title: The Recycling Folded Cascode: A General Enhancement of the Folded Cascode Amplifier

abstract: A recycling amplifier architecture based on the folded cascode transconductance amplifier is described. The proposed amplifier delivers an appreciably enhanced performance over that of the conventional folded. This is achieved by using previously idle devices in the signal path, which results in an enhanced transconductance, gain, and slew rate. Moreover, the input referred noise and offset analyses are included to demonstrate that the proposed modifications have no adverse effects on these design metrics. Transistor-level simulations and experimental results in TSMC 0.18 mum CMOS process confirm the theoretical results. When compared to the conventional folded cascode, and for the same area and power budgets, the proposed amplifier has almost twice the bandwidth (134.2 MHz versus 70.7 MHz) and better than twice the slew rate (94.1 V/mus versus 42.1 V/mus) while driving the same 5.6 pF load. Also a gain enhancement of 7.6 dB is observed.


title: Smoke detection algorithm for intelligent video surveillance system

abstract: An e‐cient smoke detection algorithm on color video sequences obtained from a stationary camera is proposed. Our algorithm considers dynamic and static features of smoke and is composed of basic steps: preprocessing; slowly moving areas and pixels segmentation in a current input frame based on adaptive background subtraction; merge slowly moving areas with pixels into blobs; classiflcation of the blobs obtained before. We use adaptive background subtraction at a stage of moving detection. Moving blobs classiflcation is based on optical ∞ow calculation, Weber contrast analysis and takes into account primary direction of smoke propagation. Real video surveillance sequences were used for smoke detection with utilization our algorithm. A set of experimental results is presented in the paper.


title: Revisiting telegony: offspring inherit an acquired characteristic of their mother's previous mate

abstract: Newly discovered non-genetic mechanisms break the link between genes and inheritance, thereby also raising the possibility that previous mating partners could influence traits in offspring sired by subsequent males that mate with the same female ('telegony'). In the fly Telostylinus angusticollis, males transmit their environmentally acquired condition via paternal effects on offspring body size. We manipulated male condition, and mated females to two males in high or low condition in a fully crossed design. Although the second male sired a large majority of offspring, offspring body size was influenced by the condition of the first male. This effect was not observed when females were exposed to the first male without mating, implicating semen-mediated effects rather than female differential allocation based on pre-mating assessment of male quality. Our results reveal a novel type of transgenerational effect with potential implications for the evolution of reproductive strategies.


title: Task-Independent Features for Automated Essay Grading

abstract: Automated scoring of student essays is increasingly used to reduce manual grading effort. State-of-the-art approaches use supervised machine learning which makes it complicated to transfer a system trained on one task to another. We investigate which currently used features are task-independent and evaluate their transferability on English and German datasets. We find that, by using our task-independent feature set, models transfer better between tasks. We also find that the transfer works even better between tasks of the same type.


title: Experts and expertise in the governance of infrastructures : Flood-risk management as an example

abstract: The current focus on risk-based approaches to the governance of technology poses new challenges to the combined effort of policymakers and experts since the current worlds of technology and governance are very different. We make use of two hegemonic perspectives in these worlds, i.e. the systems and the network perspective, to analyse the expected contributions from expertise and experts to policy-making about infrastructures. The first question we address is how these two perspectives shape the expectations of the contribution of experts and expertise to problemsolving and policy-making. Second, we explore how practitioners may deal with inevitable conflicting expectations. Based on documents and previous research and illustrated with examples of flood-defence projects from the Netherlands, this paper concludes that perspectives may be coupled or uncoupled in practices of collaboration, and that practitioners should reconsider the contingent room to manoeuvre in policy-making, and the role they may take.


title: Neural network time series forecasting of finite-element mesh adaptation

abstract: Basic learning algorithms and the neural network model are applied to the problem of mesh adaptation for the finite-element method for solving time-dependent partial differential equations. Time series prediction via the neural network methodology is used to predict the areas of ''interest'' in order to obtain an effective mesh refinement at the appropriate times. This allows for increased numerical accuracy with the same computational resources as compared with more ''traditional'' methods.


title: The Connectome Viewer Toolkit: An Open Source Framework to Manage, Analyze, and Visualize Connectomes

abstract: Abstract Advanced neuroinformatics tools are required for methods of connectome mapping, analysis and visualization. The inherent multi-modality of connectome datasets poses new challenges for data organization, integration and sharing. We have designed and implemented the Connectome Viewer Toolkit --- a set of free and extensible open-source neuroimaging tools written in Python. The key components of the toolkit are as follows: 1. The Connectome File Format is an XML-based container format to standardize multi-modal data integration and structured metadata annotation. 2. The Connectome File Format Library enables management and sharing of connectome files. 3. The Connectome Viewer is an integrated research and development environment for visualization and analysis of multi-modal connectome data. The Connectome Viewer's plugin architecture supports extensions with network analysis packages and an interactive scripting shell, to enable easy development and community contributions. Integration with tools from the scientific Python community allows the leveraging of numerous existing libraries for powerful connectome data mining, exploration and comparison. We demonstrate the applicability of the Connectome Viewer Toolkit using Diffusion MRI datasets processed by the Connectome Mapper. The Connectome Viewer Toolkit is available from http://www.cmtk.org/.


