title: PLBP: An effective local binary patterns texture descriptor with pyramid representation

abstract: Local binary pattern (LBP) is an effective texture descriptor which has successful applications in texture classification and face recognition. Many extensions are made for conventional LBP descriptors. One of the extensions is dominant local binary patterns which aim at extracting the dominant local structures in texture images. The second extension is representing LBP descriptors in Gabor transform domain (LGBP). The third extension is multi-resolution LBP (MLBP). Another extension is dynamic LBP for video texture extraction. In this paper, we extend the conventional local binary pattern to pyramid transform domain (PLBP). By cascading the LBP information of hierarchical spatial pyramids, PLBP descriptors take texture resolution variations into account. PLBP descriptors show their effectiveness for texture representation. Comprehensive comparisons are made for LBP, MLBP, LGBP, and PLBP. Performances of no sampling, partial sampling and spatial pyramid sampling approaches for the construction of PLBP texture descriptors are compared. The influences of pyramid generation approaches, and pyramid levels to PLBP based image categorization performances are discussed. Compared to the existing multi-resolution LBP descriptors, PLBP is with satisfactory performances and with low computational costs.


title: MapReduce Algorithms for GIS Polygonal Overlay Processing

abstract: Polygon overlay is one of the complex operations in computational geometry. It is applied in many fields such as Geographic Information Systems (GIS), computer graphics and VLSI CAD. Sequential algorithms for this problem are in abundance in literature but there is a lack of distributed algorithms especially for MapReduce platform. In GIS, spatial data files tend to be large in size (in GBs) and the underlying overlay computation is highly irregular and compute intensive. The MapReduce paradigm is now standard in industry and academia for processing large-scale data. Motivated by the MapReduce programming model, we revisit the distributed polygon overlay problem and its implementation on MapReduce platform. Our algorithms are geared towards maximizing local processing and minimizing the communication overhead inherent with shuffle and sort phases in MapReduce. We have experimented with two data sets and achieved up to 22x speedup with dataset 1 using 64 CPU cores.


title: Puzzle based highly secure steganography

abstract: Steganography is the insertion of secret data inside a normal cover media such as digital image, audio, video or text. The inserted message must not be damaged when processing is applied on the cover media. Several image processing operations such as format conversion, lossy and lossless compression, noise removal and other enhancement. This paper presents multi layer image encryption and decryption using random puzzle based method to embed secret data into the cover image. This approach enhances the security and provides robust embedding. In this work, a 8×8 Sudoku puzzle which has a 64×64 reference matrix is used. The proposed method gives a superior average capacity of 5 bits per pixel. The message is embedded at the sender using random permutation puzzle. At the Receiver end, the user has to rearrange the image thereby solving the puzzle. The attacker lacks knowledge of original image order and is unable to solve the puzzle. Our outcome indicate that the recommended approach provides high steganographic security while providing high embedding capacity of about 5 bits per pixel. A brief analysis of the performance and security aspects of the proposed method are presented.


title: Multi-objective workflow scheduling in Amazon EC2

abstract: Nowadays, scientists and companies are confronted with multiple competing goals such as makespan in high-performance computing and economic cost in Clouds that have to be simultaneously optimised. Multi-objective scheduling of scientific applications in these systems is therefore receiving increasing research attention. Most existing approaches typically aggregate all objectives in a single function, defined a-priori without any knowledge about the problem being solved, which negatively impacts the quality of the solutions. In contrast, Pareto-based approaches having as outcome a set of (nearly) optimal solutions that represent a tradeoff among the different objectives, have been scarcely studied. In this paper, we analyse MOHEFT, a Pareto-based list scheduling heuristic that provides the user with a set of tradeoff optimal solutions from which the one that better suits the user requirements can be manually selected. We demonstrate the potential of our method for multi-objective workflow scheduling on the commercial Amazon EC2 Cloud. We compare the quality of the MOHEFT tradeoff solutions with two state-of-the-art approaches using different synthetic and real-world workflows: the classical HEFT algorithm for single-objective scheduling and the SPEA2* genetic algorithm used in multi-objective optimisation problems. The results demonstrate that our approach is able to compute solutions of higher quality than SPEA2*. In addition, we show that MOHEFT is more suitable than SPEA2* for workflow scheduling in the context of commercial Clouds, since the genetic-based approach is unable of dealing with some of the constraints imposed by these systems.


title: Evolution of indirect reciprocity

abstract: Natural selection is conventionally assumed to favour the strong and selfish who maximize their own resources at the expense of others. But many biological systems, and especially human societies, are organized around altruistic, cooperative interactions. How can natural selection promote unselfish behaviour? Various mechanisms have been proposed, and a rich analysis of indirect reciprocity has recently emerged: I help you and somebody else helps me. The evolution of cooperation by indirect reciprocity leads to reputation building, morality judgement and complex social interactions with ever-increasing cognitive demands.


title: Improving sentence compression by learning to predict gaze

abstract: We show how eye-tracking corpora can be used to improve sentence compression models, presenting a novel multi-task learning algorithm based on multi-layer LSTMs. We obtain performance competitive with or better than state-of-the-art approaches.


title: MATCH: An Architecture For Multimodal Dialogue Systems

abstract: Mobile interfaces need to allow the user and system to adapt their choice of communication modes according to user preferences, the task at hand, and the physical and social environment. We describe a multimodal application architecture which combines finite-state multimodal language processing, a speech-act based multimodal dialogue manager, dynamic multimodal output generation, and user-tailored text planning to enable rapid prototyping of multimodal interfaces with flexible input and adaptive output. Our testbed application MATCH (Multimodal Access To City Help) provides a mobile multimodal speech-pen interface to restaurant and sub-way information for New York City.


title: Support Tensor Machines for Classification of Hyperspectral Remote Sensing Imagery

abstract: In recent years, the support vector machines (SVMs) have been very successful in remote sensing image classification, particularly when dealing with high-dimensional data and limited training samples. Nevertheless, the vector-based feature alignment of the SVM can lead to an information loss in representation of hyperspectral images, which intrinsically have a tensor-based data structure. In this paper, a new multiclass support tensor machine (STM) is specifically developed for hyperspectral image classification. Our newly proposed STM processes the hyperspectral image as a data cube and then identifies the information classes in tensor space. The multiclass STM is developed from a set of binary STM classifiers using the one-against-one parallel strategy. As a part of our tensor-based processing chain, a multilinear principal component analysis (MPCA) is used for preprocessing, in order to reduce the tensorial data redundancy and, at the same time, preserve the tensorial structure information in sparse and high-order subspaces. As a result, the contributions of this work are twofold: a new multiclass STM model for hyperspectral image classification is developed, and a tensorial image interpretation framework is constructed, which provides a system consisting of tensor-based feature representation, feature extraction, and classification. Experiments with four hyperspectral data sets, covering agricultural and urban areas, are conducted to validate the effectiveness of the proposed framework. Our experimental results show that the proposed STM and MPCA-STM can achieve better results than traditional SVM-based classifiers.


title: Advances in upper limb stroke rehabilitation: a technology push

abstract: Strokes affect thousands of people worldwide leaving sufferers with severe disabilities affecting their daily activities. In recent years, new rehabilitation techniques have emerged such as constraint-induced therapy, biofeedback therapy and robot-aided therapy. In particular, robotic techniques allow precise recording of movements and application of forces to the affected limb, making it a valuable tool for motor rehabilitation. In addition, robot-aided therapy can utilise visual cues conveyed on a computer screen to convert repetitive movement practice into an engaging task such as a game. Visual cues can also be used to control the information sent to the patient about exercise performance and to potentially address psychosomatic variables influencing therapy. This paper overviews the current state-of-the-art on upper limb robot-mediated therapy with a focal point on the technical requirements of robotic therapy devices leading to the development of upper limb rehabilitation techniques that facilitate reach-to-touch, fine motor control, whole-arm movements and promote rehabilitation beyond hospital stay. The reviewed literature suggest that while there is evidence supporting the use of this technology to reduce functional impairment, besides the technological push, the challenge ahead lies on provision of effective assessment of outcome and modalities that have a stronger impact transferring functional gains into functional independence.


title: FLP/FRT and Cre/lox recombination technology in C. elegans.

abstract: One of the most powerful aspects of biological inquiry using model organisms is the ability to control gene expression. A holy grail is both temporal and spatial control of the expression of specific gene products - that is, the ability to express or withhold the activity of genes or their products in specific cells at specific times. Ideally such a method would also regulate the precise levels of gene activity, and alterations would be reversible. The related goal of controlled or purposefully randomized expression of visible markers is also tremendously powerful. While not all of these feats have been accomplished in Caenorhabditis elegans to date, much progress has been made, and recent technologies put these goals within closer reach. Here, I present published examples of successful two-component site-specific recombination in C. elegans. These technologies are based on the principle of controlled intra-molecular excision or inversion of DNA sequences between defined sites, as driven by FLP or Cre recombinases. I discuss several prospects for future applications of this technology.


title: CRISPR/Cas, the Immune System of Bacteria and Archaea

abstract: Microbes rely on diverse defense mechanisms that allow them to withstand viral predation and exposure to invading nucleic acid. In many Bacteria and most Archaea, clustered regularly interspaced short palindromic repeats (CRISPR) form peculiar genetic loci, which provide acquired immunity against viruses and plasmids by targeting nucleic acid in a sequence-specific manner. These hypervariable loci take up genetic material from invasive elements and build up inheritable DNA-encoded immunity over time. Conversely, viruses have devised mutational escape strategies that allow them to circumvent the CRISPR/Cas system, albeit at a cost. CRISPR features may be exploited for typing purposes, epidemiological studies, host-virus ecological surveys, building specific immunity against undesirable genetic elements, and enhancing viral resistance in domesticated microbes.


