title: Time-Dependent Representation for Neural Event Sequence Prediction

abstract: Existing sequence prediction methods are mostly concerned with time-independent sequences, in which the actual time span between events is irrelevant and the distance between events is simply the difference between their order positions in the sequence. While this time-independent view of sequences is applicable for data such as natural languages, e.g., dealing with words in a sentence, it is inappropriate and inefficient for many real world events that are observed and collected at unequally spaced points of time as they naturally arise, e.g., when a person goes to a grocery store or makes a phone call. The time span between events can carry important information about the sequence dependence of human behaviors. In this work, we propose a set of methods for using time in sequence prediction. Because neural sequence models such as RNN are more amenable for handling token-like input, we propose two methods for time-dependent event representation, based on the intuition on how time is tokenized in everyday life and previous work on embedding contextualization. We also introduce two methods for using next event duration as regularization for training a sequence prediction model. We discuss these methods based on recurrent neural nets. We evaluate these methods as well as baseline models on five datasets that resemble a variety of sequence prediction tasks. The experiments revealed that the proposed methods offer accuracy gain over baseline models in a range of settings.


title: Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification

abstract: Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods rely on regions of interest (ROIs) which require great efforts to annotate. Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning (MIL) for labeling a set of instances/patches, we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned ROIs. We explore three different schemes to construct deep multi-instance networks for whole mammogram classification. Experimental results on the INbreast dataset demonstrate the robustness of proposed networks compared to previous work using segmentation and detection annotations.


title: Comparing finite mechanisms

abstract: This paper obtains finite analogues to propositions that a previous literature obtained about the informational efficiency of mechanisms whose possible messages form a continuum. Upon reaching an equilibrium message, to which all persons “agree”, a mechanism obtains an action appropriate to the organization's environment. Each person's privately observed characteristic (a part of the organization's environment) enters her agreement rule. An example is the Walrasian mechanism in an exchange economy. There a message specifies a proposed trade vector for each trader as well as a price for each non-numeraire commodity. A trader agrees if the price of each non-numeraire commodity equals her marginal utility for that commodity (at the proposed trades) divided by her marginal utility for the numeraire. At an equilibrium message, the mechanism's action consists of the trades specified in that message, and (for classic economies) those trades are Pareto-optimal and individually rational. Even though the space of environments (characteristics) is a continuum, mechanisms with a continuum of possible messages are unrealistic, since transmitting every point of a continuum is impossible. In reality, messages have to be rounded off and the number of possible messages has to be finite. Moreover, reaching a continuum mechanism's equilibrium message typically requires infinite time and that difficulty is absent if the number of possible messages is finite. The question therefore arises whether results about continuum mechanisms have finite counterparts. If we measure a continuum mechanism's communication cost by its message-space dimension, then our corresponding cost measure for a finite mechanism is the (finite) number of possible equilibrium messages. We find that if two continuum mechanisms yield the same action but the first has higher message-space dimension, then a sufficiently fine finite approximation of the first has larger error than an approximation of the second if the cost of the first approximation is no higher than the cost of the second approximation. An approximation's “error” is the largest distance between the continuum mechanism's action and the approximation's action. We obtain bounds on error. We also study the performance of Direct Revelation (DR) mechanisms relative to “indirect” mechanisms, both yielding the same action, when the environment set grows. We find that as the environment-set dimension goes to infinity, so does the extra cost of the DR approximation, if the error of the DR approximation is at least as small as the error of the indirect approximation. While the paper deals with information-processing costs and not incentives, it is related to the incentive literature, since the Revelation Principle is central to much of that literature and one of our main results is the informational inefficiency of finite Direct Revelation mechanisms. Copyright Springer-Verlag Berlin Heidelberg 2003


title: An Efficient Location Update Mechanism for Continuous Queries over Moving Objects *

abstract: In a moving-object database system that supports continuous queries (CQ), an important problem is to keep the location data consistent with the actual locations of the entities being monitored, in order to produce correct query results. This goal is often difficult to achieve due to limited network resources. However, if an object is not required by any query, its value need not be refreshed. Based on this observation, we redefine the notion of temporal consistency of data items with respect to the query result, where only data items that are relevant to the CQs need to be fresh. To exploit this correctness definition, we develop an adaptive time-based update technique called query-result update (QRU). The advantage of this technique is that it identifies objects with different levels of significance to the correctness of query results. Locations of objects that have more impact to the query result are acquired more frequently than the ones that do not. To achieve this objective, queries are classified into rank-based (i.e., ranks of objects are critical to query results) and non-rank-based. For each query class, QRU decides the time instant that an object should send a location update based on the predicted impact of the object to the query result. Moreover, the location update frequency of each object is continuously adjusted in order to adapt to the accuracy of the prediction model used. We evaluate the effectiveness of QRU by simulating execution of CQs over synthetic and real data sets. We also compare QRU experimentally with existing location update policies, namely the distance-based method, the time-based method, the speed dead-reckoning method, as well as the safe region strategy.


title: Aging model challenges in deeply scaled tri-gate technologies

abstract: As tri-gate transistor technologies continue to scale to smaller dimensions, a variety of aging mechanisms become important to include in models to accurately predict end-of-life transistor performance. Traditional aging effects such as BTI and hot carrier continue to play a role. However, modeling these mechanisms becomes more complicated with the addition of recovery, variation, and local self-heating. Further, second-order effects are starting to accumulate, such as recovery interactions, minority carrier gate injection, damage localization, and interactions between hot carrier and BTI. This work highlights the roles and impacts of these various effects and how they will need to fit into a comprehensive aging model.


title: Practical Applications of Cone-Beam Computed Tomography in Orthodontics

abstract: BACKGROUND ::: Comprehensive visualization and records of the craniofacial complex have been goals in orthodontic imaging. These tasks have been performed by means of plaster, photographs and radiographs. These approaches have evolved across time, and cone-beam computed tomography (CBCT) has emerged as a comprehensive imaging modality for orthodontics. ::: ::: ::: METHODS ::: The authors provide a practical guide for applying CBCT in orthodontics, with an emphasis on situations in which conventional imaging is limited. These situations include dental development, limits of tooth movement, airway assessment, craniofacial morphology and superimposition. ::: ::: ::: RESULTS ::: Complexities of the craniofacial complex, dentition and airway present challenges in obtaining conventional images. CBCT has image-fidelity advantages over conventional imaging that can lead to improved visualization. ::: ::: ::: CONCLUSIONS ::: CBCT is changing orthodontics with respect to clinically assessing patients and is evolving with respect to diagnosis, clinical techniques and outcomes. ::: ::: ::: CLINICAL IMPLICATIONS ::: The clinical value proposition of CBCT is to describe craniofacial anatomy accurately and provide comprehensive information regarding anatomical relationships and individual patient findings for improved diagnosis, treatment planning and prognostication.


title: A Comparative Study for Email Classification

abstract: Email has become one of the fastest and most economical forms of communication. However, the increase of email users have resulted in the dramatic increase of spam emails during the past few years. In this paper, email data was classified using four different classifiers (Neural Network, SVM classifier, Naive Bayesian Classifier, and J48 classifier). The experiment was performed based on different data size and different feature size. The final classification result should be ‘1’ if it is finally spam, otherwise, it should be ‘0’. This paper shows that simple J48 classifier which make a binary tree, could be efficient for the dataset which could be classified as binary tree.


title: Programmable Logic Controller Forensics

abstract: Programmable logic controllers (PLCs) automate the control and monitoring of physical industrial and infrastructure processes such as power generation, gas pipelines, and water management. Due to the convergence of networking infrastructure, PLCs can be exposed to cyberattacks over the network with potentially catastrophic consequences. This contribution introduces the basic mechanisms by which various attacks can be detected, analyzed, and, ultimately, remedied.


title: Learning Multilayer Channel Features for Pedestrian Detection

abstract: Pedestrian detection based on the combination of convolutional neural network (CNN) and traditional handcrafted features (i.e., HOG+LUV) has achieved great success. In general, HOG+LUV are used to generate the candidate proposals and then CNN classifies these proposals. Despite its success, there is still room for improvement. For example, CNN classifies these proposals by the fully connected layer features, while proposal scores and the features in the inner-layers of CNN are ignored. In this paper, we propose a unifying framework called multi-layer channel features (MCF) to overcome the drawback. It first integrates HOG+LUV with each layer of CNN into a multi-layer image channels. Based on the multi-layer image channels, a multi-stage cascade AdaBoost is then learned. The weak classifiers in each stage of the multi-stage cascade are learned from the image channels of corresponding layer. Experiments on Caltech data set, INRIA data set, ETH data set, TUD-Brussels data set, and KITTI data set are conducted. With more abundant features, an MCF achieves the state of the art on Caltech pedestrian data set (i.e., 10.40% miss rate). Using new and accurate annotations, an MCF achieves 7.98% miss rate. As many non-pedestrian detection windows can be quickly rejected by the first few stages, it accelerates detection speed by 1.43 times. By eliminating the highly overlapped detection windows with lower scores after the first stage, it is 4.07 times faster than negligible performance loss.