title: Handwritten Bangla Character Recognition Using the State-of-the-Art Deep Convolutional Neural Networks

abstract: In spite of advances in object recognition technology, handwritten Bangla character recognition (HBCR) remains largely unsolved due to the presence of many ambiguous handwritten characters and excessively cursive Bangla handwritings. Even many advanced existing methods do not lead to satisfactory performance in practice that related to HBCR. In this paper, a set of the state-of-the-art deep convolutional neural networks (DCNNs) is discussed and their performance on the application of HBCR is systematically evaluated. The main advantage of DCNN approaches is that they can extract discriminative features from raw data and represent them with a high degree of invariance to object distortions. The experimental results show the superior performance of DCNN models compared with the other popular object recognition approaches, which implies DCNN can be a good candidate for building an automatic HBCR system for practical applications.


A two-stage case–control association study between the tryptophan hydroxylase 2 (TPH2) gene and schizophrenia in a Japanese population


title: Stock Price Forecasting by Combining News Mining and Time Series Analysis

abstract: Stock price forecasting has aroused great concern in research of economy, machine learning and other fields. Time series analysis methods are usually utilized to deal with this task. In this paper, we propose to combine news mining and time series analysis to forecast inter-day stock prices. News reports are automatically analyzed with text mining techniques, and then the mining results are used to improve the accuracy of time series analysis algorithms. The experimental result on a half year Chinese stock market data indicates that the proposed algorithm can help to improve the performance of normal time series analysis in stock price forecasting significantly. Moreover, the proposed algorithm also performs well in stock price trend forecasting.


title: Experimental evidence on the divergence between measures of willingness to pay and willingness to accept: The role of value uncertainty, Working Paper (18

abstract: One of the most robust findings in experimental analysis in economics is that subjects often display a large discrepancy between the dollar value they are willing to accept in order to sell an item (WTA) and the dollar value they are willing to pay to purchase it (WTP). We suggest that often when individuals face purchase or sale decisions, the actual private value of an object is uncertain. This is significant because, in a large number of market environments, such uncertainty will cause individuals to display a disparity between WTA and WTP. Our review of the literature confirms that the WTA-WTP disparity does tend to grow where we might expect that estimating an object's value is more difficult. We then turn to our own experiments that attempt to measure such uncertainty more directly and examine how it relates to the WTA-WTP disparity. Our results confirm the hypothesis of a positive relationship.


title: There Is No Logical Negation Here, But There Are Alternatives: Modeling Conversational Negation with Distributional Semantics

abstract: Logical negation is a challenge for distributional semantics, because predicates and their negations tend to occur in very similar contexts, and consequently their distributional vectors are very similar. Indeed, it is not even clear what properties a "negated" distributional vector should possess. However, when linguistic negation is considered in its actual discourse usage, it often performs a role that is quite different from straightforward logical negation. If someone states, in the middle of a conversation, that "This is not a dog," the negation strongly suggests a restricted set of alternative predicates that might hold true of the object being talked about. In particular, other canids and middle-sized mammals are plausible alternatives, birds are less likely, skyscrapers and other large buildings virtually impossible. Conversational negation acts like a graded similarity function, of the sort that distributional semantics might be good at capturing. In this article, we introduce a large data set of alternative plausibility ratings for conversationally negated nominal predicates, and we show that simple similarity in distributional semantic space provides an excellent fit to subject data. On the one hand, this fills a gap in the literature on conversational negation, proposing distributional semantics as the right tool to make explicit predictions about potential alternatives of negated predicates. On the other hand, the results suggest that negation, when addressed from a broader pragmatic perspective, far from being a nuisance, is an ideal application domain for distributional semantic methods.


title: Integrated boost-sepic converter for high step-up applications

abstract: A general boost converter has limited voltage step-up ratio because of its parasitic resistances. Thus, it is not applicable for high step-up applications. As a solution, combining a boost converter with a series output module can be considered to supplement the insufficient step-up ratio. By applying this concept, a new integrated boost-sepic (IBS) converter, which provides additional step-up ratio with the help of an isolated sepic converter, is proposed in this paper. Since the boost converter and the sepic converter share a boost inductor and a switch, its structure is simple. Moreover, the proposed IBS converter needs no current- snubber for the diodes, since the transformer leakage inductor alleviates the reverse recovery. The operational principle and characteristics of proposed converter are presented, and verified experimentally with a 200-W, 42-Vdc input, 400-Vdc output converter prototype.