A Content-Based Image Meta-Search Engine using Relevance Feedback


title: Online Filtering, Smoothing and Probabilistic Modeling of Streaming data

abstract: In this paper, we address the problem of extending a relational database system to facilitate efficient real-time application of dynamic probabilistic models to streaming data. We use the recently proposed abstraction of model-based views for this purpose, by allowing users to declaratively specify the model to be applied, and by presenting the output of the models to the user as a probabilistic database view. We support declarative querying over such views using an extended version of SQL that allows for querying probabilistic data. Underneath we use particle filters, a class of sequential Monte Carlo algorithms, to represent the present and historical states of the model as sets of weighted samples (particles) that are kept up-to-date as new data arrives. We develop novel techniques to convert the queries on the model-based view directly into queries over particle tables, enabling highly efficient query processing. Finally, we present experimental evaluation of our prototype implementation over several synthetic and real datasets, that demonstrates the feasibility of online modeling of streaming data using our system and establishes the advantages of tight integration between dynamic probabilistic models and databases.


title: PAPILLON: expressive eyes for interactive characters

abstract: PAPILLON is a technology for designing highly expressive animated eyes for interactive characters, robots and toys. Expressive eyes are essential in any form of face-to-face communication [2] and designing them has been a critical challenge in robotics, as well as in interactive character and toy development.


title: Domain-Invariant Adversarial Learning for Unsupervised Domain Adaption

abstract: Unsupervised domain adaption aims to learn a powerful classifier for the target domain given a labeled source data set and an unlabeled target data set. To alleviate the effect of `domain shift', the major challenge in domain adaptation, studies have attempted to align the distributions of the two domains. Recent research has suggested that generative adversarial network (GAN) has the capability of implicitly capturing data distribution. In this paper, we thus propose a simple but effective model for unsupervised domain adaption leveraging adversarial learning. The same encoder is shared between the source and target domains which is expected to extract domain-invariant representations with the help of an adversarial discriminator. With the labeled source data, we introduce the center loss to increase the discriminative power of feature learned. We further align the conditional distribution of the two domains to enforce the discrimination of the features in the target domain. Unlike previous studies where the source features are extracted with a fixed pre-trained encoder, our method jointly learns feature representations of two domains. Moreover, by sharing the encoder, the model does not need to know the source of images during testing and hence is more widely applicable. We evaluate the proposed method on several unsupervised domain adaption benchmarks and achieve superior or comparable performance to state-of-the-art results.


title: Kernelized locality-sensitive hashing for scalable image search

abstract: Fast retrieval methods are critical for large-scale and data-driven vision applications. Recent work has explored ways to embed high-dimensional features or complex distance functions into a low-dimensional Hamming space where items can be efficiently searched. However, existing methods do not apply for high-dimensional kernelized data when the underlying feature embedding for the kernel is unknown. We show how to generalize locality-sensitive hashing to accommodate arbitrary kernel functions, making it possible to preserve the algorithm's sub-linear time similarity search guarantees for a wide class of useful similarity functions. Since a number of successful image-based kernels have unknown or incomputable embeddings, this is especially valuable for image retrieval tasks. We validate our technique on several large-scale datasets, and show that it enables accurate and fast performance for example-based object classification, feature matching, and content-based retrieval.


title: Faking contextual data for fun, profit, and privacy

abstract: The amount of contextual data collected, stored, mined, and shared is increasing exponentially. Street cameras, credit card transactions, chat and Twitter logs, e-mail, web site visits, phone logs and recordings, social networking sites, all are examples of data that persists in a manner not under individual control, leading some to declare the death of privacy. We argue here that the ability to generate convincing fake contextual data can be a basic tool in the fight to preserve privacy. One use for the technology is for an individual to make his actual data indistinguishable amongst a pile of false data. In this paper we consider two examples of contextual data, search engine query data and location data. We describe the current state of faking these types of data and our own efforts in this direction.


title: Catadioptric Stereo Using Planar Mirrors

abstract: By using mirror reflections of a scene, stereo images can be captured with a single camera (catadioptric stereo). In addition to simplifying data acquisition single camera stereo provides both geometric and radiometric advantages over traditional two camera stereo. In this paper, we discuss the geometry and calibration of catadioptric stereo with two planar mirrors. In particular, we will show that the relative orientation of a catadioptric stereo rig is restricted to the class of planar motions thus reducing the number of external calibration parameters from 6 to 5. Next we derive the epipolar geometry for catadioptric stereo and show that it has 6 degrees of freedom rather than 7 for traditional stereo. Furthermore, we show how focal length can be recovered from a single catadioptric image solely from a set of stereo correspondences. To test the accuracy of the calibration we present a comparison to Tsai camera calibration and we measure the quality of Euclidean reconstruction. In addition, we will describe a real-time system which demonstrates the viability of stereo with mirrors as an alternative to traditional two camera stereo.


title: Privacy is dead, long live privacy

abstract: Keywords: Privacy ; Fairness Reference EPFL-ARTICLE-219605doi:10.1145/2834114View record in Web of Science Record created on 2016-07-19, modified on 2017-05-12


title: Contour and boundary detection improved by surround suppression of texture edges

abstract: We propose a computational step, called surround suppression, to improve detection of object contours and region boundaries in natural scenes. This step is inspired by the mechanism of non-classical receptive field inhibition that is exhibited by most orientation selective neurons in the primary visual cortex and that influences the perception of groups of edges or lines. We illustrate the principle and the effect of surround suppression by adding this step to the Canny edge detector. The resulting operator responds strongly to isolated lines and edges, region boundaries, and object contours, but exhibits a weaker or no response to texture edges. Additionally, we introduce a new post-processing method that further suppresses texture edges. We use natural images with associated subjectively defined desired output contour and boundary maps to evaluate the performance of the proposed additional steps. In a contour detection task, the Canny operator augmented with the proposed suppression and post-processing step achieves better results than the traditional Canny edge detector and the SUSAN edge detector. The performance gain is highest at scales for which these latter operators strongly react to texture in the input image.


title: HEALTHY EATING: WHAT DOES IT MEAN TO ADOLESCENTS?

abstract: Abstract Objective The objective of this study was to investigate the meanings of “healthy” and “unhealthy” eating and the importance of healthy eating among adolescents. Design Twenty-five structured focus groups were conducted. Subjects These focus groups consisted of 203 adolescent girls and boys enrolled in three senior high schools and one junior high school. Variables Measured The variables measured were adolescents' self-report of perceptions of healthy and unhealthy eating and their descriptions of the importance of healthy eating to adolescents. Analysis Data analysis was done by general content coding and specific content coding. Results Adolescents have a significant amount of knowledge regarding healthy foods and believe that healthy eating involves moderation, balance, and variety. Despite this knowledge, they find it difficult to follow healthy eating recommendations and frequently consume foods that they perceive as unhealthy. Barriers to healthy eating include a lack of time, limited availability of healthy foods in schools, and a general lack of concern regarding following healthy eating recommendations. Implications These findings suggest that healthy eating messages based on the Dietary Guidelines for Americans are reaching adolescents, but interventions are needed that assist adolescents with the translation of this knowledge into healthy behaviors. Interventions should help make healthy eating easy for youth to apply and explain the consequences of unhealthy eating in terms that they value, stressing meaningful short-term benefits.


title: A New Similarity Measure to Understand Visitor Behavior in a Web Site

abstract: The behavior of visitors browsing in a web site offers a lot of information about their requirements and the way they use the respective site. Analyzing such behavior can provide the necessary information in order to improve the web site’s structure. The literature contains already several suggestions on how to characterize web site usage and to identify the respective visitor requirements based on clustering of visitor sessions. Here we propose to combine visitor behavior with the content of the respective web pages and the similarity between different page sequences in order to define a similarity measure between different visits. This similarity serves as input for clustering of visitor sessions. The application of our approach to a bank’s web site and its visitor sessions shows its potential for internet-based businesses. key words: web mining, browsing behavior, similarity measure, clustering.


title: Managing information sharing within an organizational setting: A social network perspective

abstract: Information sharing is critical to an organization's competitiveness and requires a free flow of information among members if the organization is to remain competitive. A review of the literature on organizational structure and information sharing was conducted to examine the research in this area. A case example illustrates how a social network approach was used to explore the process of measuring the social structure of an organization and the implementation of change interventions to increase connectivity and manage information sharing. The process of conducting social network analysis is described using the case example. Interventions for increasing information flow are discussed. The authors provide an information-sharing model that demonstrates the various domains of connectivity within an organization at any given state. The benefits of using social network analysis for information sharing and the implications for further research and practice are discussed.


title: The Signal Transmission Mechanism on the Surface of Human Body for Body Channel Communication

abstract: The signal transmission mechanism on the surface of the human body is studied for the application to body channel communication (BCC). From Maxwell's equations, the complete equation of electrical field on the human body is developed to obtain a general BCC model. The mechanism of BCC consists of three parts according to the operating frequencies and channel distances: the quasi-static near-field coupling part, the reactive induction-field radiation part, and the surface wave far-field propagation part. The general BCC model by means of the near-field and far-field approximation is developed to be valid in the frequency range from 100 kHz to 100 MHz and distance up to 1.3 m based on the measurements of the body channel characteristics. Finally, path loss characteristics of BCC are formulated for the design of BCC systems and many potential applications.


