title: Normal values of the hip joint for the evaluation of X-rays in children and adults.

abstract: X-ray measurements of the hip joints of children, with special reference to the acetabular index, suggest that the upper standard deviation of normal comprises the borderline to a critical zone where extreme values of normal and pathologic hips were found together. Above the double standard deviation only severe dysplasias were present. Investigations of the shaft-neck angle and the degree of anteversion including the wide standard deviation demonstrate that it is very difficult to determine where these angles become pathologic. It is more important to look for the relationship between femoral head and acetabulum. A new measurement--the Hip Value is based on measurements of the Idelberg- Frank angle, the Wiberg angle and MZ-distance of decentralization. By statistical methods, normal and pathological joints can be separated as follows: in adult Hip Values, between 6 and 15 indicate a normal joint form; values between 16 and 21 indicate a slight deformation and values of 22 and above are indications of a severe deformation, in children in the normal range the Hip Value reaches 14; values of 15 and up are pathological.


title: Human gait modeling and gait analysis based on Kinect

abstract: Real-time monitoring of elderly movement can provide valuable information regarding an individual's degree of functional rehabilitation. Many laboratory-based studies have described various gait detection systems with different wearable inertial sensors, but only limited number of papers addressed the issues by using some non-wearable sensors. A practical method of gait information detection and gait analysis is proposed in the paper using an inexpensive Microsoft Kinect fixed on the midpoint of lower extremity rehabilitation robot. The horizontal distances between Kinect plane and every mark pasted on lower extremity are acquired. Taken the characteristics of gait distance series into consideration, the Autoregressive Moving Average (ARMA) model is established to reflect the changing rule of gait status. Combined with the Kalman filter, gait information reflecting rehabilitation status at next moment is predicted accurately. The method regarding the gait detection and gait analysis is verified by amounts of gait experiments finally.


title: UNDERSTANDING THE INFORMATIONAL SOCIAL INFLUENCE OF ONLINE REVIEW PLATFORMS

abstract: Online review platforms have become a basis for many consumers to make informed decisions. This type of platforms is rich in review messages and review contributors. For marketers, the platforms’ practical importance is its influence on business outcomes. In the individual level, however, little research has investigated the impacts of a platform on consumer decision-making process. In this research, we use the heuristic-systematic model to explain how consumers establish their decision based on processing review messages on the platform. We build a research model and propose impacts of different constructs established from the systematic and heuristic processing of review messages. Survey data from a Chinese online review platform generally supports our hypotheses, except that the heuristic cue, source credibility, fails to affect consumers’ behavioral intention. Based on the findings, we discuss implications for both researchers and practitioners. We further point out limitations and suggest opportunities for future research.


80 GHz IMPATT amplifier


title: Order-Free RNN with Visual Attention for Multi-Label Classification

abstract: In this paper, we propose the joint learning attention and recurrent neural network (RNN) models for multi-label classification. While approaches based on the use of either model exist (e.g., for the task of image captioning), training such existing network architectures typically require pre-defined label sequences. For multi-label classification, it would be desirable to have a robust inference process, so that the prediction error would not propagate and thus affect the performance. Our proposed model uniquely integrates attention and Long Short Term Memory (LSTM) models, which not only addresses the above problem but also allows one to identify visual objects of interests with varying sizes without the prior knowledge of particular label ordering. More importantly, label co-occurrence information can be jointly exploited by our LSTM model. Finally, by advancing the technique of beam search, prediction of multiple labels can be efficiently achieved by our proposed network model.


title: Effective analysis, characterization, and detection of malicious web pages

abstract: The steady evolution of the Web has paved the way for miscreants to take advantage of vulnerabilities to embed malicious content into web pages. Up on a visit, malicious web pages steal sensitive data, redirect victims to other malicious targets, or cease control of victim's system to mount future attacks. Approaches to detect malicious web pages have been reactively effective at special classes of attacks like drive-by-downloads. However, the prevalence and complexity of attacks by malicious web pages is still worrisome. The main challenges in this problem domain are (1) fine-grained capturing and characterization of attack payloads (2) evolution of web page artifacts and (3) exibility and scalability of detection techniques with a fast-changing threat landscape. To this end, we proposed a holistic approach that leverages static analysis, dynamic analysis, machine learning, and evolutionary searching and optimization to effectively analyze and detect malicious web pages. We do so by: introducing novel features to capture fine-grained snapshot of malicious web pages, holistic characterization of malicious web pages, and application of evolutionary techniques to fine-tune learning-based detection models pertinent to evolution of attack payloads. In this paper, we present key intuition and details of our approach, results obtained so far, and future work.


title: Distributional semantic pre-filtering in context-aware recommender systems

abstract: Context-aware recommender systems improve context-free recommenders by exploiting the knowledge of the contextual situation under which a user experienced and rated an item. They use data sets of contextually-tagged ratings to predict how the target user would evaluate (rate) an item in a given contextual situation, with the ultimate goal to recommend the items with the best estimated ratings. This paper describes and evaluates a pre-filtering approach to context-aware recommendation, called distributional-semantics pre-filtering (DSPF), which exploits in a novel way the distributional semantics of contextual conditions to build more precise context-aware rating prediction models. In DSPF, given a target contextual situation (of a target user), a matrix-factorization predictive model is built by using the ratings tagged with the contextual situations most similar to the target one. Then, this model is used to compute rating predictions and identify recommendations for that specific target contextual situation. In the proposed approach, the definition of the similarity of contextual situations is based on the distributional semantics of their composing conditions: situations are similar if they influence the user's ratings in a similar way. This notion of similarity has the advantage of being directly derived from the rating data; hence it does not require a context taxonomy. We analyze the effectiveness of DSPF varying the specific method used to compute the situation-to-situation similarity. We also show how DSPF can be further improved by using clustering techniques. Finally, we evaluate DSPF on several contextually-tagged data sets and demonstrate that it outperforms state-of-the-art context-aware approaches.


title: Multiple Arousal Theory and Daily-Life Electrodermal Activity Asymmetry

abstract: Using “big data” from sensors worn continuously outside the lab, researchers have observed patterns of objective physiology that challenge some of the long-standing theoretical concepts of emotion and its measurement. One challenge is that emotional arousal, when measured as sympathetic nervous system activation through electrodermal activity, can sometimes differ significantly across the two halves of the upper body. We show that traditional measures on only one side may lead to misjudgment of arousal. This article presents daily life and controlled study data, as well as existing evidence from neuroscience, supporting the influence of multiple emotional substrates in the brain causing innervation on different sides of the body. We describe how a theory of multiple arousals explains the asymmetric EDA findings.


title: Volitional control of ankle plantar flexion in a powered transtibial prosthesis during stair-ambulation

abstract: Although great advances have been made in the design and control of lower extremity prostheses, walking on different terrains, such as ramps or stairs, and transitioning between these terrains remains a major challenge for the field. In order to generalize biomimetic behaviour of active lower-limb prostheses top-down volitional control is required but has until recently been deemed unfeasible due to the difficulties involved in acquiring an adequate electromyographic (EMG) signal. In this study, we hypothesize that a transtibial amputee can extend the functionality of a hybrid controller, designed for level ground walking, to stair ascent and descent by volitionally modulating powered plantar-flexion of the prosthesis. We here present data illustrating that the participant is able to reproduce ankle push-off behaviour of the intrinsic controller during stair ascent as well as prevent inadvertent push-off during stair descent. Our findings suggest that EMG signal from the residual limb muscles can be used to transition between level-ground walking and stair ascent/descent within a single step and significantly improve prosthesis performance during stair-ambulation.


title: A Joint Sequence Fusion Model for Video Question Answering and Retrieval

abstract: We present an approach named JSFusion (Joint Sequence Fusion) that can measure semantic similarity between any pairs of multimodal sequence data (e.g. a video clip and a language sentence). Our multimodal matching network consists of two key components. First, the Joint Semantic Tensor composes a dense pairwise representation of two sequence data into a 3D tensor. Then, the Convolutional Hierarchical Decoder computes their similarity score by discovering hidden hierarchical matches between the two sequence modalities. Both modules leverage hierarchical attention mechanisms that learn to promote well-matched representation patterns while prune out misaligned ones in a bottom-up manner. Although the JSFusion is a universal model to be applicable to any multimodal sequence data, this work focuses on video-language tasks including multimodal retrieval and video QA. We evaluate the JSFusion model in three retrieval and VQA tasks in LSMDC, for which our model achieves the best performance reported so far. We also perform multiple-choice and movie retrieval tasks for the MSR-VTT dataset, on which our approach outperforms many state-of-the-art methods.