title: Deep Sketch Hashing: Fast Free-Hand Sketch-Based Image Retrieval

abstract: Free-hand sketch-based image retrieval (SBIR) is a specific cross-view retrieval task, in which queries are abstract and ambiguous sketches while the retrieval database is formed with natural images. Work in this area mainly focuses on extracting representative and shared features for sketches and natural images. However, these can neither cope well with the geometric distortion between sketches and images nor be feasible for large-scale SBIR due to the heavy continuous-valued distance computation. In this paper, we speed up SBIR by introducing a novel binary coding method, named Deep Sketch Hashing (DSH), where a semi-heterogeneous deep architecture is proposed and incorporated into an end-to-end binary coding framework. Specifically, three convolutional neural networks are utilized to encode free-hand sketches, natural images and, especially, the auxiliary sketch-tokens which are adopted as bridges to mitigate the sketch-image geometric distortion. The learned DSH codes can effectively capture the cross-view similarities as well as the intrinsic semantic correlations between different categories. To the best of our knowledge, DSH is the first hashing work specifically designed for category-level SBIR with an end-to-end deep architecture. The proposed DSH is comprehensively evaluated on two large-scale datasets of TU-Berlin Extension and Sketchy, and the experiments consistently show DSHs superior SBIR accuracies over several state-of-the-art methods, while achieving significantly reduced retrieval time and memory footprint.


title: A parameterized cell design for high-Q, variable width and spacing spiral inductors

abstract: The on-chip planar spiral inductors having variable width (W) and spacing (S) across their turns are known to exhibit higher quality factors (Q). In this paper, we present an efficient parameterized cell (pcell) design in cadence using SKILL scripts for automatic layout generation of these complex, high-Q, variable W&S spiral inductors comprising of single ended and symmetric structures with rectangular, hexagonal, octagonal and circular spirals. Electromagnetic simulations are performed on the inductor layouts generated using the developed pcells. The constant W&S and variable W&S spiral inductor structures are fabricated in a 0.18 μm silicon on insulator process. Measurements show ∼25% improvement in the quality factor of variable W%S spiral inductors compared to their constant W&S counterparts and also validates the proper operation of the developed inductor parameterized cells. The presented variable W&S inductor pcell significantly reduces the layout design time of RF circuit designers and also helps in the design automation of these complex inductor structures to boost their own performance and the RF circuits as well.


title: Genome-wide unmasking of epigenetically silenced genes in lung adenocarcinoma from smokers and never smokers.

abstract: Lung cancer in never smokers (NS) shows striking demographic, clinicopathological and molecular distinctions from the disease in smokers (S). Studies on selected genetic and epigenetic alterations in lung cancer identified that the frequency and profile of some abnormalities significantly differ by smoking status. This study compared the transcriptome of lung adenocarcinoma cell lines derived from S (n = 3) and NS (n = 3) each treated with vehicle (control), histone deacetylation inhibitor (trichostatin A) or DNA methylation inhibitor (5-aza-2'-deoxycytidine). Among 122 genes reexpressed following 5-aza-2'-deoxycytidine but not trichostatin A treatment in two or more cell lines (including 32 genes in S-only and 12 NS-only), methylation was validated for 80% (98/122 genes). After methylation analysis of 20 normal tissue samples and 14 additional non-small cell lung cancer cell lines (total 20), 39 genes frequently methylated in normal (>20%, 4/20) and 21 genes rarely methylated in non-small cell lung cancer (≤10%, 2/20) were excluded. The prevalence for methylation of the remaining 38 genes in lung adenocarcinomas from S (n = 97) and NS (n = 75) ranged from 8-89% and significantly differs between S and NS for CPEB1, CST6, EMILIN2, LAYN and MARVELD3 (P < 0.05). Furthermore, methylation of EMILIN2, ROBO3 and IGDCC4 was more prevalent in advanced (Stage II-IV, n = 61) than early (Stage I, n = 110) tumors. Knockdown of MARVELD3, one of the novel epigenetically silenced genes, by small interfering RNA significantly reduced anchorage-independent growth of lung cancer cells (P < 0.001). Collectively, this study has identified multiple, novel, epigenetically silenced genes in lung cancer and provides invaluable resources for the development of diagnostic and prognostic biomarkers.