title: Random projection-based partial feature extraction for robust face recognition

abstract: In this paper, a novel feature extraction method for robust face recognition (FR) is proposed. The proposed method combines a simple yet effective dimensionality increasing (DI) method with an information-preserving dimensionality reduction (DR) method. For the proposed DI method, we employ the rectangle filters which sum the pixel values within a randomized rectangle window on the face image to extract the feature. By convolving the face image with all possible rectangle filters having various locations and scales, the face image in the image space is projected to a very high-dimensional feature space where more discriminative information can be incorporated. In order to significantly reduce the computational complexity while preserving the most informative features, we adopt a random projection method based on the compressed sensing theory for DR. Unlike the traditional holistic-based feature extraction methods requiring the time-consuming data-dependent training procedure, the proposed method has the partial-based and data-independent properties. Extensive experimental results on representative FR databases show that, as compared with conventional feature extraction methods, our proposed method not only achieves the higher recognition accuracy but also shows better robustness to corruption, occlusion, and disguise.


title: dentification of extract method refactoring opportunities for the ecomposition of methods

abstract: The extraction of a code fragment into a separate method is one of the most widely performed refactoring activities, since it allows the decomposition of large and complex methods and can be used in combination with other code transformations for fixing a variety of design problems. Despite the significance of Extract Method refactoring towards code quality improvement, there is limited support for the identification of code fragments with distinct functionality that could be extracted into new methods. The goal of our approach is to automatically identify Extract Method refactoring opportunities which are related with the complete computation of a given variable (complete computation slice) and the statements affecting the state of a given object (object state slice). Moreover, a set of rules regarding the preservation of existing dependences is proposed that exclude refactoring opportunities corresponding to slices whose extraction could possibly cause a change in program behavior. The proposed approach has been evaluated regarding its ability to capture slices of code implementing a distinct functionality, its ability to resolve existing design flaws, its impact on the cohesion of the decomposed and extracted methods, and its ability to preserve program behavior. Moreover, precision and recall have been computed employing the refactoring opportunities found by independent evaluators in software that they developed as a golden set.


title: MultiWiBi: The multilingual Wikipedia bitaxonomy project

abstract: Abstract We present MultiWiBi, an approach to the automatic creation of two integrated taxonomies for Wikipedia pages and categories written in different languages. In order to create both taxonomies in an arbitrary language, we first build them in English and then project the two taxonomies to other languages automatically, without the help of language-specific resources or tools. The process crucially leverages a novel algorithm which exploits the information available in either one of the taxonomies to reinforce the creation of the other taxonomy. Our experiments show that the taxonomical information in MultiWiBi is characterized by a higher quality and coverage than state-of-the-art resources like DBpedia, YAGO, MENTA, WikiNet, LHD and WikiTaxonomy, also across languages. MultiWiBi is available online at http://wibitaxonomy.org/multiwibi .


title: Balancing control of a robot bicycle with uncertain center of gravity

abstract: In this research, a small humanoid robot and a bicycle of comparable size are constructed. Like the human rider, the robot is designed to pedal, balance and steer the bicycle. We particularly focus on the design of control system for the robot to balance and steer the bicycle using the handlebar. The control system is novel that it is capable of estimating the uncertain center of gravity of the robot-bicycle system and then incorporating such an estimation to enhance control performance. The control system design is based on a general control framework which can establish asymptotic stability under unknown measurement biases. The stability of the control system is theoretically proved and a systematic procedure to compute the control parameters is given. Both simulations and experiments verify that the proposed controller can automatically counteract the mass imbalance in the system and allow the robot to perform straight-line steering.


title: Tsunami: A parasitic, indestructible botnet on Kad

abstract: While current botnets rely on a central server or bootstrap nodes for their operations, in this paper we identify and investigate a new type of botnet, called Tsunami, in which no such bottleneck nodes exist. In particular, we study how a Tsunami botnet can build a parasitic relationship with a widely deployed P2P system, Kad, to successfully issue commands to its bots, launch various attacks, including distributed denial of service (DDoS) and spam, at ease, as well as receive responses from the bots. Our evaluation shows that in a Kad network with four million nodes, even with only 6 % nodes being Tsunami bots, Tsunami can reach 75 % of its bots in less than 4 min and receive responses from 99 % of bots. We further propose how we may defend against Tsunami and evaluate the defense solution.


title: Augmenting software architectures with physical components

abstract: This paper presents an extension of exist- ing software architecture tools to model physical sys- tems, their interconnections, and the interactions between physical and cyber components. We intro- duce a new cyber-physical system (CPS) architectur- al style to support the construction of architectural descriptions of complete systems and to serve as the reference context for analysis and evaluation of de- sign alternatives using existing model-based tools. The implementation of the CPS architectural style in AcmeStudio includes behavioral annotations on com- ponents and connectors using either finite state processes (FSP) or linear hybrid automata (LHA) with plug-ins to perform behavior analysis. The application of the CPS architectural style is illustrated for the STARMAC quadrotor.


title: Cyber security for home users: A new way of protection through awareness enforcement

abstract: We are currently living in an age, where the use of the Internet has become second nature to millions of people. Not only businesses depend on the Internet for all types of electronic transactions, but more and more home users are also experiencing the immense benefit of the Internet. However, this dependence and use of the Internet bring new and dangerous risks. This is due to increasing attempts from unauthorised third parties to compromise private information for their own benefit - the whole wide area of cyber crime. It is therefore essential that all users understand the risks of using Internet, the importance of securing their personal information and the consequences if this is not done properly. It is well known that home users are specifically vulnerable, and that cyber criminals have such users squarely in their target. This vulnerability of home users is due to many factors, but one of the most important one is the fact that such home users in many cases are not aware of the risks of using the Internet, and often venture into cyber space without any awareness preparation for this journey. This paper specifically investigates the position of the home user, and proposes a new model, the E-Awareness Model (E-AM), in which home users can be forced to acquaint themselves with the risks involved in venturing into cyber space. The E-AM consists of two components: the awareness component housed in the E-Awareness Portal, and the enforcement component. This model proposes a way to improve information security awareness among home users by presenting some information security content and enforcing the absorption of this content. The main difference between the presented model and other existing information security awareness models is that in the presented model the acquiring/absorption of the awareness content is compulsory - the user is forced to proceed via the E-Awareness Portal without the option of bypassing it.


title: Dense RGB-D Semantic Mapping with Pixel-Voxel Neural Network

abstract: In this paper, a novel Pixel-Voxel network is proposed for dense 3D semantic mapping, which can perform dense 3D mapping while simultaneously recognizing and labelling the semantic category each point in the 3D map. In our approach, we fully leverage the advantages of different modalities. That is, the PixelNet can learn the high-level contextual information from 2D RGB images, and the VoxelNet can learn 3D geometrical shapes from the 3D point cloud. Unlike the existing architecture that fuses score maps from different modalities with equal weights, we propose a softmax weighted fusion stack that adaptively learns the varying contributions of PixelNet and VoxelNet and fuses the score maps according to their respective confidence levels. Our approach achieved competitive results on both the SUN RGB-D and NYU V2 benchmarks, while the runtime of the proposed system is boosted to around 13 Hz, enabling near-real-time performance using an i7 eight-cores PC with a single Titan X GPU.


OorSchot. ARP-Based Detection of Scanning Worms within an Enterprise Network


title: RFIDrone: Preliminary experiments and electromagnetic models

abstract: We introduce in a unitary way the paradigm of radiofrequency identification (RFID) merged with the technology of Unmanned Aerial Vehicles (UAV) giving rise to RFIDrone devices. Such family comprises the READER-Drone, which is a suitable UAV integrated with an autonomous RFID reader to act as mobile scanner of the environment, and the TAG-Drone, a UAV only equipped with an RFID sensor tag that hence becomes a mobile and automatically re-positioned sensor. We shows some handy electromagnetic models to identify the upper-bound communication performance of RFIDrone in close proximity of a scattering surface and we resume the results of some preliminary open-air experimentation corroborating the theoretical analysis.


title: Gender Identity and Autism Spectrum Disorders

abstract: In this review, we briefly summarize much of the existing literature on gender-related concerns and autism spectrum disorders (ASD), drawing attention to critical shortcomings in our current understanding and potential clinical implications. Some authors have concluded that gender identity disorder (GID), or gender dysphoria (GD), is more common in individuals with ASD, providing a range of potential explanations. However, existing literature is quantitatively limited, and our capacity to draw conclusions is further complicated by conceptual challenges regarding how gender identity is best understood. Discourses that emphasize gender as a component of identity formation are gaining prominence and seem particularly salient when applied to ASD. Individuals with ASD should enjoy equal rights with regard to treatment for gender dysphoria. Clinicians may be able to assist individuals in understanding this aspect of their identity by broadening the social frame and facilitating an exploration of gender roles.


