and soluble IL-1 receptors, the importance of the IL-1R accessory protein, newly discovered signal transduction pathways, naturally occurring cytokines limiting IL-1 production or activity, the effects of blocking cyclooxygenase and nitric oxide, and the outcomes of IL-1 and IL-1 Ra in human trials. Special attention is paid to IL-1beta converting enzyme and programmed cell death. The roles of IL-1 in hematopoiesis, leukemia, atherosclerosis, and growth of solid tumors are also discussed. This is a lengthy review, with 586 citations chosen to illustrate specific areas of interest rather than a compendium of references. At the end of each section, a short commentary summarizes what the author considers established or controversial topics linking the biology of IL-1 to mechanisms of disease.


title: Social Media: A Contextual Framework to Guide Research and Practice

abstract: Social media are a broad collection of digital platforms that have radically changed the way people interact and communicate. However, we argue that social media are not simply a technology but actually represent a context that differs in important ways from traditional (e.g., face-to-face) and other digital (e.g., email) ways of interacting and communicating. As a result, social media is a relatively unexamined type of context that may affect the cognition, affect, and behavior of individuals within organizations. We propose a contextual framework that identifies the discrete and ambient stimuli that distinguish social media contexts from digital communication media (e.g., email) and physical (e.g., face-to-face) contexts. We then use this contextual framework to demonstrate how it changes more person-centered theories of organizational behavior (e.g., social exchange, social contagion, and social network theories). These theoretical insights are also used to identify a number of practical implications for individuals and organizations. This study’s major contribution is creating a theoretical understanding of social media features so that future research may proceed in a theory-based, rather than platform-based, manner. Overall, we intend for this article to stimulate and broadly shape the direction of research on this ubiquitous, but poorly understood, phenomenon.


Public-private partnerships in hospital development: lessons from the UK's `private finance initiative'


title: Clickbait detection using word embeddings

abstract: Clickbait is a pejorative term describing web content that is aimed at generating online advertising revenue, especially at the expense of quality or accuracy, relying on sensationalist headlines or eye-catching thumbnail pictures to attract click-throughs and to encourage forwarding of the material over online social networks. We use distributed word representations of the words in the title as features to identify clickbaits in online news media. We train a machine learning model using linear regression to predict the cickbait score of a given tweet. Our methods achieve an F1-score of 64.98\% and an MSE of 0.0791. Compared to other methods, our method is simple, fast to train, does not require extensive feature engineering and yet moderately effective.


Detecting Leaks in Water-Distribution Pipes


Evaluating coherence in open domain conversational systems.


title: High-performance active gate drive for high-power IGBT's

abstract: This paper deals with an active gate drive (AGD) technology for high-power insulated gate bipolar transistors (IGBTs). It is based on an optimal combination of several requirements necessary for good switching performance under hard-switching conditions. The scheme specifically combines together the slow drive requirements for low noise and switching stress and the fast drive requirements for high-speed switching and low switching energy loss. The gate drive can also effectively dampen oscillations during low-current turn-on transient in the IGBT. This paper looks at the conflicting requirements of the conventional gate drive circuit design and demonstrates using experimental results that the proposed three-stage AGD technique can be an effective solution.


title: P-CNN: Pose-Based CNN Features for Action Recognition

abstract: This work targets human action recognition in video. While recent methods typically represent actions by statistics of local video features, here we argue for the importance of a representation derived from human pose. To this end we propose a new Pose-based Convolutional Neural Network descriptor (P-CNN) for action recognition. The descriptor aggregates motion and appearance information along tracks of human body parts. We investigate different schemes of temporal aggregation and experiment with P-CNN features obtained both for automatically estimated and manually annotated human poses. We evaluate our method on the recent and challenging JHMDB and MPII Cooking datasets. For both datasets our method shows consistent improvement over the state of the art.


title: Robust Sclera Recognition System With Novel Sclera Segmentation and Validation Techniques

abstract: Sclera blood veins have been investigated recently as a biometric trait which can be used in a recognition system. The sclera is the white and opaque outer protective part of the eye. This part of the eye has visible blood veins which are randomly distributed. This feature makes these blood veins a promising factor for eye recognition. The sclera has an advantage in that it can be captured using a visible-wavelength camera. Therefore, applications which may involve the sclera are wide ranging. The contribution of this paper is the design of a robust sclera recognition system with high accuracy. The system comprises of new sclera segmentation and occluded eye detection methods. We also propose an efficient method for vessel enhancement, extraction, and binarization. In the feature extraction and matching process stages, we additionally develop an efficient method, that is, orientation, scale, illumination, and deformation invariant. The obtained results using UBIRIS.v1 and UTIRIS databases show an advantage in terms of segmentation accuracy and computational complexity compared with state-of-the-art methods due to Thomas, Oh, Zhou, and Das.


title: Multiprecision multiplication on AVR revisited

abstract: This paper presents new speed records for multiprecision multiplication on the AVR ATmega family of 8-bit microcontrollers. For example, our software takes only 1,969 cycles for the multiplication of two 160-bit integers; this is more than 15 % faster than that demonstrated in previous work. For 256-bit inputs, our software is not only the first to break through the 6,000-cycle barrier; with only 4,771 cycles it also breaks through the 5,000-cycle barrier and is more than 21 % faster than previous work. We achieve these speed records by carefully optimizing the Karatsuba multiplication technique for AVR ATmega. One might expect that subquadratic-complexity Karatsuba multiplication is only faster than algorithms with quadratic complexity for large inputs. This paper shows that it is in fact faster than fully unrolled product-scanning multiplication already for surprisingly small inputs, starting at 48 bits. Our results thus make Karatsuba multiplication the method of choice for high-performance implementations of elliptic-curve cryptography on AVR ATmega microcontrollers.


title: Dynamics, Multiplicity and Conceptual Blends in HCI

abstract: Discussions on what makes user interfaces "natural" or "intuitive" have led researchers to apply Fauconnier and Turner's theory of Conceptual Blends to explain how users rely on familiar and real-world concepts when they learn to use new digital technologies -- as a blend of experiences from the --physical and the "digital" world. This pursuit has multiple challenges of which we address four: The continuous dynamic development of experiences; the multiplicity and complexity involved; the distinction between "real" and "virtual" experiences, and finally applying descriptive concepts predictively. Based on our background in activity theoretical HCI we discuss two cases to nuance the discussion of conceptual blends and HCI. We provide an understanding of conceptual blends beyond one-to-one static blends, and immediately recognizable concepts. We focus on multiplicity, dynamics and learning, and in that we provide a more advanced methodological scaffolding of analyses of conceptual blends, hence we propose that designers need to seed blends in design.


