title: A Survey on Multiple-Antenna Techniques for Physical Layer Security

abstract: As a complement to high-layer encryption techniques, physical layer security has been widely recognized as a promising way to enhance wireless security by exploiting the characteristics of wireless channels, including fading, noise, and interference. In order to enhance the received signal power at legitimate receivers and impair the received signal quality at eavesdroppers simultaneously, multiple-antenna techniques have been proposed for physical layer security to improve secrecy performance via exploiting spatial degrees of freedom. This paper provides a comprehensive survey on various multiple-antenna techniques in physical layer security, with an emphasis on transmit beamforming designs for multiple-antenna nodes. Specifically, we provide a detailed investigation on multiple-antenna techniques for guaranteeing secure communications in point-to-point systems, dual-hop relaying systems, multiuser systems, and heterogeneous networks. Finally, future research directions and challenges are identified.


title: Fake News or Truth? Using Satirical Cues to Detect Potentially Misleading News

abstract: Satire is an attractive subject in deception detection research: it is a type of deception that intentionally incorporates cues revealing its own deceptiveness. Whereas other types of fabrications aim to instill a false sense of truth in the reader, a successful satirical hoax must eventually be exposed as a jest. This paper provides a conceptual overview of satire and humor, elaborating and illustrating the unique features of satirical news, which mimics the format and style of journalistic reporting. Satirical news stories were carefully matched and examined in contrast with their legitimate news counterparts in 12 contemporary news topics in 4 domains (civics, science, business, and “soft” news). Building on previous work in satire detection, we proposed an SVMbased algorithm, enriched with 5 predictive features (Absurdity, Humor, Grammar, Negative Affect, and Punctuation) and tested their combinations on 360 news articles. Our best predicting feature combination (Absurdity, Grammar and Punctuation) detects satirical news with a 90% precision and 84% recall (F-score=87%). Our work in algorithmically identifying satirical news pieces can aid in minimizing the potential deceptive impact of satire.


Successful sexual reproduction of the scleractinian coral Porites panamensis: Evidence of planktonic larvae and recruitment


Optimizing Expensive Queries in Complex Event Processing


title: Audio-Visual Event Localization in Unconstrained Videos

abstract: In this paper, we introduce a novel problem of audio-visual event localization in unconstrained videos. We define an audio-visual event as an event that is both visible and audible in a video segment. We collect an Audio-Visual Event (AVE) dataset to systemically investigate three temporal localization tasks: supervised and weakly-supervised audio-visual event localization, and cross-modality localization. We develop an audio-guided visual attention mechanism to explore audio-visual correlations, propose a dual multimodal residual network (DMRN) to fuse information over the two modalities, and introduce an audio-visual distance learning network to handle the cross-modality localization. Our experiments support the following findings: joint modeling of auditory and visual modalities outperforms independent modeling, the learned attention can capture semantics of sounding objects, temporal alignment is important for audio-visual fusion, the proposed DMRN is effective in fusing audio-visual features, and strong correlations between the two modalities enable cross-modality localization.


title: Configurable Error Control Scheme for NoC Signal Integrity

abstract: In this paper we propose a novel error control scheme to cope with errors affecting the communication links of a NoC. Our scheme can be configured in correction mode, detection mode, and mixed mode, depending on the particular application, thus allowing to meet different quality of service (QoS) levels in terms of error control. For each configuration mode, we propose different error control policies and we consider SEC hamming codes SEC/DED Hsiao codes, and symbol error correcting codes. We evaluate advantages and drawbacks of each approach, in terms of signal integrity, area overhead and impact on performance.


title: A GaN transistor based 90W AC/DC adapter with a buck-PFC stage and an isolated Quasi-switched-capacitor DC/DC stage

abstract: This paper presents a GaN Transistor based 90W ac/dc adapter with a Buck-PFC stage and an isolated Quasi-Switched-Capacitor (QSC) dc/dc stage. In the dc/dc stage, two different QSC converters are proposed. Compared to Flyback and LLC resonant converters, the QSC converters feature: 1) reduced voltage stress on the primary-side switches to 2/3 of the input voltage; 2) reduced voltage stress on the transformer to 1/3 of the input voltage and a lower transformer turns ratio; 3) a wide range for soft-switching operation and high efficiency; 4) a simple control strategy. The operation principles and simulation results are presented. A 90 W, 85 V/19 V, 1 MHz QSC resonant converter is built, using 100 V EPC eGaN FETs for all switches. This prototype achieves: 1) a high power density of 10.5 W/cm3; 2) wide-range soft switching and a peak efficiency of 92.8% at 900 kHz in preliminary test results. A Buck-PFC evaluation module from TI is tested with a GaN HEMT and a SiC Schottky diode. The peak efficiency reached 97.1%, and the experimental results are compared with those from the Si based version.


title: iMapReduce: A Distributed Computing Framework for Iterative Computation

abstract: Iterative computation is pervasive in many applications such as data mining, web ranking, graph analysis, online social network analysis, and so on. These iterative applications typically involve massive data sets containing millions or billions of data records. This poses demand of distributed computing frameworks for processing massive data sets on a cluster of machines. MapReduce is an example of such a framework. However, MapReduce lacks built-in support for iterative process that requires to parse data sets iteratively. Besides specifying MapReduce jobs, users have to write a driver program that submits a series of jobs and performs convergence testing at the client. This paper presents iMapReduce, a distributed framework that supports iterative processing. iMapReduce allows users to specify the iterative computation with the separated map and reduce functions, and provides the support of automatic iterative processing within a single job. More importantly, iMapReduce significantly improves the performance of iterative implementations by (1) reducing the overhead of creating new MapReduce jobs repeatedly, (2) eliminating the shuffling of static data, and (3) allowing asynchronous execution of map tasks. We implement an iMapReduce prototype based on Apache Hadoop, and show that iMapReduce can achieve up to 5 times speedup over Hadoop for implementing iterative algorithms.


title: DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified Text

abstract: Existing text generation methods tend to produce repeated and "boring" expressions. To tackle this problem, we propose a new text generation model, called Diversity-Promoting Generative Adversarial Network (DP-GAN). The proposed model assigns low reward for repeated text and high reward for "novel" text, encouraging the generator to produce diverse and informative text. Moreover, we propose a novel language-model based discriminator, which can better distinguish novel text from repeated text without the saturation problem compared with existing classifier-based discriminators. The experimental results on review generation and dialogue generation tasks demonstrate that our method can generate substantially more diverse and informative text than existing baseline methods. The code is available at this https URL


title: Modeling task complexity in crowdsourcing

abstract: Complexity is crucial to characterize tasks performed by humans through computer systems. Yet, the theory and practice of crowdsourcing currently lacks a clear understanding of task complexity, hindering the design of effective and efficient execution interfaces or fair monetary rewards. To understand how complexity is perceived and distributed over crowdsourcing tasks, we instrumented an experiment where we asked workers to evaluate the complexity of 61 real-world re-instantiated crowdsourcing tasks. We show that task complexity, while being subjective, is coherently perceived across workers; on the other hand, it is significantly influenced by task type. Next, we develop a high-dimensional regression model, to assess the influence of three classes of structural features (metadata, content, and visual) on task complexity, and ultimately use them to measure task complexity. Results show that both the appearance and the language used in task description can accurately predict task complexity. Finally, we apply the same feature set to predict task performance, based on a set of 5 years-worth tasks in Amazon MTurk. Results show that features related to task complexity can improve the quality of task performance prediction, thus demonstrating the utility of complexity as a task modeling property.


title: Machine Learning-Based Prototyping of Graphical User Interfaces for Mobile Apps

abstract: It is common practice for developers of user-facing software to transform a mock-up of a graphical user interface (GUI) into code. This process takes place both at an application's inception and in an evolutionary context as GUI changes keep pace with evolving features. Unfortunately, this practice is challenging and time-consuming. In this paper, we present an approach that automates this process by enabling accurate prototyping of GUIs via three tasks: detection , classification , and assembly . First, logical components of a GUI are detected from a mock-up artifact using either computer vision techniques or mock-up metadata. Then, software repository mining, automated dynamic analysis, and deep convolutional neural networks are utilized to accurately classify GUI-components into domain-specific types (e.g., toggle-button). Finally, a data-driven, K-nearest-neighbors algorithm generates a suitable hierarchical GUI structure from which a prototype application can be automatically assembled . We implemented this approach for Android in a system called ReDraw . Our evaluation illustrates that ReDraw achieves an average GUI-component classification accuracy of 91 percent and assembles prototype applications that closely mirror target mock-ups in terms of visual affinity while exhibiting reasonable code structure. Interviews with industrial practitioners illustrate ReDraw's potential to improve real development workflows.


title: Fusing Inertial Sensor Data in an Extended Kalman Filter for 3D Camera Tracking

abstract: In a setup where camera measurements are used to estimate 3D egomotion in an extended Kalman filter (EKF) framework, it is well-known that inertial sensors (i.e., accelerometers and gyroscopes) are especially useful when the camera undergoes fast motion. Inertial sensor data can be fused at the EKF with the camera measurements in either the correction stage (as measurement inputs) or the prediction stage (as control inputs). In general, only one type of inertial sensor is employed in the EKF in the literature, or when both are employed they are both fused in the same stage. In this paper, we provide an extensive performance comparison of every possible combination of fusing accelerometer and gyroscope data as control or measurement inputs using the same data set collected at different motion speeds. In particular, we compare the performances of different approaches based on 3D pose errors, in addition to camera reprojection errors commonly found in the literature, which provides further insight into the strengths and weaknesses of different approaches. We show using both simulated and real data that it is always better to fuse both sensors in the measurement stage and that in particular, accelerometer helps more with the 3D position tracking accuracy, whereas gyroscope helps more with the 3D orientation tracking accuracy. We also propose a simulated data generation method, which is beneficial for the design and validation of tracking algorithms involving both camera and inertial measurement unit measurements in general.