Subspace Linear Discriminant Analysis for Face Recognition


title: A bodyguard of lies: the use of honey objects in information security

abstract: Decoy objects, often labeled in computer security with the term honey, are a powerful tool for compromise detection and mitigation. There has been little exploration of overarching theories or set of principles or properties, however. This short paper (and accompanying keynote talk) briefly explore two properties of honey systems, indistinguishability and secrecy. The aim is to illuminate a broad design space that might encompass a wide array of areas in information security, including access control, the main topic of this symposium. Dr. Ari Juels is a roving chief scientist specializing in computer security. He was Chief Scientist of RSA (The Security Division of EMC), Director of RSA Laboratories, and a Distinguished Engineer at EMC, where he worked until September 2013. He joined RSA in 1996 after receiving his Ph.D. in computer science from U.C. Berkeley. His recent areas of interest include "big data" security analytics, cybersecurity, cloud security, user authentication, privacy, medical-device security, biometric security, and RFID / NFC security. As an industry scientist, Dr. Juels has helped incubate innovative new product features and products and advised on the science behind security-industry strategy. He is also a frequent public speaker, and has published highly cited scientific papers on many topics in computer security. In 2004, MIT's Technology Review Magazine named Dr. Juels one of the world's top 100 technology innovators under the age of 35. Computerworld honored him in its "40 Under 40" list of young industry leaders in 2007. He has received other distinctions, but sadly no recent ones acknowledging his youth.


title: Using PSO Algorithm for Producing Best Rules in Diagnosis of Heart Disease

abstract: Heart disease is still a growing global health issue. In the health care system, limiting human experience and expertise in manual diagnosis leads to inaccurate diagnosis, and the information about various illnesses is either inadequate or lacking in accuracy as they are collected from various types of medical equipment. Since the correct prediction of a person's condition is of great importance, equipping medical science with intelligent tools for diagnosing and treating illness can reduce doctors' mistakes and financial losses. In this paper, the Particle Swarm Optimization (PSO) algorithm, which is one of the most powerful evolutionary algorithms, is used to generate rules for heart disease. First the random rules are encoded and then they are optimized based on their accuracy using PSO algorithm. Finally we compare our results with the C4.5 algorithm.


title: T/R-module technologies today and possible evolutions

abstract: After many years of development the active electronically scanned array (AESA) radar technology reached a mature technology level. Many of today's and future radar systems will be equipped with the ASEA technology. T/R-modules are key elements in active phased array antennas for radar and electronic warfare applications. Meanwhile T/R-modules using GaAs MMICs are in mass production with high quantities. Top priority is on continuous improvement of yield figures by optimizing the spread of key performance parameters to come down with cost. To fulfill future demands on power, bandwidth, robustness, weight, multifunctional sensor capability, and overall sensor cost, new emerging semiconductor and packaging technologies have to be implemented for the next generation T/R-modules. Using GaN MMICs as HPAs and also as robust LNAs is a promising approach. Higher integration at the amplitude and phase setting section of the T/R-module is realized with GaAs core chips or even with SiGe multifunction chips. With increasing digital signal processing capability the digital beam forming will get more importance with a high impact on the T/R-modules. For lower production costs but also for sensor integration new packaging concepts are necessary. This includes the transition towards organic packages or the transition from brick style T/R-module to a tile T/R-module.


title: Lightness and Retinex Theory

abstract: Sensations of color show a strong correlation with reflectance, even though the amount of visible light reaching the eye depends on the product of reflectance and illumination. The visual system must achieve this remarkable result by a scheme that does not measure flux. Such a scheme is described as the basis of retinex theory. This theory assumes that there are three independent cone systems, each starting with a set of receptors peaking, respectively, in the long-, middle-, and short-wavelength regions of the visible spectrum. Each system forms a separate image of the world in terms of lightness that shows a strong correlation with reflectance within its particular band of wavelengths. These images are not mixed, but rather are compared to generate color sensations. The problem then becomes how the lightness of areas in these separate images can be independent of flux. This article describes the mathematics of a lightness scheme that generates lightness numbers, the biologic correlate of reflectance, independent of the flux from objects


title: A hybrid approach of topic model and matrix factorization based on two-step recommendation framework

abstract: Recommender systems become increasingly significant in solving the information explosion problem. Two typical kinds of techniques treat the recommendation problem as either a rating prediction or a ranking prediction one. In contrast, we propose a two-step framework that considers recommendation as a simulation of users' behaviors to generate ratings. The first step is to predict the probability that a user rates an item, and the second step is to predict rating values. After that, the predicted results from both steps are combined to compute the expectations of users' ratings on items, which are used to generate recommendations. Based on this framework, we propose a hybrid approach which uses topic model in the first step and matrix factorization in the second to solve the recommendation problem. Experiments with MovieLens and EachMovie datasets demonstrate the effectiveness of the proposed framework and the recommendation approach.


title: UniDrive: Synergize Multiple Consumer Cloud Storage Services

abstract: Consumer cloud storage (CCS) services have become popular among users for storing and synchronizing files via apps installed on their devices. A single CCS, however, has intrinsic limitations on networking performance, service reliability, and data security. To overcome these limitations, we present UniDrive, a CCS app that synergizes multiple CCSs (multi-cloud) by using only few simple public RESTful Web APIs. UniDrive follows a server-less, client-centric design, in which synchronization logic is purely implemented at client devices and all communication is conveyed through file upload and download operations. Strong consistency of the metadata is guaranteed via a quorum-based distributed mutual-exclusive lock mechanism. UniDrive improves reliability and security by judiciously distributing erasure coded files across multiple CCSs. To boost networking performance, UniDrive leverages all available clouds to maximize parallel transfer opportunities, but the key insight behind is the concept of data block over-provisioning and dynamic scheduling. This suite of techniques masks the diversified and varying network conditions of the underlying clouds, and exploits more the faster clouds via a simple yet effective in-channel probing scheme. Extensive experimental results on the global Amazon EC2 platform and a real-world trial by 272 users confirmed significantly superior and consistent sync performance of UniDrive over any single CCS.


title: DoublePlay: Parallelizing Sequential Logging and Replay

abstract: Deterministic replay systems record and reproduce the execution of a hardware or software system. In contrast to replaying execution on uniprocessors, deterministic replay on multiprocessors is very challenging to implement efficiently because of the need to reproduce the order or values read by shared memory operations performed by multiple threads. In this paper, we present DoublePlay, a new way to efficiently guarantee replay on commodity multiprocessors. Our key insight is that one can use the simpler and faster mechanisms of single-processor record and replay, yet still achieve the scalability offered by multiple cores, by using an additional execution to parallelize the record and replay of an application. DoublePlay timeslices multiple threads on a single processor, then runs multiple time intervals (epochs) of the program concurrently on separate processors. This strategy, which we call uniparallelism, makes logging much easier because each epoch runs on a single processor (so threads in an epoch never simultaneously access the same memory) and different epochs operate on different copies of the memory. Thus, rather than logging the order of shared-memory accesses, we need only log the order in which threads in an epoch are timesliced on the processor. DoublePlay runs an additional execution of the program on multiple processors to generate checkpoints so that epochs run in parallel. We evaluate DoublePlay on a variety of client, server, and scientific parallel benchmarks; with spare cores, DoublePlay reduces logging overhead to an average of 15% with two worker threads and 28% with four threads.


title: Privacy Concerns and Internet Use – A Model of Trade-off Factors

abstract: This study focuses on the trade-offs between personal benefits and privacy costs associated with Internet use. The results indicate that trust encourages e-commerce. When personal interest and ability to control information outweigh privacy concerns and perceptions of vulnerability, individuals are encouraged to use the Internet for e-commerce purposes.


title: Semi-supervised learning

abstract: We study semi-supervised learning when the data consists of multiple intersecting manifolds. We give a finite sample analysis to quantify the potential gain of using unlabeled data in this multi-manifold setting. We then propose a semi-supervised learning algorithm that separates different manifolds into decision sets, and performs supervised learning within each set. Our algorithm involves a novel application of Hellinger distance and size-constrained spectral clustering. Experiments demonstrate the benefit of our multimanifold semi-supervised learning approach.


title: Measuring the thickness of the human cerebral cortex from magnetic resonance images

abstract: Accurate and automated methods for measuring the thickness of human cerebral cortex could provide powerful tools for diagnosing and studying a variety of neurodegenerative and psychiatric disorders. Manual methods for estimating cortical thickness from neuroimaging data are labor intensive, requiring several days of effort by a trained anatomist. Furthermore, the highly folded nature of the cortex is problematic for manual techniques, frequently resulting in measurement errors in regions in which the cortical surface is not perpendicular to any of the cardinal axes. As a consequence, it has been impractical to obtain accurate thickness estimates for the entire cortex in individual subjects, or group statistics for patient or control populations. Here, we present an automated method for accurately measuring the thickness of the cerebral cortex across the entire brain and for generating cross-subject statistics in a coordinate system based on cortical anatomy. The intersubject standard deviation of the thickness measures is shown to be less than 0.5 mm, implying the ability to detect focal atrophy in small populations or even individual subjects. The reliability and accuracy of this new method are assessed by within-subject test–retest studies, as well as by comparison of cross-subject regional thickness measures with published values.


title: Unsupervised learning of an IS-A taxonomy from a limited domain-specific corpus

abstract: Taxonomies hierarchically organize concepts in a domain. Building and maintaining them by hand is a tedious and time-consuming task. This paper proposes a novel, unsupervised algorithm for automatically learning an IS-A taxonomy from scratch by analyzing a given text corpus. Our approach is designed to deal with infrequently occurring concepts, so it can effectively induce taxonomies even from small corpora. Algorithmically, the approach makes two important contributions. First, it performs inference based on clustering and the distributional semantics, which can capture links among concepts never mentioned together. Second, it uses a novel graph-based algorithm to detect and remove incorrect is-a relations from a taxonomy. An empirical evaluation on five corpora demonstrates the utility of our proposed approach.


title: Service Function Chaining Resource Allocation: A Survey

abstract: Service Function Chaining (SFC) is a crucial technology for future Internet. It aims to overcome the limitation of current deployment models which is rigid and static. Application of this technology relies on algorithms that can optimally mapping SFC to substrate network. This category of algorithms is referred as "Service Function Chaining Resource Allocation (SFC-RA)" algorithms or "VNF placement (VNFP)" algorithms. This paper presents a survey of current researches in SFC-RA algorithms. After presenting the formulation and related problems, several variants of SFC-RA problem are summarized. At last, we discussed several future research directions.