title: A new version of the RDP (Ribosomal Database Project)

abstract: The Ribosomal Database Project (RDP-II), previously described by Maidak et al. [ Nucleic Acids Res. (1997), 25, 109-111], is now hosted by the Center for Microbial Ecology at Michigan State University. RDP-II is a curated database that offers ribosomal RNA (rRNA) nucleotide sequence data in aligned and unaligned forms, analysis services, and associated computer programs. During the past two years, data alignments have been updated and now include >9700 small subunit rRNA sequences. The recent development of an ObjectStore database will provide more rapid updating of data, better data accuracy and increased user access. RDP-II includes phylogenetically ordered alignments of rRNA sequences, derived phylogenetic trees, rRNA secondary structure diagrams, and various software programs for handling, analyzing and displaying alignments and trees. The data are available via anonymous ftp (ftp.cme.msu. edu) and WWW (http://www.cme.msu.edu/RDP). The WWW server provides ribosomal probe checking, approximate phylogenetic placement of user-submitted sequences, screening for possible chimeric rRNA sequences, automated alignment, and a suggested placement of an unknown sequence on an existing phylogenetic tree. Additional utilities also exist at RDP-II, including distance matrix, T-RFLP, and a Java-based viewer of the phylogenetic trees that can be used to create subtrees.


title: 3D Integration technology: Status and application development

abstract: As predicted by the ITRS roadmap, semiconductor industry development dominated by shrinking transistor gate dimensions alone will not be able to overcome the performance and cost problems of future IC fabrication. Today 3D integration based on through silicon vias (TSV) is a well-accepted approach to overcome the performance bottleneck and simultaneously shrink the form factor. Several full 3D process flows have been demonstrated, however there are still no microelectronic products based on 3D TSV technologies in the market — except CMOS image sensors. 3D chip stacking of memory and logic devices without TSVs is already widely introduced in the market. Applying TSV technology for memory on logic will increase the performance of these advanced products and simultaneously shrink the form factor. In addition to the enabling of further improvement of transistor integration densities, 3D integration is a key technology for integration of heterogeneous technologies. Miniaturized MEMS/IC products represent a typical example for such heterogeneous systems demanding for smart system integration rather than extremely high transistor integration densities. The European 3D technology platform that has been established within the EC funded e-CUBES project is focusing on the requirements coming from heterogeneous systems. The selected 3D integration technologies are optimized concerning the availability of devices (packaged dies, bare dies or wafers) and the requirements of performance and form factor. There are specific technology requirements for the integration of MEMS/NEMS devices which differ from 3D integrated ICs (3D-IC). While 3D-ICs typically show a need for high interconnect densities and conductivities, TSV technologies for the integration of MEMS to ICs may result in lower electrical performance but have to fulfill other requirements, e. g. mechanical stability issues. 3D integration of multiple MEMS/IC stacks was successfully demonstrated for the fabrication of miniaturized sensor systems (e-CUBES), as for automotive, health & fitness and aeronautic applications.


title: Using depth in visual simultaneous localisation and mapping

abstract: We present a method of utilizing depth information as provided by RGBD sensors for robust real-time visual simultaneous localisation and mapping (SLAM) by augmenting monocular visual SLAM to take into account depth data. This is implemented based on the feely available software “Parallel Tracking and Mapping” by Georg Klein. Our modifications allow PTAM to be used as a 6D visual SLAM system even without any additional information about odometry or from an inertial measurement unit.


title: A spectral transform approach to stochastic circuits

abstract: Stochastic computing (SC) processes data in the form of long pseudo-random bit-streams denoting probabilities. Its key advantages are simple computational elements and high soft-error tolerance. Recent technology developments have revealed important new SC applications such as image processing and LDPC decoding. Despite its long history, SC still lacks a comprehensive design methodology; existing methods tend to be ad hoc and limited to a few arithmetic functions. We demonstrate a fundamental relation between stochastic circuits and spectral transforms. Based on this, we propose a transform approach to the analysis and synthesis of SC circuits. We illustrate the approach for a variety of basic combinational SC design problems, and show that the area cost associated with stochastic number generation can be significantly reduced.


title: Eye movements in natural behavior

abstract: The classic experiments of Yarbus over 50 years ago revealed that saccadic eye movements reflect cognitive processes. But it is only recently that three separate advances have greatly expanded our understanding of the intricate role of eye movements in cognitive function. The first is the demonstration of the pervasive role of the task in guiding where and when to fixate. The second has been the recognition of the role of internal reward in guiding eye and body movements, revealed especially in neurophysiological studies. The third important advance has been the theoretical developments in the fields of reinforcement learning and graphic simulation. All of these advances are proving crucial for understanding how behavioral programs control the selection of visual information.


title: A CNN Cascade for Landmark Guided Semantic Part Segmentation

abstract: This paper proposes a CNN cascade for semantic part segmentation guided by pose-specific information encoded in terms of a set of landmarks (or keypoints). There is large amount of prior work on each of these tasks separately, yet, to the best of our knowledge, this is the first time in literature that the interplay between pose estimation and semantic part segmentation is investigated. To address this limitation of prior work, in this paper, we propose a CNN cascade of tasks that firstly performs landmark localisation and then uses this information as input for guiding semantic part segmentation. We applied our architecture to the problem of facial part segmentation and report large performance improvement over the standard unguided network on the most challenging face datasets. Testing code and models will be published online at http://cs.nott.ac.uk/~psxasj/.


title: Cognitive computing and proposed approaches to conceptual organization of case law knowledge bases: a proposed model for information preparation, indexing, and analysis

abstract: Carole Hafner's scholarship on the conceptual organization of case law knowledge bases (COC) was an original approach to distilling a library's worth of cases into a manageable subset that any given legal researcher could review. Her approach applied concept indexation and concept search based on an annotation model of three interacting components combined with a system of expert legal reasoning to aid in the retrieval of pertinent case law. Despite the clear value this tripartite approach would afford to researchers in search of cases with similar fact patterns and desired (or undesired) outcomes, this approach has not been applied consistently in the intervening years since its introduction. Specifically, the conceptual representation of domain concepts and the case frames were not pursued by researchers, and they were not applied by the legal case indexing services that came to dominate the electronic case law market. Advances since Hafner's original scholarship in the form of (1) digitized case law and related materials; (2) computer science analytical protocols; and (3) more advanced forms of artificial intelligence approaches present the question of whether Hafner's COC model could move from the hypothetical to the real.


title: Low-Complexity Range-Azimuth FMCW Radar Sensor Using Joint Angle and Delay Estimation Without SVD and EVD

abstract: A low complexity range-azimuth frequency-modulated continuous-waveform (FMCW) radar sensor using joint angle and delay estimation method without singular value decomposition (SVD) and eigenvalue decomposition (EVD) is presented in this paper. Conventional joint angle and delay estimation techniques exploit the dual-shift-invariant structure of received signals through matrix decompositions, such as SVD and EVD, which increases the computational burden. The proposed method utilizes the dual-shift-invariant structure through matrix inversion and performs angle and delay estimation using extended one-dimensional pseudospectrum searching instead of two-dimensional pseudospectrum searching to reduce the computational complexity. We demonstrate the effectiveness of the proposed method through Monte-Carlo simulations. The proposed algorithm is also verified by processing real FMCW data collected in an anechoic chamber.


title: Clustering header categories extracted from web tables

abstract: Revealing related content among heterogeneous web tables is part of our long term objective of formulating queries over ::: multiple sources of information. Two hundred HTML tables from institutional web sites are segmented and each table ::: cell is classified according to the fundamental indexing property of row and column headers. The categories that ::: correspond to the multi-dimensional data cube view of a table are extracted by factoring the (often multi-row/column) ::: headers. To reveal commonalities between tables from diverse sources, the Jaccard distances between pairs of category ::: headers (and also table titles) are computed. We show how about one third of our heterogeneous collection can be ::: clustered into a dozen groups that exhibit table-title and header similarities that can be exploited for queries.


title: Double dissociation of value computations in orbitofrontal and anterior cingulate neurons

abstract: This study reports a double dissociation in the neuronal correlates of value-based decision making in monkey prefrontal cortex, with orbitofrontal cortex neurons encoding choice value relative to recent choice values, while anterior cingulate cortex neurons flexibly encode multiple decision parameters and reward prediction errors using a 'common valuation currency'.


title: Analyzing and Managing Role-Based Access Control Policies

abstract: Today more and more security-relevant data is stored on computer systems; security-critical business processes are mapped to their digital counterparts. This situation applies to various domains such as health care industry, digital government, and financial service institutes requiring that different security requirements must be fulfilled. Authorisation constraints can help the policy architect design and express higher-level organisational rules. Although the importance of authorisation constraints has been addressed in the literature, there does not exist a systematic way to verify and validate authorisation constraints. In this paper, we specify both non-temporal and history-based authorisation constraints in the Object Constraint Language (OCL) and first-order linear temporal logic (LTL). Based upon these specifications, we attempt to formally verify role-based access control policies with the help of a theorem prover and to validate policies with the USE system, a validation tool for OCL constraints. We also describe an authorisation engine, which supports the enforcement of authorisation constraints.