title: Multipolicy decision-making for autonomous driving via changepoint-based behavior prediction: Theory and experiment

abstract: This paper reports on an integrated inference and decision-making approach for autonomous driving that models vehicle behavior for both our vehicle and nearby vehicles as a discrete set of closed-loop policies. Each policy captures a distinct high-level behavior and intention, such as driving along a lane or turning at an intersection. We first employ Bayesian changepoint detection on the observed history of nearby cars to estimate the distribution over potential policies that each nearby car might be executing. We then sample policy assignments from these distributions to obtain high-likelihood actions for each participating vehicle, and perform closed-loop forward simulation to predict the outcome for each sampled policy assignment. After evaluating these predicted outcomes, we execute the policy with the maximum expected reward value. We validate behavioral prediction and decision-making using simulated and real-world experiments.


Efficient Knowledge Distillation from an Ensemble of Teachers


33 Caffeine , Sleep , and Quality of Life


title: Intravesical Migration of Intrauterine Device Mimicking Bladder Stone on Radiologic Imaging: A Case Report

abstract: Due to its high cost-effectiveness, intrauterine device (IUD) is one of the widely used contraception methods worldwide. Intravesical migration of an IUD via perforation of the uterus and bladder is very rare. Endoscopic approach is recommended in the treatment, but open surgery may also be needed rarely. In this report, we present the case of a 37-year-old female who was misdiagnosed radiologically with bladder stone, but later on, it was understood that an IUD migrated to the bladder and resulted in stone formation. Laser lithotripsy was performed, and the migrated IUD was unearthed. Removal of the IUD with cystoscopic forceps was unsuccessful. Postoperative pelvic computed tomography revealed that a part of the IUD was outside the bladder. At the next operation session, laparoscopic removal of the IUD was applied. The patient was followed up for 5 days with a Foley catheter and discharged after performing cystography, assuring us that the bladder contours were normal.


title: Thermal and electrical characterization of eWLB (embedded Wafer Level BGA)

abstract: Demand for Wafer Level Package (WLP) is being driven by the need to shrink package size and height, simplify the supply chain and provide a lower overall cost by using the infrastructure of a batch process. WLP is the upcoming future packaging technology. The driving factors for the implementation of this packaging technology are the low packaging and test cost, the excellent electrical and thermal performance, the ability to work with increasing interconnect density on chip side and the potential for integration of functionality. The increasing demand for new and more advanced electronic products with smaller form factor and superior functionality and performance, is driving the integration of functionality into the third dimension. There are some restrictions in possible applications for Fan-in (FI) WLPs since global chip trends tend toward smaller chip areas with an increasing number of interconnects. The shrinkage of the pitches and pads at the chip to package interface is happening much faster than the shrinkage at the package to board interface. This interconnection gap requires Fan-out (FO) packaging, where the package size is larger than the chip size in order to provide a sufficient area to accommodate the second level interconnect. eWLB is a type of FO-WLP that has the potential to realize any number of interconnects with standard pitches at any shrink stage of the wafer node technology. This paper will highlight some of the recent characterization works of thermal and electrical performance of eWLB package. The performance compared with other packaging format, i.e. FI-WLP and other array packages is discussed with simulation data. Based on computational fluidic dynamics (CFD) simulation, Θja of eWLB package is calculated and it was comparable with FI-WLP's. Thermal characterization activity is carried out to investigate the effect on eWLB configuration with power loading. Thin film based passives on the FO area (mold material) of the eWLB is also analyzed during electrical characterization. Due to the low-loss property of the mold material, plated Cu inductors showed high quality-factor (Q) performance. The mold material in FO area is not only used as a supporting substrate, but also serves as the package substrate, which allows the high-Q inductors to be implemented with other RF chips in one single package. In addition, parasitic (RLC) electrical performance results will be presented in this paper too.


title: Screen Time Tantrums: How Families Manage Screen Media Experiences for Toddlers and Preschoolers

abstract: Prior work shows that setting limits on young children's screen time is conducive to healthy development but can be a challenge for families. We investigate children's (age 1 - 5) transitions to and from screen-based activities to understand the boundaries families have set and their experiences living within them. We report on interviews with 27 parents and a diary study with a separate 28 families examining these transitions. These families turn on screens primarily to facilitate parents' independent activities. Parents feel this is appropriate but self-audit and express hesitation, as they feel they are benefiting from an activity that can be detrimental to their child's well-being. We found that families turn off screens when parents are ready to give their child their full attention and technology presents a natural stopping point. Transitioning away from screens is often painful, and predictive factors determine the pain of a transition. Technology-mediated transitions are significantly more successful than parent-mediated transitions, suggesting that the design community has the power to make this experience better for parents and children by creating technologies that facilitate boundary-setting and respect families' self-defined limits.


title: On the structure of linear programs with overlapping cardinality constraints

abstract: Abstract Cardinality constraints enforce an upper bound on the number of variables that can be nonzero. This article investigates linear programs with cardinality constraints that mutually overlap, i.e., share variables. We present the components of a branch-and-cut solution approach, including new branching rules that exploit the structure of the corresponding conflict hypergraph. We also investigate valid or facet defining cutting planes for the convex hull of the feasible solution set. Our approach can be seen as a continuous analogue of independence system polytopes. We study three different classes of cutting planes: hyperclique bound cuts, implied bound cuts, and flow cover cuts. In a computational study, we examine the effectiveness of an implementation based on the presented concepts.


title: Neural Machine Translation with Reconstruction

abstract: Although end-to-end Neural Machine Translation (NMT) has achieved remarkable progress in the past two years, it suffers from a major drawback: translations generated by NMT systems often lack of adequacy. It has been widely observed that NMT tends to repeatedly translate some source words while mistakenly ignoring other words. To alleviate this problem, we propose a novel encoder-decoder-reconstructor framework for NMT. The reconstructor, incorporated into the NMT model, manages to reconstruct the input source sentence from the hidden layer of the output target sentence, to ensure that the information in the source side is transformed to the target side as much as possible. Experiments show that the proposed framework significantly improves the adequacy of NMT output and achieves superior translation result over state-of-the-art NMT and statistical MT systems.