title: Analysis of preprocessing methods on classification of Turkish texts

abstract: Preprocessing is an important task and critical step in information retrieval and text mining. The objective of this study is to analyze the effect of preprocessing methods in text classification on Turkish texts. We compiled two large datasets from Turkish newspapers using a crawler. On these compiled data sets and using two additional datasets, we perform a detailed analysis of preprocessing methods such as stemming, stopword filtering and word weighting for Turkish text classification on several different Turkish datasets. We report the results of extensive experiments.


title: Adversarially Robust Generalization Requires More Data

abstract: Machine learning models are often susceptible to adversarial perturbations of their inputs. Even small perturbations can cause state-of-the-art classifiers with high "standard" accuracy to produce an incorrect prediction with high confidence. To better understand this phenomenon, we study adversarially robust learning from the viewpoint of generalization. We show that already in a simple natural data model, the sample complexity of robust learning can be significantly larger than that of "standard" learning. This gap is information theoretic and holds irrespective of the training algorithm or the model family. We complement our theoretical results with experiments on popular image classification datasets and show that a similar gap exists here as well. We postulate that the difficulty of training robust classifiers stems, at least partially, from this inherently larger sample complexity.


title: Ant colony algorithms in MANETs: A review

abstract: Mobile ad-hoc networks (MANETs) consist of special kind of wireless mobile nodes which form a temporary network without using any infrastructure or centralized administration. MANETs can be used in wide range of future applications as they have the capability to establish networks at anytime, anywhere without aid of any established infrastructure. It is a challenging task to find most efficient routing due to the changing topology and the dynamic behavior of the nodes in MANET. It has been found that ant colony optimization (ACO) algorithms can give better results as they are having characterization of Swarm Intelligence (SI) which is highly suitable for finding the adaptive routing for such type of volatile network. ACO algorithms are inspired by a foraging behavior of group of ants which are able to find optimal path based upon some defined metric which is evaluated during the motion of ants. ACO routing algorithms use simple agents called artificial ants which establish optimum paths between source and destination that communicate indirectly with each other by means of stigmergy. Keeping in view of the above, in this paper we provide a taxonomy of various ant colony algorithms with advantages and disadvantages of each others with respect to various metrics.


title: Concurrency design patterns, software quality attributes and their tactics

abstract: With the prevalent application of multi-core CPUs, software practitioners are facing the challenge of developing high quality multi-threaded programs. Applying concurrency design patterns is one of the best practices in multi-core software engineering. We comprehensively surveyed 28 concurrency design patterns, and provided a problem-oriented guide that navigates software developers towards the "right" pattern(s) with minimal search/reading effort. The guide also illustrates the relationship between concurrency design patterns and the quality attributes they address. Additionally, further investigation was conducted on how these concurrency design patterns implement quality attributes tactics. We present a mapping between surveyed concurrency design patterns and the tactics for two important quality attributes: performance and modifiability. The results of these studies provide an insight into concurrency design patterns for software developers who are seeking appropriate or improved solutions to multi-threaded software development issues.


title: Grid Converters and Power Electronics Grid Converters for Photovoltaic and Wind Power

abstract: This new book, coauthored by Remus Teodorescu from Aalborg University, Denmark, Marco Liserre from the Politecnica di Bari, Italy, and Pedro Rodriguez from the Universitat Politecnica di Catalu nya (UPC-BarcelonaTech), Spain, presents problems related to power electronic grid interface for wind and photovoltaic (PV)-generated electrical energy. The book's concept originated during biannual industrial/Ph.D. courses organized in Aalborg University, Institute of Energy Technology. These courses started in 2005 and lasted until the end of 2010 and were attended by more than 250 Ph.D. students and industrial participants.


title: Choline chloride-based deep eutectic solvent systems as a pretreatment for nanofibrillation of ramie fibers

abstract: As a new type of green solvents, deep eutectic solvents (DESs) were used as pretreatment media for the nanofibrillation of ramie fibers (RFs). Two DESs, choline chloride-oxalic acid dihydrate (CO) and choline chloride-urea (CU), were formed at 100 °C and then applied to decompose RFs. The DES-pretreated fibers were nanofibrillated using a planetary ball mill, and their properties were analyzed. During the DES pretreatment, CU DES could only dissolve some of hemicelluloses in raw RFs, while CO DES could degrade some oligosaccharides and a part of amorphous cellulose to loosen the fiber structure. Therefore, only the CO DES pretreatment could significantly enhance nanofibrillation of the RFs with a low hemicellulose content in this study, as indicated by the decrease of milling time (from 11 to 5 h) to obtain desirable cellulose nanofibrils (CNFs). After 7 h CO DES pretreatment at room temperature and a subsequent ball milling process, a turbid and gel-like CNF suspension with a mean equivalent spherical diameter of 262 nm was obtained from RFs, and the mass yield of CNFs reached 94.06%. The CNFs also showed a crystallinity index of 66.51% and a thermal stability with Tmax = 322.6 °C, and the produced CNF film had a tensile strength of 52 MPa. Overall, the CO DES pretreatment has great potential for use in the production of CNFs from RFs.


A Neural Network Approach to Early Risk Detection of Depression and Anorexia on Social Media Text


Overview of ImageCLEF 2018 Medical Domain Visual Question Answering Task


title: Design and Evaluation of an RDMA-aware Data Shuffling Operator for Parallel Database Systems

abstract: The commoditization of high-performance networking has sparked research interest in the RDMA capability of this hardware. One-sided RDMA primitives, in particular, have generated substantial excitement due to the ability to directly access remote memory from within an application without involving the TCP/IP stack or the remote CPU. This paper considers how to leverage RDMA to improve the analytical performance of parallel database systems. To shuffle data efficiently using RDMA, one needs to consider a complex design space that includes (1) the number of open connections, (2) the contention for the shared network interface, (3) the RDMA transport function, and (4) how much memory should be reserved to exchange data between nodes during query processing. We contribute six designs that capture salient trade-offs in this design space. We comprehensively evaluate how transport-layer decisions impact the query performance of a database system for different generations of InfiniBand. We find that a shuffling operator that uses the RDMA Send/Receive transport function over the Unreliable Datagram transport service can transmit data up to 4× faster than an RDMA-capable MPI implementation in a 16-node cluster. The response time of TPC-H queries improves by as much as 2×.


title: The oral fungal mycobiome: characteristics and relation to periodontitis in a pilot study

abstract: Background ::: The oral fungal microbiome (mycobiome) is not well characterized, particularly in relation to oral diseases such as periodontal disease. We aimed to describe and compare the oral mycobiome of subjects with and without periodontal disease.


title: LEMNA: Explaining Deep Learning based Security Applications

abstract: While deep learning has shown a great potential in various domains, the lack of transparency has limited its application in security or safety-critical areas. Existing research has attempted to develop explanation techniques to provide interpretable explanations for each classification decision. Unfortunately, current methods are optimized for non-security tasks ( e.g., image analysis). Their key assumptions are often violated in security applications, leading to a poor explanation fidelity. In this paper, we propose LEMNA, a high-fidelity explanation method dedicated for security applications. Given an input data sample, LEMNA generates a small set of interpretable features to explain how the input sample is classified. The core idea is to approximate a local area of the complex deep learning decision boundary using a simple interpretable model. The local interpretable model is specially designed to (1) handle feature dependency to better work with security applications ( e.g., binary code analysis); and (2) handle nonlinear local boundaries to boost explanation fidelity. We evaluate our system using two popular deep learning applications in security (a malware classifier, and a function start detector for binary reverse-engineering). Extensive evaluations show that LEMNA's explanation has a much higher fidelity level compared to existing methods. In addition, we demonstrate practical use cases of LEMNA to help machine learning developers to validate model behavior, troubleshoot classification errors, and automatically patch the errors of the target models.


title: STRANGER: An Automata-based String Analysis Tool for PHP ⋆

abstract: Stranger is an automata-based string analysis tool for finding and eliminating string-related security vulnerabilities in PHP applications. Stranger uses symbolic forward and backward reachability analyses to compute the possible values that the string expressions can take during program execution. Stranger can automatically (1) prove that an application is free from specified attacks or (2) generate vulnerability signatures that characterize all malicious inputs that can be used to generate attacks.


title: Obfuscation of executable code to improve resistance to static disassembly

abstract: A great deal of software is distributed in the form of executable code. The ability to reverse engineer such executables can create opportunities for theft of intellectual property via software piracy, as well as security breaches by allowing attackers to discover vulnerabilities in an application. The process of reverse engineering an executable program typically begins with disassembly, which translates machine code to assembly code. This is then followed by various decompilation steps that aim to recover higher-level abstractions from the assembly code. Most of the work to date on code obfuscation has focused on disrupting or confusing the decompilation phase. This paper, by contrast, focuses on the initial disassembly phase. Our goal is to disrupt the static disassembly process so as to make programs harder to disassemble correctly. We describe two widely used static disassembly algorithms, and discuss techniques to thwart each of them. Experimental results indicate that significant portions of executables that have been obfuscated using our techniques are disassembled incorrectly, thereby showing the efficacy of our methods.


title: Heterogeneous Unsupervised Cross-domain Transfer Learning.