Immersive Virtual Reality application using Google Cardboard and Leap Motion technologies.


title: Training Complex Models with Multi-Task Weak Supervision

abstract: As machine learning models continue to increase in complexity, collecting large hand-labeled training sets has become one of the biggest roadblocks in practice. Instead, weaker forms of supervision that provide noisier but cheaper labels are often used. However, these weak supervision sources have diverse and unknown accuracies, may output correlated labels, and may label different tasks or apply at different levels of granularity. We propose a framework for integrating and modeling such weak supervision sources by viewing them as labeling different related sub-tasks of a problem, which we refer to as the multi-task weak supervision setting. We show that by solving a matrix completion-style problem, we can recover the accuracies of these multi-task sources given their dependency structure, but without any labeled data, leading to higher-quality supervision for training an end model. Theoretically, we show that the generalization error of models trained with this approach improves with the number of unlabeled data points, and characterize the scaling with respect to the task and dependency structures. On three fine-grained classification problems, we show that our approach leads to average gains of 20.2 points in accuracy over a traditional supervised approach, 6.8 points over a majority vote baseline, and 4.1 points over a previously proposed weak supervision method that models tasks separately.


title: Design of a CMOS Broadband Transimpedance Amplifier With Active Feedback

abstract: In this paper, a novel current-mode transimpedance amplifier (TIA) exploiting the common gate input stage with common source active feedback has been realized in CHRT 0.18 ?m -1.8 V RFCMOS technology. The proposed active feedback TIA input stage is able to achieve a low input impedance similar to that of the well-known regulated cascode (RGC) topology. The proposed TIA also employs series inductive peaking and capacitive degeneration techniques to enhance the bandwidth and the gain. The measured transimpedance gain is 54.6 dB? with a -3 dB bandwidth of about 7 GHz for a total input parasitic capacitance of 0.3 pF. The measured average input referred noise current spectral density is about 17.5 pA/?{Hz} up to 7 GHz. The measured group delay is within 65 ± 10 ps over the bandwidth of interest. The chip consumes 18.6 mW DC power from a single 1.8 V supply. The mathematical analysis of the proposed TIA is presented together with a detailed noise analysis based on the van der Ziel MOSFET noise model. The effect of the induced gate noise in a broadband TIA is included.


title: A CBT-Based Psychoeducational Intervention for Suicide Survivors

abstract: Background: Bereavement following suicide is associated with an increased vulnerability for depression, complicated grief, suicidal ideation, and suicide. There is, however, a paucity of studies of the effects of interventions in suicide survivors. Aims: This study therefore ex- amined the effects of a cognitive behavioral therapy (CBT)-based psychoeducational intervention on depression, complicated grief, and suicide risk factors in suicide survivors. Method: In total, 83 suicide survivors were randomized to the intervention or the control condition in a cluster randomized controlled trial. Primary outcome measures included maladaptive grief reactions, depression, suicidal ideation, and hopelessness. Secondary outcome measures included grief-related cognitions and coping styles. Results: There was no signifi cant effect of the intervention on the outcome measures. However, the intensity of symptoms of grief, depressive symptoms, and passive coping styles decreased signifi cantly in the intervention group but not in the control group. Conclusion: The CBT-based psychoeducational intervention has no signifi cant effect on the development of complicated grief reactions, depression, and suicide risk factors among suicide survivors. The intervention may, however, serve as supportive counseling for suicide survivors.


title: An exploratory analysis of mobile development issues using stack overflow

abstract: Question & answer (Q&A) websites, such as Stack Overflow (SO), are widely used by developers to find and provide answers to technical issues and concerns in software development. Mobile development is not an exception to the rule. In the latest SO dump, more than 400K questions were labeled with tags related to mobile technologies. Although, previous works have analyzed the main topics and trends in SO threads, there are no studies devoted specifically to mobile development. In this paper we used topic modeling techniques to extract hot-topics from mobile-development related questions. Our findings suggest that most of the questions include topics related to general questions and compatibility issues, and the most specific topics, such as crash reports and database connection, are present in a reduced set of questions.


title: Slice Finder: Automated Data Slicing for Model Validation

abstract: As machine learning (ML) systems become democratized, it becomes increasingly important to help users easily debug their models. However, current data tools are still primitive when it comes to helping users trace model performance problems all the way to the data. We focus on the particular problem of slicing data to identify subsets of the validation data where the model performs poorly. This is an important problem in model validation because the overall model performance can fail to reflect that of the smaller subsets, and slicing allows users to analyze the model performance on a more granular-level. Unlike general techniques (e.g., clustering) that can find arbitrary slices, our goal is to find interpretable slices (which are easier to take action compared to arbitrary subsets) that are large and problematic. We propose Slice Finder, which is an interactive framework for identifying such slices using statistical techniques. Applications include diagnosing model fairness and fraud detection, where identifying slices that are interpretable to humans is crucial.


title: A Strategy of MR Brain Tissue Images' Suggestive Annotation Based on Modified U-Net

abstract: Accurate segmentation of MR brain tissue is a crucial step for diagnosis,surgical planning, and treatment of brain abnormalities. However,it is a time-consuming task to be performed by medical experts. So, automatic and reliable segmentation methods are required. How to choose appropriate training dataset from limited labeled dataset rather than the whole also has great significance in saving training time. In addition, medical data labeled is too rare and expensive to obtain extensively, so choosing appropriate unlabeled dataset instead of all the datasets to annotate, which can attain at least same performance, is also very meaningful. To solve the problem above, we design an automatic segmentation method based on U-shaped deep convolutional network and obtain excellent result with average DSC metric of 0.8610, 0.9131, 0.9003 for Cerebrospinal Fluid (CSF), Gray Matter (GM) and White Matter (WM) respectively on the well-known IBSR18 dataset. We use bootstrapping algorithm for selecting the most effective training data and get more state-of-the-art segmentation performance by using only 50% of training data. Moreover, we propose a strategy of MR brain tissue images' suggestive annotation for unlabeled medical data based on the modified U-net. The proposed method performs fast and can be used in clinical.


title: Attend and Rectify: a Gated Attention Mechanism for Fine-Grained Recovery

abstract: We propose a novel attention mechanism to enhance Convolutional Neural Networks for fine-grained recognition. It learns to attend to lower-level feature activations without requiring part annotations and uses these activations to update and rectify the output likelihood distribution. In contrast to other approaches, the proposed mechanism is modular, architecture-independent and efficient both in terms of parameters and computation required. Experiments show that networks augmented with our approach systematically improve their classification accuracy and become more robust to clutter. As a result, Wide Residual Networks augmented with our proposal surpasses the state of the art classification accuracies in CIFAR-10, the Adience gender recognition task, Stanford dogs, and UEC Food-100.


title: Inverted indexes for phrases and strings

abstract: Inverted indexes are the most fundamental and widely used data structures in information retrieval. For each unique word occurring in a document collection, the inverted index stores a list of the documents in which this word occurs. Compression techniques are often applied to further reduce the space requirement of these lists. However, the index has a shortcoming, in that only predefined pattern queries can be supported efficiently. In terms of string documents where word boundaries are undefined, if we have to index all the substrings of a given document, then the storage quickly becomes quadratic in the data size. Also, if we want to apply the same type of indexes for querying phrases or sequence of words, then the inverted index will end up storing redundant information. In this paper, we show the first set of inverted indexes which work naturally for strings as well as phrase searching. The central idea is to exclude document d in the inverted list of a string P if every occurrence of P in d is subsumed by another string of which P is a prefix. With this we show that our space utilization is close to the optimal. Techniques from succinct data structures are deployed to achieve compression while allowing fast access in terms of frequency and document id based retrieval. Compression and speed trade-offs are evaluated for different variants of the proposed index. For phrase searching, we show that our indexes compare favorably against a typical inverted index deploying position-wise intersections. We also show efficient top-k based retrieval under relevance metrics like frequency and tf-idf.


title: Multilevel inverters: a survey of topologies, controls, and applications

abstract: Multilevel inverter technology has emerged recently as a very important alternative in the area of high-power medium-voltage energy control. This paper presents the most important topologies like diode-clamped inverter (neutral-point clamped), capacitor-clamped (flying capacitor), and cascaded multicell with separate DC sources. Emerging topologies like asymmetric hybrid cells and soft-switched multilevel inverters are also discussed. This paper also presents the most relevant control and modulation methods developed for this family of converters: multilevel sinusoidal pulsewidth modulation, multilevel selective harmonic elimination, and space-vector modulation. Special attention is dedicated to the latest and more relevant applications of these converters such as laminators, conveyor belts, and unified power-flow controllers. The need of an active front end at the input side for those inverters supplying regenerative loads is also discussed, and the circuit topology options are also presented. Finally, the peripherally developing areas such as high-voltage high-power devices and optical sensors and other opportunities for future development are addressed.


title: Sedimentation in the World Ocean: With Emphasis On the Nature, Distribution and Behavior of Marine Suspensions

abstract: The World Oceans covers the greater part of the earth’s surface and is 2.5 times as large as the area of land. Nearly 84 percent of the southern hemisphere is blanketed by the oceanic waters. The continents can be thought of as huge islands in the ocean. Therefore, the oceanic processes of matter and energy transformation are of global significance. Originally presented as a series of lectures by Alexander P. Lisitzin.