title: Design of a MOSFET-Based Pulsed Power Supply for Electroporation

abstract: The mould arrangement comprises two supports which support movable side portions of a mould and two plenum chambers each associated with one of the supports and movable therewith. Each plenum chamber extends above the mould side portion without obstructing an upwardly-opening entrance to the mould and has one or more exits which communicate with entrances of cooling passages formed in the side portion. Air supplied to the plenum chambers passes through the exits into the cooling passages and serves to cool the side portions.


title: Annotation of Computer Science Papers for Semantic Relation Extrac-tion

abstract: We designed a new annotation scheme for formalising relation structures in research papers, through the investigation of computer science papers. The annotation scheme is based on the hypothesis that identifying the role of entities and events that are described in a paper is useful for intelligent information retrieval in academic literature, and the role can be determined by the relationship between the author and the described entities or events, and relationships among them. Using the scheme, we have annotated research abstracts from the IPSJ Journal published in Japanese by the Information Processing Society of Japan. On the basis of the annotated corpus, we have developed a prototype information extraction system which has the facility to classify sentences according to the relationship between entities mentioned, to help find the role of the entity in which the searcher is interested.


title: Kolmogorov’s spline network

abstract: In this paper, an innovative neural-network architecture is proposed and elucidated. This architecture, based on the Kolmogorov's superposition theorem (1957) and called the Kolmogorov's spline network (KSN), utilizes more degrees of adaptation to data than currently used neural-network architectures (NNAs). By using cubic spline technique of approximation, both for activation and internal functions, more efficient approximation of multivariate functions can be achieved. The bound on approximation error and number of adjustable parameters, derived in this paper, favorably compares KSN with other one-hidden layer feedforward NNAs. The training of KSN, using the ensemble approach and the ensemble multinet, is described. A new explicit algorithm for constructing cubic splines is presented.


title: Data Mining and Clinical Decision Support Systems

abstract: Data mining is a process of pattern and relationship discovery within large sets of data. Because of the large volume of data generated in healthcare settings, it is not surprising that healthcare organizations have been interested in data mining to enhance physician practices, disease management, and resource utilization. This chapter discusses a variety of data mining techniques that have been used to develop clinical decision support systems, including decision trees, neural networks, logistic regression, nearest neighbor classifiers. In addition, genetic algorithms, biologic and quantum computing, and big data analytics as well as methods of evaluating and comparing the different approaches are also discussed.


title: Effects of the 2nd order harmonic leg current in a MMC converter

abstract: In this paper an exhaustive analysis of the utilization of the second harmonic component of the circulating currents in a modular multilevel converter for HVDC application is presented, with the aim of highlighting the effects on the maximum arm energy ripple, i.e. capacitors voltage ripple, and on semiconductor losses. Simulation results are presented and design remarks are indicated.


title: Intrinsic Plagiarism Detection using Complexity Analysis

abstract: We introduce Kolmogorov Complexity measures as a way of extracting structural information from texts for Intrinsic Plagiarism Detection. Kolmogorov complexity measures have been used as features in a variety of machine learning tasks including image recognition, radar signal classification, EEG classification, DNA analysis, speech recognition and some text classification tasks (Chi and Kong, 1998; Zhang, Hu, and Jin, 2003; Bhattacharya, 2000; Menconi, Benci, and Buiatti, 2008; Frank, Chui, and Witten, 2000; Dalkilic et al., 2006; Seaward and Saxton, 2007; Seaward, Inkpen, and Nayak, 2008). Intrinsic Plagiarism detection uses no external corpus for document comparison and thus plagiarism must be detected solely on the basis of style shifts within the text to be analyzed. Given the small amount of text to be analyzed, feature extraction is of particular importance. We give a theoretical background as to why complexity measures are meaningful and we introduce some experimental results on the PAN'09 Intrinsic Plagiarism Corpus. We show complexity features based on the Lempel-Ziv compression algorithm slightly increase performance over features based on normalized counts. Furthermore we believe that more sophisticated compression algorithms which are suited to com- pressing the English language show great promise for feature extraction for various text classification problems.


title: AN INTRODUCTION TO SYNTHETIC APERTURE RADAR (SAR)

abstract: This paper outlines basic principle of Synthetic Aperture Radar (SAR). Matched filter approaches for processing the received data and pulse compression technique are presented. Besides the SAR radar equation, the linear frequency modulation (LFM) waveform and matched filter response are also discussed. Finally the system design consideration of various parameters and aspects are also highlighted.


DIZK: A Distributed Zero Knowledge Proof System.


title: Understanding Organizational Level ERP Assimilation: A Multi-Case Study

abstract: Assimilation of ERP technology into organizational routine has received increased attention in the literature. The main objectives of this study are to examine the different levels of ERP assimilation in companies and to understand the primary drivers of organizational level ERP assimilation. Using a multi-case study method in this exploratory research, we conceptualize ERP assimilation at organizational level in a hierarchical model from supporting business process, to supporting operational decision marking, and to supporting business strategies. We then identify some primary drivers of organizational level ERP assimilation, including environment uncertainty, perceived usefulness, and internal managerial need. Finally, we identify some facilitating factors for organizational level ERP assimilation, including interaction with vendor, cautious implementation strategy, and strong IT champion. Theoretical contributions and managerial implications are discussed.


title: Perspectives of microbial oils for biodiesel production

abstract: Biodiesel has become more attractive recently because of its environmental benefits, and the fact that it is made from renewable resources. Generally speaking, biodiesel is prepared through transesterification of vegetable oils or animal fats with short chain alcohols. However, the lack of oil feedstocks limits the large-scale development of biodiesel to some extent. Recently, much attention has been paid to the development of microbial, oils and it has been found that many microorganisms, such as algae, yeast, bacteria, and fungi, have the ability to accumulate oils under some special cultivation conditions. Compared to other plant oils, microbial oils have many advantages, such as short life cycle, less labor required, less affection by venue, season and climate, and easier to scale up. With the rapid expansion of biodiesel, microbial oils might become one of potential oil feedstocks for biodiesel production in the future, though there are many works associated with microorganisms producing oils need to be carried out further. This review is covering the related research about different oleaginous microorganisms producing oils, and the prospects of such microbial oils used for biodiesel production are also discussed.


title: How nutrition and the maternal microbiota shape the neonatal immune system

abstract: The mucosal surfaces of mammals are densely colonized with microorganisms that are commonly referred to as the commensal microbiota. It is believed that the fetus in utero is sterile and that colonization with microorganisms starts only after birth. Nevertheless, the unborn fetus is exposed to a multitude of metabolites that originate from the commensal microbiota of the mother that reach systemic sites of the maternal body. The intestinal microbiota is strongly personalized and influenced by environmental factors, including nutrition. Members of the maternal microbiota can metabolize dietary components, pharmaceuticals and toxins, which can subsequently be passed to the developing fetus or the breast-feeding neonate. In this Review, we discuss the complex interplay between nutrition, the maternal microbiota and ingested chemicals, and summarize their effects on immunity in the offspring.


title: Extractive text summarization system to aid data extraction from full text in systematic review development

abstract: Abstract Objectives Extracting data from publication reports is a standard process in systematic review (SR) development. However, the data extraction process still relies too much on manual effort which is slow, costly, and subject to human error. In this study, we developed a text summarization system aimed at enhancing productivity and reducing errors in the traditional data extraction process. Methods We developed a computer system that used machine learning and natural language processing approaches to automatically generate summaries of full-text scientific publications. The summaries at the sentence and fragment levels were evaluated in finding common clinical SR data elements such as sample size, group size, and PICO values. We compared the computer-generated summaries with human written summaries (title and abstract) in terms of the presence of necessary information for the data extraction as presented in the Cochrane review’s study characteristics tables. Results At the sentence level, the computer-generated summaries covered more information than humans do for systematic reviews (recall 91.2% vs. 83.8%, p Conclusion Computer-generated summaries are potential alternative information sources for data extraction in systematic review development. Machine learning and natural language processing are promising approaches to the development of such an extractive summarization system.


title: Named Entity Recognition with Bidirectional LSTM-CNNs

abstract: Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance. In this paper, we present a novel neural network architecture that automatically detects word- and character-level features using a hybrid bidirectional LSTM and CNN architecture, eliminating the need for most feature engineering. We also propose a novel method of encoding partial lexicon matches in neural networks and compare it to existing approaches. Extensive evaluation shows that, given only tokenized text and publicly available word embeddings, our system is competitive on the CoNLL-2003 dataset and surpasses the previously reported state of the art performance on the OntoNotes 5.0 dataset by 2.13 F1 points. By using two lexicons constructed from publicly-available sources, we establish new state of the art performance with an F1 score of 91.62 on CoNLL-2003 and 86.28 on OntoNotes, surpassing systems that employ heavy feature engineering, proprietary lexicons, and rich entity linking information.