abstract: Transfer learning addresses the problem of how to leverage previously acquired knowledge (a source domain) to improve the efficiency of learning in a new domain (the target domain). Although transfer learning has been widely researched in the last decade, existing research still has two restrictions: 1) the feature spaces of the domains must be homogeneous; and 2) the target domain must have at least a few labeled instances. These restrictions significantly limit transfer learning models when transferring knowledge across domains, especially in the big data era. To completely break through both of these bottlenecks, a theorem for reliable unsupervised knowledge transfer is proposed to avoid negative transfers, and a Grassmann manifold is applied to measure the distance between heterogeneous feature spaces. Based on this theorem and the Grassmann manifold, this study proposes two heterogeneous unsupervised knowledge transfer (HeUKT) models - known as RLG and GLG. The RLG uses a linear monotonic map (LMM) to reliably project two heterogeneous feature spaces onto a latent feature space and applies geodesic flow kernel (GFK) model to transfers knowledge between two the projected domains. The GLG optimizes the LMM to achieve the highest possible accuracy and guarantees that the geometric properties of the domains remain unchanged during the transfer process. To test the overall effectiveness of two models, this paper reorganizes five public datasets into ten heterogeneous cross-domain tasks across three application fields: credit assessment, text classification, and cancer detection. Extensive experiments demonstrate that the proposed models deliver superior performance over current benchmarks, and that these HeUKT models are a promising way to give computers the associative ability to judge unknown things using related known knowledge.


title: Neuroaesthetics: A Coming of Age Story

abstract: Neuroaesthetics is gaining momentum. At this early juncture, it is worth taking stock of where the field is and what lies ahead. Here, I review writings that fall under the rubric of neuroaesthetics. These writings include discussions of the parallel organizational principles of the brain and the intent and practices of artists, the description of informative anecdotes, and the emergence of experimental neuroaesthetics. I then suggest a few areas within neuroaesthetics that might be pursued profitably. Finally, I raise some challenges for the field. These challenges are not unique to neuroaesthetics. As neuroaesthetics comes of age, it might take advantage of the lessons learned from more mature domains of inquiry within cognitive neuroscience.


title: OntBot: Ontology based chatbot

abstract: A new ontology based approach is proposed to model and operate chatbots (OntBot). OntBot uses appropriate mapping technique to transform ontologies and knowledge into relational database and then use that knowledge to drive its chats. The proposed approach overcomes a number of traditional chatbots drawbacks including: the need to learn and use chatbot specific language such as AIML, high botmaster interference, and the use of non-matured technology. OntBot has the additional power of easy users interactions using their natural language, and the seamless support of different application domains. This gives the proposed approach a number of unique scalability and interoperability properties that are going to be evaluated in future phases of this research project.


title: A Load Balancing Model Based on Cloud Partitioning for the Public Cloud

abstract: Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.


title: Identifying Buzzing Stories via Anomalous Temporal Subgraph Discovery

abstract: Story identification from online user-generated content has recently raised increasing attention. Existing approaches fall into two categories. Approaches in the first category extract stories as cohesive substructures in a graph representing the strength of association between terms. The latter category includes approaches that analyze the temporal evolution of individual terms and identify stories by grouping terms with similar anomalous temporal behavior. Both categories have limitations. In this work we advance the literature on story identification by devising a novel method that profitably combines the peculiarities of the two main existing approaches, thus also addressing their weaknesses. Experiments on a dataset extracted from a real-world web-search log demonstrate the superiority of the proposed method over the state of the art.


title: A sub-100-milliwatt dual-core HOG accelerator VLSI for real-time multiple object detection

abstract: In this paper, a Histogram of Oriented Gradients (HOG) feature extraction accelerator for real-time multiple object detection is presented. The processor employs three techniques: a VLSI-oriented HOG algorithm with early classification in Support Vector Machine (SVM) classification, a dual core architecture for parallel feature extraction, and a detection-window-size scalable architecture with a reconfigurable MAC array for processing objects of different shapes. Early classification reduces the number of computations in SVM classification. The dual core architecture and the detection-window-size scalable architecture enable the processor to operate in several modes: high-speed mode, low-power mode, multiple object detection mode, and multiple shape object detection mode. These techniques expand the processor flexibility required for versatile application. The test chip was fabricated using 65 nm CMOS technology. The proposed architecture is designed to process HDTV resolution video (1920 × 1080 pixels) at 30 frames per second (fps). The performance of this accelerator is demonstrated on a pedestrian detection system.


title: A Survey of Algorithms and Analysis for Adaptive Online Learning

abstract: We present tools for the analysis of Follow-The-Regularized-Leader (FTRL), Dual Averaging, and Mirror Descent algorithms when the regularizer (equivalently, prox-function or learning rate schedule) is chosen adaptively based on the data. Adaptivity can be used to prove regret bounds that hold on every round, and also allows for data-dependent regret bounds as in AdaGrad-style algorithms (e.g., Online Gradient Descent with adaptive per-coordinate learning rates). We present results from a large number of prior works in a unified manner, using a modular and tight analysis that isolates the key arguments in easily re-usable lemmas. This approach strengthens pre-viously known FTRL analysis techniques to produce bounds as tight as those achieved by potential functions or primal-dual analysis. Further, we prove a general and exact equivalence between an arbitrary adaptive Mirror Descent algorithm and a correspond- ing FTRL update, which allows us to analyze any Mirror Descent algorithm in the same framework. The key to bridging the gap between Dual Averaging and Mirror Descent algorithms lies in an analysis of the FTRL-Proximal algorithm family. Our regret bounds are proved in the most general form, holding for arbitrary norms and non-smooth regularizers with time-varying weight.


title: Artificial Intelligence for Automatic Text Summarization

abstract: Automatic text summarization has played a critical role in helping people obtain key information from increasing huge data with the advantaged development of technology. In the past, few literatures are related to solve the problem of generating titles (short summaries) by using artificial intelligence (AI). The purpose of this study is that we proposed an AI approach for automatic text summarization. We developed an AI text summarization system architecture with three models, namely, statistical model, machine learning model, and deep learning model as well as evaluating the performance of three models. Essay titles and essay abstracts are used to train artificial intelligence deep learning model to generate the candidate titles and evaluated by ROUGE for performance evaluation. The contribution of this paper is that we proposed an AI automatic text summarization system by applying deep learning to generate short summaries from the titles and abstracts of the Web of Science (WOS) database.


title: A Survey of the Connected Vehicle Landscape—Architectures, Enabling Technologies, Applications, and Development Areas

abstract: This paper summarizes the state of the art in connected vehicles—from the need for vehicle data and applications thereof, to enabling technologies, challenges, and identified opportunities. Connectivity is increasing around the world and its expansion to vehicles is no exception. With improvements in connectivity, sensing, and computation, the future will see vehicles used as development platforms capable of generating rich data, acting based on inference, and effecting great change in transportation, the human-vehicle dynamic, the environment, and the economy. Connected vehicle technologies have already been used to improve fleet safety and efficiency, with emerging technologies additionally allowing data to be used to inform aspects of vehicle design, ownership, and use. While the demand for connected vehicles and its enabling technology has progressed significantly in recent years, there remain challenges to connected and collaborative vehicle application deployment before the full potential of connected cars may be realized. From extensibility and scalability to privacy and security, this paper informs the reader about key enabling technologies, opportunities, and challenges in the connected vehicle landscape.


title: In situ synchrotron analysis of phase transformation at high temperatures in ODS ferritic steel

abstract: Texture and microstructure evolution in a ferritic/martensitic 9wt%Cr oxide dispersion strengthened (ODS) steels during heat treatment after cold tube forming are studied via in situ synchrotron X-ray diffraction analysis. The initial textures of cold-formed ODS steel tubes present a well-known α-fiber ( //RD). During high-temperature heat treatment, this initial sharp fiber texture, inherited from cold forming, is transformed by the phase transition to austenite into a weak fiber. During cooling, a texture memory effect is observed with a strong dependency of the fiber intensity on the cooling rate. After quenching into martensite, the fiber becomes weaker than in the initial ferrite. By contrast, after slow cooling into ferrite, the fiber develops and becomes stronger than initially. For many materials, texture memory effect can be explained by a variant selection phenomenon. Accordingly, experimental results are compared to the double Kurdjumov–Sachs (DKS) model. During heating, a limited texture heritage is exhibited with essentially a Kurdjumov–Sachs (K–S) relation between the two phases. During cooling the DKS relation correctly predicts the texture fiber components. However, the strong enhancement of texture during slow cooling remains not completely explained. A Williamson–Hall analysis of the size and strain evolutions during the heating cycle showed a remarkable stability of the crystallites size which is the same in ferrite, austenite and martensite.


title: ThreatVectors: contextual workflows and visualizations for rapid cyber event triage

abstract: Cyber security operations face a daily flood of security events generated by automated security tools and analytics. These events must be rapidly and accurately triaged to remove false positives and focus investigations on those presenting the greatest risks to the enterprise and requiring immediate remediation. We introduce ThreatVectors as a contextual triage workflow and event visualization tool to aid operators in event triage. ThreatVectors use a streaming event processing framework for event correlation, aggregation and prioritization based on user definable event collections and a cyber-triage domain specific language. Triage work progress is shown using a novel progress bar matrix. Event collection visualization includes abstract event thumbnails for event overview and a dynamic filtering mechanism based on metafield hierarchies. Bulk adjudication of filtered event views and event clusters is supported. User testing on large enterprise networks indicates the approach has significant potential for aiding in identifying multievent campaigns, supporting collaborative triage and reducing total time spent triaging events.