title: Compensation topology for flat spiral coil inductive power transfer systems

abstract: Inductive power transfer technology is now widely used to provide power into various devices, such as battery based vehicles, implant devices, portable equipment etc. Despite the rapid development of power electronics components, inductive power transfer technology still has some drawbacks. The output power is vulnerable to the magnetic coupling efficiency of two coils. If misalignment and displacement of the coils occur the output power can drop sharply. This study proposes a new design that can transfer rated power at various coupling coefficients. The characteristics of the proposed topology are discussed. All discussions and conclusions are obtained for the condition that the coil self-inductance remains unchanged during misalignment. Analytical and experimental results are presented in this article, to support the proposed design.


title: The Association of Backpack Use and Back Pain in Adolescents

abstract: Study Design. A cross-sectional study comprising the first phase of an ongoing, longitudinal prospective study was conducted. Objective. To investigate the relation between backpack use and back pain in adolescents. of Background Data. The prevalence of nonspecific back pain increases dramatically during adolescence from less than 10% in the pre-teen-age years up to 50% in 15- to 16-year-olds. There is widespread concern that heavy backpacks carried by adolescents contribute to the development of back pain. Methods. A total of 1126 children, ages 12 to 18 years, participated by completing a questionnaire about their health, activities, and backpack use. Each child's body weight height, and backpack weight were measured. A child was classified as having back pain if one or more of the following were reported during the preceding month: neck or back pain that had interfered with school or leisure, neck or back pain with a severity rating of 2 or more on a scale 0 to 10, a visit to a physician or therapist for neck or back pain, or exemption from physical education or sports because of neck or back pain. Results. Of 1122 backpack users, 74.4% were classified as having back pain, validated by significantly poorer general health, more limited physical functioning, and more bodily pain. As compared with no or low use of backpacks at school, heavy use (odds ratio, 1.98; P < 0.0001) was independently associated with back pain. Female gender and larger body mass index also were significantly associated with back pain. As compared with adolescents who had no back pain, adolescents with back pain carried significantly heavier backpacks that represented a significantiy greater percentage of their body weights. Conclusion. The use of backpacks during the school day and backpack weights are independently associated with back pain.


title: Comparative Analysis of 2- and 3-D Methods for Computing Electric and Magnetic Fields Generated by Overhead Transmission Lines

abstract: This paper presents a comparative analysis of 2- and 3-D methods for computing the electromagnetic field generated by overhead transmission lines operating at the power frequency. The study focuses on the catenary effect of cables and the effect of electric charges and currents present in tower elements when computing the electric field on the cables' surface and the electric and magnetic fields near the earth's surface. Results for a typical three-phase 138-kV line are presented as well as the proper range of the application for a modified and fast 2-D solution.


title: On-the-fly model checking from interval logic specifications

abstract: Future Interval Logic (FIL) and its intuitive graphical representation, Graphical Interval Logic (GIL), can be used as the formal description language of model checking tools to verify hardware and software systems. An interval clearly defines the temporal scope over which properties are evaluated. From interval formulas specifying the temporal behavior of a system we obtain their semantically equivalent Buchi automata, but in such a way that our algorithm can be integrated into an on-the-fly model checking tool. As the property automaton can be generated simultaneously with, and guided by, the construction of the system model, it is possible to detect that a property is violated by constructing only a part of both state spaces. This is the first time that this kind of algorithm has been developed for an interval logic. The relations with other automated verification techniques from linear temporal logic are also discussed. Moreover, an appendix with proof of the correctness of our algorithm is included.


title: Privacy Preserving Web Query Log Publishing: A Survey on Anonymization Techniques

abstract: Releasing Web query logs which contain valuable information for research or marketing, can breach the privacy of search engine users. Therefore rendering query logs to limit linking a query to an individual while preserving the data usefulness for analysis, is an important research problem. This survey provides an overview and discussion on the recent studies on this direction.


title: Continuum limit of total variation on point clouds

abstract: We consider point clouds obtained as random samples of a measure on a Euclidean domain. A graph representing the point cloud is obtained by assigning weights to edges based on the distance between the points they connect. Our goal is to develop mathematical tools needed to study the consistency, as the number of available data points increases, of graph-based machine learning algorithms for tasks such as clustering. In particular, we study when is the cut capacity, and more generally total variation, on these graphs a good approximation of the perimeter (total variation) in the continuum setting. We address this question in the setting of $\Gamma$-convergence. We obtain almost optimal conditions on the scaling, as number of points increases, of the size of the neighborhood over which the points are connected by an edge for the $\Gamma$-convergence to hold. Taking the limit is enabled by a transportation based metric which allows to suitably compare functionals defined on different point clouds.


title: Informational Influence in Organizations: An Integrated Approach to Knowledge Adoption

abstract: This research investigates how knowledge workers are influenced to adopt the advice that they receive in mediated contexts. The research integrates the Technology Acceptance Model (Davis 1989) with dual-process models of informational influence (e.g., Petty and Cacioppo 1986, Chaiken and Eagly 1976) to build a theoretical model of information adoption. This model highlights the assessment of information usefulness as a mediator of the information adoption process. Importantly, the model draws on the dual-process models to make predictions about the antecedents of informational usefulness under different processing conditions.The model is investigated qualitatively first, using interviews of a sample of 40 consultants, and then quantitatively on another sample of 63 consultants from the same international consulting organization. Data reflect participants' perceptions of actual e-mails they received from colleagues consisting of advice or recommendations. Results support the model, suggesting that the process models used to understand information adoption can be generalized to the field of knowledge management, and thatusefulness serves a mediating role between influence processes and information adoption. Organizational knowledge work is becoming increasingly global. This research offers a model for understanding knowledge transfer using computer-mediated communication.


title: Video Captioning With Attention-Based LSTM and Semantic Consistency

abstract: Recent progress in using long short-term memory (LSTM) for image captioning has motivated the exploration of their applications for video captioning. By taking a video as a sequence of features, an LSTM model is trained on video-sentence pairs and learns to associate a video to a sentence. However, most existing methods compress an entire video shot or frame into a static representation, without considering attention mechanism which allows for selecting salient features. Furthermore, existing approaches usually model the translating error, but ignore the correlations between sentence semantics and visual content. To tackle these issues, we propose a novel end-to-end framework named aLSTMs, an attention-based LSTM model with semantic consistency, to transfer videos to natural sentences. This framework integrates attention mechanism with LSTM to capture salient structures of video, and explores the correlation between multimodal representations (i.e., words and visual content) for generating sentences with rich semantic content. Specifically, we first propose an attention mechanism that uses the dynamic weighted sum of local two-dimensional convolutional neural network representations. Then, an LSTM decoder takes these visual features at time $t$ and the word-embedding feature at time $t$ $-$ 1 to generate important words. Finally, we use multimodal embedding to map the visual and sentence features into a joint space to guarantee the semantic consistence of the sentence description and the video visual content. Experiments on the benchmark datasets demonstrate that our method using single feature can achieve competitive or even better results than the state-of-the-art baselines for video captioning in both BLEU and METEOR.