title: Design of Compensated Coupled-Stripline 3-dB Directional Couplers, Phase Shifters, and Magic-T's—Part II: Broadband Coupled-Line Circuits

abstract: The problem of discontinuities in broadband multisection coupled-stripline 3-dB directional couplers, phase shifters, high-pass tapered-line 3-dB directional couplers, and magic-T's, regarding the connections of coupled and terminating signal lines, is comprehensively investigated in this paper for the first time. The equivalent circuit of these discontinuities proposed in Part I has been used for accurate modeling of the broadband multisection and ultra-broadband high-pass coupled-stripline circuits. It has been shown that parasitic reactances, which result from the connections of signal and coupled lines, severely deteriorate the return losses and the isolation of such circuits and also-in case of tapered-line directional couplers-the coupling responses. Moreover, it has been proven theoretically and experimentally that these discontinuity effects can be substantially reduced by introducing compensating shunt capacitances in a number of cross sections of coupled and signal lines. Results of measurements carried out for various designed and manufactured coupled-line circuits have been very promising and have proven the efficiency of the proposed broadband compensation technique. The theoretical and measured data are given for the following coupled-stripline circuits: a decade-bandwidth asymmetric three-section 3-dB directional coupler, a decade-bandwidth three-section phase-shifter compensator, and a high-pass asymmetric tapered-line 3-dB coupler


Biometric Recognition - Security and Privacy Concerns.


title: ProductRec: Product Bundle Recommendation Based on User's Sequential Patterns in Social Networking Service Environment

abstract: With the overload of information on the Web, Recommender Systems (RSs) are becoming increasingly popular and have been employed to provide suggestions to meet different requirements. RSs are utilized in a variety of areas including movies, music, social tags, user group and products as Web services evoked on the Internet either as mobile Apps or PC-based applications. However, it is challenging to achieve personalized recommendations instead of offering up too many lowest common denominator recommendations. Understanding how products relate to each other is important because it has great impact on the performance. Furthermore, the personalized sequential behavior, which is closely related to a particular product, is essential for recommender systems. Most models simply integrate features from users and items without considering potential product bundle relationships between products exposed by users' personalized sequential behaviors. In this paper, a novel method based on Factorizing Personalized Markov Chain (FPMC) is proposed to comprehensively explore the latent bundle relations from users perspective, along with the hidden correlative semantics between products obtained from logic regression method, which provides a unified view to describe the user preferences, product/item features, and the user sequential patterns in timely manner. The involved semantic features are extracted using deep learning models. We evaluate our method on real-world Amazon datasets and our framework significantly outperforms other baseline models, especially on sparse datasets. The experimental results show that our approach qualitatively captures personalized behaviors with superior recommendation performance.


title: SketchSeeker: Finding Similar Sketches

abstract: Searching is a necessary tool for managing and navigating the massive amounts of data available in today's information age. While new searching methods have become increasingly popular and reliable in recent years, such as image-based searching, these methods may be more limited than text-based means in that they do not allow generic user input. Sketch-based searching is a method that allows users to draw generic search queries and return similar drawn images, giving more user control over their search content. In this paper, we present SketchSeeker, a system for indexing and searching across a large number of sketches quickly based on their similarity. SketchSeeker introduces a technique for indexing sketches in extremely compressed representations, which allows for fast, accurate retrieval augmented with a multilevel ranking subsystem. SketchSeeker was tested on a large set of sketches against existing sketch similarity metrics, and it shows significant improvements in terms of storage requirements, speed, and accuracy.


title: Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation

abstract: We introduce a model for constructing vector representations of words by composing characters using bidirectional LSTMs. Relative to traditional word representation models that have independent vectors for each word type, our model requires only a single vector per character type and a fixed set of parameters for the compositional model. Despite the compactness of this model and, more importantly, the arbitrary nature of the form‐function relationship in language, our “composed” word representations yield state-of-the-art results in language modeling and part-of-speech tagging. Benefits over traditional baselines are particularly pronounced in morphologically rich languages (e.g., Turkish).


title: Human cloning - the science and ethics of nuclear transplantation.

abstract: Many believe that the practice of somatic-cell nuclear transfer with the goal of generating an embryonic stem-cell line is justified. Dr. Rudolf Jaenisch discusses the science and ethics of nuclear transplantation.


title: A Small Dual-Band CPW-Fed Monopole Antenna for GSM and WLAN Applications

abstract: A unique and miniaturized dual-band coplanar waveguide (CPW)-fed antenna is presented. The proposed antenna comprises a rectangular patch that is surrounded by upper and lower ground-plane sections that are interconnected by a high-impedance microstrip line. The proposed antenna structure generates two separate impedance bandwidths to cover frequency bands of GSM and Wi-Fi/WLAN. The antenna realized is relatively small in size $(17\times 20\ {\hbox{mm}}^{2})$ and operates over frequency ranges 1.60–1.85 and 4.95–5.80 GHz, making it suitable for GSM and Wi-Fi/WLAN applications. In addition, the antenna is circularly polarized in the GSM band. Experimental results show the antenna exhibits monopole-like radiation characteristics and a good antenna gain over its operating bands. The measured and simulated results presented show good agreement.


title: Approximation Algorithms for the Maximum Carpool Matching Problem

abstract: The Maximum Carpool Matching problem is a star packing problem in directed graphs. Formally, given a directed graph $G = (V, A)$, a capacity function $ c: V \to N $, and a weight function $w : A \to R $, a feasible \emph{carpool matching} is a triple $(P, D, M)$, where $P$ (passengers) and $D$ (drivers) form a partition of $V$, and $M$ is a subset of $A \cap (P \times D)$, under the constraints that for every vertex $d \in D$, $din(d) \leq c(d)$, and for every vertex $p \in P$, $dout(p) \leq 1$. In the Maximum Carpool Matching problem we seek for a matching $(P, D, M)$ that maximizes the total weight of $M$. The problem arises when designing an online carpool service, such as Zimride~\cite{zimride}, that tries to connect between passengers and drivers based on (arbitrary) similarity function. The problem is known to be NP-hard, even for uniform weights and without capacity constraints. We present a $3$-approximation algorithm for the problem and $2$-approximation algorithm for the unweighted variant of the problem.


title: A fast rendering method for refractive and reflective caustics due to water surfaces

abstract: In order to synthesize realistic images of scenes that include water surfaces, the rendering of optical effectscaused by waves on the water surface, such as caustics and reflection, is necessary. However, rendering causticsis quite complex and time-consuming. In recent years, the performance of graphics hardware has made significantprogress. This fact encourages researchers to study the acceleration of realistic image synthesis. We present herea method for the fast rendering of refractive and reflective caustics due to water surfaces. In the proposed method,an object is expressed by a set of texture mapped slices. We calculate the intensities of the caustics on the objectby using the slices and store the intensities as textures. This makes it possible to render caustics at interactive rateby using graphics hardware. Moreover, we render objects that are reflected and refracted due to the water surfaceby using reflection/refraction mapping of these slices. ::: ::: ::: ::: Categories and Subject Descriptors (according to ACM CCS): I.3.1 [Computer Graphics]: Hardware Architecture I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism


title: A Trust Based Security Scheme for RREQ Flooding Attack in MANET

abstract: Mobile ad hoc are gaining popularity because of availability of low cost mobile devices and its ability to provide instant wireless networking capabilities where implementation of wired network is not possible or costly. MANETs are vulnerable to various types of attack because of its features like continuous changing topology, resource constraints and unavailability of any centralized infrastructure. Many denial of service type of attacks are possible in the MANET and one of these type attack is flooding attack in which malicious node sends the useless packets to consume the valuable network resources. Flooding attack is possible in all most all on demand routing protocol. In this paper we present a novel technique to mitigate the effect of RREQ flooding attack in MANET using trust estimation function in DSR on demand routing protocol.


title: Automatic Number Plate Recognition for Motorcyclists Riding Without Helmet

abstract: Motorcycles have always been the primary mode of transport in developing countries. In recent years, there has been a rise in motorcycle accidents. One of the major reasons for fatalities in accidents is the motorcyclist not wearing a protective helmet. The most prevalent method for ensuring that motorcyclists wear helmet is traffic police manually monitoring motorcyclists at road junctions or through CCTV footage and penalizing those without helmet. But, it requires human intervention and efforts. This paper proposes an automated system for detecting motorcyclists not wearing helmet and retrieving their motorcycle number plates from CCTV footage video. The proposed system first does background subtraction from video to get moving objects. Then, moving objects are classified as motorcyclist or non-motorcyclist. For classified motorcyclist, head portion is located and it is classified as helmet or non-helmet. Finally, for identified motorcyclist without helmet, number plate of motorcycle is detected and the characters on it are extracted. The proposed system uses Convolutional Neural Networks trained using transfer learning on top of pre-trained model for classification which has helped in achieving greater accuracy. Experimental results on traffic videos show an accuracy of 98.72% on detection of motorcyclists without helmet.


title: Regeneration of gingival papillae after single-implant treatment.

abstract: An index to assess the size of the interproximal gingival papillae adjacent to single implant restorations was described and preliminary tested in a pilot study of retrospective material comprising 25 crowns in 21 patients. The result indicated a significant spontaneous regeneration of papillae (P < .001) after a mean follow-up period of 1.5 years. Based on these results, the general conclusion was made that the proposed index allows scientific assessment of soft tissue contour adjacent to single-implant restorations. The results also indicated that soft tissue changed in a systematic manner during the time period between insertion of the crowns and follow-up 1 to 3 years later.