title: Applications of the extent analysis method on fuzzy AHP

abstract: In this paper, a new approach for handling fuzzy AHP is introduced, with the use of triangular fuzzy numbers for pairwise comprison scale of fuzzy AHP, and the use of the extent analysis method for the synthetic extent value Si of the pairwise comparison. By applying the principle of the comparison of fuzzy numbers, that is, V(M1 ⩾ M2) = 1 iff m1 ⩾ m2, V(M2 ⩾ M1) = hgt(M1 ∩ M2) = μM1 (d), the vectors of weight with respect to each element under a certaine criterion are represented by d(Ai) = min V(Si ⩾ Sk), k = 1, 2,…, n; k ≠ i. This decision process is demonstrated by an example.


title: A new designed quadruped robot with elastic joints

abstract: The study of quadruped robots is an important branch of robotics. As is known to all, quadruped robots can go through the rough terrain easily and they are useful in field operation. Whether quadruped robot can interact softly with the environment is an important index to value its property. This paper presents the preliminary research of a quadruped robot, mainly focus on the mechanical design and corresponding configurations. A new kind of elastic joint is designed to improve the flexibility of the robot to adapt to the environment. This elastic joint has a widely portability and it can be used in many occasions where elastic actuators are needed. The dynamics and control of the elastic module are analyzed. The simulation results are also shown at the end of the paper to demonstrate the effectiveness of the elastic module.


title: HERMES : an infrastructure for low area overhead packet-switching networks on chip

abstract: The increasing complexity of integrated circuits drives the research of new on-chip interconnection architectures. A network on chip draws on concepts inherited from distributed systems and computer networks subject areas to interconnect IP cores in a structured and scalable way. The main goal pursued is to achieve superior bandwidth when compared to conventional on-chip bus architectures. This paper reviews the state of the art in networks on chip. Then, it describes an infrastructure called Hermes, targeted to implement packet-switching mesh and related interconnection architectures and topologies. The basic element of Hermes is a switch with five bi-directional ports, connecting to four other switches and to a local IP core. The switch employs an XY routing algorithm, and uses input queuing. The main design objective was to develop a small size switch, enabling its immediate practical use. The paper also presents the design validation of the Hermes switch and of a network on chip based on it. A Hermes NoC case study has been successfully prototyped in hardware as described in the paper, demonstrating the functionality of the approach. Quantitative data for the Hermes infrastructure is advanced.


Policy-Based Adaptation of Byzantine Fault Tolerant Systems


title: Lifetime investigation of high power IGBT modules

abstract: This article concerns lifetime investigation of high power IGBT modules. Other research investigating windturbine failures of modern wind turbines observed that the power converter counts for 15 % of all failures. A test bench capable of testing the IGBT power modules in an accelerated application specific working point was constructed. The working point was given with regards to voltage, current, power and phase. An analysis of failure mechanisms in modern IGBT modules was conducted. From this it was concluded that V CE of the IGBTs can be used as an indication of wear out of an IGBT module. Through circuit analysis and other studies it was concluded that a change of around 30 mV was to be expected. A innovative measuring system capable of measuring V CE of the IGBTs and V D of the diodes with high accuracy was implemented. Three test periods was conducted, and each period lasted 7–14 days. Every 5 minutes all V CE and V D voltage drop were measured and saved. From the measurements it was concluded that the wear out of IGBT modules can be traced through V CE and V D .


title: An Embedded Systems Laboratory to Support Rapid Prototyping of Robotics and the Internet of Things

abstract: This paper describes a new approach for a course and laboratory designed to allow students to develop low-cost prototypes of robotic and other embedded devices that feature Internet connectivity, I/O, networking, a real-time operating system (RTOS), and object-oriented C/C++. The application programming interface (API) libraries provided permit students to work at a higher level of abstraction. A low-cost 32-bit SOC RISC microcontroller module with flash memory, numerous I/O interfaces, and on-chip networking hardware is used to build prototypes. A cloud-based C/C++ compiler is used for software development. All student files are stored on a server, and any Web browser can be used for software development. Breadboards are used in laboratory projects to rapidly build prototypes of robots and embedded devices using the microcontroller, networking, and other I/O subsystems on small breakout boards. The commercial breakout boards used provide a large assortment of modern sensors, drivers, display ICs, and external I/O connectors. Resources provided include eBooks, laboratory assignments, and extensive Wiki pages with schematics and sample microcontroller application code for each breakout board.


title: Publicly Auditable Secure Multi-Party Computation

abstract: In the last few years the efficiency of secure multi-party computation (MPC) increased in several orders of magnitudes. However, this alone might not be enough if we want MPC protocols to be used in practice. A crucial property that is needed in many applications is that everyone can check that a given (secure) computation was performed correctly – even in the extreme case where all the parties involved in the computation are corrupted, and even if the party who wants to verify the result was not participating. This is especially relevant in the clients-servers setting, where many clients provide input to a secure computation performed by a few servers. An obvious example of this is electronic voting, but also in many types of auctions one may want independent verification of the result. Traditionally, this is achieved by using non-interactive zero-knowledge proofs during the computation.


title: Are you serious?: Rhetorical Questions and Sarcasm in Social Media Dialog

abstract: Effective models of social dialog must understand a broad range of rhetorical and figurative devices. Rhetorical questions (RQs) are a type of figurative language whose aim is to achieve a pragmatic goal, such as structuring an argument, being persuasive, emphasizing a point, or being ironic. While there are computational models for other forms of figurative language, rhetorical questions have received little attention to date. We expand a small dataset from previous work, presenting a corpus of 10,270 RQs from debate forums and Twitter that represent different discourse functions. We show that we can clearly distinguish between RQs and sincere questions (0.76 F1). We then show that RQs can be used both sarcastically and non-sarcastically, observing that non-sarcastic (other) uses of RQs are frequently argumentative in forums, and persuasive in tweets. We present experiments to distinguish between these uses of RQs using SVM and LSTM models that represent linguistic features and post-level context, achieving results as high as 0.76 F1 for "sarcastic" and 0.77 F1 for "other" in forums, and 0.83 F1 for both "sarcastic" and "other" in tweets. We supplement our quantitative experiments with an in-depth characterization of the linguistic variation in RQs.


title: CRISPR/Cas-induced double-strand breaks boost the frequency of gene replacements for humanizing the mouse Cnr2 gene

abstract: Abstract The CRISPR/Cas technology has been successfully used to stimulate the integration of small DNA sequences in a target locus to produce gene mutations. However, many applications require homologous recombination using large gene-targeting constructs. Here we address the potential of CRISPR/Cas-mediated double-strand breaks to enhance the genetic engineering of large target sequences using a construct for “humanizing” the mouse Cnr2 gene locus. We designed a small-guide RNA that directs the induction of double strand breaks by Cas9 in the Cnr2 coding exon. By co-transfection of the CRISPR/Cas system with the 10 kb targeting construct we were able to boost the recombination frequency more than 200-fold from 0.27% to 67%. This simple technology can thus be used for the homologous integration of large gene fragments and should greatly enhance our ability to generate any kind of genetically altered mouse models.


title: MetaBank: A Knowledge-Base of Metaphoric Language Conventions

abstract: The frequent and conventional use of nonliteral language has been a major stumbling block for natural language processing systems since the early machine translation efforts. Metaphor, metonymy, and indirect speech acts are among the most troublesome phenomena. Recent computational efforts addressing these problems have taken an approach that emphasizes the use of systematic knowledge about nonliteral language conventions. We are currently engaged in an effort to supply this knowledge in the case of conventional metaphor. We are constructing MetaBank: an empirically derived and theoretically motivated knowledge-base of English metaphorical conventions. This article describes our three-part approach to the construction of MetaBank: the collection of on-line textual resources and databases of linguistic generalizations, the development of a methodology for analyzing these resources, and the construction of a knowledge-base based on the preceding analyses.


title: Bidirectional DC-DC converter topologies and control strategies for interfacing energy storage systems in microgrids: An overview

abstract: A microgrid is defined as a local electric power distribution system with diverse distributed generation (DG), energy storage systems, and loads, which can operate as a part of the distribution system or when needed can operate in an islanded mode. Energy storage systems play a key role in improving security, stability, and power quality of the microgrid. During grid-connected mode, these storage units are charged from various DG sources as well as the main grid. During islanded mode, DG sources along with the storage units need to supply the load. Power electronic interfaces between the microgrid buses and the storage units should be able to detect the mode of operation, allow seamless transition between the modes, and allow power flow in both directions, while maintaining stability and power quality. An overview of bidirectional converter topologies relevant to microgrid energy storage application and their control strategies will be presented in this paper.


title: Treating PTSD in refugees and asylum seekers within the general health care system. A randomized controlled multicenter study.

abstract: OBJECTIVE ::: There has been uncertainty about whether refugees and asylum seekers with PTSD can be treated effectively in standard psychiatric settings in industrialized countries. In this study, Narrative Exposure Therapy (NET) was compared to Treatment As Usual (TAU) in 11 general psychiatric health care units in Norway. The focus was on changes in symptom severity and in the diagnostic status for PTSD and depression. ::: ::: ::: METHOD ::: Refugees and asylum seekers fulfilling the DSM-IV criteria for PTSD (N = 81) were randomized with an a-priori probability of 2:1 to either NET (N = 51) or TAU (N = 30). The patients were assessed with Clinician Administered PTSD Scale, Hamilton rating scale for depression and the MINI Neuropsychiatric Interview before treatment, and again at one and six months after the completion. ::: ::: ::: RESULTS ::: Both NET and TAU gave clinically relevant symptom reduction both in PTSD and in depression. NET gave significantly more symptom reduction compared to TAU as well as significantly more reduction in participants with PTSD diagnoses. No difference in treatment efficacy was found between refugees and asylum seekers. ::: ::: ::: CONCLUSIONS ::: The study indicated that refugees and asylum seekers can be treated successfully for PTSD and depression in the general psychiatric health care system; NET appeared to be a promising treatment for both groups.