title: Community structure of microbial biofilms associated with membrane-based water purification processes as revealed using a polyphasic approach

abstract: The microbial communities of membrane biofilms occurring in two full-scale water purification processes employing microfiltration (MF) and reverse osmosis (RO) membranes were characterized using a polyphasic approach that employed bacterial cultivation, 16S rDNA clone library and fluorescence in situ hybridization techniques. All methods showed that the alpha-Proteobacteria was the largest microbial fraction in the samples, followed by the gamma-Proteobacteria. This suggested that members of these two groups could be responsible for the biofouling on the membranes studied. Furthermore, the microbial community structures between the MF and RO samples were considerably different in composition of the most predominant 16S rDNA clones and bacterial isolates from the alpha-Proteobacteria and only shared two common groups ( Bradyrhizobium, Bosea) out of more than 17 different bacterial groups observed. The MF and RO samples further contained Planctomycetes and Fibroacter/ Acidobacteria as the second predominant bacterial clones, respectively, and differed in minor bacterial clones and isolates. The community structure differences were mainly attributed to differences in feed water, process configurations and operating environments, such as the pressure and hydrodynamic conditions present in the water purification systems.


title: Psychosocial and psychiatric risk factors for suicide: Case--control psychological autopsy study

abstract: Background Few studies of suicide have simultaneously examined the individual and combined effects of psychosocial and psychiatric risk factors. ::: ::: Aims To do so in a representative sample of suicides. ::: ::: Method A case—control psychological autopsy was conducted among 113 consecutive suicides and 226 living controls matched for age, gender, ethnicity and area of residence in Taiwan. ::: ::: Results Five major risk factors (loss event, suicidal behaviour in first-degree relatives, ICD-10 major depressive episode, emotionally unstable personality disorder and substance dependence) were found to have independent effects on suicide from multivariate conditional logistic regression analysis. ::: ::: Conclusions Effective intervention and management for loss event and major depressive episode among emotionally unstable subjects with a family tendency of suicidal behaviour, frequently also comorbid with alcohol or other substance dependence, may prove to be most effective for suicide prevention in different populations.


title: 3D LIDAR–camera intrinsic and extrinsic calibration: Identifiability and analytical least-squares-based initialization

abstract: In this paper we address the problem of estimating the intrinsic parameters of a 3D LIDAR while at the same time computing its extrinsic calibration with respect to a rigidly connected camera. Existing approaches to solve this nonlinear estimation problem are based on iterative minimization of nonlinear cost functions. In such cases, the accuracy of the resulting solution hinges on the availability of a precise initial estimate, which is often not available. In order to address this issue, we divide the problem into two least-squares sub-problems, and analytically solve each one to determine a precise initial estimate for the unknown parameters. We further increase the accuracy of these initial estimates by iteratively minimizing a batch nonlinear least-squares cost function. In addition, we provide the minimal identifiability conditions, under which it is possible to accurately estimate the unknown parameters. Experimental results consisting of photorealistic 3D reconstruction of indoor and outdoor scenes, as well as standard metrics of the calibration errors, are used to assess the validity of our approach.


title: Using String Matching for Deep Packet Inspection

abstract: String matching has sparked renewed research interest due to its usefulness for deep packet inspection in applications such as intrusion detection, virus scanning, and Internet content filtering. Matching expressive pattern specifications with a scalable and efficient design, accelerating the entire packet flow, and string matching with high-level semantics are promising topics for further study.


title: Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

abstract: From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.


title: Learning from incomplete ratings using non-negative matrix factorization

abstract: We use a low-dimensional linear model to describe the user rating matrix in a recommendation system. A non-negativity constraint is enforced in the linear model to ensure that each user’s rating profile can be represented as an additive linear combination of canonical coordinates. In order to learn such a constrained linear model from an incomplete rating matrix, we introduce two variations on Non-negative Matrix Factorization (NMF): one based on the Expectation-Maximization (EM) procedure and the other a Weighted Nonnegative Matrix Factorization (WNMF). Based on our experiments, the EM procedure converges well empirically and is less susceptible to the initial starting conditions than WNMF, but the latter is much more computationally efficient. Taking into account the advantages of both algorithms, a hybrid approach is presented and shown to be effective in real data sets. Overall, the NMF-based algorithms obtain the best prediction performance compared with other popular collaborative filtering algorithms in our experiments; the resulting linear models also contain useful patterns and features corresponding to user communities.


title: The assessment of school engagement: examining dimensionality and measurement invariance by gender and race/ethnicity.

abstract: The construct of school engagement has attracted growing interest as a way to ameliorate the decline in academic achievement and increase in dropout rates. The current study tested the fit of a second-order multidimensional factor model of school engagement, using large-scale representative data on 1103 students in middle school. In order to make valid model comparisons by group, we evaluated the extent to which the measurement structure of this model was invariant by gender and by race/ethnicity (European-American vs. African-American students). Finally, we examined differences in latent factor means by these same groups. From our confirmatory factor analyses, we concluded that school engagement was a multidimensional construct, with evidence to support the hypothesized second-order engagement factor structure with behavioral, emotional, and cognitive dimensions. In this sample, boys and girls did not substantially differ, nor did European-American and African-American students, in terms of the underlying constructs of engagement and the composition of these constructs. Finally, there were substantial differences in behavioral and emotional engagement by gender and by racial/ethnic groups in terms of second-order factor mean differences. Language: en


title: Second-Order Terahertz Bandpass Frequency Selective Surface With Miniaturized Elements

abstract: In this paper, a second-order frequency selective surface (FSS) made of miniaturized elements is proposed and designed for terahertz applications. The FSS is composed of two layers of metallic arrays separated from each other by a polymer dielectric spacer. The unit cells on the front and back layers are smaller than $\lambda _{0}/5$ , where $\lambda _{0}$ is the free space wavelength. The operation principle of the proposed FSS is described through a circuit model, and a synthesis procedure is presented for designing a desired filtering response. A prototype of the FSS is synthesized to operate at a center frequency of 0.42 THz with 45% fractional bandwidth. The designed FSS is fabricated by using microfabrication process. The performance is evaluated by using terahertz time-domain spectroscopy. Measurement results show a low sensitivity of the FSS response to oblique angles of incidence for both of the TE and TM polarizations.


title: Analysis and Design of a 1.6–28-GHz Compact Wideband LNA in 90-nm CMOS Using a $ \pi $-Match Input Network

abstract: This paper presents a wideband low-noise amplifier (LNA) based on the cascode configuration with resistive feedback. Wideband input-impedance matching was achieved using a shunt-shunt feedback resistor in conjunction with a preceding π -match network, while the wideband gain response was obtained using a post-cascode inductor (LP), which was inserted between the output of the cascoding transistor and the input of the shunt-shunt resistive feedback network to enhance the gain and suppress noise. Theoretical analysis shows that the frequency response of the power gain, as well as the noise figure (NF), can be described by second-order functions with quality factors or damping ratios as parameters. Implemented in 90-nm CMOS technology, the die area of this wideband LNA is only 0.139 mm2 including testing pads. It dissipates 21.6-mW power and achieves S11 below -10 dB, S22 below -10 dB, flat S21 of 9.6 ±1.1 dB, and flat NF of 3.68 ± 0.72 dB over the 1.6-28-GHz band. Besides, excellent input third-order inter-modulation point of +4 dBm is also achieved. The analytical, simulated, and measured results are mutually consistent.


title: SATIN: a toolkit for informal ink-based applications

abstract: Software support for making effective pen-based applications is currently rudimentary. To facilitate the creation of such applications, we have developed SATIN, a Java-based toolkit designed to support the creation of applications that leverage the informal nature of pens. This support includes a scenegraph for manipulating and rendering objects; support for zooming and rotating objects, switching between multiple views of an object, integration of pen input with interpreters, libraries for manipulating ink strokes, widgets optimized for pens, and compatibility with Java's Swing toolkit. SATIN includes a generalized architecture for handling pen input, consisting of recognizers, interpreters, and multi-interpreters. In this paper, we describe the functionality and architecture of SATIN, using two applications built with SATIN as examples.


title: Thematic development for measuring cohesion and coherence between sentences in English paragraph

abstract: Writing is a skill of marking coherent words on a paper and composing a text. There are several criteria of good writing, two of them are cohesion and coherence. Research of cohesion and coherence writing has been conducted by using Centering Theory and Entity Transition Value method. However, the result can still be improved. Therefore, in this research, we tried to use Thematic Development approach which focused on the use of Theme and Rheme as a method to analyze coherence level of a paragraph, combined with CogNIAC rules and Centering Theory to analyze its cohesion. Besides improving the result of previous methods, this research aims to help users in evaluating and assessing their writing text. To achieve these objectives, the proposed method is compared to previous works as well as human judge. Based on the experiment, the proposed method yields average result of 91% which is nearly equivalent to human judge which is 92%. Thematic Development also yields better result compared to Centering Theory which is 29% and Entity Transition Value which is 0%, given the same data set of beginner and intermediate simple writing.