title: Clusterers: a comparison of partitioning and density-based algorithms and a discussion of optimisations

abstract: Though data mining is a relatively recent innovation, the improvements it offers over traditional data analysis have seen the field expand rapidly. Given the critical requirement for the efficient and accurate delivery of useful information in today's data-rich climate, significant research in the topic continues. ::: ::: Clustering is one of the fundamental techniques adopted by data mining tools across a range of applications. It provides several algorithms that can assess large data sets based on specific parameters and group related data points. ::: ::: This paper compares two widely used clustering algorithms, K-Medoids and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), against other well-known techniques. The initial testing conducted on each technique utilises the standard implementation of each algorithm. Further experimental work proposes and tests potential improvements to these methods, and presents the UltraK-Medoids and UltraDBScan algorithms. Various key applications of clustering methods are detailed, and several ::: areas of future work have been suggested.


title: Im2Text: Describing Images Using 1 Million Captioned Photographs

abstract: We develop and demonstrate automatic image description methods using a large captioned photo collection. One contribution is our technique for the automatic collection of this new dataset – performing a huge number of Flickr queries and then filtering the noisy results down to 1 million images with associated visually relevant captions. Such a collection allows us to approach the extremely challenging problem of description generation using relatively simple non-parametric methods and produces surprisingly effective results. We also develop methods incorporating many state of the art, but fairly noisy, estimates of image content to produce even more pleasing results. Finally we introduce a new objective performance measure for image captioning.


Real-coded Genetic Algorithms with Simulated Binary Crossover: Studies on Multimodal and Multiobjective Problems.


title: Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A

abstract: Rapid progress in unconstrained face recognition has resulted in a saturation in recognition accuracy for current benchmark datasets. While important for early progress, a chief limitation in most benchmark datasets is the use of a commodity face detector to select face imagery. The implication of this strategy is restricted variations in face pose and other confounding factors. This paper introduces the IARPA Janus Benchmark A (IJB-A), a publicly available media in the wild dataset containing 500 subjects with manually localized face images. Key features of the IJB-A dataset are: (i) full pose variation, (ii) joint use for face recognition and face detection benchmarking, (iii) a mix of images and videos, (iv) wider geographic variation of subjects, (v) protocols supporting both open-set identification (1∶N search) and verification (1∶1 comparison), (vi) an optional protocol that allows modeling of gallery subjects, and (vii) ground truth eye and nose locations. The dataset has been developed using 1,501,267 million crowd sourced annotations. Baseline accuracies for both face detection and face recognition from commercial and open source algorithms demonstrate the challenge offered by this new unconstrained benchmark.


Intersensory comparisons of temporal judgments.


Indian Estuaries : Part I-Physico-Chemical Features of Water & Sediment Nutrients of Ashtamudi Estuary


title: Improved Mixed-Example Data Augmentation

abstract: In order to reduce overfitting, neural networks are typically trained with data augmentation, the practice of artificially generating additional training data via label-preserving transformations of existing training examples. While these types of transformations make intuitive sense, recent work has demonstrated that even non-label-preserving data augmentation can be surprisingly effective, examining this type of data augmentation through linear combinations of pairs of examples. Despite their effectiveness, little is known about why such methods work. In this work, we aim to explore a new, more generalized form of this type of data augmentation in order to determine whether such linearity is necessary. By considering this broader scope of "mixed-example data augmentation", we find a much larger space of practical augmentation techniques, including methods that improve upon previous state-of-the-art. This generalization has benefits beyond the promise of improved performance, revealing a number of types of mixed-example data augmentation that are radically different from those considered in prior work, which provides evidence that current theories for the effectiveness of such methods are incomplete and suggests that any such theory must explain a much broader phenomenon.


title: BioWatch: A Noninvasive Wrist-Based Blood Pressure Monitor That Incorporates Training Techniques for Posture and Subject Variability

abstract: Noninvasive continuous blood pressure (BP) monitoring is not yet practically available for daily use. Challenges include making the system easily wearable, reducing noise level and improving accuracy. Variations in each person's physical characteristics, as well as the possibility of different postures, increase the complexity of continuous BP monitoring, especially outside the hospital. This study attempts to provide an easily wearable solution and proposes training to specific posture and individual for further improving accuracy. The wrist watch-based system we developed can measure electrocardiogram and photoplethysmogram. From these two signals, we measure pulse transit time through which we can obtain systolic and diastolic blood pressure through regression techniques. In this study, we investigate various functions to perform the training to obtain blood pressure. We validate measurements on different postures and subjects, and show the value of training the device to each posture and each subject. We observed that the average RMSE between the measured actual systolic BP and calculated systolic BP is between 7.83 to 9.37 mmHg across 11 subjects. The corresponding range of error for diastolic BP is 5.77 to 6.90 mmHg. The system can also automatically detect the arm position of the user using an accelerometer with an average accuracy of 98%, to make sure that the sensor is kept at the proper height. This system, called BioWatch, can potentially be a unified solution for heart rate, SPO2 and continuous BP monitoring.


title: Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks

abstract: Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.


title: Adversarial Removal of Demographic Attributes from Text Data

abstract: Recent advances in Representation Learning and Adversarial Training seem to succeed in removing unwanted features from the learned representation. We show that demographic information of authors is encoded in -- and can be recovered from -- the intermediate representations learned by text-based neural classifiers. The implication is that decisions of classifiers trained on textual data are not agnostic to -- and likely condition on -- demographic attributes. When attempting to remove such demographic information using adversarial training, we find that while the adversarial component achieves chance-level development-set accuracy during training, a post-hoc classifier, trained on the encoded sentences from the first part, still manages to reach substantially higher classification accuracies on the same data. This behavior is consistent across several tasks, demographic properties and datasets. We explore several techniques to improve the effectiveness of the adversarial component. Our main conclusion is a cautionary one: do not rely on the adversarial training to achieve invariant representation to sensitive features.