title: Three level buck converter with control and soft startup

abstract: The three level buck converter can offer high efficiency and high power density in VR and POL applications. The gains are made possible by adding a flying capacitor that reduces the MOSFET voltage stress by half allowing for the use of low voltage devices, doubles the effective switching frequency, and decreases the inductor size by reducing the volt-second across the inductor. To achieve high efficiency and power density the flying capacitor must be balanced at half of the input voltage and the circuit must be started up without the MOSFETs seeing the full input voltage for protection purposes. This paper provides a new novel control method to balance the flying capacitor with the use of current control and offers a simple startup solution to protect the MOSFETs during start up. Experimental verification shows the efficiency gains and inductance reduction.


title: BIOLOGICAL FOUNDATIONS OF LANGUAGE: Clues from Sign Language

abstract: Revue des fondements biologiques du bugage humain: organisation architecturale du langage, sa representation dans le cerveau, systeme gestuel visuel developpe chez les sourds


title: Outdoor air pollution and asthma

abstract: Traffic and power generation are the main sources of urban air pollution. The idea that outdoor air pollution can cause exacerbations of pre-existing asthma is supported by an evidence base that has been accumulating for several decades, with several studies suggesting a contribution to new-onset asthma as well. In this Series paper, we discuss the effects of particulate matter (PM), gaseous pollutants (ozone, nitrogen dioxide, and sulphur dioxide), and mixed traffic-related air pollution. We focus on clinical studies, both epidemiological and experimental, published in the previous 5 years. From a mechanistic perspective, air pollutants probably cause oxidative injury to the airways, leading to inflammation, remodelling, and increased risk of sensitisation. Although several pollutants have been linked to new-onset asthma, the strength of the evidence is variable. We also discuss clinical implications, policy issues, and research gaps relevant to air pollution and asthma.


title: Tenth USA/Europe Air Traffic Management Research and Development Seminar (ATM2013) Airspace Sectorisation using Constraint-Based Local Search

abstract: Airspace sectorisation is the optimisation problem of dividing a given airspace into sectors in a way that promotes efficient air traffic management by some quantifiable measure. In this thesis, we ...


title: Vigi4Med Scraper: A Framework for Web Forum Structured Data Extraction and Semantic Representation

abstract: View at Publisher| ::: Export ::: | Download | Add to List| More... ::: ::: PLoS ONE ::: Volume 12, Issue 1, January 2017, Article number e0169658 ::: Open Access ::: Vigi4Med scraper: A framework for web forum structured data extraction and semantic representation (Article) ::: Audeh, B.a , ::: Beigbeder, M.a, ::: Zimmermann, A.a, ::: Jaillon, P.b, ::: Bousquet, C.c ::: ::: a University of Lyon, MINES Saint-Etienne, CNRS, Hubert Curien Laboratory, UMR 5516, Saint-Etienne, France ::: b Ecole Nationale Superieure des Mines de Saint-Etienne, Saint-Etienne, France ::: c INSERM, U1142, LIMICS, Paris, France ::: View additional affiliations ::: View references (36) ::: Abstract ::: ::: The extraction of information from social media is an essential yet complicated step for data analysis in multiple domains. In this paper, we present Vigi4Med Scraper, a generic open source framework for extracting structured data from web forums. Our framework is highly configurable; using a configuration file, the user can freely choose the data to extract from any web forum. The extracted data are anonymized and represented in a semantic structure using Resource Description Framework (RDF) graphs. This representation enables efficient manipulation by data analysis algorithms and allows the collected data to be directly linked to any existing semantic resource. To avoid server overload, an integrated proxy with caching functionality imposes a minimal delay between sequential requests. Vigi4Med Scraper represents the first step of Vigi4Med, a project to detect adverse drug reactions (ADRs) from social networks founded by the French drug safety agency Agence Nationale de Securite du Medicament (ANSM). Vigi4Med Scraper has successfully extracted greater than 200 gigabytes of data from the web forums of over 20 different websites. © 2017 Audeh et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


title: Distributed multi‐vehicle coordinated control via local information exchange

abstract: This paper describes a distributed coordination scheme with local information exchange for multiple vehicle systems. We introduce second-order consensus protocols that take into account motions of the information states and their derivatives, extending first-order protocols from the literature. We also derive necessary and sufficient conditions under which consensus can be reached in the context of unidirectional information exchange topologies. This work takes into account the general case where information flow may be unidirectional due to sensors with limited fields of view or vehicles with directed, power-constrained communication links. Unlike the first-order case, we show that having a (directed) spanning tree is a necessary rather than a sufficient condition for consensus seeking with second-order dynamics. This work focuses on a formal analysis of information exchange topologies that permit second-order consensus. Given its importance to the stability of the coordinated system, an analysis of the consensus term control gains is also presented, specifically the strength of the information states relative to their derivatives. As an illustrative example, consensus protocols are applied to coordinate the movements of multiple mobile robots. Copyright © 2006 John Wiley & Sons, Ltd.


title: Compact solid-State switched pulsed power and its applications

abstract: Power semiconductor devices, such as insulated-gate bipolar transistors, metal-oxide-semiconductor field-effect transistors, and static-induction thyristors, are used in different kinds of pulsed power generators developed for different applications. In addition, the semiconductor opening switch is found to have very effective applications in pulsed power generation by inductive energy storage. Semiconductor switches have greatly extended the scales of pulsed power parameters, especially in repetition rate and lifetime. They have also enabled new areas of pulsed power applications, such as accelerators, flue-gas treatment, and gas lasers.


title: Identigram/watermark removal using cross-channel correlation

abstract: We introduce a method to repair an image which has been stamped by an identigram or a watermark. Our method is based on the cross-channel correlation which assures the co-occurrence of image discontinuities and correlation of color distributions across different color channels of an image. Using blind source separation, we find the transformation of color space which separates the structures of identigram and that of the original image into two different individual color channels. To repair the image contents in the corrupted channel, we formulate the problem using Bayes' rule where the prior and the likelihood probabilities are defined based on the cross-channel correlation assumption. We compare our results with results from inpainting and texture synthesis-based hole filling techniques. Our results are pleasable for real-world examples and have the maximum PSNR for synthetic examples.


title: ACPN: A Novel Authentication Framework with Conditional Privacy-Preservation and Non-Repudiation for VANETs

abstract: In Vehicular Ad hoc NETworks (VANETs), authentication is a crucial security service for both inter-vehicle and vehicle-roadside communications. On the other hand, vehicles have to be protected from the misuse of their private data and the attacks on their privacy, as well as to be capable of being investigated for accidents or liabilities from non-repudiation. In this paper, we investigate the authentication issues with privacy preservation and non-repudiation in VANETs. We propose a novel framework with preservation and repudiation (ACPN) for VANETs. In ACPN, we introduce the public-key cryptography (PKC) to the pseudonym generation, which ensures legitimate third parties to achieve the non-repudiation of vehicles by obtaining vehicles’ real IDs. The self-generated PKC-based pseudonyms are also used as identifiers instead of vehicle IDs for the privacy-preserving authentication, while the update of the pseudonyms depends on vehicular demands. The existing ID-based signature (IBS) scheme and the ID-based online/offline signature (IBOOS) scheme are used, for the authentication between the road side units (RSUs) and vehicles, and the authentication among vehicles, respectively. Authentication, privacy preservation, non-repudiation and other objectives of ACPN have been analyzed for VANETs. Typical performance evaluation has been conducted using efficient IBS and IBOOS schemes. We show that the proposed ACPN is feasible and adequate to be used efficiently in the VANET environment.


title: Large-Scale System Problems Detection by Mining Console Logs

abstract: Surprisingly, console logs rarely help operators detect problems in large-scale datacenter services, for they often consist of the voluminous intermixing of messages from many software components written by independent developers. We propose a general methodology to mine this rich source of information to automatically detect system runtime problems. We first parse console logs by combining source code analysis with information retrieval to create composite features. We then analyze these features using machine learning to detect operational problems. We show that our method enables analyses that are impossible with previous methods because of its superior ability to create sophisticated features. We also show how to distill the results of our analysis to an operator-friendly one-page decision tree showing the critical messages associated with the detected problems. We validate our approach using the Darkstar online game server and the Hadoop File System, where we detect numerous real problems with high accuracy and few false positives. In the Hadoop case, we are able to analyze 24 million lines of console logs in 3 minutes. Our methodology works on textual console logs of any size and requires no changes to the service software, no human input, and no knowledge of the software’s internals.


title: Predicting star ratings based on annotated reviews of mobile apps

abstract: This paper presents and evaluates different computational models for review rating prediction. The models rely solely on star ratings from an annotated corpus of customer reviews of mobile apps that were collected from the Google Play Store in a related work. Fine-granular opinions and the classification of their sentiment orientation were already available. The models build upon them to make predictions based on their polarity. Predicting star ratings is of importance to the sentiment analysis community because it can better be understood how customers subjectively rate products. Rating them consistently with corresponding written reviews, however, remains a difficult task for automated predictors. This paper sheds new light in that direction.