Towards a European Jurisdiction based on Brain Computer Interfaces


title: Sparsifying Neural Network Connections for Face Recognition

abstract: This paper proposes to learn high-performance deep ConvNets with sparse neural connections, referred to as sparse ConvNets, for face recognition. The sparse ConvNets are learned in an iterative way, each time one additional layer is sparsified and the entire model is re-trained given the initial weights learned in previous iterations. One important finding is that directly training the sparse ConvNet from scratch failed to find good solutions for face recognition, while using a previously learned denser model to properly initialize a sparser model is critical to continue learning effective features for face recognition. This paper also proposes a new neural correlation-based weight selection criterion and empirically verifies its effectiveness in selecting informative connections from previously learned models in each iteration. When taking a moderately sparse structure (26%-76% of weights in the dense model), the proposed sparse ConvNet model significantly improves the face recognition performance of the previous state-of-theart DeepID2+ models given the same training data, while it keeps the performance of the baseline model with only 12% of the original parameters.


title: A Model-Theoretic Coreference Scoring Scheme

abstract: This note describes a scoring scheme for the coreference task in MUC6. It improves on the original approach by: (1) grounding the scoring scheme in terms of a model; (2) producing more intuitive recall and precision scores; and (3) not requiring explicit computation of the transitive closure of coreference. The principal conceptual difference is that we have moved from a syntactic scoring model based on following coreference links to an approach defined by the model theory of those links.


title: Analysis of a denial of service attack on TCP

abstract: The paper analyzes a network based denial of service attack for IP (Internet Protocol) based networks. It is popularly called SYN flooding. It works by an attacker sending many TCP (Transmission Control Protocol) connection requests with spoofed source addresses to a victim's machine. Each request causes the targeted host to instantiate data structures out of a limited pool of resources. Once the target host's resources are exhausted, no more incoming TCP connections can be established, thus denying further legitimate access. The paper contributes a detailed analysis of the SYN flooding attack and a discussion of existing and proposed countermeasures. Furthermore, we introduce a new solution approach, explain its design, and evaluate its performance. Our approach offers protection against SYN flooding for all hosts connected to the same local area network, independent of their operating system or networking stack implementation. It is highly portable, configurable, extensible, and requires neither special hardware, nor modifications in routers or protected end systems.


title: Automated Migration of Hierarchical Data to Relational Tables using Programming-by-Example

abstract: While many applications export data in hierarchical formats like XML and JSON, it is often necessary to convert such hierarchical documents to a relational representation. This paper presents a novel programming-by-example approach, and its implementation in a tool called Mitra, for automatically migrating tree-structured documents to relational tables. We have evaluated the proposed technique using two sets of experiments. In the first experiment, we used Mitra to automate 98 data transformation tasks collected from StackOverflow. Our method can generate the desired program for 94% of these benchmarks with an average synthesis time of 3.8 seconds. In the second experiment, we used Mitra to generate programs that can convert real-world XML and JSON datasets to full-fledged relational databases. Our evaluation shows that Mitra can automate the desired transformation for all datasets.


title: LoRaWAN as an e-Health Communication Technology

abstract: LoRaWAN is a Low Power Wide Area Network (LPWAN) technology which enables low cost and low power IoT device communication even in dense urban areas. LoRa modulation is capable of extracting data from a weak signal in noisy environments. This modulation technique can be useful for the delivery of critical data in the noisy environment. In this study, we investigate LoRaWAN technology in the context of data transmission for health care systems (or critical health data in disaster environments). In the study, we explore the standard and evaluate data frame transmission in LoRaWAN with a custom test-bed system.


HumorTools : A Microtask Workflow for Writing News Satire


title: Research and Implementation of Mobile Application Security Detection Combining Static and Dynamic

abstract: With the popularity of the Internet and mobile intelligent terminals, the number of mobile applications is exploding. Mobile intelligent terminals trend to be the mainstream way of people's work and daily life online in place of PC terminals. Mobile application system brings some security problems inevitably while it provides convenience for people, and becomes a main target of hackers. Therefore, it is imminent to strengthen the security detection of mobile applications. This paper divides mobile application security detection into client security detection and server security detection. We propose a combining static and dynamic security detection method to detect client-side. We provide a method to get network information of server by capturing and analyzing mobile application traffic, and propose a fuzzy testing method based on HTTP protocol to detect server-side security vulnerabilities. Finally, on the basis of this, an automated platform for security detection of mobile application system is developed. Experiments show that the platform can detect the vulnerabilities of mobile application client and server effectively, and realize the automation of mobile application security detection. It can also reduce the cost of mobile security detection and enhance the security of mobile applications.


title: Dissociable Effects of Surprising Rewards on Learning and Memory

abstract: Reward-prediction errors track the extent to which rewards deviate from expectations, and aid in learning. How do such errors in prediction interact with memory for the rewarding episode? Existing findings point to both cooperative and competitive interactions between learning and memory mechanisms. Here, we investigated whether learning about rewards in a high-risk context, with frequent, large prediction errors, would give rise to higher fidelity memory traces for rewarding events than learning in a low-risk context. Experiment 1 showed that recognition was better for items associated with larger absolute prediction errors during reward learning. Larger prediction errors also led to higher rates of learning about rewards. Interestingly we did not find a relationship between learning rate for reward and recognition-memory accuracy for items, suggesting that these two effects of prediction errors were caused by separate underlying mechanisms. In Experiment 2, we replicated these results with a longer task that posed stronger memory demands and allowed for more learning. We also showed improved source and sequence memory for items within the high-risk context. In Experiment 3, we controlled for the difficulty of reward learning in the risk environments, again replicating the previous results. Moreover, this control revealed that the high-risk context enhanced item-recognition memory beyond the effect of prediction errors. In summary, our results show that prediction errors boost both episodic item memory and incremental reward learning, but the two effects are likely mediated by distinct underlying systems. (PsycINFO Database Record


Antidiabetic effect of bitter melon/Kerala (Momordica charantia) in alloxan induced diabetic rat


title: Anserini: Enabling the Use of Lucene for Information Retrieval Research

abstract: Software toolkits play an essential role in information retrieval research. Most open-source toolkits developed by academics are designed to facilitate the evaluation of retrieval models over standard test collections. Efforts are generally directed toward better ranking and less attention is usually given to scalability and other operational considerations. On the other hand, Lucene has become the de facto platform in industry for building search applications (outside a small number of companies that deploy custom infrastructure). Compared to academic IR toolkits, Lucene can handle heterogeneous web collections at scale, but lacks systematic support for evaluation over standard test collections. This paper introduces Anserini, a new information retrieval toolkit that aims to provide the best of both worlds, to better align information retrieval practice and research. Anserini provides wrappers and extensions on top of core Lucene libraries that allow researchers to use more intuitive APIs to accomplish common research tasks. Our initial efforts have focused on three functionalities: scalable, multi-threaded inverted indexing to handle modern web-scale collections, streamlined IR evaluation for ad hoc retrieval on standard test collections, and an extensible architecture for multi-stage ranking. Anserini ships with support for many TREC test collections, providing a convenient way to replicate competitive baselines right out of the box. Experiments verify that our system is both efficient and effective, providing a solid foundation to support future research.


title: Complex problem solving: a case for complex cognition?

abstract: Complex problem solving (CPS) emerged in the last 30 years in Europe as a new part of the psychology of thinking and problem solving. This paper introduces into the field and provides a personal view. Also, related concepts like macrocognition or operative intelligence will be explained in this context. Two examples for the assessment of CPS, Tailorshop and MicroDYN, are presented to illustrate the concept by means of their measurement devices. Also, the relation of complex cognition and emotion in the CPS context is discussed. The question if CPS requires complex cognition is answered with a tentative “yes.”


title: Design Thinking and Participation: Lessons Learned from Three Case Studies

abstract: This paper examines how design thinking and serious games approaches can be used to support participation through the analysis of three case studies. Indeed we will analyze these approaches in three different contexts: (i) a state-owned multi-utilities company; (ii) a political party; (iii) an information system strategic committee. Our analysis framework relies on the concepts of "perceived usefulness" and "perceived ease of use" and we will use it to discuss the lessons learned. Our main finding is that these approaches really contributing in making complex and abstract matters more "tangible" and thus understandable.


title: Comprehensively and efficiently protecting the heap

abstract: The goal of this paper is to propose a scheme that provides comprehensive security protection for the heap. Heap vulnerabilities are increasingly being exploited for attacks on computer programs. In most implementations, the heap management library keeps the heap meta-data (heap structure information) and the application's heap data in an interleaved fashion and does not protect them against each other. Such implementations are inherently unsafe: vulnerabilities in the application can cause the heap library to perform unintended actions to achieve control-flow and non-control attacks.Unfortunately, current heap protection techniques are limited in that they use too many assumptions on how the attacks will be performed, require new hardware support, or require too many changes to the software developers' toolchain. We propose Heap Server, a new solution that does not have such drawbacks. Through existing virtual memory and inter-process protection mechanisms, Heap Server prevents the heap meta-data from being illegally overwritten, and heap data from being meaningfully overwritten. We show that through aggressive optimizations and parallelism, Heap Server protects the heap with nearly-negligible performance overheads even on heap-intensive applications. We also verify the protection against several real-world exploits and attack kernels.


title: Discovering Statistically Significant Biclusters in Gene Expression Data

abstract: In gene expression data, a bicluster is a subset of the genes exhibiting consistent patterns over a subset of the conditions. We propose a new method to detect significant biclusters in large expression datasets. Our approach is graph theoretic coupled with statistical modelling of the data. Under plausible assumptions, our algorithm is polynomial and is guaranteed to find the most significant biclusters. We tested our method on a collection of yeast expression profiles and on a human cancer dataset. Cross validation results show high specificity in assigning function to genes based on their biclusters, and we are able to annotate in this way 196 uncharacterized yeast genes. We also demonstrate how the biclusters lead to detecting new concrete biological associations. In cancer data we are able to detect and relate finer tissue types than was previously possible. We also show that the method outperforms the biclustering algorithm of Cheng and Church (2000).


title: CONET: a content centric inter-networking architecture

abstract: CONET is a content-centric inter-network that provides users with a network access to remote named-resources, rather than to remote hosts. Named-resources can be either data (named-data) or service-access-points (named-sap), identified by a network-identifier (a name). CONET interconnects CONET Sub Systems, which can be layer-2 networks, layer-3 networks or couples of nodes connected by a point-to-point link. CONET supports the already proposed "clean-slate" and "overlay" deployment approaches. In addition, CONET supports a novel "integration" approach, which extends the IP layer with a new header option that makes IP itself content-aware. CONET limits the size of name-based routing tables by including only a subset of all named-resources; missing entries are looked up in a name-system and then cached. CONET does not maintain states in network nodes, to deliver contents.