Fine-grained Sentiment Analysis of Chinese Reviews Using LSTM Network


title: Goal-directed arm movements change eye-head coordination

abstract: We compared the head movements accompanying gaze shifts while our subjects executed different manual operations, requiring gaze shifts of about 30°. The different tasks yielded different latencies between gaze shifts and hand movements, and different maximum velocities of the hand. These changes in eye-hand coordination had a clear effect on eye-head coordination: the latencies and maximum velocities of head and hand were correlated. The same correlation between movements of the head and hand was also found within a task. Therefore, the changes in eye-head coordination are not caused by changes in the strategy of the subjects. We conclude that head movements and saccades during gaze shifts are not based on the same command: head movements depend both on the actual saccade and on possible future gaze shifts.


title: Inverted indexing for cross-lingual NLP

abstract: We present a novel, count-based approach to obtaining inter-lingual word representations based on inverted indexing of Wikipedia. We present experiments applying these representations to 17 datasets in document classification, POS tagging, dependency parsing, and word alignment. Our approach has the advantage that it is simple, computationally efficient and almost parameter-free, and, more importantly, it enables multi-source crosslingual learning. In 14/17 cases, we improve over using state-of-the-art bilingual embeddings.


Photometric model of a rain drop


title: Computationally Manageable Combinational Auctions

abstract: There is interest in designing simultaneous auctions for situations such as the recent FCC radio spectrum auctions, in which the value of assets to a bidder depends on which other assets he or she wins. In such auctions, bidders may wish to submit bids for combinations of assets. When this is allowed, the problem of determining the revenue maximizing set of nonconflicting bids can be difficult. We analyze this problem, identifying several different structures of permitted combinational bids for which computational tractability is constructively demonstrated and some structures for which computational tractability cannot be guaranteed.


title: Refinement of strong Stackelberg equilibria in security games

abstract: Given the real-world deployments of attacker-defender Stackelberg security games, robustness to deviations from expected attacker behaviors has now emerged as a critically important issue. This paper provides four key contributions in this context. First, it identifies a fundamentally problematic aspect of current algorithms for security games. It shows that there are many situations where these algorithms face multiple equilibria, and they arbitrarily select one that may hand the defender a significant disadvantage, particularly if the attacker deviates from its equilibrium strategies due to unknown constraints. Second, for important subclasses of security games, it identifies situations where we will face such multiple equilibria. Third, to address these problematic situations, it presents two equilibrium refinement algorithms that can optimize the defender's utility if the attacker deviates from equilibrium strategies. Finally, it experimentally illustrates that the refinement approach achieved significant robustness in consideration of attackers' deviation due to unknown constraints.


title: Systematic review for orthodontic and orthopedic treatments for anterior open bite in the mixed dentition

abstract: BACKGROUND ::: The treatment options for the early treatment of anterior open bite are still controversial. The aim of this study was to evaluate the actual available evidence on treatments of anterior open bite in the mixed dentition in order to assess the effectiveness of the early treatment in reducing open bite, the most efficacious treatment strategy and the stability of the results. ::: ::: ::: MATERIALS AND METHODS ::: A literature survey was done on November 15, 2015, by means of appropriate Medical Subject Headings (MeSH) using the following databases: PubMed, EMBASE, Cochrane Library, LILACS, VHL, and WEB OF SCIENCE. Randomized clinical trials and studies with a control group (treated or untreated) were then selected by two authors. Trials including patients with syndromes or in the permanent dentition and studies concerning treatment with extractions, full-fixed appliances, or surgery were not considered. Full articles were retrieved for abstracts or titles that met the initial inclusion criteria or lacked sufficient detail for immediate exclusion. ::: ::: ::: RESULTS ::: Two thousand five hundred sixty-nine studies about open bite were available; the search strategy selected 240 of them. Twenty-four articles have been judged suitably for the final review, and their relevant data were analyzed. ::: ::: ::: DISCUSSION ::: Although this review confirms the effectiveness of early treatment of open bite, particularly when no-compliance strategies are employed, meta-analysis was unfeasible due to lack of standardization, important methodological limitations, and shortcomings of the studies. ::: ::: ::: CONCLUSIONS ::: A more robust approach to trial design in terms of methodology and error analysis is needed. Besides, more studies with longer periods of follow-up are required.


title: Using species distribution models to assess the importance of Egypt's protected areas for the conservation of medicinal plants

abstract: Abstract Human activities affect the distribution and abundance of plants, with impacts on ecosystem services and human well-being; it is thus vital that a network of Protected Areas is capable of conserving plants that are useful. Using the species distribution (SDM) model algorithm MaxEnt, we tested whether Egypt's network of Protected Areas performs well in conserving the region's important medicinal plant species. We constructed individual SDMs for each species, and then combined the models into a single ‘species-richness’ layer, which we then compared to the distribution of the existing Protected Areas. Temperature was the most important of eleven predictor variables used to build the SDMs. Assuming the SDM's prediction of suitable habitat was accurate and corresponded to the occurrence of the medicinal plant species, then on average species richness was significantly higher within than outside the Protected Areas. Based on our findings, Egypt's Protected Areas are effective at conserving its medicinal plants.


title: An Analysis of Buck Converter Efficiency in PWM/PFM Mode with Simulink

abstract: This technical paper takes a study into efficiency comparison between PWM and PFM control modes in DC-DC buck converters. Matlab Simulink Models are built to facilitate the analysis of various effects on power loss and converting efficiency, including different load conditions, gate switching frequency, setting of voltage and current thresholds, etc. From efficiency vs. load graph, a best switching frequency is found to achieve a good efficiency throughout the wide load range. This simulation point is then compared to theoretical predictions, justifying the effectiveness of computer based simulation. Efficiencies at two different control modes are compared to verify the improvement of PFM scheme.


title: Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 19: 368–375

abstract: Motivation: DNA microarrays have recently been used for the purpose of monitoring expression levels of thousands of genes simultaneously and identifying those genes that are differentially expressed. The probability that a false identification (type I error) is committed can increase sharply when the number of tested genes gets large. Correlation between the test statistics attributed to gene co-regulation and dependency in the measurement errors of the gene expression levels further complicates the problem. In this paper we address this very large multiplicity problem by adopting the false discovery rate (FDR) controlling approach. In order to address the dependency problem, we present three resampling-based FDR controlling procedures, that account for the test statistics distribution, and compare their performance to that of the