title: Remembering can cause forgetting: Retrieval dynamics in long-term memory

abstract: Three studies show that the retrieval process itself causes long-lasting forgetting. Ss studied 8 categories (e.g., Fruit). Half the members of half the categories were then repeatedly practiced through retrieval tests (e.g., Fruit Or ). Category-cued recall of unpracticed members of practiced categories was impaired on a delayed test. Experiments 2 and 3 identified 2 significant features of this retrieval-induced forgetting: The impairment remains when output interference is controlled, suggesting a retrieval-base d suppression that endures for 20 min or more, and the impairment appears restricted to high-frequenc y members. Low-frequency members show little impairment, even in the presence of strong, practiced competitors that might be expected to block access to those items. These findings suggest a critical role for suppression in models of retrieval inhibition and implicate the retrieval process itself in everyday forgetting. A striking implication of current memory theory is that the very act of remembering may cause forgetting. It is not that the remembered item itself becomes more susceptible to forgetting; in fact, recalling an item increases the likelihood that it will be recallable again at a later time. Rather, it is other items—items that are associated to the same cue or cues guiding retrieval—that may be put in greater jeopardy of being forgotten. Impaired recall of such related items may arise if access to them is blocked by the newly acquired strength of their successfully retrieved competitors (Blaxton & Neely, 1983; Brown, 1981; Brown, Whiteman, Cattoi, & Bradley, 1985; Roediger, 1974, 1978; Roediger & Schmidt, 1980; Rundus, 1973). This implication follows from three assumptions underlying what we herein refer to as strength-dependent competition models of interference: (a) the competition assumption—that memories associated to a common cue compete for access to conscious recall when that cue is presented; (b) the strengthdependence assumption—that the cued recall of an item will decrease as a function of increases in the strengths of its


title: Phylogeography of the Japanese eight-barbel loach Lefua echigonia from the Yamagata area of the Tohoku district, Japan

abstract: The Japanese eight-barbel loach Lefua echigonia, which is a freshwater fish native to Japan, is distributed from the Tohoku to Kinki districts and is divided into six regional populations according to mtDNA analysis. In this study, we investigated L. echigonia collected from several locations in Yamagata Prefecture and neighboring prefectures using mtDNA control region sequences and confirmed the spatial distribution pattern among the new regional population (Yamagata population). The new population was limited to the Mogami river system in the inland area of Yamagata Prefecture and is distinguished from other regional populations by high sequence divergences.


title: Design, fabrication and testing of smart lighting system

abstract: Smart lighting is an illumination technology designed for energy efficiency. This may include high efficiency features and automated controls that make adjustments in light intensity based on conditions such as availability of daylight or occupancy. Lighting is an application of light to realize both appeal and visual comfort. Nowadays, modern systems are able to reproduce intensities and pivot on smart sensors and actuators incorporating information and communication technologies. This research work aims at developing a smart lighting system that combines heterogeneous lighting technologies enabling intelligent functions. The light intensities can be shifted according to the visual comfort or control according to the occupant's activity based needs. This is achieved by design, development and testing of smart lighting system through ZigBee according to the availability of natural daylight, occupancy and required lighting level by the occupants.


title: Examining linkages between Smart Villages and Smart Cities: Learning from rural youth accessing the internet in India

abstract: Abstract The literature on Smart Cities has not as yet paid adequate attention to the rural sector, and the potential in villages for creating smart and sustainable solutions for the 21st century. This paper focuses on linking proposed smart city strategies to smart village policies to ensure that rural youth have improved opportunities for employment through ICT initiatives to ensure digital inclusion, using primary surveys undertaken in India. The motivation was to understand how mobile telephony could be a catalyst to create Smart Villages in India, where young people can chart out new pathways to realize their aspirations with regard to tertiary education and new avenues for diversifying into rural non-agricultural employment. We use data obtained from a household survey in villages in the states of Punjab and Tamil Nadu to examine the mobile phone usage preferences of rural educated youth to identify the way forward in improving the accessibility of services on the supply side. We make the case that where youth are using mobile phone access to increase their social information base it is indeed possible that the new social media groups formed by rural youth become a powerful conduit for generating new employment opportunities. The key to accessing this solution is to use a demand driven model for mobile services that would permit a bottom of governance model to generate sustainable growth of enterprises and improved human development of these villages.


title: Infrastructure for Usable Machine Learning: The Stanford DAWN Project

abstract: Despite incredible recent advances in machine learning, building machine learning applications remains prohibitively time-consuming and expensive for all but the best-trained, best-funded engineering organizations. This expense comes not from a need for new and improved statistical models but instead from a lack of systems and tools for supporting end-to-end machine learning application development, from data preparation and labeling to productionization and monitoring. In this document, we outline opportunities for infrastructure supporting usable, end-to-end machine learning applications in the context of the nascent DAWN (Data Analytics for What's Next) project at Stanford.


TESTING DEFECT PREDICTION MODEL-A PRACTICAL APPROACH


title: On the Security of A Dynamic ID-based Authentication Scheme for Telecare Medical Information Systems

abstract: Telecare medical information systems (TMISs) are increasingly popular technologies for healthcare applications. Using TMISs, physicians and caregivers can monitor the vital signs of patients remotely. Since the database of TMISs stores patients’ electronic medical records (EMRs), only authorized users should be granted the access to this information for the privacy concern. To keep the user anonymity, recently, Chen et al. proposed a dynamic ID-based authentication scheme for telecare medical information system. They claimed that their scheme is more secure and robust for use in a TMIS. However, we will demonstrate that their scheme fails to satisfy the user anonymity due to the dictionary attacks. It is also possible to derive a user password in case of smart card loss attacks. Additionally, an improved scheme eliminating these weaknesses is also presented.


title: The antiviral innate immune response in fish: evolution and conservation of the IFN system.

abstract: Innate immunity constitutes the first line of the host defense after pathogen invasion. Viruses trigger the expression of interferons (IFNs). These master antiviral cytokines induce in turn a large number of interferon-stimulated genes, which possess diverse effector and regulatory functions. The IFN system is conserved in all tetrapods as well as in fishes, but not in tunicates or in the lancelet, suggesting that it originated in early vertebrates. Viral diseases are an important concern of fish aquaculture, which is why fish viruses and antiviral responses have been studied mostly in species of commercial value, such as salmonids. More recently, there has been an interest in the use of more tractable model fish species, notably the zebrafish. Progress in genomics now makes it possible to get a relatively complete image of the genes involved in innate antiviral responses in fish. In this review, by comparing the IFN system between teleosts and mammals, we will focus on its evolution in vertebrates.


title: Selecting optimum maintenance approach based on relative membership grade under fuzzy environment

abstract: Maintenance planning, because of its high effects on manufacturing performance such as production quality, failure cost, is one of the most important decision problems. Allowing for the imprecision judgments of decision makers, this paper employs triangular fuzzy number to express the ambiguity and uncertainty, adopt relative membership grade(RMG) method to solve the multiple-criteria decision-making(MCDM) problem existing in maintenance selection. Comparing it with TOPSIS, illustrate the feasibility and superiority of RMG.


title: Two neural correlates of consciousness

abstract: Neuroscientists continue to search for ‘the' neural correlate of consciousness (NCC). In this article, I argue that a framework in which there are at least two distinct NCCs is increasingly making more sense of empirical results than one in which there is a single NCC. I outline the distinction between phenomenal NCC and access NCC, and show how they can be distinguished by experimental approaches, in particular signal-detection theory approaches. Recent findings in cognitive neuroscience provide an empirical case for two different NCCs.


title: Situation Assessment in Tactical Lane Change Behavior Planning for Automated Vehicles

abstract: Automated driving within a lane is a fascinating experience already. However, more exiting but also technically more challenging is to dare the next step of automating tactical behavior decisions for lane changes, as well. In this paper, we present our approach for situation assessment in tactical behavior planning for lane changes, whether lane changes are beneficial and/or possible. We present a way to tackle perception uncertainties and how to monitor the system's abilities and current skills. This is achieved by a dynamic Bayesian network and an unscented variance transform. Our approach is evaluated not only in a simulation, but also in real traffic. Our implementation has recently been demonstrated to the public in the Audi A7 piloted driving concept vehicle, driving 550 miles from Stanford to Las Vegas to the Consumer Electronics Show (CES) 2015.


title: Team innovation climate and knowledge sharing among healthcare managers : mediating effects of altruistic intentions

abstract: Background: This paper aims to provide empirical evidence concerning the impact of team climate on knowledge sharing behavior and the mediating effects of individuals' altruistic intentions in the context of healthcare settings. ::: Methods: Questionnaire data were collected from 212 administrators employed at a medical center in Taiwan. Team climate was assessed by the Team Climate Inventory composed of four factors, participative safety, support for innovation, vision, and task orientation. The proposed hypotheses were tested using structural equation modeling. ::: Results: The influence of the team innovation climate on knowledge sharing behavior was evident. Furthermore, individuals' altruistic intentions played a full mediating role in the relationship between team innovation climate and knowledge sharing behavior. ::: Conclusions: These results contribute to the field of the people-orientated perspective in knowledge management. The full mediating effect of employees' altruistic intentions provides healthcare team managers the direction to accelerate knowledge sharing behavior.


title: Development of an ultra-wideband elliptical disc planar monopole antenna with improved omni-directional performance using a modified ground

abstract: A planar monopole antenna has many attractive features such as a simple structure, exhibition of ultra-wideband characteristics, and a near omni-directional radiation pattern (J. Liang et al., 2004 - Z.N. Low et al., 2005). However, the ability to sustain an omni-directional pattern over ultra-wide band has not been adequately addressed in the literature up until now. The use of a modified ground plane can aid in providing such a feature.