title: Applied Geometry:Discrete Differential Calculus for Graphics

abstract: Geometry has been extensively studied for centuries, almost exclusively from a differential point of view. However, with the advent of the digital age, the interest directed to smooth surfaces has now partially shifted due to the growing importance of discrete geometry. From 3D surfaces in graphics to higher dimensional manifolds in mechanics, computational sciences must deal with sampled geometric data on a daily basis-hence our interest in Applied Geometry. ::: ::: ::: ::: In this talk we cover different aspects of Applied Geometry. First, we discuss the problem of Shape Approximation, where an initial surface is accurately discretized (i.e., remeshed) using anisotropic elements through error minimization. Second, once we have a discrete geometry to work with, we briefly show how to develop a full- blown discrete calculus on such discrete manifolds, allowing us to manipulate functions, vector fields, or even tensors while preserving the fundamental structures and invariants of the differential case. We will emphasize the applicability of our discrete variational approach to geometry by showing results on surface parameterization, smoothing, and remeshing, as well as virtual actors and thin-shell simulation. ::: ::: ::: ::: Joint work with: Pierre Alliez (INRIA), David Cohen-Steiner (Duke U.), Eitan Grinspun (NYU), Anil Hirani (Caltech), Jerrold E. Marsden (Caltech), Mark Meyer (Pixar), Fred Pighin (USC), Peter Schroder (Caltech), Yiying Tong (USC).


title: Deep Functional Maps: Structured Prediction for Dense Shape Correspondence

abstract: We introduce a new framework for learning dense correspondence between deformable 3D shapes. Existing learning based approaches model shape correspondence as a labelling problem, where each point of a query shape receives a label identifying a point on some reference domain; the correspondence is then constructed a posteriori by composing the label predictions of two input shapes. We propose a paradigm shift and design a structured prediction model in the space of functional maps, linear operators that provide a compact representation of the correspondence. We model the learning process via a deep residual network which takes dense descriptor fields defined on two shapes as input, and outputs a soft map between the two given objects. The resulting correspondence is shown to be accurate on several challenging benchmarks comprising multiple categories, synthetic models, real scans with acquisition artifacts, topological noise, and partiality.


title: Small size NFC antenna with high performance

abstract: In this paper, a new type of NFC antenna that has small size but high performance is proposed. In particular, the antenna consists of a ferrite and an 8 shape coil. When this NFC antenna is placed closely to ground plane, stronger positive eddy current is generated on the ground plane. Compared to previous NFC antenna design, therefore, very significant improvement in antenna performance can be obtained. Antenna prototypes are fabricated and measured; and the improvement is confirmed by the measurement results.


title: Visual Question Answering as a Meta Learning Task

abstract: The predominant approach to Visual Question Answering (VQA) demands that the model represents within its weights all of the information required to answer any question about any image. Learning this information from any real training set seems unlikely, and representing it in a reasonable number of weights doubly so. We propose instead to approach VQA as a meta learning task, thus separating the question answering method from the information required. At test time, the method is provided with a support set of example questions/answers, over which it reasons to resolve the given question. The support set is not fixed and can be extended without retraining, thereby expanding the capabilities of the model. To exploit this dynamically provided information, we adapt a state-of-the-art VQA model with two techniques from the recent meta learning literature, namely prototypical networks and meta networks. Experiments demonstrate the capability of the system to learn to produce completely novel answers (i.e. never seen during training) from examples provided at test time. In comparison to the existing state of the art, the proposed method produces qualitatively distinct results with higher recall of rare answers, and a better sample efficiency that allows training with little initial data. More importantly, it represents an important step towards vision-and-language methods that can learn and reason on-the-fly.


title: Design of synchronous reluctance motor for hybrid electric vehicles

abstract: The paper deals with the design of a synchronous reluctance motor suited for hybrid electric vehicles. At first, the focus is on rotor design. In particular an useful approach is proposed to choose the proper angles of the flux-barrier ends with the aim of reducing the torque ripple due to the slot harmonics. In the second part a comparison among different rotor arrangements (one, two and three flux-barriers per pole) is presented. Furthermore an improvement in terms of low ripple and high average torque is given thanks to the ”Machaon” configuration. The third part of the paper deals with the analysis of the improved layout. The current vector control method used to maximize the torque according to the voltage and current constraints is shown. Saturation effects are taken into account. The impact of electrical design on the mechanical characteristics of the rotor (and natural frequencies) is discussed as well. At last the predicted mechanical characteristics of the reluctance motor are presented: torque versus speed behavior, power losses and power factor. The efficiency map of the machine based on Finite Elements analysis is also reported.


title: Supporting Wicked Problems with Procedural Decision Support Systems

abstract: Wicked problems are immensely complex problems that do not lend themselves to resolution through traditional problem-solving approaches. This capriciousness is particularly troublesome for organizations that encounter such problems due to their very nature being highly prone to fluctuation. Further, any attempts to resolve wicked problems tend to be irreversible --- for better or for worse. Decision support systems (DSSs) have long been hailed as a panacea of sorts for highly complex problems due to their ability to assist in storing, retrieving, and manipulating information to arrive at a decision. However, traditional DSSs, being originally aimed at semi-structured problems, are rendered almost ineffectual in the face of the high levels of complexity inherent in wicked problems. Consequently, this paper outlines the argument that posits procedural rationality as an alternative underlying mechanism for developing DSSs for wicked problems. An empirical literature review is utilized to locate the required characteristics of such a DSS, to evaluate cognizance thereof in the research, and to present an integrated model for the design, development, and utilisation of procedural DSSs specifically for resolution of wicked problems.


title: Subdivision surface fitting from a dense triangle mesh

abstract: This paper presents a new approach for fitting a subdivision surface from an irregular and dense triangle mesh of arbitrary topological type. All feature edges and feature vertices of the original mesh model are first identified. A topology- and feature-preserving mesh simplification algorithm is developed to further simplify the dense triangle mesh into a coarse mesh. A subdivision surface with exactly the same topology and sharp features as that of the simplified mesh is finally fitted from a subset of the vertices of the original dense mesh. During the fitting process, both the position masks and subdivision rules are used for setting up the fitting equation. Examples are provided to demonstrate the proposed approach.


title: Learning to rank for question routing in community question answering

abstract: This paper focuses on the problem of Question Routing (QR) in Community Question Answering (CQA), which aims to route newly posted questions to the potential answerers who are most likely to answer them. Traditional methods to solve this problem only consider the text similarity features between the newly posted question and the user profile, while ignoring the important statistical features, including the question-specific statistical feature and the user-specific statistical features. Moreover, traditional methods are based on unsupervised learning, which is not easy to introduce the rich features into them. This paper proposes a general framework based on the learning to rank concepts for QR. Training sets consist of triples (q, asker, answerers) are first collected. Then, by introducing the intrinsic relationships between the asker and the answerers in each CQA session to capture the intrinsic labels/orders of the users about their expertise degree of the question q, two different methods, including the SVM-based and RankingSVM-based methods, are presented to learn the models with different example creation processes from the training set. Finally, the potential answerers are ranked using the trained models. Extensive experiments conducted on a real world CQA dataset from Stack Overflow show that our proposed two methods can both outperform the traditional query likelihood language model (QLLM) as well as the state-of-the-art Latent Dirichlet Allocation based model (LDA). Specifically, the RankingSVM-based method achieves statistical significant improvements over the SVM-based method and has gained the best performance.


title: Solar powered UAV: Design and experiments

abstract: Unmanned solar powered aircraft offer a unique set of advanced capabilities and have set general aviation records for longest continuous flight and greatest sustained altitude. However, the application of solar powered flight to small scale solar powered unmanned aerial vehicles (UAVs) has seen sparse research activity and is only partially explored. The use of solar power as an energy resource allows small scale UAVs to carry heavier, more powerful sensor payloads, and can extend flight times to over 24 hours, thereby achieving multi-day flight. This work focuses on recent developments by the Center for Distributed Robotics on a four meter wingspan solar UAV designed for low altitude aerial sensing applications. Highlighted in this paper are aspects of airframe, propulsion, and electronics hardware design as well as experiments that quantify the solar power system and airframe performance.


title: Diet and hair loss: effects of nutrient deficiency and supplement use

abstract: Patients presenting with hair loss should be screened by medical history, dietary history and physical exam for risk factors for nutrient deficiency. If warranted, laboratory studies may be performed. In patients with no risk factors, further laboratory evaluation searching for nutritional deficiencies is not warranted. For patients with nutritional deficiencies, it is clear that those deficiencies should be corrected. Further research is required to determine whether any benefit exists for nutrient supplementation in the absence of documented deficiency. At this time, patients must be informed that such research is lacking and that in fact some supplements carry the risk of worsening hair loss or the risk of toxicity.


title: A Small-Size Dual-Feed Broadband Circularly Polarized U-Slot Patch Antenna

abstract: A small-size broadband circularly polarized U-slot patch antenna with dual-feed is proposed. By introducing an additional feeding probe near the vertical slot of the conventional singly fed square U-slot patch antenna printed on a high-permittivity substrate, two series resonances in close proximity are excited. The two resonant frequencies are found to be independent of the orientation of U-slot with respect to the patch, and broadband circular polarization is achieved by introducing a nonquadrature phase difference between two feeding ports. Experimental results show that the overlapped bandwidth of ${\hbox{VSWR} }\leq 1.5$ and ${\hbox{AR} }\leq \hbox{3~dB}$ is over 20% with a small overall size of $0.33\lambda_0 \times 0.33\lambda_0 \times 0.068\lambda_0$ , where $\lambda_0$ is the free-space wavelength at the center frequency within the operating band.