Electronic QED coherence in brain microtubules


title: Introduction to Optical Fiber Communication in Lightwave Systems

abstract: From the last 10-15 years Optical Fibers are replacing the conventional ways of data transmission i.e. wires, where an optical fiber is a transparent, hair like cylindrical structure which carries data in form of light signals. As the number of users is growing day by day so as the transmission data, in order to accumulate this high amount of data the traditional wires was not an good option because with the enlargement of users, their needs also expand. One of the need of users is speed, although the wires were transmitting data but the speed was low with other losses in the transmission. Optical fiber transmits data in the form of light, so naturally the speed of data is 3*10 m/s (speed of light). In modern world almost everything will rely on optical cabels like that of telephone, television, internet lines etc. This paper focuses on the implementation of the optical network in metro span area that with what parameters, components, losses and efficiency an network working on light signal will handle the large amount of an metropolitan area.


title: The Semantics/pragmatics Distinction: A View From Relevance Theory

abstract: The assumption that sentence types encode proposition types was shaken by Donnellan’s observation that a sentence with a definite description subject could express either a general or a singular proposition. In other words, a single sentence type could have different truth conditions on different occasions of use. Relevance Theory holds a strong version of this “semantic underdeterminacy” thesis, according to which natural language sentences standardly fall far short of encoding propositions or proposition types. The relevance-driven pragmatic inferential mechanism is part of our “theory of mind” capacity and functions independently of any code; it follows that linguistically encoded utterance meaning need be only schematic.


title: Emerging Memory Technologies: Recent Trends and Prospects

abstract: This tutorial introduces the basics of emerging nonvolatile memory (NVM) technologies including spin-transfer-torque magnetic random access memory (STTMRAM), phase-change random access memory (PCRAM), and resistive random access memory (RRAM). Emerging NVM cell characteristics are summarized, and device-level engineering trends are discussed. Emerging NVM array architectures are introduced, including the onetransistor?one-resistor (1T1R) array and the cross-point array with selectors. Design challenges such as scaling the write current and minimizing the sneak path current in cross-point array are analyzed. Recent progress on megabit-to gigabit-level prototype chip demonstrations is summarized. Finally, the prospective applications of emerging NVM are discussed, ranging from the last-level cache to the storage-class memory in the memory hierarchy. Topics of three-dimensional (3D) integration and radiation-hard NVM are discussed. Novel applications beyond the conventional memory applications are also surveyed, including physical unclonable function for hardware security, reconfigurable routing switch for field-programmable gate array (FPGA), logic-in-memory and nonvolatile cache/register/flip-flop for nonvolatile processor, and synaptic device for neuro-inspired computing.


THE HEATS OF SOLUTION OF THE THREE FORMS OF MILK-SUGAR.


title: Zero-Shot Dialog Generation with Cross-Domain Latent Actions

abstract: This paper introduces zero-shot dialog generation (ZSDG), as a step towards neural dialog systems that can instantly generalize to new situations with minimal data. ZSDG enables an end-to-end generative dialog system to generalize to a new domain for which only a domain description is provided and no training dialogs are available. Then a novel learning framework, Action Matching, is proposed. This algorithm can learn a cross-domain embedding space that models the semantics of dialog responses which, in turn, lets a neural dialog generation model generalize to new domains. We evaluate our methods on a new synthetic dialog dataset, and an existing human-human dialog dataset. Results show that our method has superior performance in learning dialog models that rapidly adapt their behavior to new domains and suggests promising future research.


title: Learning Action Primitives for Multi-level Video Event Understanding

abstract: Human action categories exhibit significant intra-class variation. Changes in viewpoint, human appearance, and the temporal evolution of an action confound recognition algorithms. In order to address this, we present an approach to discover action primitives, sub-categories of action classes, that allow us to model this intra-class variation. We learn action primitives and their interrelations in a multi-level spatio-temporal model for action recognition. Action primitives are discovered via a data-driven clustering approach that focuses on repeatable, discriminative sub-categories. Higher-level interactions between action primitives and the actions of a set of people present in a scene are learned. Empirical results demonstrate that these action primitives can be effectively localized, and using them to model action classes improves action recognition performance on challenging datasets.


title: Data-driven multi-touch attribution models

abstract: In digital advertising, attribution is the problem of assigning credit to one or more advertisements for driving the user to the desirable actions such as making a purchase. Rather than giving all the credit to the last ad a user sees, multi-touch attribution allows more than one ads to get the credit based on their corresponding contributions. Multi-touch attribution is one of the most important problems in digital advertising, especially when multiple media channels, such as search, display, social, mobile and video are involved. Due to the lack of statistical framework and a viable modeling approach, true data-driven methodology does not exist today in the industry. While predictive modeling has been thoroughly researched in recent years in the digital advertising domain, the attribution problem focuses more on accurate and stable interpretation of the influence of each user interaction to the final user decision rather than just user classification. Traditional classification models fail to achieve those goals. In this paper, we first propose a bivariate metric, one measures the variability of the estimate, and the other measures the accuracy of classifying the positive and negative users. We then develop a bagged logistic regression model, which we show achieves a comparable classification accuracy as a usual logistic regression, but a much more stable estimate of individual advertising channel contributions. We also propose an intuitive and simple probabilistic model to directly quantify the attribution of different advertising channels. We then apply both the bagged logistic model and the probabilistic model to a real-world data set from a multi-channel advertising campaign for a well-known consumer software and services brand. The two models produce consistent general conclusions and thus offer useful cross-validation. The results of our attribution models also shed several important insights that have been validated by the advertising team. We have implemented the probabilistic model in the production advertising platform of the first author's company, and plan to implement the bagged logistic regression in the next product release. We believe availability of such data-driven multi-touch attribution metric and models is a break-through in the digital advertising industry.


title: Using Voronoi diagrams to improve classification performances when modeling imbalanced datasets

abstract: An over-sampling technique called V-synth is proposed and compared to borderline SMOTE (bSMOTE), a common methodology used to balance an imbalanced dataset for classification purposes. V-synth is a machine learning methodology that allows synthetic minority points to be generated based on the properties of a Voronoi diagram. A Voronoi diagram is a collection of geometric regions that encapsulate classifying points in such a way that any point within the region is closest to the encapsulated classifier than any other adjacent classifiers based on their distance from one another. Because of properties inherent to Voronoi diagrams, V-synth identifies exclusive regions of feature space where it is ideal to create synthetic minority samples. To test the generalization and application of V-synth, six databases from various problem domains were selected from the University of California Irvine's Machine Learning Repository. Though not always guaranteed due to the random nature of synthetic over-sampling, significant evidence is presented that supports the hypothesis that V-synth more consistently leads to the creation of more accurate and better-balanced classification models than bSMOTE when the classification complexity of a dataset is high.


title: Sketch-based shape retrieval

abstract: We develop a system for 3D object retrieval based on sketched feature lines as input. For objective evaluation, we collect a large number of query sketches from human users that are related to an existing data base of objects. The sketches turn out to be generally quite abstract with large local and global deviations from the original shape. Based on this observation, we decide to use a bag-of-features approach over computer generated line drawings of the objects. We develop a targeted feature transform based on Gabor filters for this system. We can show objectively that this transform is better suited than other approaches from the literature developed for similar tasks. Moreover, we demonstrate how to optimize the parameters of our, as well as other approaches, based on the gathered sketches. In the resulting comparison, our approach is significantly better than any other system described so far.