title: Poisson-Based Regression Analysis of Aggregate Crime Rates

abstract: This article introduces the use of regression models based on the Poisson distribution as a tool for resolving common problems in analyzing aggregate crime rates. When the population size of an aggregate unit is small relative to the offense rate, crime rates must be computed from a small number of offenses. Such data are ill-suited to least-squares analysis. Poisson-based regression models of counts of offenses are preferable because they are built on assumptions about error distributions that are consistent with the nature of event counts. A simple elaboration transforms the Poisson model of offense counts to a model of per capita offense rates. To demonstrate the use and advantages of this method, this article presents analyses of juvenile arrest rates for robbery in 264 nonmetropolitan counties in four states. The negative binomial variant of Poisson regression effectively resolved difficulties that arise in ordinary least-squares analyses.


title: CityGML – Interoperable Access to 3D City Models

abstract: Virtual 3D city models provide important information for different aspects of disaster management. In this context, up-to-dateness of and flexible access to 3D city models are of utmost importance. Spatial Data Infrastructures (SDI) provide the appropriate framework to cover both aspects, integrating distributed data sources on demand. In this paper we present CityGML, a multi-purpose and multi-scale representation for the storage of and interoperable access to 3D city models in SDIs. CityGML is based on the standard GML3 of the Open Geospatial Consortium and covers the geometrical, topological, and semantic aspects of 3D city models. The class taxonomy distinguishes between buildings and other man-made artifacts, vegetation objects, waterbodies, and transportation facilities like streets and railways. Spatial as well as semantic properties are structured in five consecutive levels of detail. Throughout the paper, special focus is on the utilization of model concepts with respect to different tasks in disaster management.


title: A Graph-based Recommendation across Heterogeneous Domains

abstract: Given the users from a social network site, who have been tagged with a set of terms, how can we recommend the movies tagged with a completely different set of terms hosted by another website? Given the users from a website dedicated to Type I and Type II diabetes, how can we recommend the discussion threads from another website dedicated to gestational diabetes, where the keywords used in the two websites might be quite diverse? In other words, how can we recommend across heterogeneous domains characterized by barely overlapping feature sets? Despite the vast amount of existing work devoted to recommendation within homogeneous domains (e.g., with the same set of features), or collaborative filtering, emerging applications call for new techniques to address the problem of recommendation across heterogeneous domains, such as recommending movies hosted by one website to users from another website with barely overlapping tags. To this end, in this paper, we propose a graph-based approach for recommendation across heterogeneous domains. Specifically, for each domain, we use a bipartite graph to represent the relationships between its entities and features. Furthermore, to bridge the gap among multiple heterogeneous domains with barely overlapping sets of features, we propose to infer their semantic relatedness through concept-based interpretation distilled from online encyclopedias, e.g., Wikipedia and Baike. Finally, we propose an efficient propagation algorithm to obtain the similarity between entities from heterogeneous domains. Experimental results on both Weibo-Douban data set and Diabetes data set demonstrate the effectiveness and efficiency of our algorithm.


title: Analysis and synthesis of 3D shape families via deep-learned generative models of surfaces

abstract: We present a method for joint analysis and synthesis of geometrically diverse 3D shape families. Our method first learns part-based templates such that an optimal set of fuzzy point and part correspondences is computed between the shapes of an input collection based on a probabilistic deformation model. In contrast to previous template-based approaches, the geometry and deformation parameters of our part-based templates are learned from scratch. Based on the estimated shape correspondence, our method also learns a probabilistic generative model that hierarchically captures statistical relationships of corresponding surface point positions and parts as well as their existence in the input shapes. A deep learning procedure is used to capture these hierarchical relationships. The resulting generative model is used to produce control point arrangements that drive shape synthesis by combining and deforming parts from the input collection. The generative model also yields compact shape descriptors that are used to perform fine-grained classification. Finally, it can be also coupled with the probabilistic deformation model to further improve shape correspondence. We provide qualitative and quantitative evaluations of our method for shape correspondence, segmentation, fine-grained classification and synthesis. Our experiments demonstrate superior correspondence and segmentation results than previous state-of-the-art approaches.


title: Building a Digital Wind Farm

abstract: The purpose of this paper is to provide a high level, holistic overview of the work being undertaken in the wind energy industry. It summarises the main techniques used to simulate both aerodynamic and structural issues associated with wind turbines and farms. The motivation behind this paper is to provide new researchers with an outlook of the modelling and simulation landscape, whilst highlighting the trends and direction research is taking. Each section summarises an individual area of simulation and modelling, covering the important historical research findings and a comprehensive analysis of recent work. This segregated approach emphasises the key components of wind energy. Topics range in geometric scales and detail, ranging from atmospheric boundary layer modelling, to fatigue and fracture in the turbine blades. More recent studies have begun to combine a range of scales and physics to better approximate real systems and provide higher fidelity and accurate analyses to manufacturers and companies. This paper shows a clear trend towards coupling both scales and physics into singular models utilising high performance computing system.


title: Exploiting temporal and depth information for multi-frame face anti-spoofing

abstract: Face anti-spoofing is significant to the security of face recognition systems. Previous works on depth supervised learning have proved the effectiveness for face anti-spoofing. Nevertheless, they only considered the depth as an auxiliary supervision in the single frame. Different from these methods, we develop a new method to estimate depth information from multiple RGB frames and propose a depth-supervised architecture which can efficiently encodes spatiotemporal information for presentation attack detection. It includes two novel modules: optical flow guided feature block (OFFB) and convolution gated recurrent units (ConvGRU) module, which are designed to extract short-term and long-term motion to discriminate living and spoofing faces. Extensive experiments demonstrate that the proposed approach achieves state-of-the-art results on four benchmark datasets, namely OULU-NPU, SiW, CASIA-MFSD, and Replay-Attack.


title: Incorporating Copying Mechanism in Image Captioning for Learning Novel Objects

abstract: Image captioning often requires a large set of training image-sentence pairs. In practice, however, acquiring sufficient training pairs is always expensive, making the recent captioning models limited in their ability to describe objects outside of training corpora (i.e., novel objects). In this paper, we present Long Short-Term Memory with Copying Mechanism (LSTM-C) --- a new architecture that incorporates copying into the Convolutional Neural Networks (CNN) plus Recurrent Neural Networks (RNN) image captioning framework, for describing novel objects in captions. Specifically, freely available object recognition datasets are leveraged to develop classifiers for novel objects. Our LSTM-C then nicely integrates the standard word-by-word sentence generation by a decoder RNN with copying mechanism which may instead select words from novel objects at proper places in the output sentence. Extensive experiments are conducted on both MSCOCO image captioning and ImageNet datasets, demonstrating the ability of our proposed LSTM-C architecture to describe novel objects. Furthermore, superior results are reported when compared to state-of-the-art deep models.


title: Improving Deep Learning with Generic Data Augmentation

abstract: Deep artificial neural networks require a large corpus of training data in order to effectively learn, where collection of such training data is often expensive and laborious.Data augmentationovercomes this issue by artificially inflating the training set with label preserving transformations. Recently there has been extensive use of generic data augmentation to improveConvolutional Neural Network(CNN) task performance. This study benchmarks various popular data augmentation schemes to allow researchers to make informed decisions as to which training methods are most appropriate for their data sets. Various geometric and photometric schemes are evaluated on a coarse-grained data set using a relatively simple CNN. Experimental results, run using 4-fold cross-validation and reported in terms of Top-1 and Top-5 accuracy, indicate that croppingin geometric augmentationsignificantly increases CNN task performance.


title: Data-intensive document clustering on graphics processing unit (GPU) clusters

abstract: Document clustering is a central method to mine massive amounts of data. Due to the explosion of raw documents generated on the Internet and the necessity to analyze them efficiently in various intelligent information systems, clustering techniques have reached their limitations on single processors. Instead of single processors, general-purpose multi-core chips are increasingly deployed in response to diminishing returns in single-processor speedup due to the frequency wall, but multi-core benefits only provide linear speedups while the number of documents in the Internet is growing exponentially. Accelerating hardware devices represent a novel promise for improving the performance for data-intensive problems such as document clustering. They offer more radical designs with a higher level of parallelism but adaptation to novel programming environments. In this paper, we assess the benefits of exploiting the computational power of graphics processing units (GPUs) to study two fundamental problems in document mining, namely to calculate the term frequency-inverse document frequency (TF-IDF) and cluster a large set of documents. We transform traditional algorithms into accelerated parallel counterparts that can be efficiently executed on many-core GPU architectures. We assess our implementations on various platforms, ranging from stand-alone GPU desktops to Beowulf-like clusters equipped with contemporary GPU cards. We observe at least one order of magnitude speedups over CPU-only desktops and clusters. This demonstrates the potential of exploiting GPU clusters to efficiently solve massive document mining problems. Such speedups combined with the scalability potential and accelerator-based parallelization are unique in the domain of document-based data mining, to the best of our knowledge.