title: Ontologies for Network Security and Future Challenges

abstract: Efforts have been recently made to construct ontologies for network security. The proposed ontologies are related to specific aspects of network security. Therefore, it is necessary to identify the specific aspects covered by existing ontologies for network security. A review and analysis of the principal issues, challenges, and the extent of progress related to distinct ontologies was performed. Each example was classified according to the typology of the ontologies for network security. Some aspects include identifying threats, intrusion detection systems (IDS), alerts, attacks, countermeasures, security policies, and network management tools. The research performed here proposes the use of three stages: 1. Inputs; 2. Processing; and 3. Outputs. The analysis resulted in the introduction of new challenges and aspects that may be used as the basis for future research. One major issue that was discovered identifies the need to develop new ontologies that relate to distinct aspects of network security, thereby facilitating management tasks.


title: A Single-center Analysis on the Learning Curve of Male-to-Female Penoscrotal Vaginoplasty by Multiple Surgical Measures.

abstract: OBJECTIVE ::: To asses and quantify the learning curve (LC) of the penoscrotal inversion flap vaginoplasty (PSV). ::: ::: ::: PATIENTS AND METHODS ::: We retrospectively reviewed clinical records of 69 patients who underwent PSV from January 2005 to January 2015. Two validated methods were used: a scatterplot representation and a splitting group. We selected as primary outcomes the operative time and vaginal depth. Surgical outcomes including blood losses, hospital stay, and postoperative complications such as vaginal stenosis or atresia or urethral meatus stenosis were also evaluated. ::: ::: ::: RESULTS ::: The overall median operative time was 245 minutes. Severe intraoperative complications were not reported. The overall incidence of postoperative major complications was 21.7 %, most of them being urethral issues. The splitting group analysis revealed a statistically remarkable difference between groups for the operative time (P < .01), the vaginal depth (P = .01), the hospital stay (P < .01), and the intraoperative complication rate (P = .01). On the contrary, no differences were evidenced between the cohorts for the amount of blood loss (P = .08). The scatterplot logarithmic analysis demonstrated a clear visible LC for most parameters. The operative time showed a sharp decrease within the first 20-30 cases, reaching a plateau after 40 cases. Considering the analysis of the vaginal depth, the logarithmic scatterplot curve evidenced a slight increase within the first 10 cases, reaching a clear stabilization after nearly 30-40 cases. ::: ::: ::: CONCLUSION ::: An evident LC for PSV is detectable, consisting of at least 40 cases needed to the surgical team to develop adequate skills to guarantee a safe and high-quality procedure.


title: Effect of intracoronary agents on the no-reflow phenomenon during primary percutaneous coronary intervention in patients with ST-elevation myocardial infarction: a network meta-analysis

abstract: BACKGROUND ::: Despite the restoration of epicardial flow after primary percutaneous coronary intervention (PPCI), myocardial reperfusion remains impaired in a significant proportion of patients. We performed a network meta-analysis to assess the effect of 7 intracoronary agents (adenosine, anisodamine, diltiazem, nicorandil, nitroprusside, urapidil, and verapamil) on the no-reflow phenomenon in patients with ST-elevation myocardial infarction (STEMI) undergoing PPCI. ::: ::: ::: METHODS ::: Database searches were conducted to identify randomized controlled trials (RCTs) comparing the 7 agents with each other or with standard PPCI. Outcome measures included thrombolysis in myocardial infarction flow grade (TFG), ST-segment resolution (STR), left ventricular ejection fraction (LVEF), major adverse cardiovascular events (MACEs), and adverse events. ::: ::: ::: RESULTS ::: Forty-one RCTs involving 4069 patients were analyzed. The addition of anisodamine to standard PPCI for STEMI was associated with improved post-procedural TFG, more occurrences of STR, and improvement of LVEF. The cardioprotective effect of anisodamine conferred a MACE-free survival benefit. Additionally, nitroprusside was regarded as efficient in improving coronary flow and clinical outcomes. Compared with standard care, adenosine, nicorandil, and verapamil improved coronary flow but had no corresponding benefits regarding cardiac function and clinical outcomes. The ranking probability for the 7 treatment drugs showed that anisodamine consistently ranked the highest in efficacy outcomes (TFG < 3, STR, LVEF, and MACEs). No severe adverse events, such as hypotension and malignant arrhythmia, were observed in patients treated with anisodamine. Network meta-regression analysis showed that age, the time to reperfusion, and study follow-up did not affect the treatment effects. ::: ::: ::: CONCLUSIONS ::: The intracoronary administration of anisodamine appears to improve myocardial reperfusion, cardiac function, and clinical outcomes in patients with STEMI undergoing PPCI. Given the limited quality and quantity of the included studies, more rigorous RCTs are needed to verify the role of this inexpensive and well-tolerated regimen.


title: Using Text Mining and Natural Language Processing for Health Care Claims Processing

abstract: A health care claims processing application is introduced which processes both structured and unstructured information associated with medical insurance claims. The application makes use of a natural language processing (NLP) engine, together with application-specific knowledge, written in a concept specification language. Using NLP techniques, the entities and relationships that act as indicators of recoverable claims are mined from management notes, call centre logs and patient records to identify medical claims that require further investigation. Text mining techniques can then be applied to find dependencies between different entities, and to combine indicators to provide scores to individual claims. Claims are scored to determine whether they involve potential fraud or abuse, or to determine whether claims should be paid by or in conjunction with other insurers or organizations. Dependencies between claims and other records can then be combined to create cases. Issues related to the design of the application are discussed, specifically the use of rule-based techniques which provide a capability for deeper analysis than traditionally found in statistical techniques.


title: Structured Dictionary Learning for Classification

abstract: Sparsity driven signal processing has gained tremendous popularity in the last decade. At its core, the assumption is that the signal of interest is sparse with respect to either a fixed transformation or a signal dependent dictionary. To better capture the data characteristics, various dictionary learning methods have been proposed for both reconstruction and classification tasks. For classification particularly, most approaches proposed so far have focused on designing explicit constraints on the sparse code to improve classification accuracy while simply adopting $l_0$-norm or $l_1$-norm for sparsity regularization. Motivated by the success of structured sparsity in the area of Compressed Sensing, we propose a structured dictionary learning framework (StructDL) that incorporates the structure information on both group and task levels in the learning process. Its benefits are two-fold: (i) the label consistency between dictionary atoms and training data are implicitly enforced; and (ii) the classification performance is more robust in the cases of a small dictionary size or limited training data than other techniques. Using the subspace model, we derive the conditions for StructDL to guarantee the performance and show theoretically that StructDL is superior to $l_0$-norm or $l_1$-norm regularized dictionary learning for classification. Extensive experiments have been performed on both synthetic simulations and real world applications, such as face recognition and object classification, to demonstrate the validity of the proposed DL framework.


CLOUD SECURITY AND PRIVACY Security and Privacy in Cloud Computing : Vision , Trends , and Challenges


title: Underdetermined Convolutive Blind Source Separation via Frequency Bin-Wise Clustering and Permutation Alignment

abstract: This paper presents a blind source separation method for convolutive mixtures of speech/audio sources. The method can even be applied to an underdetermined case where there are fewer microphones than sources. The separation operation is performed in the frequency domain and consists of two stages. In the first stage, frequency-domain mixture samples are clustered into each source by an expectation-maximization (EM) algorithm. Since the clustering is performed in a frequency bin-wise manner, the permutation ambiguities of the bin-wise clustered samples should be aligned. This is solved in the second stage by using the probability on how likely each sample belongs to the assigned class. This two-stage structure makes it possible to attain a good separation even under reverberant conditions. Experimental results for separating four speech signals with three microphones under reverberant conditions show the superiority of the new method over existing methods. We also report separation results for a benchmark data set and live recordings of speech mixtures.


title: Adaptive gait pattern control of a quadruped locomotion robot

abstract: The authors have proposed a control system of a quadruped locomotion robot by using nonlinear oscillators. It is composed of a leg motion controller and a gait pattern controller. The leg motion controller drives the actuators of the legs by using local feedback control. The gait pattern controller involves nonlinear oscillators with mutual interactions. In the paper, capability of adaptation of the proposed control system to variance of the environment is verified through numerical simulations and hardware experiments. With the input signals from the touch sensors at the tips of the legs, the nonlinear oscillators tune the phase differences among them through mutual entertainments. As a result, a gait pattern corresponding to the states of the system or to the properties of the environment emerges. The robot changes its gait pattern adaptively to variance of the environment and establishes a stable locomotion while suppressing the energy consumption.