A Differential Variational Inequality Approach for the Simulation of Heterogeneous Materials


title: 2006a) The process of innovation assimilation by firms in different countries: a technology diffusion perspective on e-business

abstract: This paper extends our previous studies on the assimilation of Internet-based e-business innovations by firms in an international setting. Drawing upon theories on the process and contexts of technology diffusion, we develop an integrative model to examine three assimilation stages: initiation → adoption → routinization. The model features technological, organizational, and environmental contexts as prominent antecedents of this three-stage assimilation process. Based on this model, we hypothesize how technology readiness, technology integration, firm size, global scope, managerial obstacles, competition intensity, and regulatory environment influence e-business assimilation at the firm level. A unique data set of 1,857 firms from 10 countries is used to test the conceptual model and hypotheses. To probe deeper into the influence of the environmental context, we compare two subsamples from developed and developing countries. Our empirical analysis leads to several key findings: (1) Competition positively affects initiation and adoption, but negatively impacts routinization, suggesting that too much competition is not necessarily good for technology assimilation because it drives firms to chase the latest technologies without learning how to use existing ones effectively. (2) Large firms tend to enjoy resource advantages at the initiation stage, but have to overcome structural inertia in later stages. (3) We also find that economic environments shape innovation assimilation: Regulatory environment plays a more important role in developing countries than in developed countries. Moreover, while technology readiness is the strongest factor facilitating assimilation in developing countries, technology integration turns out to be the strongest in developed countries, implying that as e-business evolves, the key determinant of its assimilation shifts from accumulation to integration of technologies. Together, these findings offer insights into how innovation assimilation is influenced by contextual factors, and how the effects may vary across different stages and in different environments.


title: A survey of patients' understanding and expectations of persistent postsurgical pain in a preoperative testing center.

abstract: STUDY OBJECTIVE ::: Acute postoperative pain may transition to persistent/chronic pain in up to 50% or more of patients after certain surgeries. Despite this concern, it is unclear that patients' preprocedure understanding and expectations are aligned with these potential outcomes. This study was designed to evaluate the extent of this alignment and the potential impact on the quality of risk/benefit discussions before procedures. ::: ::: ::: DESIGN ::: Prospective survey. ::: ::: ::: SETTING ::: A large, tertiary care preoperative assessment clinic. ::: ::: ::: PATIENTS ::: A total of 1481 adult patients. ::: ::: ::: INTERVENTIONS ::: Survey administration. ::: ::: ::: MEASUREMENTS ::: The survey items evaluated patients' expectations of postoperative pain and how familiar patients were with the risk of persistent postsurgical pain based on their specific characteristics and procedure type. ::: ::: ::: MAIN RESULTS ::: The overwhelming majority (80%) of patients were unaware of the risk of persistent postsurgical pain. Given the choice, most patients (65%) wanted to be informed of their risk, and 25% stated that it might even affect their decision to proceed with surgery. ::: ::: ::: CONCLUSIONS ::: There is great need for health care providers to discuss the significant risk of persistent postsurgical pain with patients in the preoperative setting. Patients need to be armed with realistic data to ensure high-quality discussions of risk/benefit, align expectations with outcomes, and potentially identify high-risk groups in which preoperative intervention can reduce the likelihood or severity of persistent postoperative pain syndromes.


title: Challenges for advanced end of the roadmap, beyond Si and beyond CMOS technologies

abstract: Future technologies put stringent demands on materials, process modules and device architectures. Design considerations favor the future use of vertical devices like tunnelFETs and nanowires. Heterogenous integration of Ge and III-V technologies on a silicon platform enables to fabricate System-on-Chip applications, while increased functionality is achieved by 3D integration. Attention is also given to the trend and progress in the use of 2D material and devices, spintronics and neuromorphic computing.


title: Applicability Investigation of SAW Devices in the 3 to 5 GHz range

abstract: This paper investigates applicability of new type SAW devices comprising a thin piezoelectric crystal plate and an acoustic energy confinement portion to the 3 to 5 GHz range unexplored for SAW industries. First, it is demonstrated that excellent performances even in the 3.5 GHz range are achievable by using a KrF stepper/scanner that has commonly used in SAW mass production lines when I.H.P. SAW (Incredible High-perfor-mance SAW), one of the new type SAW devices, is used. Next, the same interdigital transducer periodicity of the 3.5 GHz I.H.P.SAW is applied to an LLSAW devices, another new type SAW device, and it is revealed that good performance is still achievable even in the 5 GHz range.


title: End-to-End Channel Capacity of a Wireless Sensor Network Under Reachback

abstract: Many wireless sensor network applications will impose a many-to-one traffic flow pattern. This phenomena in communication networks is referred to as reachback. The higher volume of traffic in traffic hotspots, which usually form around the base station, limits the number of sensors that can participate in the formation of a wireless sensor network with a fixed per sensor data rate. To alleviate the effects of reachback, spatial correlation of sensor data has enabled the use of Slepian-Wolf coding as a compression technique to reduce the volume of traffic in sensor networks. Within a cluster, Slepian-Wolf coding is used to compress the sensor readings. Clusterheads then transmit the compressed sensor readings over the overlay network which is formed by all clusterheads. We develop an expression for the end-to-end (sensor-to-base station) channel capacity of such a wireless sensor network and observe the effect of variations in the number of clusterheads on end-to-end capacity.


title: Tracing the development of teacher knowledge in a design seminar: Integrating content, pedagogy and technology

abstract: Effective technology integration for teaching subject matter requires knowledge not just of content, technology and pedagogy, but also of their relationship to each other. Building on Schulman's [Schulman, L. S. (1987). Knowledge and teaching: foundations for a new reform. Harvard Educational Review, 57(1), 1-22] concept of pedagogical content knowledge, we introduce a framework for conceptualizing Technological Pedagogical Content Knowledge-TPCK [Mishra, P., Koehler, M.J., (in press). Technological pedagogical content knowledge: A new framework for teacher knowledge. Teachers College Record]. We report the results of a semester-long investigation of the development of TPCK during a faculty development design seminar, whereby faculty members worked together with masters students to develop online courses. Quantitative discourse analysis of 15 weeks of field notes for two of the design teams show participants moved from considering technology, pedagogy and content as being independent constructs towards a richer conception that emphasized connections among the three knowledge bases. Our analyses suggests that developing TPCK is a multigenerational process, involving the development of deeper understandings of the complex web of relationships between content, pedagogy and technology and the contexts in which they function. Pedagogic, pragmatic, theoretical, and methodological contributions are discussed.


Probabilistic Roadmaps for Path Planning in High-Dimensional Configuration Spaces


title: Developing a Speech Activity Detection System for the DARPA RATS Program.

abstract: This paper describes the speech activity detection (SAD) system developed by the Patrol team for the first phase of the DARPA RATS (Robust Automatic Transcription of Speech) program, which seeks to advance state of the art detection capabilities on audio from highly degraded communication channels. We present two approaches to SAD, one based on Gaussian mixture models, and one based on multi-layer perceptrons. We show that significant gains in SAD accuracy can be obtained by careful design of acoustic front end, feature normalization, incorporation of long span features via data-driven dimensionality reducing transforms, and channel dependent modeling. We also present a novel technique for normalizing detection scores from different systems for the purpose of system combination.


title: Why do people use information technology ? A critical review of the technology acceptance model

abstract: Information systems (IS) implementation is costly and has a relatively low success rate. Since the seventies. IS research has contributed to a better understanding of this process and its outcomes. The early efforts concentrated on the identification of factors that facilitated IS use. This produced a long list of items that proved to be of little practical value. It became obvious that, for practical reasons, the factors had to be grouped into a model in a way that would facilitate analysis of IS use.In 1985, Fred Davis suggested the technology acceptance model (TAM). It examines the mediating role of perceived ease of use and perceived usefulness in their relation between systems characteristics (external variables) and the probability of system use (an indicator of system success). More recently, Davis proposed a new version of his model: TAM2. It includes subjective norms, and was tested with longitudinal research designs. Overall the two explain about 40% of system's use. Analysis of empirical research using TAM shows that results are not totally consistent or clear. This suggests that significant factors are not included in the models.We conclude that TAM is a useful model, but has to be integrated into a broader one which would include variables related to both human and social change processes, and to the adoption of the innovation model.