Framework for Academic Advice through Mobile Applications


title: An Algebra and Equivalences to Transform Graph Patterns in Neo4j

abstract: Modern query optimizers of relational database systems embody more than three decades of research and practice in the area of data management and processing. Key advances include algebraic query transformation, intelligent search space pruning, and modular optimizer architectures. Surprisingly, many of these contributions seem to have been overlooked in the emerging field of graph databases so far. In particular, we believe that query optimization based on a general graph algebra and its equivalences can greatly improve on the current state of the art. Although some graph algebras have already been proposed, they have often been developed in a context, in which a relational database system is used as a backend to process graph data. As a consequence, these algebras are typically tightly coupled to the relational algebra, making them unsuitable for native graph databases. While we support the approach of extending the relational algebra, we argue that graph-specific operations should be defined at a higher level, independent of the database backend. In this paper, we introduce such a general graph algebra and corresponding equivalences. We demonstrate how it can be used to optimize Cypher queries in the setting of the Neo4j native graph database.


The Evolution of Constructivist Learning Environments : Immersion in Distributed , Virtual Worlds


title: Email classification with co-training

abstract: The main problems in text classification are lack of labeled data, as well as the cost of labeling the unlabeled data. We address these problems by exploring co-training - an algorithm that uses unlabeled data along with a few labeled examples to boost the performance of a classifier. We experiment with co-training on the email domain. Our results show that the performance of co-training depends on the learning algorithm it uses. In particular, Support Vector Machines significantly outperforms Naive Bayes on email classification.


title: Multi-task Maximum Entropy Inverse Reinforcement Learning

abstract: Multi-task Inverse Reinforcement Learning (IRL) is the problem of inferring multiple reward functions from expert demonstrations. Prior work, built on Bayesian IRL, is unable to scale to complex environments due to computational constraints. This paper contributes a formulation of multi-task IRL in the more computationally efficient Maximum Causal Entropy (MCE) IRL framework. Experiments show our approach can perform one-shot imitation learning in a gridworld environment that single-task IRL algorithms need hundreds of demonstrations to solve. We outline preliminary work using meta-learning to extend our method to the function approximator setting of modern MCE IRL algorithms. Evaluating on multi-task variants of common simulated robotics benchmarks, we discover serious limitations of these IRL algorithms, and conclude with suggestions for further work.


title: Biophotons, microtubules and CNS, is our brain a "holographic computer"?

abstract: Several experiments show that there is a cell to cell communication by light in different cell types. This article describes theoretical mechanisms and subcellular structures that could be involved in this phenomenon. Special consideration is given to the nervous system, since it would have excellent conditions for such mechanisms. Neurons are large colourless cells with wide arborisations, have an active metabolism generating photons, contain little pigment, and have a prominent cytoskeleton consisting of hollow microtubules. As brain and spinal cord are protected from environmental light by bone and connective tissue, the signal to noise ratio should be high for photons as signal. Fluorescent and absorbing substances should interfere with such a communication system. Of all biogenic amines nature has chosen the ones with the strongest fluorescence as neurotransmitters for mood reactions: serotonin, dopamine and norepinephrine. If these mechanisms are of relevance our brain would have to be looked upon as a "holographic computer".


title: Static test case prioritization using topic models

abstract: Software development teams use test suites to test changes to their source code. In many situations, the test suites are so large that executing every test for every source code change is infeasible, due to time and resource constraints. Development teams need to prioritize their test suite so that as many distinct faults as possible are detected early in the execution of the test suite. We consider the problem of static black-box test case prioritization (TCP), where test suites are prioritized without the availability of the source code of the system under test (SUT). We propose a new static black-box TCP technique that represents test cases using a previously unused data source in the test suite: the linguistic data of the test cases, i.e., their identifier names, comments, and string literals. Our technique applies a text analysis algorithm called topic modeling to the linguistic data to approximate the functionality of each test case, allowing our technique to give high priority to test cases that test different functionalities of the SUT. We compare our proposed technique with existing static black-box TCP techniques in a case study of multiple real-world open source systems: several versions of Apache Ant and Apache Derby. We find that our static black-box TCP technique outperforms existing static black-box TCP techniques, and has comparable or better performance than two existing execution-based TCP techniques. Static black-box TCP methods are widely applicable because the only input they require is the source code of the test cases themselves. This contrasts with other TCP techniques which require access to the SUT runtime behavior, to the SUT specification models, or to the SUT source code.


title: CV-SLAM using Ceiling Boundary Celing Vision based SLAM and Kidnapping Solution

abstract: This paper deals with simultaneous localization and mapping(SLAM) problem for a mobile robot that travels around the indoor environments. A single camera looking up the ceiling is used as the only sensor. Line features are extracted from the boundaries between the ceiling and walls and parameterized for SLAM update. Extended Kalman Filter(EKF) is used for simultaneously estimating the current robot pose and building a map with the line features. When the robot is kidnapped, Monte Carlo Localization(MCL) is used for finding the robot pose. To improve the localization performance, the resampling method is modified. The experiment is practiced in our indoor test bed and the proposed algorithms are proved by the experimental results.


title: Superpixel tracking

abstract: While numerous algorithms have been proposed for object tracking with demonstrated success, it remains a challenging problem for a tracker to handle large change in scale, motion, shape deformation with occlusion. One of the main reasons is the lack of effective image representation to account for appearance variation. Most trackers use high-level appearance structure or low-level cues for representing and matching target objects. In this paper, we propose a tracking method from the perspective of mid-level vision with structural information captured in superpixels. We present a discriminative appearance model based on superpixels, thereby facilitating a tracker to distinguish the target and the background with mid-level cues. The tracking task is then formulated by computing a target-background confidence map, and obtaining the best candidate by maximum a posterior estimate. Experimental results demonstrate that our tracker is able to handle heavy occlusion and recover from drifts. In conjunction with online update, the proposed algorithm is shown to perform favorably against existing methods for object tracking.