title: The networked sensor tapestry (NeST): a privacy enhanced software architecture for interactive analysis of data in video-sensor networks

abstract: This paper details the architecture of a test-bed under development for secure sharing, capture, distributed processing, and archiving of surveillance data called the Networked Sensor Tapestry (NeST). The test-bed consists of core software modules including a centralized server, client interface library, a layered XML messaging scheme. Mobile hardware clients are interfaced to the NeST using a Tiny-OS based microcontroller with sensor data collected over a 1-wire data bus. Maintaining subject privacy in video and other sensor monitoring scenarios can be imperative for the successful deployment of surveillance networks. Subject privacy is integrated into the architecture and can (if desired) operate as a buffer to the server core, denying access to identity specific information to any or all modules or operators. We introduce 3 fundamental privacy concepts: The privacy buffer: is a core component of the NeST server and utilizes programmable plug-in privacy filters operating on incoming sensor data to prevent access to or transform data to remove personally identifiable information. These privacy filters are developed and specified using a privacy grammar that can connect multiple low-level data filters and features to create arbitrary data-dependent privacy definitions. The utility of the architecture is demonstrated with a connection to a variety of hardware/software clients including PDA based client hardware, remote sensor interface devices, software modules for sensor data inferencing, data visualization, sensor control and data archival applications.


title: Numerical relation extraction with minimal supervision

abstract: We study a novel task of numerical relation extraction with the goal of extracting relations where one of the arguments is a number or a quantity (e.g., atomic number(Aluminium, 13), inflation rate(India, 10.9%)). This task presents peculiar challenges not found in standard Information Extraction (IE), such as the difficulty of matching numbers in distant supervision and the importance of units. We design two extraction systems that require minimal human supervision per relation: (1) NumberRule, a rule based extractor, and (2) NumberTron, a probabilistic graphical model. We find that both systems dramatically outperform MultiR, a state-of-the-art non-numerical IE model, obtaining up to 25 points F-score improvement.


title: Semi-Supervised Clustering via Matrix Factorization

abstract: The recent years have witnessed a surge of interests of semi-supervised clustering methods, which aim to cluster the data set under the guidance of some supervisory information. Usually those supervisory information takes the form of pairwise constraints that indicate the similarity/dissimilarity between the two points. In this paper, we propose a novel matrix factorization based approach for semi-supervised clustering. In addition, we extend our algorithm to co-cluster the data sets of different types with constraints. Finally the experiments on UCI data sets and real world Bulletin Board Systems (BBS) data sets show the superiority of our proposed method.


title: Stochastic Neighbor Embedding under f-divergences

abstract: The t-distributed Stochastic Neighbor Embedding (t-SNE) is a powerful and popular method for visualizing high-dimensional data. It minimizes the Kullback-Leibler (KL) divergence between the original and embedded data distributions. In this work, we propose extending this method to other f-divergences. We analytically and empirically evaluate the types of latent structure-manifold, cluster, and hierarchical-that are well-captured using both the original KL-divergence as well as the proposed f-divergence generalization, and find that different divergences perform better for different types of structure. ::: A common concern with $t$-SNE criterion is that it is optimized using gradient descent, and can become stuck in poor local minima. We propose optimizing the f-divergence based loss criteria by minimizing a variational bound. This typically performs better than optimizing the primal form, and our experiments show that it can improve upon the embedding results obtained from the original $t$-SNE criterion as well.


title: A comparison of implicit and explicit links for web page classification

abstract: It is well known that Web-page classification can be enhanced by using hyperlinks that provide linkages between Web pages. However, in the Web space, hyperlinks are usually sparse, noisy and thus in many situations can only provide limited help in classification. In this paper, we extend the concept of linkages from explicit hyperlinks to implicit links built between Web pages. By observing that people who search the Web with the same queries often click on different, but related documents together, we draw implicit links between Web pages that are clicked after the same queries. Those pages are implicitly linked. We provide an approach for automatically building the implicit links between Web pages using Web query logs, together with a thorough comparison between the uses of implicit and explicit links in Web page classification. Our experimental results on a large dataset confirm that the use of the implicit links is better than using explicit links in classification performance, with an increase of more than 10.5% in terms of the Macro-F1 measurement.


title: √M-best candidates based soft-demapper for rotated M-QAM constellation

abstract: In the second generation terrestrial digital video broadcasting (DVB-T2) systems, one of the important features is the rotated quadrature amplitude modulation (QAM) with cyclic Q delay exploiting signal space diversity to improve detection performance. However, the complexity of soft-demapper is increased, because it needs to calculate the Euclidean distances between the received signal and all candidate points, and then to find the minimums for log-likelihood ratio (LLR) calculation. In this paper, we propose a reduced complexity soft-demapper that selects only √M-best candidates for LLR calculation of either even or odd bits. The candidate set is formed by selecting the closest points to the image made by projecting the received signal onto the constellation grid lines. In total, it only calculates the Euclidean distances for 2√M points, which hence can reduce the complexity significantly. In spite of the reduced complexity, the √M-best candidate set is optimal because it always includes the minimum-distance point in the original full set. Finally, our results show that the proposed soft-demapper achieves the same bit error rate (BER) performance as the Max-Log based full search one, but the complexity is reduced by 67.5% and 85.9%, respectively for 64-QAM and 256-QAM cases.


title: SecVisor: a tiny hypervisor to provide lifetime kernel code integrity for commodity OSes

abstract: We propose SecVisor, a tiny hypervisor that ensures code integrity for commodity OS kernels. In particular, SecVisor ensures that only user-approved code can execute in kernel mode over the entire system lifetime. This protects the kernel against code injection attacks, such as kernel rootkits. SecVisor can achieve this propertyeven against an attacker who controls everything but the CPU, the memory controller, and system memory chips. Further, SecVisor can even defend against attackers with knowledge of zero-day kernel exploits. Our goal is to make SecVisor amenable to formal verificationand manual audit, thereby making it possible to rule out known classes of vulnerabilities. To this end, SecVisor offers small code size and small external interface. We rely on memory virtualization to build SecVisor and implement two versions, one using software memory virtualization and the other using CPU-supported memory virtualization. The code sizes of the runtime portions of these versions are 1739 and 1112 lines, respectively. The size of the external interface for both versions of SecVisor is 2 hypercalls. It is easy to port OS kernels to SecVisor. We port the Linux kernel version 2.6.20 by adding 12 lines and deleting 81 lines, out of a total of approximately 4.3 million lines of code in the kernel.


title: Please cite this paper as: Donner, Jonathan. (forthcoming). Research Approaches to Mobile Use in the Developing World: A Review of the Literature. The Information Society 24(3)"

abstract: This paper reviews roughly 200 recent studies of mobile (cellular) phone use in the developing world, and identifies major concentrations of research. It categorizes studies along two dimensions. One dimension distinguishes studies of the determinants of mobile adoption from those that assess the impacts of mobile use, and from those focused on the interrelationships between mobile technologies and users. A secondary dimension identifies a subset of studies with a strong economic development perspective. The discussion considers the implications of the resulting review and typology for future research.


title: An End-to-end 3D Convolutional Neural Network for Action Detection and Segmentation in Videos

abstract: In this paper, we propose an end-to-end 3D CNN for action detection and segmentation in videos. The proposed architecture is a unified deep network that is able to recognize and localize action based on 3D convolution features. A video is first divided into equal length clips and next for each clip a set of tube proposals are generated based on 3D CNN features. Finally, the tube proposals of different clips are linked together and spatio-temporal action detection is performed using these linked video proposals. This top-down action detection approach explicitly relies on a set of good tube proposals to perform well and training the bounding box regression usually requires a large number of annotated samples. To remedy this, we further extend the 3D CNN to an encoder-decoder structure and formulate the localization problem as action segmentation. The foreground regions (i.e. action regions) for each frame are segmented first then the segmented foreground maps are used to generate the bounding boxes. This bottom-up approach effectively avoids tube proposal generation by leveraging the pixel-wise annotations of segmentation. The segmentation framework also can be readily applied to a general problem of video object segmentation. Extensive experiments on several video datasets demonstrate the superior performance of our approach for action detection and video object segmentation compared to the state-of-the-arts.


title: Growth of single-crystal pyrite films by atmospheric pressure chemical vapor deposition

abstract: Single-crystal thin films of pyrite were successfully grown by means of atmospheric pressure chemical vapor deposition (AP-CVD) using FeCl3 and CH3CSNH2 as starting materials. The pyrite films deposited onto Si(100) substrates at 673 K have smooth shinny metallic surfaces free from cracks. It was observed that the carrier concentration and Hall mobility of the films at 298 K was 5.5 × 1017cm-3 and 280 cm2/V·s, respectively. Also, the band gap and the optical absorption coefficient were 1.0 eV and 5 × 104 cm-1, respectively.


title: Parallelizing K-Means-Based Clustering on Spark

abstract: K-means is in fact a family of clustering algorithms with different distance functions and a variety of extension, e.g., fuzzy clustering and consensus clustering. Nevertheless, K-means-based clustering algorithms employ the similar two-phase iterative procedure including distance computation and centroids updating. Therefore, to explore the parallel implementations of this two-phase iterative procedure on Spark is not only universal to a wealth of clustering algorithms but also meets the practical needs addressed by big data. This paper contributes to reveal implementation details for parallelizing K-means-based clustering on Spark. In particular, we first introduce the boundary of so-called K-means-based clustering, and then present the overall parallelizable framework on Spark. We discuss the technical barrier and their alternative strategies for each step. Experimental results on both large-scale UCI datasets and text datasets demonstrate the effectiveness and efficiency of our implementations.