title: Multiple Resolution Residually Connected Feature Streams for Automatic Lung Tumor Segmentation From CT Images

abstract: Volumetric lung tumor segmentation and accurate longitudinal tracking of tumor volume changes from computed tomography images are essential for monitoring tumor response to therapy. Hence, we developed two multiple resolution residually connected network (MRRN) formulations called incremental-MRRN and dense-MRRN. Our networks simultaneously combine features across multiple image resolution and feature levels through residual connections to detect and segment the lung tumors. We evaluated our method on a total of 1210 non-small cell (NSCLC) lung tumors and nodules from three data sets consisting of 377 tumors from the open-source Cancer Imaging Archive (TCIA), 304 advanced stage NSCLC treated with anti- PD-1 checkpoint immunotherapy from internal institution MSKCC data set, and 529 lung nodules from the Lung Image Database Consortium (LIDC). The algorithm was trained using 377 tumors from the TCIA data set and validated on the MSKCC and tested on LIDC data sets. The segmentation accuracy compared to expert delineations was evaluated by computing the dice similarity coefficient, Hausdorff distances, sensitivity, and precision metrics. Our best performing incremental-MRRN method produced the highest DSC of 0.74 ± 0.13 for TCIA, 0.75±0.12 for MSKCC, and 0.68±0.23 for the LIDC data sets. There was no significant difference in the estimations of volumetric tumor changes computed using the incremental-MRRN method compared with the expert segmentation. In summary, we have developed a multi-scale CNN approach for volumetrically segmenting lung tumors which enables accurate, automated identification of and serial measurement of tumor volumes in the lung.


title: SMOL: a systemic methodology for ontology learning from heterogeneous sources

abstract: Organizations are demanding an efficacious knowledge management. Consequently, they are increasing their system innovation investments to turn information into useful knowledge for decision making obtained from heterogeneous Knowledge Sources (KSOs) such as databases, documents, and even ontologies. Methodological Resources (MRs) for the required knowledge discovering and recovering purposes have gradually become more elaborated and mature in the framework of Knowledge Engineering. Particularly, in the Ontology Learning (OL) field, there is a lack of integrated and open methodologies that could involve all the optional KSOs. In this sense, a systemic perspective is introduced combining MRs associated to diverse KSOs to improve the quality of an integral and continuous Knowledge Acquisition (KA) process. The main contributions provided by this work are on one hand, a novel Systemic Methodology for OL (SMOL) from heterogeneous KSOs which is applied for a case study and on the other hand, an evaluation of SMOL.


title: Competitive Strategies for Brick-and-Mortar Stores to Counter “Showrooming”

abstract: Customers often evaluate products at brick-and-mortar stores to identify their “best-fit” product but buy it for a lower price at a competing online retailer. This free-riding behavior by customers is referred to as “showrooming,” and we show that this is detrimental to the profits of the brick-and-mortar stores. We first analyze price matching as a short-term strategy to counter showrooming. Price matching allows customers to purchase a product from the store for less than the store’s posted price, so one would expect the price matching strategy to be less effective as the fraction of customers who seek the matching increases. However, our results show that with an increase in the fraction of customers who seek price matching, the store’s profits initially decrease and then increase. While price matching could be used even when customers do not exhibit showrooming behavior, we find that it is more effective when customers do showrooming. We then study exclusivity of product assortments as a long-term str...


title: Where Do Human Heuristics Come From?

abstract: Human decision-making deviates from the optimal solution, that maximizes cumulative rewards, in many situations. Here we approach this discrepancy from the perspective of bounded rationality and our goal is to provide a justification for such seemingly sub-optimal strategies. More specifically we investigate the hypothesis, that humans do not know optimal decision-making algorithms in advance, but instead employ a learned, resource-bounded approximation. The idea is formalized through combining a recently proposed meta-learning model based on Recurrent Neural Networks with a resource-bounded objective. The resulting approach is closely connected to variational inference and the Minimum Description Length principle. Empirical evidence is obtained from a two-armed bandit task. Here we observe patterns in our family of models that resemble differences between individual human participants.


title: BoundShield: Comprehensive Mitigation for Memory Disclosure Attacks via Secret Region Isolation

abstract: Address space layout randomization (ASLR) is now widely adopted in modern operating systems to thwart code reuse attacks. However, an adversary can still bypass fine-grained ASLR by exploiting memory corruption vulnerabilities and performing memory disclosure attacks. Although Execute-no-Read schemes have been proven to be an efficient solution against read-based memory disclosures, existing solutions need modifications to kernel or hypervisor. Besides, the defense of execution-based memory disclosures has been ignored. In this paper, we propose BoundShield, a self-protection scheme that provides comprehensive protection against memory disclosure attacks, especially against those based on executing arbitrary code by leveraging Intel Memory Protection Extension . BoundShield protects code memory by defending not only read-based memory disclosure attacks but also execution-based memory disclosure attacks. On one hand, read-based memory disclosures can be eliminated by hiding all code sections and code pointers in a secret region separated from the user address space. On the other hand, BoundShield prevents return addresses from being corrupted and ensures that all function pointers point to the legitimate entries whenever they are dereferenced, which significantly reduces the attack surface for execution-based memory disclosures. We have implemented a prototype of BoundShield based on a set of modifications to compiler toolchain and the standard C library. Our evaluation results show that the BoundShield can provide strong defenses against memory disclosure attacks while incurring a small performance overhead.


title: Path and travel time inference from gps probe vehicle data

abstract: We consider the problem of estimating real-time traffic conditions from sparse, noisy GPS probe vehicle data. We specifically address arterial roads, which are also known as the secondary road network (highways are considered the primary road network). We consider several estimation problems: historical traffic patterns, real-time traffic conditions, and forecasting future traffic conditions. We assume that the data available for these estimation problems is a small set of sparsely traced vehicle trajectories, which represents a small fraction of the total vehicle flow through the network. We present an expectation maximization algorithm that simultaneously learns the likely paths taken by probe vehicles as well as the travel time distributions through the network. A case study using data from San Francisco taxis is used to illustrate the performance of the algorithm.


title: Variation in Women’s Preferences Regarding Male Facial Masculinity Is Better Explained by Genetic Differences Than by Previously Identified Context-Dependent Effects

abstract: Women's preferences for masculine versus feminine male faces are highly variable. According to a dominant theory in evolutionary psychology, this variability results from adaptations that optimize preferences by calibrating them to certain contextual factors, including women's self-perceived attractiveness, short- versus long-term relationship orientation, pathogen disgust sensitivity, and stage of the menstrual cycle. The theory does not account for the possible contribution of genetic variation on women's facial masculinity preference. Using a large sample (N = 2,160) of identical and nonidentical female Finnish twins and their siblings, we showed that the proportion of variation in women's preferences regarding male facial masculinity that was attributable to genetic variation (38%) dwarfed the variation due to the combined effect of contextual factors (< 1%). These findings cast doubt on the importance of these context-dependent effects and may suggest a need for refocusing in the field toward understanding the wide genetic variation in these preferences and how this variation relates to the evolution of sexual dimorphism in faces.


title: Prioritizing test cases with string distances

abstract: Test case prioritisation aims at finding an ordering which enhances a certain property of an ordered test suite. Traditional techniques rely on the availability of code or a specification of the program under test. We propose to use string distances on the text of test cases for their comparison and elaborate a prioritisation algorithm. Such a prioritisation does not require code or a specification and can be useful for initial testing and in cases when code is difficult to instrument. In this paper, we also report on experiments performed on the "Siemens Test Suite", where the proposed prioritisation technique was compared with random permutations and four classical string distance metrics were evaluated. The obtained results, confirmed by a statistical analysis, indicate that prioritisation based on string distances is more efficient in finding defects than random ordering of the test suite: the test suites prioritized using string distances are more efficient in detecting the strongest mutants, and, on average, have a better APFD than randomly ordered test suites. The results suggest that string distances can be used for prioritisation purposes, and Manhattan distance could be the best choice.


title: Controlling your control flow graph

abstract: Code Reuse Attacks (CRAs) are software exploits in which an attacker directs program control flow through existing code without injecting malicious code to achieve his objective. In this paper, we propose Dynamic Sequence Checker (DSC), a framework to verify the validity of control flow between basic blocks in the program. Unique codes are assigned to every basic block in the program at compile time in such a way that the Hamming distance between two legally connected basic blocks is a known constant. At runtime, Hamming distance between the codes assigned to the source and destination basic blocks are calculated and compared against the known constant, to verify the control flow. Execution is aborted if the Hamming distance comparison does not match. We implemented DSC on a cycle-accurate x86 simulator. DSC has been able to detect all the CRA gadgets reported by the ROPGadget tool. The average performance overhead is 4.7% over a baseline processor.


title: Sparse Spatio-Spectral LapSVM With Semisupervised Kernel Propagation for Hyperspectral Image Classification

abstract: The information contained in the hyperspectral data allows the characterization, identification, and classification of the land covers with improved accuracy and robustness. Many methods have been explored in the hyperspectral image classification (HIC). Among these methods, spatio-spectral Laplacian support vector machine (SS-LapSVM) combines the spatial and spectral information on both the labeled and unlabeled samples together through the weight sum of a spectral regularization term and a spatial regularization term. Thus, it can achieve accurate classification with very few labeled samples and has proved to be effective in HIC. In this paper, a sparse SS-LapSVM with semisupervised Kernel Propagation (S3LapSVM-KP) is constructed to achieve higher accuracy and efficiency in HIC. First, data-driven semisupervised KP is proposed to carefully learn a kernel matrix from a small number of labeled pixels. Furthermore, a one-step sparse pruning algorithm is advanced by solving sparse weight vectors associated with network nodes in SS-LapSVM. By combining semisupervised KP with sparse coding, S3LapSVM-KP can not only automatically determine kernels from data, but also avoid overfitting and reduce computation cost resulted from the nonsparse topology of SS-LapSVM. The performance of S3LapSVM-KP is evaluated on several real hyperspectral datasets, and the results show that S3LapSVM-KP can achieve accurate and rapid classification with very few labeled data.