title: Sectorization based pilot reuse for improving net spectral efficiency in the multicell massive MIMO system

abstract: A sectorization method using the uniform circular array (UCA) is proposed to improve the net spectral efficiency (SE) of the multicell massive MIMO system, which is an important index for evaluating the performance of a communication system. We derive the ergodic achievable uplink net SE per cell of a general sectorized system and obtain its deterministic approximation based on the large random matrix theory. Different weight matrices are considered for the sectorized system and the one with the best performance is utilized for further analysis. The consistency of the deterministic approximation with the result of Monte-Carlo simulation is proved at the same time. At last, numerical results indicate that the net SE per cell can be greatly improved compared to the conventional multicell massive MIMO system, which validates the effectiveness of the sectorization method. Moreover, comparisons with other pilot reuse methods are also made in this paper.


title: Alignment of Ni- and Co-related centres during the growth of high-pressure–high-temperature diamond

abstract: An extensive study of Co- and Ni-related centres in high-pressure synthetic diamond, using electron spin resonance, optical absorption and luminescence techniques, has revealed that most of these defects, in samples grown at low temperature (), exhibit preferential alignment, i.e. a non-equal distribution over crystallographically equivalent orientations. Remarkably, this observation applies not only to defects assumed to involve only an Ni or a Co atom, but also to some Ni–N complexes. Annealing at T>1600 °C induces nitrogen mobility and the corresponding formation of numerous Ni–N- and Co–N-related centres. This annealing eventually destroys the preferential polarization of low-temperature centres.


title: Teaching Computational Thinking by Playing Games and Building Robots

abstract: Computing in schools has gained momentum in the last two years resulting in GCSEs in Computing and teachers looking to up skill from Digital Literacy (ICT). For many students the subject of computer science concerns software code but writing code can be challenging, due to specific requirements on syntax and spelling with new ways of thinking required. Not only do many undergraduate students lack these ways of thinking, but there is a general misrepresentation of computing in education. Were computing taught as a more serious subject like science and mathematics, public understanding of the complexities of computer systems would increase, enabling those not directly involved with IT make better informed decisions and avoid incidents such as over budget and underperforming systems. We present our exploration into teaching a variety of computing skills, most significantly "computational thinking", to secondary-school age children through three very different engagements. First, we discuss Print craft, in which participants learn about computer-aided design and additive manufacturing by designing and building a miniature world from scratch using the popular open-world game Mine craft and 3D printers. Second, we look at how students can get a new perspective on familiar technology with a workshop using App Inventor, a graphical Android programming environment. Finally, we look at an ongoing after school robotics club where participants face a number of challenges of their own making as they design and create a variety of robots using a number of common tools such as Scratch and Arduino.


title: Discriminant Context Information Analysis for Post-Ranking Person Re-Identification

abstract: Existing approaches for person re-identification are mainly based on creating distinctive representations or on learning optimal metrics. The achieved results are then provided in the form of a list of ranked matching persons. It often happens that the true match is not ranked first but it is in the first positions. This is mostly due to the visual ambiguities shared between the true match and other “similar” persons. At the current state, there is a lack of a study of such visual ambiguities which limit the re-identification performance within the first ranks. We believe that an analysis of the similar appearances of the first ranks can be helpful in detecting, hence removing, such visual ambiguities. We propose to achieve such a goal by introducing an unsupervised post-ranking framework. Once the initial ranking is available, content and context sets are extracted. Then, these are exploited to remove the visual ambiguities and to obtain the discriminant feature space which is finally exploited to compute the new ranking. An in-depth analysis of the performance achieved on three public benchmark data sets support our believes. For every data set, the proposed method remarkably improves the first ranks results and outperforms the state-of-the-art approaches.


title: The Radical Plasticity Thesis: How the Brain Learns to be Conscious

abstract: In this paper, I explore the idea that consciousness is something that the brain learns to do rather than an intrinsic property of certain neural states and not others. Starting from the idea that neural activity is inherently unconscious, the question thus becomes: How does the brain learn to be conscious? I suggest that consciousness arises as a result of the brain's continuous attempts at predicting not only the consequences of its actions on the world and on other agents, but also the consequences of activity in one cerebral region on activity in other regions. By this account, the brain continuously and unconsciously learns to redescribe its own activity to itself, so developing systems of meta-representations that characterise and qualify the target first-order representations. Such learned redescriptions, enriched by the emotional value associated with them, form the basis of conscious experience. Learning and plasticity are thus central to consciousness, to the extent that experiences only occur in experiencers that have learned to know they possess certain first-order states and that have learned to care more about certain states than about others. This is what I call the “Radical Plasticity Thesis”. In a sense thus, this is the enactive perspective, but turned both inwards and (further) outwards. Consciousness involves “signal detection on the mind”; the mind is the brain's (non-conceptual, implicit) theory about itself. I illustrate these ideas through neural network models that simulate the relationships between performance and awareness in different tasks.


title: Joint Relational Embeddings for Knowledge-based Question Answering

abstract: Transforming a natural language (NL) question into a corresponding logical form (LF) is central to the knowledge-based question answering (KB-QA) task. Unlike most previous methods that achieve this goal based on mappings between lexicalized phrases and logical predicates, this paper goes one step further and proposes a novel embedding-based approach that maps NL-questions into LFs for KBQA by leveraging semantic associations between lexical representations and KBproperties in the latent space. Experimental results demonstrate that our proposed method outperforms three KB-QA baseline methods on two publicly released QA data sets.


title: Estimation of matrix pH in isolated heart mitochondria using a fluorescent probe.

abstract: Isolated heart mitochondria hydrolyze the acetoxymethyl esters of the Ca2+-sensitive fluorescent probe fura-2 and the pH-sensitive 2',7'-biscarboxyethyl-5(6)-carboxyfluorescein (BCECF). The resulting charged forms of the probes are retained in the mitochondrial matrix and appear well-suited for the estimation of pCa and pH in this compartment. The mitochondria esterase activity is stimulated by Ca2+, inhibited by butacaine and quinine, and shows an alkaline pH optimum. The esterase has a similar affinity for the two probes (Km about 1.5 microM) and a somewhat higher Vmax for BCECF. Intramitochondrial pH can be determined by recording the ratio of the fluorescence of matrix BCECF at its excitation maximum of 509 nm to that at 450 nm, an excitation wavelength that is unresponsive to pH. A calibration plot relating the fluorescence ratio to pH is constructed using detergent-lysed mitochondria and the excitation maximum of 500 nm for BCECF in aqueous solution. Estimates of matrix pH by BCECF fluorescence in its useful range (pH 6 to 8) agree well with values obtained using the distribution of 5,5-dimethyl-2,4-oxazolidenedione. In protocols in which the fluorescence with excitation at 450 nm does not vary, a direct recording of BCECF fluorescence with excitation at 509 nm can be used to follow the kinetics of matrix pH changes.


title: Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks

abstract: Abstract Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn (www.nitrc.org/projects/conn) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. Compared to methods that rely on global signal regression, the CompCor noise reduction method all...


title: High Sensitivity Flexible Capacitive Pressure Sensor Using Polydimethylsiloxane Elastomer Dielectric Layer Micro-Structured by 3-D Printed Mold

abstract: 3-D printing is used to fabricate mold for micro-structuring the polydimethylsiloxane (PDMS) elastomeric dielectric layer in capacitive flexible pressure sensors. It is shown that, despite of the limited resolution of the used commercial 3-D printer for producing the mold, the fabricated sensor with the micro-structured PDMS layers can achieve sensitivity higher than previous work using micro-fabricated silicon wafer molds. The devices also present very low detection limit, fast response/recovery speed, excellent durability and good tolerance to variations of ambient temperature and humidity, which enables to reliably monitor weak human physiological signals in real time. As an application example, the flexible pressure sensor is integrated in a wearable system to monitor wrist pulse.


title: An accident detection technique using inertial measurement unit and odometry

abstract: Despite the prevention effort, car accident is still one of the major causes of death. Thus, to avoid as many casualties, helps and assistance should reach the accident sites as soon as possible. This can be enabled by quickly detecting the accidents and reporting them to the related official units. To this end, we propose an accident detection device using an inertial measurement unit (IMU) and 3G cellular module along with an accident detection method. Our detection approach combines both IMU sensory data and odometry in order to detect an accident and then send the accident coordinate over cellular network to be reported on a map. The preliminary result based on test scenarios proves the practicality of our approach with satisfactory detection rates.


title: A 6 A, 93% Peak Efficiency, 4-Phase Digitally Synchronized Hysteretic Buck Converter With ±1.5% Frequency and ±3.6% Current-Sharing Error

abstract: A four-phase, quasi-current-mode hysteretic buck converter with digital frequency synchronization, online comparator offset-calibration and, digital current-sharing control is presented. The switching frequency of the hysteretic converter is digitally synchronized to the input clock reference with less than ±1.5% error in the switching frequency range of 3–9.5 MHz. The online offset calibration cancels the input-referred offset of the hysteretic comparator and enables ±1.1% voltage regulation accuracy. Maximum current-sharing error of ±3.6% is achieved by a duty-cycle-calibrated delay line-based pulsewidth modulation generator, without affecting the phase synchronization timing sequence. In light-load conditions, individual converter phases can be disabled, and the final stage power converter output stage is segmented for high efficiency. The dc–dc converter achieves 93% peak efficiency for $V_{i}= 2$ V and $V_{o}= 1.6$ V.