title: Facial markers in second- and third-trimester fetuses with trisomy 18 or 13, triploidy or Turner syndrome

abstract: OBJECTIVE ::: To examine the effectiveness of nasal bone (NB) evaluation (including NB length (NBL)), prenasal thickness (PT) measurement, the PT:NBL ratio and the prefrontal space ratio (PFSR) in the identification of fetuses with trisomy 18 or 13, triploidy or Turner syndrome. ::: ::: ::: METHODS ::: This was a retrospective study using stored midsagittal two-dimensional images of the facial profile of fetuses with trisomy 18 or 13, triploidy or Turner syndrome in the second and third trimesters. For images of acceptable quality, measurements were obtained of NBL (where NB was present), PT, the PT:NBL ratio and PFSR, and these measurements were compared with previously published normal ranges. ::: ::: ::: RESULTS ::: The search of databases identified 189 fetuses that met the study criteria: 132 (69.8%) with trisomy 18, 40 (21.2%) with trisomy 13, 10 (5.3%) with triploidy and seven (3.7%) with Turner syndrome. The NB was either absent or its measurement was below the 5(th) centile in 67 (50.8%), 20 (50.0%), five (50.0%) and two (28.6%) of the fetuses with trisomy 18, trisomy 13, triploidy and Turner syndrome, respectively. The PT measurement was above the 95(th) centile in 24 (18.2%), six (15.0%), one (10.0%) and one (14.3%) of the affected fetuses, respectively. The PFSR was abnormal in 72 (54.5%), 29 (72.5%), seven (70%) and four (57.1%) of the cases and the PT:NBL ratio was above the 95(th) centile or the nasal bone was absent in 72 (54.5%), 20 (50.0%), six (60.0%) and four (57.1%) cases, respectively. ::: ::: ::: CONCLUSION ::: Although each of the facial markers considered provides some useful information in screening for trisomy 18, trisomy 13, triploidy and Turner syndrome, the performance of none of the markers appears to be as good as that in screening for trisomy 21.


title: Continuous deployment of software intensive products and services: A systematic mapping study

abstract: Abstract The software intensive industry is moving towards the adoption of a value-driven and adaptive real-time business paradigm. The traditional view of software as an item that evolves through releases every few months is being replaced by the continuous evolution of software functionality. This study aims to classify and analyse the literature related to continuous deployment in the software domain in order to scope the phenomenon, provide an overview of the state-of-the-art, investigate the scientific evidence in the reported results and identify areas suitable for further research. We conducted a systematic mapping study and classified the continuous deployment literature. The benefits and challenges related to continuous deployment were also analysed. RESULTS: The systematic mapping study includes 50 primary studies published between 2001 and 2014. An in-depth analysis of the primary studies revealed ten recurrent themes that characterize continuous deployment and provide researchers with directions for future work. In addition, a set of benefits and challenges of which practitioners may take advantage were identified. CONCLUSION: Overall, although the topic area is very promising, it is still in its infancy, thus offering a plethora of new opportunities for both researchers and software intensive companies.


title: African cognitive ability : Research , results , divergences and recommendations

abstract: In the past different researchers have come to diverging cognitive ability estimates for people in Africa and of African descent. The paper tries to check the validity of past results by comparing them with outcomes of two new psychometric test studies from East and South Africa; with results from student assessment studies; with predictions based on those variables which, outside Africa, correlate most strongly with intelligence; and by comparing them with further indicators of cognitive ability (descriptions of everyday life and human accomplishment). Integrating these cognitive ability measures with the application of several corrections (due to the higher age of students in Africa, lower African school enrollment, selectivity of samples and higher African secular IQ rise), the best guess for an African average is IQ 75. Finally, possible environmental and genetic (evolutionary, therefore past environmental) causes are discussed and suggestions are given how to enhance cognitive development in African countries.


title: COGENT: Certified Compilation for a Functional Systems Language

abstract: We present a self-certifying compiler for the COGENT systems language. COGENT is a restricted, polymorphic, higher-order, and purely functional language with linear types and without the need for a trusted runtime or garbage collector. It compiles to efficient C code that is designed to interoperate with existing C functions. The language is suited for layered systems code with minimal sharing such as file systems or network protocol control code. For a well-typed COGENT program, the compiler produces C code, a high-level shallow embedding of its semantics in Isabelle/HOL, and a proof that the C code correctly implements this embedding. The aim is for proof engineers to reason about the full semantics of real-world systems code productively and equationally, while retaining the interoperability and leanness of C. We describe the formal verification stages of the compiler, which include automated formal refinement calculi, a switch from imperative update semantics to functional value semantics formally justified by the linear type system, and a number of standard compiler phases such as type checking and monomorphisation. The compiler certificate is a series of language-level meta proofs and per-program translation validation phases, combined into one coherent top-level theorem in Isabelle/HOL.


title: Viscoelastic properties of passive skeletal muscle in compression: stress-relaxation behaviour and constitutive modelling.

abstract: The compressive properties of skeletal muscle are important in impact biomechanics, rehabilitation engineering and surgical simulation. However, the mechanical behaviour of muscle tissue in compression remains poorly characterised. In this paper, the time-dependent properties of passive skeletal muscle were investigated using a combined experimental and theoretical approach. Uniaxial ramp and hold compression tests were performed in vitro on fresh porcine skeletal muscle at various rates and orientations of the tissue fibres. Results show that above a very small compression rate, the viscoelastic component plays a significant role in muscle mechanical properties; it represents approximately 50% of the total stress reached at a compression rate of 0.5% s(-1). A stiffening effect with compression rate is observed especially in directions closer to the muscle fibres. Skeletal muscle viscoelastic behaviour is thus dependent on compression rate and fibre orientation. A model is proposed to represent the observed experimental behaviour, which is based on the quasi-linear viscoelasticity framework. A previously developed strain-dependent Young's Moduli formulation was extended with Prony series to account for the tissue viscoelastic properties. Parameters of the model were obtained by fitting to stress-relaxation data obtained in the muscle fibre, cross-fibre and 45 degrees directions. The model then successfully predicted stress-relaxation behaviour at 60 degrees from the fibre direction (errors <25%). Simultaneous fitting to data obtained at compression rates of 0.5% s(-1), 1%s(-1) and 10% s(-1) was performed and the model provided a good fit to the data as well as good predictions of muscle behaviour at rates of 0.05% s(-1) and 5% s(-1) (errors <25%).


title: Generalized Convolutional Neural Networks for Point Cloud Data

abstract: The introduction of cheap RGB-D cameras, stereo cameras, and LIDAR devices has given the computer vision community 3D information that conventional RGB cameras cannot provide. This data is often stored as a point cloud. In this paper, we present a novel method to apply the concept of convolutional neural networks to this type of data. By creating a mapping of nearest neighbors in a dataset, and individually applying weights to spatial relationships between points, we achieve an architecture that works directly with point clouds, but closely resembles a convolutional neural net in both design and behavior. Such a method bypasses the need for extensive feature engineering, while proving to be computationally efficient and requiring few parameters.


AI Thinking for Cloud Education Platform with Personalized Learning


title: An Efficient Algorithm for Robot Maze-Solving

abstract: This paper presents an efficient IEEE standard robot maze-solving algorithm. According to the actual situation of the robot searching maze, this algorithm improved the flood fill algorithm in maze-solving. Comparing with the results of using flood-fill algorithm directly, experiments show that this algorithm works better and more efficiently, and also, it has the advantage of little searching time and high speed of maze-solving. So it can be used to some areas like robot finding path.


title: PARALLEL IMPLEMENTATION OF AN ANT COLONY OPTIMIZATION METAHEURISTIC WITH OPENMP

abstract: This paper presents a parallel implementation of an ant colony optimization metaheuristic for the solution of an industrial scheduling problem in an aluminum casting center. The usefulness and efficiency of the algorithm, in its sequential form, to solve that particular optimization problem has already been shown in previous work. However, even if this method, as well as metaheuristics in general, offers good quality of solution, it still needs considerable computational time and resources. Moreover, the structure of the algorithm makes it well suited for parallelization, so a considerable improvement can be achieved that way. In this paper an efficient and straightforward OpenMP implementation on a shared memory architecture is presented and the main algorithmic and programming issues that had to be adressed are discussed. The code is written in C and the application has been executed on a Silicon Graphics Origin2000 parallel machine.


title: Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms

abstract: Many clustering and segmentation algorithms both suffer from the limitation that the number of clusters/segments is specified by a human user. It is often impractical to expect a human with sufficient domain knowledge to be available to select the number of clusters/segments to return. We investigate techniques to determine the number of clusters or segments to return from hierarchical clustering and segmentation algorithms. We propose an efficient algorithm, the L method that finds the "knee" in a '# of clusters vs. clustering evaluation metric' graph. Using the knee is well-known, but is not a particularly well-understood method to determine the number of clusters. We explore the feasibility of this method, and attempt to determine in which situations it will and will not work. We also compare the L method to existing methods based on the accuracy of the number of clusters that are determined and efficiency. Our results show favorable performance for these criteria compared to the existing methods that were evaluated.


title: A preview and exploratory technique for large scale scientific simulations

abstract: Successful in-situ and remote visualization solutions must have minimal storage requirements and account for only a small percentage of supercomputing time. One solution that meets these requirements is to store a compact intermediate representation of the data, instead of a 3D volume itself. Recent work explores the use of attenuation functions as a data representation that summarizes the distribution of attenuation along the rays. This representation goes beyond conventional static images and allows users to dynamically explore their data, for example, to change color and opacity parameters, without accessing the original 3D data. The computation and storage costs of this method may still be prohibitively expensive for large and time-varying data sets, thus limiting its applicability in the real-world scenarios. In this paper, we present an efficient algorithm for computing attenuation functions in parallel. We exploit the fact that the distribution of attenuation can be constructed recursively from a hierarchy of blocks or intervals of the data, which is a highly parallelizeable process. We have developed a library of routines that can be used in a distance visualization scenario or can be called directly from a simulation code to generate explorable images in-situ. Through a number of examples, we demonstrate the application of this work to large-scale scientific simulations in a real-world parallel environment with thousands of processors. We also explore various compression methods for reducing the size of the RAF. Finally, we present a method for computing an alternative RAF representation, which more closely encodes the actual distribution of samples along a ray, using kernel density estimation.