title: A survey of autonomous mobile robot path planning approaches

abstract: Navigation is one of the pivotal and defining features of an autonomous robot. Essentially, the autonomous robot navigation problem can be divided into four parts namely: perception, localization, cognition and path planning, out of which path planning is important and has been a constant research topic. In this paper, a brief introduction and review of some of the path planning approaches (both classical and heuristic) is provided. The advantages and disadvantages of the methods are discussed.


title: Incentives for Mobile Crowd Sensing: A Survey

abstract: Recent years have witnessed the fast proliferation of mobile devices (e.g., smartphones and wearable devices) in people's lives. In addition, these devices possess powerful computation and communication capabilities and are equipped with various built-in functional sensors. The large quantity and advanced functionalities of mobile devices have created a new interface between human beings and environments. Many mobile crowd sensing applications have thus been designed which recruit normal users to contribute their resources for sensing tasks. To guarantee good performance of such applications, it's essential to recruit sufficient participants. Thus, how to effectively and efficiently motivate normal users draws growing attention in the research community. This paper surveys diverse strategies that are proposed in the literature to provide incentives for stimulating users to participate in mobile crowd sensing applications. The incentives are divided into three categories: entertainment, service, and money. Entertainment means that sensing tasks are turned into playable games to attract participants. Incentives of service exchanging are inspired by the principle of mutual benefits. Monetary incentives give participants payments for their contributions. We describe literature works of each type comprehensively and summarize them in a compact form. Further challenges and promising future directions concerning incentive mechanism design are also discussed.


title: Robots in organizations: the role of workflow, social, and environmental factors in human-robot interaction

abstract: Robots are becoming increasingly integrated into the workplace, impacting organizational structures and processes, and affecting products and services created by these organizations. While robots promise significant benefits to organizations, their introduction poses a variety of design challenges. In this paper, we use ethnographic data collected at a hospital using an autonomous delivery robot to examine how organizational factors affect the way its members respond to robots and the changes engendered by their use. Our analysis uncovered dramatic differences between the medical and post-partum units in how people integrated the robot into their workflow and their perceptions of and interactions with it. Different patient profiles in these units led to differences in workflow, goals, social dynamics, and the use of the physical environment. In medical units, low tolerance for interruptions, a discrepancy between the perceived cost and benefits of using the robot, and breakdowns due to high traffic and clutter in the robot's path caused the robot to have a negative impact on the workflow and staff resistance. On the contrary, post-partum units integrated the robot into their workflow and social context. Based on our findings, we provide design guidelines for the development of robots for organizations.


title: Compact substrate integrated waveguide Wilkinson power dividers

abstract: Two half-mode SIW Wilkinson power dividers are investigated in this paper. The presented compact half-mode SIW power dividers benefit from the excellent shielded transmission characteristics of the SIW in the power splitting branch, unlike the previously published HMSIW Wilkinson power divider. Two structures with waveguide and microstrip line feeds were simulated. The simulations of the half-mode power divider with microstrip launchers demonstrate similar results to those achieved with the all-waveguide half-mode SIW structure. A prototype was fabricated to investigate the half-mode power divider with microstrip feed lines. Measurement results demonstrate improved isolation characteristics in comparison with the previously published literature on all SIW “Y” branch power dividers. A passband of 4.2GHz, or 40% bandwidth centered at 10.5GHz, was measured. The measured input return loss was less than 10dB, and the measured isolation was greater than 10dB over the entire operating bandwidth. The maximum isolation was found to be over 40dB at 12.4GHz.


title: 3 D Face recognition by ICP-based shape matching

abstract: In this paper, we propose a novel face recognition approach based on 2.5D/3D shape matching. While most of existing methods use facial intensity image, we aim to develop a method using three-dimensional information of the human face. This is the main innovation of our technology. In our approach, the 3D dimensional information is introduced in order to overcome classical face recognition problems which are pose, illumination and facial expression variations. The paradigm is to build a 3D face gallery using a laser-based scanner: the off-line stage. In the on-line stage, the recognition, we capture one 2.5D face model at any view point and with any facial expressions. Our processing allows the identification of the presented person by performing the captured model with all faces from the database. Here, the Iterative Closest Point-based matching algorithm provides the pose of the probe whereas the region-based metric provides a spatial deviation between the probe and each face from the gallery. In this metric, we calculate the global recognition score as a weighted sum of region-based distances already labelled as mimic or static regions. For automatic 3D face segmentation, we use an immersion version of watershed segmentation algorithm. This paper also presents some experiments in order to shown illumination, pose and facial expression compensations.


Molecular biology of flaviviruses.


title: The use of focused ultrasound for non-invasive body contouring in Asians.

abstract: INTRODUCTION ::: Previous studies demonstrated that multiple treatments using focused ultrasound can be effective as an non-invasive method for reducing unwanted localized fat deposits. The objective of the study is to investigate the safety and efficacy of this focused ultrasound device in body contouring in Asians. ::: ::: ::: METHOD ::: Fifty-three (51 females and 2 males) patients were enrolled into the study. Subjects had up to three treatment sessions with approximately 1-month interval in between treatment. Efficacy was assessed by changes in abdominal circumference, ultrasound fat thickness, and caliper fat thickness. Weight change was monitored to distinguish weight loss induced changes in these measurements. Patient questionnaire was completed after each treatment. The level of pain or discomfort, improvement in body contour and overall satisfaction were graded with a score of 1-5 (1 being the least). Any adverse effects such as erythema, pain during treatment or blistering were recorded. ::: ::: ::: RESULT ::: The overall satisfaction amongst subjects was poor. Objective measurements by ultrasound, abdominal circumference, and caliper did not show significant difference after treatment. There is a negative correlation between the abdominal fat thickness and number of shots per treatment session. ::: ::: ::: CONCLUSION ::: Focused ultrasound is not effective for non-invasive body contouring among Southern Asians as compared with Caucasian. Such observation is likely due to smaller body figures. Design modifications can overcome this problem and in doing so, improve clinical outcome.


title: A reconfigurable-winding system for electric vehicle drive applications

abstract: In this paper, a reconfigurable-winding system for electric vehicles (EV) applications is discussed. It features fewer solid-state ac switches compared with state-of-the-art designs and does not have a dc circuit short-circuit failure mode. Simulation results show that the proposed design can achieve significant inverter loss reduction in low and high speed modes. Preliminary experiments demonstrate the ability to change winding configurations in about 5 ms.


title: Fiber optics in sensing and measurement

abstract: Optical techniques for measurement-interferometry, spectrometry and polarimetry"have long been used in materials measurement and environmental evaluation. The optical fiber lends get more flexibility in the implementation of these basic concepts. Fiber-optic technology has, for over 30 years, made important contributions to the science of measurement. The paper presents a perspective on these contributions which while far from exhaustive highlights the important conceptual advances made in the early days of optical fiber technology and the breadth of application which has emerged. There are also apparent opportunities for yet more imaginative research in applying guided-wave optics to emerging and challenging measurement requirements ranging from microsystems characterization to cellular biochemistry to art restoration.


title: An Improved IEEE 802.16 WiMAX Module for the NS-3 Simulator

abstract: IEEE 802.16 WiMAX is a promising new wireless technology for providing broadband ubiquitous network access. As more and more researchers and industrials are interested in simulating such networks, a number of WiMAX simulators have been emerged in the networking community. One of the most recent WiMAX simulator is the one developed for ns-3. This module provides a standard compliant and well designed implementation of the standard and benefits from the major enhancements provided by ns-3 (compared to other network simulators) which has all the capabilities of becoming the leading network simulator in near future. However, this WiMAX module still lacks some important features which motivated this work. In this paper, we first provide a snapshot of existing WiMAX simulators available in the public domain, while highlighting their limitations. Then, we describe the new features and enhancements we have integrated within the ns-3 WiMAX module, and in particular: a realistic and scalable physical model, an IP packet classifier for the convergence sub-layer, efficient uplink and downlink schedulers, support for multicast traffic and pcap packet tracing functionality. The new design of the physical layer has improved the simulation time by several magnitude orders while still providing a realistic implementation of the standard. Furthermore, the IP classifier has enabled the simulation of an unlimited number of service flows per subscriber station, while the proposed schedulers improve the management of the QoS requirements for the different service flows.


title: Cryptography with chaos at the physical level

abstract: In this work, we devise a chaos-based secret key cryptography scheme for digital communication where the encryption is realized at the physical level, that is, the encrypting transformations are applied to the wave signal instead to the symbolic sequence. The encryption process consists of transformations applied to a two-dimensional signal composed of the message carrying signal and an encrypting signal that has to be a chaotic one. The secret key, in this case, is related to the number of times the transformations are applied. Furthermore, we show that due to its chaotic nature, the encrypting signal is able to hide the statistics of the original signal.


title: ED plaster-of-Paris jacket for infantile scoliosis

abstract: BACKGROUND ::: There are various articles published in last few years which consider surgical methods like growing rod instrumentation and modulation of the growth as a "gold standard" for the treatment of early onset severe scoliosis. We emphasize orthopaedic correction with serial casting as another option for such progressive deformity. The key to the success of this treatment is to understand the strategy and the technique involved in the effective casting. ::: ::: ::: METHODS ::: The conventional technique of elongation, derotation, flexion cast (named EDF by Cotrel) is described with some modifications like wedging the cast (gypsotomy) in order to produce the flexion component. ::: ::: ::: RESULTS ::: Serial casting with ED casts for the treatment of progressive idiopathic infantile scoliosis is an effective tool for the benign types of curves (Mehta) and spinal fusion was not necessary in two-third of our cases. ::: ::: ::: CONCLUSION ::: Surgical option for treatment of early onset scoliosis is not a "gold standard". Orthopaedic treatment with serial elongation, derotation casts remain the centerpiece of this treatment. Each detail to understand the technique must be known in order to obtain the best result.