CAEMSI : A Cross-Domain Analytic Evaluation Methodology for Style Imitation


title: Cancer statistics, 2016.

abstract: Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data were collected by the National Cancer Institute (Surveillance, Epidemiology, and End Results [SEER] Program), the Centers for Disease Control and Prevention (National Program of Cancer Registries), and the North American Association of Central Cancer Registries. Mortality data were collected by the National Center for Health Statistics. In 2016, 1,685,210 new cancer cases and 595,690 cancer deaths are projected to occur in the United States. Overall cancer incidence trends (13 oldest SEER registries) are stable in women, but declining by 3.1% per year in men (from 2009-2012), much of which is because of recent rapid declines in prostate cancer diagnoses. The cancer death rate has dropped by 23% since 1991, translating to more than 1.7 million deaths averted through 2012. Despite this progress, death rates are increasing for cancers of the liver, pancreas, and uterine corpus, and cancer is now the leading cause of death in 21 states, primarily due to exceptionally large reductions in death from heart disease. Among children and adolescents (aged birth-19 years), brain cancer has surpassed leukemia as the leading cause of cancer death because of the dramatic therapeutic advances against leukemia. Accelerating progress against cancer requires both increased national investment in cancer research and the application of existing cancer control knowledge across all segments of the population.


title: Does the 6-minute walk test predicts functional capacity in a sample of elderly women? A pilot-study

abstract: Introduction: Functional capacity is the capacity to conduct daily activities in an independent way. It can be estimated with the 6-minutes’ walk test (6MWT) and other validated functional tests. Objectives: Verify associations between functional capacity measured with two different instruments (6MWT and Composite Physical Function (CPF) scale) and levels of physical activity and between those and characterization variables. Methods: This sample consisted of 30 apparently healthy elderly women from Loures municipality. Essentially they should be independent and community-dwelling. Characterization data were collected, containing characterization of physical activity levels and anthropometric data. Functional capacity was assessed with CPF scale and distance walked by the 6MWT. Results were analysed using a SPSS v21.0 through correlation tests. Results: The walked distance in 6MWT was positively associated with height (r = 0.406; p = 0.026), physical activity level (r = 0.594; p = 0.001) and functional capacity (r = 0.682; p = 0.000). For each point more obtained in CPF, the distance walked increases on average by 7.5 meters. Relatively to sedentary participants, being insufficiently active increases, on average, the distance walked in 85.8 meters; and being active increases, on average, the distance walked in 108.8 meters. No other associations were observed in our sample. Conclusion: Based on the collected sample, walked distance in 6MWT has a high correlation with results in CPF scale, so this test can be used to predict functional capacity. More attention should be taken to promote strategies to increase walking in older adults.


Towards Automation and Classification of Bharatanatyam Dance Sequences


title: Boolean functions over nano-fabrics: Improving resilience through coding

abstract: This paper determines mechanisms to mitigate errors when implementing Boolean functions in nano-circuits. Nano-fabrics are expected to have high defect rates as atomic variations directly impact such materials. This paper develops a coding mechanism that uses a combination of cheap, but unreliable nano-device as the main function and reliable, but expensive CMOS devices to implement the coding mechanism. The unique feature of this paper is that it exploits the don't-cares that naturally occur in Boolean functions to construct better codes. The reliable Boolean function problem is cast as a constraint satisfaction problem and then solved using a tree-based dynamic programming algorithm. (Here, the word “dynamic programming” is used in the same sense as computer-science literature, i.e., and as an efficient search algorithm over trees).


title: Software Defined Optical Networks (SDONs): A Comprehensive Survey

abstract: The emerging software defined networking (SDN) paradigm separates the data plane from the control plane and centralizes network control in an SDN controller. Applications interact with controllers to implement network services, such as network transport with quality of service. SDN facilitates the virtualization of network functions so that multiple virtual networks can operate over a given installed physical network infrastructure. Due to the specific characteristics of optical (photonic) communication components and the high optical transmission capacities, SDN-based optical networking poses particular challenges, but holds also great potential. In this article, we comprehensively survey studies that examine the SDN paradigm in optical networks; in brief, we survey the area of software defined optical networks (SDONs). We mainly organize the SDON studies into studies focused on the infrastructure layer, the control layer, and the application layer. Moreover, we cover SDON studies focused on network virtualization, as well as SDON studies focused on the orchestration of multilayer and multidomain networking. Based on the survey, we identify open challenges for SDONs and outline future directions.


title: Socioeconomic status and selected behavioral determinants as risk factors for dental caries.

abstract: The purpose of this review is to summarize a systematic review evaluating the evidence regarding the association between the incidence and prevalence of dental caries and: 1) socioeconomic status; 2) tooth-brushing; and 3) the use of the baby bottle. Literature was drawn from two databases, Medline and EmBase. Because of limited resources, we did not conduct hand-searching or search unpublished studies. Three thousand one hundred thirty-eight abstracts were identified, 358 reviewed, and 272 papers included in the systematic review. There is fairly strong evidence for an inverse relationship between SES and the prevalence of caries among children less than twelve years of age. The evidence for this relationship is weaker for older children and for adults because of the relatively small number of studies and methodological limitations. There is weak evidence that tooth-brushing prevents dental caries, but it is uncertain whether the effects of tooth-brushing are due to use of a fluoride dentifrice or from mechanical removal of plaque. Finally, the evidence for the relationship between prolonged use of the baby bottle and dental caries is weak. More studies directly aimed at analyzing the relationship between SES and dental caries are needed to identify factors associated with SES that contribute to dental caries risk. Tooth-brushing should continue to be recommended as a measure to prevent dental caries, particularly using a fluoride dentifrice. Recommendations regarding bottle use should continue until clear evidence about the relationship between prolonged bottle use and dental caries can be obtained.


title: Semantic Amodal Segmentation

abstract: Common visual recognition tasks such as classification, object detection, and semantic segmentation are rapidly reaching maturity, and given the recent rate of progress, it is not unreasonable to conjecture that techniques for many of these problems will approach human levels of performance in the next few years. In this paper we look to the future: what is the next frontier in visual recognition? ::: We offer one possible answer to this question. We propose a detailed image annotation that captures information beyond the visible pixels and requires complex reasoning about full scene structure. Specifically, we create an amodal segmentation of each image: the full extent of each region is marked, not just the visible pixels. Annotators outline and name all salient regions in the image and specify a partial depth order. The result is a rich scene structure, including visible and occluded portions of each region, figure-ground edge information, semantic labels, and object overlap. ::: We create two datasets for semantic amodal segmentation. First, we label 500 images in the BSDS dataset with multiple annotators per image, allowing us to study the statistics of human annotations. We show that the proposed full scene annotation is surprisingly consistent between annotators, including for regions and edges. Second, we annotate 5000 images from COCO. This larger dataset allows us to explore a number of algorithmic ideas for amodal segmentation and depth ordering. We introduce novel metrics for these tasks, and along with our strong baselines, define concrete new challenges for the community.


title: Inteligent Crack Detecting Algorithm on the Concrete Crack Image Using Neural Network

abstract: Safety inspection of concrete structures should be strictly carried out since it is closely related with the structural health and reliability. However, it is difficult to find cracks by a visual check for the extremely large structures. So, the development of crack detecting systems has been a significant issue. Final objective of this research is to develop an automatic crack detection system that can analyze the concrete surface and visualize the cracks efficiently. The algorithm is composed of two parts; image processing and image classification. In the first step, cracks are distinguished from background image easily using the filtering, the improved subtraction method, and the morphological operation. The particular data such as the number of pixel and the ratio of the major axis to minor axis for connected pixels area are also extracted. In the second step, the existence of cracks are identified. Backpropagation neural network is used to automate the image classification. Target data values in the training process were generated by inspector’s manual classification. In order to verify the first and second step of the proposed algorithm, the algorithm was tested using real surface images of concrete bridge. Backpropagation neural network was trained using 105 images of concrete structure, and the trained network was tested for new 120 new images. The recognition rate of the crack image was 90% and non-crack image was 92%. This method is useful for non expert inspectors, enabling them to perform crack monitoring tasks effectively.


title: Service-Oriented Computing: State of the Art and Research Challenges

abstract: Service-oriented computing promotes the idea of assembling application components into a network of services that can be loosely coupled to create flexible, dynamic business processes and agile applications that span organizations and computing platforms. An SOC research road map provides a context for exploring ongoing research activities.