title: Using machine teaching to identify optimal training-set attacks on machine learners

abstract: We investigate a problem at the intersection of machine learning and security: training-set attacks on machine learners. In such attacks an attacker contaminates the training data so that a specific learning algorithm would produce a model profitable to the attacker. Understanding training-set attacks is important as more intelligent agents (e.g. spam filters and robots) are equipped with learning capability and can potentially be hacked via data they receive from the environment. This paper identifies the optimal training-set attack on a broad family of machine learners. First we show that optimal training-set attack can be formulated as a bilevel optimization problem. Then we show that for machine learners with certain Karush-Kuhn-Tucker conditions we can solve the bilevel problem efficiently using gradient methods on an implicit function. As examples, we demonstrate optimal training-set attacks on Support Vector Machines, logistic regression, and linear regression with extensive experiments. Finally, we discuss potential defenses against such attacks.


title: A novel low cost portable integrated solar PV, fuel cell and battery emulator with fast tracking algorithm

abstract: Applications based on renewable energy sources are getting worldwide attention. Laboratory level research involving renewable energy sources, poses many difficulties like high cost, dependency on environmental conditions and its repeatability, space requirements etc. These difficulties can be sorted out by using a hardware emulator that can accurately mimic the characteristics of a particular renewable energy sources. An emulator is supposed to replicate the voltage and current (V–I) steady state and dynamic characteristics of any source such as solar Photovoltaics (PV), Fuel Cell (FC) etc. Typically it is a low cost and a portable alternative for the use of actual renewable sources. Unfortunately, many of them suffer from poor dynamic response as compared to their actual counterparts. This paper proposes a new integrated emulator that provides emulation with fast load tracking capability for three energy sources viz. solar PV, Proton Exchange Membrane fuel cell (PEMFC) and battery. This can offer a low cost solution for test-bed setup facilitating the continuous research efforts irrespective of the actual environmental conditions in the field of renewable energy and NCE. The proposed emulator is designed and built around a Texas Instrument's TMS320F28027 controlled flyback converter and National Instrument's NIDAQ USB6008 interfaced with LabVIEW based GUI. Key results of the proposed system along with control implementation and modeling are presented.


title: An Overview of Dynamic Spectrum Sharing: Ongoing Initiatives, Challenges, and a Roadmap for Future Research

abstract: We are in the midst of a major paradigm shift in how we manage radio spectrum. This paradigm shift is necessitated by the growth of wireless services of all types and the demand pressure imposed on limited spectrum resources under legacy management regimes. The shift is feasible because of advances in radio and networking technologies that make it possible to share spectrum dynamically in all possible dimensions-i.e., across frequencies, time, location, users, uses, and networks. Realizing the full potential of this shift to Dynamic Spectrum Sharing will require the co-evolution of wireless technologies, markets, and regulatory policies; a process which is occurring on a global scale. This paper provides a current overview of major technological and regulatory reforms that are leading the way toward a global paradigm shift to more flexible, dynamic, market-based ways to manage and share radio spectrum resources. We focus on current efforts to implement database-driven approaches for managing the shared co-existence of users with heterogeneous access and interference protection rights, and discuss open research challenges.


title: Adversarial and Clean Data Are Not Twins

abstract: Adversarial attack has cast a shadow on the massive success of deep neural networks. Despite being almost visually identical to the clean data, the adversarial images can fool deep neural networks into wrong predictions with very high confidence. In this paper, however, we show that we can build a simple binary classifier separating the adversarial apart from the clean data with accuracy over 99%. We also empirically show that the binary classifier is robust to a second-round adversarial attack. In other words, it is difficult to disguise adversarial samples to bypass the binary classifier. Further more, we empirically investigate the generalization limitation which lingers on all current defensive methods, including the binary classifier approach. And we hypothesize that this is the result of intrinsic property of adversarial crafting algorithms.


title: Step-Response Model Development for Dynamic Matrix Control of a Drum-Type Boiler–Turbine System

abstract: This paper presents the application of dynamic matrix control (DMC) to a drum-type boiler-turbine system. Two types of step-response models for DMC are investigated in designing the DMC; one is developed with the linearization of the nonlinear plant model and the other is developed with the process step-response data. Then, the control performances of the DMC based on both types of models are evaluated. Because of severe nonlinearity of drum water-level dynamics, it is observed that the simulation with the step-response model based on the test data shows satisfactory results, while the linearized model is not suitable for controller design of the drum-type boiler-turbine system.


title: Implementation issues of sliding mode observer for sensorless field oriented control of PMSM using TMS320F2812

abstract: The sliding mode observer is a very promising approach for position sensorless control of Permanent Magnet Synchronous Motors due to its robustness and parameter insensitivity. However it was observed that estimation of rotor flux position was affected due to measurement noise and phase shift introduced due to low pass filtering. In this paper, the authors discuss key issues related to implementation of sliding mode observer based sensorless field oriented control of Permanent Magnet Synchronous Motor and propose different approaches to improve rotor flux estimation. An experimental lab setup is designed and implemented on a 400W PMSM motor. Texas Instrument's TMS320F2812 DSP is used in the drive as the central controller to fulfill the demands of exhaustive real time computations by vector control and sliding mode observer.


title: Fast, Compact, and Discriminative: Evaluation of Binary Descriptors for Mobile Applications

abstract: Local feature descriptors underpin many diverse applications, supporting object recognition, image registration, database search, 3D reconstruction, and more. The recent phenomenal growth in mobile devices and mobile computing in general has created demand for descriptors that are not only discriminative, but also compact in size and fast to extract and match. In response, a large number of binary descriptors have been proposed, each claiming to overcome some limitations of the predecessors. This paper provides a comprehensive evaluation of several promising binary designs. We show that existing evaluation methodologies are not sufficient to fully characterize descriptors’ performance and propose a new evaluation protocol and a challenging dataset. In contrast to the previous reviews, we investigate the effects of the matching criteria, operating points, and compaction methods, showing that they all have a major impact on the systems’ design and performance. Finally, we provide descriptor extraction times for both general-purpose systems and mobile devices, in order to better understand the real complexity of the extraction task. The objective is to provide a comprehensive reference and a guide that will help in selection and design of the future descriptors.