title: High-Performance Graph Analytics on Manycore Processors

abstract: The divergence in the computer architecture landscape has resulted in different architectures being considered mainstream at the same time. For application and algorithm developers, a dilemma arises when one must focus on using underlying architectural features to extract the best performance on each of these architectures, while writing portable code at the same time. We focus on this problem with graph analytics as our target application domain. In this paper, we present an abstraction-based methodology for performance-portable graph algorithm design on manicure architectures. We demonstrate our approach by systematically optimizing algorithms for the problems of breadth-first search, color propagation, and strongly connected components. We use Kokkos, a manicure library and programming model, for prototyping our algorithms. Our portable implementation of the strongly connected components algorithm on the NVIDIA Tesla K40M is up to 3.25× faster than a state-of-the-art parallel CPU implementation on a dual-socket Sandy Bridge compute node.


title: Identifying Local Spatially Dependent Driving Factors of Village Development in Jambi Province

abstract: Village is the smallest unit of administrative boundaries in Indonesia. There are more than 74,000 villages in our country with various characteristics. One way that can be used to determine the hierarchy of village development is by analyzing the village development index (VDI). The objectives of this study are to calculate VDI and to identify spatial variations in the relationship between VDI and its driving factors in every location. In this study, Jambi Province was selected as the research location. There are 1543 villages in Jambi. The VDI in this study were determined by considering the number of facilities/infrastructure and the accessibility ::: to access those facilities. Villages with higher VDI are more developed rather than other villages. To identify the driving factors of village development, 62 variables from PODES and spatial data were included in the Ordinary Least Square (OLS) model. Based on OLS results, we found 17 variables which statistically significant affecting village development in Jambi. Then, Geographically Weighted Regression (GWR) model was employed to identify the spatial relationship of driving factors to village development. The seventeen variables included number of education, health, and economic facilities; accessibility; percentage of built-up area, household working in agricultural sector, household using electricity, household living in slum area, poor people; number of criminality and people suffering from malnutrition. Studies of the interdependencies between these driving forces which affecting village development in the region remain limited. The presented findings show that the local driving forces affecting village development in Jambi Province vary spatially.


title: Tracking and Segmentation of the Airways in Chest CT Using a Fully Convolutional Network

abstract: Airway segmentation plays an important role in analyzing chest computed tomography (CT) volumes such as lung cancer detection, chronic obstructive pulmonary disease (COPD), and surgical navigation. However, due to the complex tree-like structure of the airways, obtaining segmentation results with high accuracy for a complete 3D airway extraction remains a challenging task. In recent years, deep learning based methods, especially fully convolutional networks (FCN), have improved the state-of-the-art in many segmentation tasks. 3D U-Net is an example that optimized for 3D biomedical imaging. It consists of a contracting encoder part to analyze the input volume and a successive decoder part to generate integrated 3D segmentation results. While 3D U-Net can be trained for any 3D segmentation task, its direct application to airway segmentation is challenging due to differently sized airway branches. In this work, we combine 3D deep learning with image-based tracking in order to automatically extract the airways. Our method is driven by adaptive cuboidal volume of interest (VOI) analysis using a 3D U-Net model. We track the airways along their centerlines and set VOIs according to the diameter and running direction of each airway. After setting a VOI, the 3D U-Net is utilized to extract the airway region inside the VOI. All extracted candidate airway regions are unified to form an integrated airway tree. We trained on 30 cases and tested our method on an additional 20 cases. Compared with other state-of-the-art airway tracking and segmentation methods, our method can increase the detection rate by 5.6 while decreasing the false positives (FP) by 0.7 percentage points.


title: Functional and longitudinal data analysis: perspectives on smoothing

abstract: The perspectives and methods of functional data analysis and longitu- dinal data analysis for smoothing are contrasted and compared. Topics include kernel methods and random effects models for smoothing, basis function methods, and examination of correlates of curve shapes. Some directions in which method- ology might advance are identified. Until recently, functional data analysis (FDA) and longitudinal data anal- ysis (LDA) have been rather disjoint enterprises. Both are concerned with the analysis of data consisting of repeated measurements of objects over time. Mea- surements treated in the FDA literature typically are recorded by high frequency automatic sensing equipment, whereas those treated in the LDA literature are more typically sparsely, and often irregularly, spaced measurements on human or other biological subjects. The aims of the analysis are often somewhat different, partly because of different scientific subject matter (it is interesting to read and compare the introductions of the two classic texts of Diggle, Liang, and Zeger (1994) and Ramsay and Silverman (1997)). Those of FDA tend to be exploratory − to represent and display data in order to highlight interesting characteristics, perhaps as input for further analysis − whereas those of LDA have a stronger inferential component. This contrast can be seen in the use of estimated time- correlation functions in the two areas; correlation functions are used in the FDA literature in a descriptive manner to characterize time dependencies in the curves, whereas an important aim in the LDA literature of estimating these correlation functions is to draw valid inferences. Another important difference is that the LDA literature has had to pay much more attention to data that are missing because of a variety of mechanisms. Despite these differences in focus, there are many common aims, among them are the following:


title: How bad is selfish routing in practice?

abstract: Routing games are one of the most successful domains of application of game theory. It is well understood that simple dynamics converge to equilibria, whose performance is nearly optimal regardless of the size of the network or the number of agents. These strong theoretical assertions prompt a natural question: How well do these pen-and-paper calculations agree with the reality of everyday traffic routing? We focus on a semantically rich dataset from Singapore's National Science Experiment that captures detailed information about the daily behavior of thousands of Singaporean students. Using this dataset, we can identify the routes as well as the modes of transportation used by the students, e.g. car (driving or being driven to school) versus bus or metro, estimate source and sink destinations (home-school) and trip duration, as well as their mode-dependent available routes. We quantify both the system and individual optimality. Our estimate of the Empirical Price of Anarchy lies between 1.11 and 1.22. Individually, the typical behavior is consistent from day to day and nearly optimal, with low regret for not deviating to alternative paths.


title: SPred: Large-scale Harvesting of Semantic Predicates

abstract: We present SPred, a novel method for the creation of large repositories of semantic predicates. We start from existing collocations to form lexical predicates (e.g., break ) and learn the semantic classes that best fit the argument. To do this, we extract all the occurrences in Wikipedia which match the predicate and abstract its arguments to general semantic classes (e.g., break BODY PART, break AGREEMENT, etc.). Our experiments show that we are able to create a large collection of semantic predicates from the Oxford Advanced Learner’s Dictionary with high precision and recall, and perform well against the most similar approach.


title: Data Mining With Computational Intelligence

abstract: Finding information hidden in data is as theoretically difficult as it is practically important. With the objective of discovering unknown patterns from data, the methodologies of data mining were derived from statistics, machine learning, and artificial intelligence, and are being used successfully in application areas such as bioinformatics, banking, retail, and many others. Wang and Fu present in detail the state of the art on how to utilize fuzzy neural networks, multilayer perceptron neural networks, radial basis function neural networks, genetic algorithms, and support vector machines in such applications. They focus on three main data mining tasks: data dimensionality reduction, classification, and rule extraction. The book is targeted at researchers in both academia and industry, while graduate students and developers of data mining systems will also profit from the detailed algorithmic descriptions.


title: COMMON PHASES OF COMPUTER FORENSICS INVESTIGATION MODELS

abstract: The increasing criminal activities using digital information as the means or targets warrant for a structured manner in dealing with them. Since 1984 when a formalized process been introduced, a great number of new and improved computer forensic investigation processes have been developed. In this paper, we reviewed a few selected investigation processes that have been produced throughout the years and then identified the commonly shared processes. Hopefully, with the identification of the commonly shard process, it would make it easier for the new users to understand the processes and also to serve as the basic underlying concept for the development of a new set of processes. Based on the commonly shared processes, we proposed a generic computer forensics investigation model, known as GCFIM.


title: IMPLEMENTING CFD (COMPUTATIONAL FLUID DYNAMICS) IN OPENCL FOR BUILDING SIMULATION

abstract: Though researchers in computer graphics have started to use the GPGPU (General Purposed Graphics Processing Unit) method to speed up their procedural programs, these techniques are seldom used in the building simulation field. It is possible to apply the GPGPU method to many simulation scenarios (i.e. human evacuation, shadow simulation) to speed up performance. In this paper, CFD is used as an example to introduce how the GPGPU method will provide benefits to building simulation. CFD is widely used for building performance analysis. However CFD is computationally expansive so large scale problems often take a long time to perform in a simulation.