title: Modeling and cont PWA

abstract: The inverted pendulum is the most popular benchmark for teaching and researches in control theory and robotics. Modeling and control of inverted pendulum on a cart is as one of the most important case in control theory. In this paper a new method in Piecewise Affine (PWA) models identification based on numerical analysis is proposed. The evaluation of this algorithm is investigated on a real system. Also the PWA-FUZZY approach as a solution to obviate the problem of hard switching in PWA system is used. A patched LQR and LQG controller in order to stabilize open-loop unstable equilibrium point of this system is proposed.


title: Autonomous Underwater Vehicle Navigation

abstract: This paper considers the vehicle navigation problem for an autonomous underwater vehicle (AUV) with six degrees of freedom. We approach this problem using an error state formulation of the Kalman filter. Integration of the vehicle's high-rate inertial measurement unit's (IMU's) accelerometers and gyros allow time propagation while other sensors provide measurement corrections. The low-rate aiding sensors include a Doppler velocity log (DVL), an acoustic long baseline (LBL) system that provides round-trip travel times from known locations, a pressure sensor for aiding depth, and an attitude sensor. Measurements correct the filter independently as they arrive, and as such, the filter is not dependent on the arrival of any particular measurement. We propose novel tightly coupled techniques for the incorporation of the LBL and DVL measurements. In particular, the LBL correction properly accounts for the error state throughout the measurement cycle via the state transition matrix. Alternate tightly coupled approaches ignore the error state, utilizing only the navigation state to account for the physical latencies in the measurement cycle. These approaches account for neither the uncertainty of vehicle trajectory between interrogation and reply, nor the error state at interrogation. The navigation system also estimates critical sensor calibration parameters to improve performance. The result is a robust navigation system. Simulation and experimental results are provided.


title: Exploring the Phenomenon of Zero Waste and Future Cities

abstract: The evolving phenomenon of zero waste encompasses the theory, practice, and learning of individuals, families, businesses, communities, and government organisations, responding to perceptions of crisis and failure around conventional waste management. The diverse and growing body of international zero waste experience, can be portrayed as both, an entirely new and alternative waste management paradigm, and or, interpreted as overlapping, extending, and synergetic with a general evolution towards more sustainable waste/resource management practices. Combining the terms zero and waste provokes creative, intellectual, and pragmatic tensions, which provide a contemporary axis for necessary debate and innovation in this sphere of resource management. This commentary draws on an interdisciplinary perspective and utilises some elements of the critique of zero waste, as a lens to examine and better understand this heterogeneous global community of practice. In particular, how the concept and implementation of a zero waste goal can increase community engagement and be a catalyst for the design and management of a more circular urban metabolism and hence, more adaptive, resilient, and sustainable future (zero waste) cities.


title: Deep Multitask Gaze Estimation with a Constrained Landmark-Gaze Model

abstract: As an indicator of attention, gaze is an important cue for human behavior and social interaction analysis. Recent deep learning methods for gaze estimation rely on plain regression of the gaze from images without accounting for potential mismatches in eye image cropping and normalization. This may impact the estimation of the implicit relation between visual cues and the gaze direction when dealing with low resolution images or when training with a limited amount of data. In this paper, we propose a deep multitask framework for gaze estimation, with the following contributions. (i) we proposed a multitask framework which relies on both synthetic data and real data for end-to-end training. During training, each dataset provides the label of only one task but the two tasks are combined in a constrained way. (ii) we introduce a Constrained Landmark-Gaze Model (CLGM) modeling the joint variation of eye landmark locations (including the iris center) and gaze directions. By relating explicitly visual information (landmarks) to the more abstract gaze values, we demonstrate that the estimator is more accurate and easier to learn. (iii) by decomposing our deep network into a network inferring jointly the parameters of the CLGM model and the scale and translation parameters of eye regions on one hand, and a CLGM based decoder deterministically inferring landmark positions and gaze from these parameters and head pose on the other hand, our framework decouples gaze estimation from irrelevant geometric variations in the eye image (scale, translation), resulting in a more robust model. Thorough experiments on public datasets demonstrate that our method achieves competitive results, improving over state-of-the-art results in challenging free head pose gaze estimation tasks and on eye landmark localization (iris location) ones.


title: Detecting price manipulation in the financial market

abstract: Market abuse has attracted much attention from financial regulators around the world but it is difficult to fully prevent. One of the reasons is the lack of thoroughly studies of the market abuse strategies and the corresponding effective market abuse approaches. In this paper, the strategies of reported price manipulation cases are analysed as well as the related empirical studies. A transformation is then defined to convert the time-varying financial trading data into pseudo-stationary time series, where machine learning algorithms can be easily applied to the detection of the price manipulation. The evaluation experiments conducted on four stocks from NASDAQ show a promising improved performance for effectively detecting such manipulation cases.


title: Simulink model for economic analysis and environmental impacts of a PV with diesel-battery system for remote villages

abstract: This paper discusses the economic analysis and environmental impacts of integrating a photovoltaic (PV) array into diesel-electric power systems for remote villages. MATLAB Simulink is used to match the load with the demand and apportion the electrical production between the PV and diesel-electric generator. The economic part of the model calculates the fuel consumed, the kilowatthours obtained per gallon of fuel supplied, and the total cost of fuel. The environmental part of the model calculates the CO/sub 2/, particulate matter (PM), and the NO/sub x/ emitted to the atmosphere. Simulations based on an actual system in the remote Alaskan community of Lime Village were performed for three cases: 1) diesel only; 2) diesel-battery; and 3) PV with diesel-battery using a one-year time period. The simulation results were utilized to calculate the energy payback, the simple payback time for the PV module, and the avoided costs of CO/sub 2/, NO/sub x/, and PM. Post-simulation analysis includes the comparison of results with those predicted by Hybrid Optimization Model for Electric Renewables (HOMER). The life-cycle cost (LCC) and air emissions results of our Simulink model were comparable to those predicted by HOMER.


title: Computational stereo camera system with programmable control loop

abstract: Stereoscopic 3D has gained significant importance in the entertainment industry. However, production of high quality stereoscopic content is still a challenging art that requires mastering the complex interplay of human perception, 3D display properties, and artistic intent. In this paper, we present a computational stereo camera system that closes the control loop from capture and analysis to automatic adjustment of physical parameters. Intuitive interaction metaphors are developed that replace cumbersome handling of rig parameters using a touch screen interface with 3D visualization. Our system is designed to make stereoscopic 3D production as easy, intuitive, flexible, and reliable as possible. Captured signals are processed and analyzed in real-time on a stream processor. Stereoscopy and user settings define programmable control functionalities, which are executed in real-time on a control processor. Computational power and flexibility is enabled by a dedicated software and hardware architecture. We show that even traditionally difficult shots can be easily captured using our system.


title: Survey of Test Vector Compression Techniques

abstract: Test data compression consists of test vector compression on the input side and response, compaction on the output side. This vector compression has been an active area of research. This article summarizes and categories these techniques. The focus is on hardware-based test vector compression techniques for scan architectures. Test vector compression schemes fall broadly into three categories: code-based schemes use data compression codes to encode test cubes; linear-decompression-based schemes decompress the data using only linear operations (that is LFSRs and XOR networks) and broadcast-scan-based schemes rely on broadcasting the same values to multiple scan chains


title: Going beyond Mean and Median Programs Performances

abstract: In the area of code performance optimisation and tuning, we are faced on the difficult problem of selecting the "best" code version based on empirical experiments and statistical analysis. With the massive introduction of general purpose multicore processors, programs performances become more and more instable, especially parallel programs. Usual statistical methods for computing performance speedups and comparing between programs are based on testing mean or median values. In this article, we explain why these metrics may be inadequate for making relevent decisions, and we propose new performance metrics based on parametric statistics using gaussian mixture models. Our new statistical methods are more accurate for decision making, they are formally defined, computed, implemented and distributed as free software in [1].


Internal Organization of the Alpha 21164, a 300-MHz 64-bit Quad-issue CMOS RISC Microprocessor


title: Clinical data mining: a review.

abstract: Objective: Clinical data mining is the application of data mining techniques using clinical data. We review the literature in order to provide a general overview by identifying the status-of-practice and the challenges ahead. Methods: The nine data mining steps proposed by Fayyad in 1996 [4] were used as the main themes of the review. MEDLINE was used as primary source and 84 papers were retained based on our inclusion criteria. Results: Clinical data mining has three objectives: understanding the clinical data, assist healthcare professionals, and develop a data analysis methodology suitable for medical data. Classification is the most frequently used data mining function with a predominance of the implementation of Bayesian classifiers, neural networks, and SVMs (Support Vector Machines). A myriad of quantitative performance measures were proposed with a predominance of accuracy, sensitivity, specificity, and ROC curves. The latter are usually associated with qualitative evaluation. Conclusion: Clinical data mining respects its commitment to extracting new and previously unknown knowledge from clinical databases. More efforts are still needed to obtain a wider acceptance from the healthcare professionals and for generalization of the knowledge and reproducibility of its extraction process: better description of variables, systematic report of algorithm parameters including the method to obtain them, use of easy-to-understand models and comparisons of the efficiency of clinical data mining with traditional statistical analyses. More and more data will be available for data miners and they have to develop new methodologies and infrastructures to analyze the increasingly complex medical data.