title: RTOS based vehicle tracking system

abstract: The ability to track vehicles is useful in many applications including security of personal vehicles, public transportation systems, School buses and others. Therefore, the development of vehicle tracking system using the Global Positioning System (GPS) and Global System for Mobile Communications (GSM) modem is with the aim of enabling users to locate their vehicles with ease and in a useful manner. The system will provide users with the capability to track vehicle remotely through the mobile network. This project mainly deals with concept of Vehicle tracking, Monitoring and so gives a better supervision to the target vehicle. This system is based on ARM7, GSM and GPS. GSM technology is used to send information about the vehicle. This information contains exact location of the vehicle. ARM7 TDMI core LPC 2148 processor with collects the information and sends to the Monitoring system using GSM modem. The Monitoring system uses GUI (Graphical User Interface) to display the received information on Google Map.


title: Image Super-Resolution Using Dense Skip Connections

abstract: Recent studies have shown that the performance of single-image super-resolution methods can be significantly boosted by using deep convolutional neural networks. In this study, we present a novel single-image super-resolution method by introducing dense skip connections in a very deep network. In the proposed network, the feature maps of each layer are propagated into all subsequent layers, providing an effective way to combine the low-level features and high-level features to boost the reconstruction performance. In addition, the dense skip connections in the network enable short paths to be built directly from the output to each layer, alleviating the vanishing-gradient problem of very deep networks. Moreover, deconvolution layers are integrated into the network to learn the upsampling filters and to speedup the reconstruction process. Further, the proposed method substantially reduces the number of parameters, enhancing the computational efficiency. We evaluate the proposed method using images from four benchmark datasets and set a new state of the art.


title: Detecting self-mutating malware using control-flow graph matching

abstract: Next generation malware will by be characterized by the intense use of polymorphic and metamorphic techniques aimed at circumventing the current malware detectors, based on pattern matching. In order to deal with this new kind of threat, novel techniques have to be devised for the realization of malware detectors. Recent papers started to address such an issue and this paper represents a further contribution in such a field. More precisely in this paper we propose a strategy for the detection of metamorphic malicious code inside a program P based on the comparison of the control flow graphs of P against the set of control flow graphs of known malware. We also provide experimental data supporting the validity of our strategy


title: Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data

abstract: We present hidden fluid mechanics (HFM), a physics informed deep learning framework capable of encoding an important class of physical laws governing fluid motions, namely the Navier-Stokes equations. In particular, we seek to leverage the underlying conservation laws (i.e., for mass, momentum, and energy) to infer hidden quantities of interest such as velocity and pressure fields merely from spatio-temporal visualizations of a passive scaler (e.g., dye or smoke), transported in arbitrarily complex domains (e.g., in human arteries or brain aneurysms). Our approach towards solving the aforementioned data assimilation problem is unique as we design an algorithm that is agnostic to the geometry or the initial and boundary conditions. This makes HFM highly flexible in choosing the spatio-temporal domain of interest for data acquisition as well as subsequent training and predictions. Consequently, the predictions made by HFM are among those cases where a pure machine learning strategy or a mere scientific computing approach simply cannot reproduce. The proposed algorithm achieves accurate predictions of the pressure and velocity fields in both two and three dimensional flows for several benchmark problems motivated by real-world applications. Our results demonstrate that this relatively simple methodology can be used in physical and biomedical problems to extract valuable quantitative information (e.g., lift and drag forces or wall shear stresses in arteries) for which direct measurements may not be possible.


title: Common Mode EMI Noise Suppression for Bridgeless PFC Converters

abstract: The goal of this paper is to study the possibility of minimizing common mode (CM) noise emission in bridgeless power factor correction (PFC) converters. Two approaches are proposed. In the first approach, the bridgeless PFC is modified to achieve symmetry. A CM noise model for symmetric topology is derived and the conditions for symmetry are summarized. Parasitics critical to the symmetrical condition are studied and carefully controlled. As a result, CM noise can be minimized with good cancellation. The second approach is to introduce a balance technique to bridgeless PFC converters. The topology is modified so that the balance technique can be applied so as to minimize CM noise. Experimental results validate that both approaches can greatly reduce CM noise up to 30 dBmuV. The two approaches are compared in terms of both its effects on CM noise and their implementations.


title: Adaptive Feature Mapping for Customizing Deep Learning Based Facial Expression Recognition Model

abstract: Automated facial expression recognition can greatly improve the human–machine interface. The machine can provide better and more personalized services when it knows the human’s emotion. This kind of improvement is an important progress in this artificial intelligence era. Many deep learning approaches have been applied in recent years due to their outstanding recognition accuracy after training with large amounts of data. The performance is limited, however, by the specific environmental conditions and variations in different persons involved. Hence, this paper addresses the issue of how to customize the generic model without label information from the testing samples. Weighted Center Regression Adaptive Feature Mapping (W-CR-AFM) is mainly proposed to transform the feature distribution of testing samples into that of trained samples. By means of minimizing the error between each feature of testing sample and the center of the most relevant category, W-CR-AFM can bring the features of testing samples around the decision boundary to the centers of expression categories; therefore, their predicted labels can be corrected. When the model which is tuned by W-CR-AFM is tested on extended Cohn-Kanade (CK+), Radboud Faces database, and Amsterdam dynamic facial expression set, our approach can improve the recognition accuracy by about 3.01%, 0.49%, and 5.33%, respectively. Compared to the competing deep learning architectures with the same training data, our approach shows the better performance.


title: Gain weight by “going diet?” Artificial sweeteners and the neurobiology of sugar cravings

abstract: America's obesity epidemic has gathered much media attention recently. A rise in the percent of the population who are obese coincides with an increase in the widespread use of non-caloric artificial sweeteners, such as aspartame (e.g., Diet Coke) and sucralose (e.g., Pepsi One), in food products (Figure 1). Both forward and reverse causalities have been proposed. While people often choose "diet" or "light" products to lose weight, research studies suggest that artificial sweeteners may contribute to weight gain. In this mini-review, inspired by a discussion with Dr. Dana Small at Yale's Neuroscience 2010 conference in April, I first examine the development of artificial sweeteners in a historic context. I then summarize the epidemiological and experimental evidence concerning their effects on weight. Finally, I attempt to explain those effects in light of the neurobiology of food reward.


title: An International Urogynecological Association (IUGA) / International Continence Society (ICS) joint report on the terminology for female pelvic organ prolapse (POP)

abstract: INTRODUCTION ::: The terminology for female pelvic floor prolapse (POP) should be defined and organized in a clinically-based consensus Report. ::: ::: ::: METHODS ::: This Report combines the input of members of two International Organizations, the International Urogynecological Association (IUGA) and the International Continence Society (ICS), assisted at intervals by external referees. Appropriate core clinical categories and a sub-classification were developed to give a coding to definitions. An extensive process of fourteen rounds of internal and external review was involved to exhaustively examine each definition, with decision-making by collective opinion (consensus). ::: ::: ::: RESULTS ::: A Terminology Report for female POP, encompassing over 230 separate definitions, has been developed. It is clinically-based with the most common diagnoses defined. Clarity and user-friendliness have been key aims to make it interpretable by practitioners and trainees in all the different specialty groups involved in female pelvic floor dysfunction and POP. Female-specific imaging (ultrasound, radiology and MRI) and conservative and surgical managements are major additions and appropriate figures have been included to supplement and clarify the text. Emerging concepts and measurements, in use in the literature and offering further research potential, but requiring further validation, have been included as an appendix. Interval (5-10 year) review is anticipated to keep the document updated and as widely acceptable as possible. ::: ::: ::: CONCLUSION ::: A consensus-based Terminology Report for female POP has been produced to aid clinical practice and research.


title: GLADNet: Low-Light Enhancement Network with Global Awareness

abstract: In this paper, we address the problem of lowlight enhancement. Our key idea is to first calculate a global illumination estimation for the low-light input, then adjust the illumination under the guidance of the estimation and supplement the details using a concatenation with the original input. Considering that, we propose a GLobal illumination Aware and Detail-preserving Network (GLADNet). The input image is rescaled to a certain size and then put into an encoder-decoder network to generate global priori knowledge of the illumination. Based on the global prior and the original input image, a convolutional network is employed for detail reconstruction. For training GLADNet, we use a synthetic dataset generated from RAW images. Extensive experiments demonstrate the superiority of our method over other compared methods on the real low-light images captured in various conditions.


title: Data Change Exploration Using Time Series Clustering

abstract: Analysis of static data is one of the best studied research areas. However, data changes over time. These changes may reveal patterns or groups of similar values, properties, and entities. We study changes in large, publicly available data repositories by modelling them as time series and clustering these series by their similarity. In order to perform change exploration on real-world data we use the publicly available revision data of Wikipedia Infoboxes and weekly snapshots of IMDB.


Plasma Gasification as a Viable Waste-to-Energy Treatment of Municipal Solid Waste