abstract: Kalermo, Jonna Maria Rissanen, Jenni Karoliina Agile software development in theory and practice / Jonna Kalermo and Jenni Rissanen Jyvaskyla, University of Jyvaskyla, 2002. 188 pp. Master's thesis In turn of the millennium, new software development ideas were presented in the form of Agile Manifesto as a counteraction to the traditional, rigorous development methods and process models. Agile Manifesto consists of four values and twelve principles of which the authors of this thesis formed a conceptual framework. It assists in analysing different aspects of agile software development one by one but also as a whole. Agile software development aims at fast, light and efficient development that supports customer's business without being chaotic or following any rigorous method. The aim of this study was to analyse Agile Manifesto and its applicability. This was done by conducting a literature review and an empirical case study. Agile Manifesto gives an ideological background for agile methods, such as Extreme Programming (XP). Such methods are most feasible for small organisations developing innovative software products. We analysed Agile Manifesto and found out that literature supported the ideas of it, although no revolutionary ideas were discovered. We used an empirical case study as an example of an agile in-house software development method in corporate venturing context. The case study supported principles and values of Agile Manifesto and confirmed the assumption that agility is heavily based on tacit knowledge, skilled and motivated employees and frequent communication. This sets several requirements for management and employees working in an agile mode: they have to be communicative, social, responsible and skilled.


title: VCSEL-Based Interconnects for Current and Future Data Centers

abstract: The vast majority of optical links within the data center are based on vertical cavity surface emitting lasers (VCSELs) operating at 850 nm over multimode optical fiber. Deployable links have evolved in speed from 1 Gb/s in 1996 to 28 Gb/s in 2014. Serial data links at 40 and 56 Gb/s are now under development and place even more demand on the VCSEL and photodiodes. In this paper, we present the characteristics of VCSELs and photodiodes used in current generation 28 Gb/s links and present several methods to extend link distances using more advanced data encoding schemes. Finally, we will present results on wavelength division multiplexing on multimode optical fiber that demonstrate 40 Gb/s Ethernet connections up to 300 m on duplex OM3 optical fiber, and present results on fiber optimized for modal bandwidth in the 850 to 980 nm range.


title: CityGuard: A Watchdog for Safety-Aware Conflict Detection in Smart Cities

abstract: Nowadays, increasing number of smart services are being developed and deployed in cities around the world. IoT platforms have emerged to integrate smart city services and city resources, and thus improve city performance in the domains of transportation, emergency, environment, public safety, etc. Despite the increasing intelligence of smart services and the sophistication of platforms, the safety issues in smart cities are not addressed adequately, especially the safety issues arising from the integration of smart services. Therefore, CityGuard, a safety-aware watchdog architecture is developed. To the best of our knowledge, it is the first architecture that detects and resolves conflicts among actions of different services considering both safety and performance requirements. Prior to developing CityGuard, safety and performance requirements and a spectrum of conflicts are specified. Sophisticated models are used to analyze secondary effects, and detect device and environmental conflicts. A simulation based on New York City is used for the evaluation. The results show that CityGuard (i) identifies unsafe actions and thus helps to prevent the city from safety hazards, (ii) detects and resolves two major types of conflicts, i.e., device and environmental conflicts, and (iii) improves the overall city performance.


title: Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser

abstract: Neural networks are vulnerable to adversarial examples, which poses a threat to their application in security sensitive systems. We propose high-level representation guided denoiser (HGD) as a defense for image classification. Standard denoiser suffers from the error amplification effect, in which small residual adversarial noise is progressively amplified and leads to wrong classifications. HGD overcomes this problem by using a loss function defined as the difference between the target model's outputs activated by the clean image and denoised image. Compared with ensemble adversarial training which is the state-of-the-art defending method on large images, HGD has three advantages. First, with HGD as a defense, the target model is more robust to either white-box or black-box adversarial attacks. Second, HGD can be trained on a small subset of the images and generalizes well to other images and unseen classes. Third, HGD can be transferred to defend models other than the one guiding it. In NIPS competition on defense against adversarial attacks, our HGD solution won the first place and outperformed other models by a large margin.1


title: Evaluation of Objective Quality Measures for Speech Enhancement

abstract: In this paper, we evaluate the performance of several objective measures in terms of predicting the quality of noisy speech enhanced by noise suppression algorithms. The objective measures considered a wide range of distortions introduced by four types of real-world noise at two signal-to-noise ratio levels by four classes of speech enhancement algorithms: spectral subtractive, subspace, statistical-model based, and Wiener algorithms. The subjective quality ratings were obtained using the ITU-T P.835 methodology designed to evaluate the quality of enhanced speech along three dimensions: signal distortion, noise distortion, and overall quality. This paper reports on the evaluation of correlations of several objective measures with these three subjective rating scales. Several new composite objective measures are also proposed by combining the individual objective measures using nonparametric and parametric regression analysis techniques.


title: Forecasting Spatiotemporal Impact of Traffic Incidents on Road Networks

abstract: The advances in sensor technologies enable real-time collection of high-fidelity spatiotemporal data on transportation networks of major cities. In this paper, using two real-world transportation datasets: 1) incident data and 2) traffic data, we address the problem of predicting and quantifying the impact of traffic incidents. Traffic incidents include any non-recurring events on road networks, including accidents, weather hazard, road construction or work zone closures. By analyzing archived incident data, we classify incidents based on their features (e.g., time, location, type of incident). Subsequently, we model the impact of each incident class on its surrounding traffic by analyzing the archived traffic data at the time and location of the incidents. Consequently, in real-time, if we observe a similar incident (from real-time incident data), we can predict and quantify its impact on the surrounding traffic using our developed models. This information, in turn, can help drivers to effectively avoid impacted areas in real-time. To be useful for such real-time navigation application, and unlike current approaches, we study the dynamic behavior of incidents and model the impact as a quantitative time varying spatial span. In addition to utilizing incident features, we improve our classification approach further by analyzing traffic density around the incident area and the initial behavior of the incident. We evaluated our approach with very large traffic and incident datasets collected from the road networks of Los Angeles County and the results show that we can improve our baseline approach, which solely relies on incident features, by up to 45%.


title: Long non‐coding RNA MALAT1 mediates hypoxia‐induced pro‐survival autophagy of endometrial stromal cells in endometriosis

abstract: Endometriosis is a common gynecological disease characterized by diminished apoptosis, sustained ectopic survival of dysfunctional endometrial cells. Hypoxia has been implicated as a crucial microenvironmental factor that contributes to endometriosis. It has been reported that long non-coding RNA MALAT1 (lncRNA-MALAT1) highly expressed in endometriosis and up-regulated by hypoxia. Hypoxia may also induce autophagy, which might act as cell protective mechanism. However, the relationship between lncRNA-MALAT1 and autophagy under hypoxia conditions in endometriosis remains unknown. In the present study, we found that both lncRNA-MALAT1 and autophagy level were up-regulated in ectopic endometrium from patients with endometriosis, and its expression level correlates positively with that of hypoxia-inducible factor-1α (HIF-1α). In cultured human endometrial stromal cells, both lncRNA-MALAT1 and autophagy were induced by hypoxia in a time-dependent manner and lncRNA-MALAT1 up-regulation was dependent on HIF-1α signalling. Our analyses also show that knockdown of lncRNA-MALAT1 suppressed hypoxia induced autophagy. Furthermore, inhibiting autophagy with specific inhibitor 3-Methyladenine (3-MA) and Beclin1 siRNA enhanced apoptosis of human endometrial stromal cells under hypoxia condition. Collectively, our findings identify that lncRNA-MALAT1 mediates hypoxia-induced pro-survival autophagy of endometrial stromal cells in endometriosis.


title: Vibration-based Terrain Analysis for Mobile Robots

abstract: Safe, autonomous mobility in rough terrain is an important requirement for planetary exploration rovers. Knowledge of local terrain properties is critical to ensure a rover’s safety on slopes and uneven surfaces. This paper presents a method to classify terrain based on vibrations induced in the rover structure by wheel-terrain interaction during driving. Vibrations are measured using an accelerometer on the rover structure. The classifier is trained using labeled vibration data during an off-line learning phase. Linear discriminant analysis is used for on-line identification of terrain classes such as sand, gravel, or clay. This approach is experimentally validated on a laboratory testbed.


title: Parallel peak pruning for scalable SMP contour tree computation

abstract: As data sets grow to exascale, automated data analysis and visualisation are increasingly important, to intermediate human understanding and to reduce demands on disk storage via in situ analysis. Trends in architecture of high performance computing systems necessitate analysis algorithms to make effective use of combinations of massively multicore and distributed systems. One of the principal analytic tools is the contour tree, which analyses relationships between contours to identify features of more than local importance. Unfortunately, the predominant algorithms for computing the contour tree are explicitly serial, and founded on serial metaphors, which has limited the scalability of this form of analysis. While there is some work on distributed contour tree computation, and separately on hybrid GPU-CPU computation, there is no efficient algorithm with strong formal guarantees on performance allied with fast practical performance. We report the first shared SMP algorithm for fully parallel contour tree computation, withfor-mal guarantees of O(lgnlgt) parallel steps and O(n lgn) work, and implementations with up to 10x parallel speed up in OpenMP and up to 50x speed up in NVIDIA Thrust.


title: Trajectory generation and control of a quadrotor with a cable-suspended load - A differentially-flat hybrid system

abstract: A quadrotor with a cable-suspended load with eight degrees of freedom and four degrees underactuation is considered and the system is established to be a differentially-flat hybrid system. Using the flatness property, a trajectory generation method is presented that enables finding nominal trajectories with various constraints that not only result in minimal load swing if required, but can also cause a large swing in the load for dynamically agile motions. A control design is presented for the system specialized to the planar case, that enables tracking of either the quadrotor attitude, the load attitude or the position of the load. Stability proofs for the controller design and experimental validation of the proposed controller are presented.