title: Structural effects of the antimicrobial peptide maculatin 1.1 on supported lipid bilayers

abstract: The interactions of the antimicrobial peptide maculatin 1.1 (GLFGVLAKVAAHVVPAIAEHF-NH(2)) with model phospholipid membranes were studied by use of dual polarisation interferometry and neutron reflectometry and dimyristoylphosphatidylcholine (DMPC) and mixed DMPC-dimyristoylphosphatidylglycerol (DMPG)-supported lipid bilayers chosen to mimic eukaryotic and prokaryotic membranes, respectively. In DMPC bilayers concentration-dependent binding and increasing perturbation of bilayer order by maculatin were observed. By contrast, in mixed DMPC-DMPG bilayers, maculatin interacted more strongly and in a concentration-dependent manner with retention of bilayer lipid order and structure, consistent with pore formation. These results emphasise the importance of membrane charge in mediating antimicrobial peptide activity and emphasise the importance of using complementary methods of analysis in probing the mode of action of antimicrobial peptides.


title: A Comparative Analysis of Gender Classification Techniques

abstract: Over the period of time, automated classification of gender has gained enormous significance and has become an active area of research. Many researchers have put a lot of effort and have produced quality research in this area. Still, there is an immense potential in this field because of its utility in many areas like monitoring, surveillance, commercial profiling and human-computer interaction. Security applications have utmost importance in this area. Gender classification can be used as part of a face recognition process. This paper presents a comprehensive comparison of state-of-the-art research techniques. We have divided the classification process into three stages and have presented a categorical review of existing literatures. Their analysis has been presented along-with their strengths and weaknesses. We have also discussed standard data sets. This can help the novel researcher a comprehensive review. Future dimensions are presented considering the limitations found in the literature.


title: Learning using privileged information: similarity control and knowledge transfer

abstract: This paper describes a new paradigm of machine learning, in which Intelligent Teacher is involved. During training stage, Intelligent Teacher provides Student with information that contains, along with classification of each example, additional privileged information (for example, explanation) of this example. The paper describes two mechanisms that can be used for significantly accelerating the speed of Student's learning using privileged information: (1) correction of Student's concepts of similarity between examples, and (2) direct Teacher-Student knowledge transfer.


title: Dielectric properties of pure and Nb-doped SrTiO3 surfaces: a reflection electron energy loss spectroscopy study

abstract: Reflection Electron Energy Loss Spectroscopy (REELS) is a valuable tool to characterize the electronic structure of insulating surfaces and nanometric films. The REELS inelastic single scattering cross section obtained after removing multiple losses is related to the complex dielectric function in the surface layers. Moreover the probing depth can be varied by changing the primary energy of electrons. One can expect in essence to obtain non-destructive REELS depth profiles, where the evolution of the REELS spectrum translates to the in-depth evolution of the dielectric constant. We present a REELS study of pure and Nb-doped SrTiO3 single crystals, with primary energies ranging from 200 eV to 1500 eV so as to vary the probing depth between 0.7 and 3 nm. The resulting variations in the shape of the spectra are qualitatively interpreted by considering the energy loss functions corresponding to a double interface SrTiO3/carbon monolayer/vacuum, on the basis of the experimental dielectric function derived from TEELS measurements on strontium titanate and amorphous carbon.


Opinion Polarity Detection in Twitter Data combining Sequence Mining and Topic Modeling


Analysis of Different Access Control Mechanism in Cloud


title: Organizations as complex adaptive systems: Implications of Complexity Theory for leadership research

abstract: This article contrasts the assumptions of General Systems Theory, the framework for much prior leadership research, with those of Complexity Theory, to further develop the latter's implications for the definition of leadership and the leadership process. We propose that leadership in a Complex Adaptive System (CAS) may affect the organization indirectly, through the mediating variables of organizational identity and social movements. A rudimentary model of leadership in a CAS is presented. We then outline two non-linear methodologies, dynamic systems simulation and artificial neural networks, as appropriate to enable development and testing of a model leadership under the assumptions of Complexity Theory.


title: Shape, Illumination, and Reflectance from Shading

abstract: A fundamental problem in computer vision is that of inferring the intrinsic, 3D structure of the world from flat, 2D images of that world. Traditional methods for recovering scene properties such as shape, reflectance, or illumination rely on multiple observations of the same scene to overconstrain the problem. Recovering these same properties from a single image seems almost impossible in comparison—there are an infinite number of shapes, paint, and lights that exactly reproduce a single image. However, certain explanations are more likely than others: surfaces tend to be smooth, paint tends to be uniform, and illumination tends to be natural. We therefore pose this problem as one of statistical inference, and define an optimization problem that searches for the most likely explanation of a single image. Our technique can be viewed as a superset of several classic computer vision problems (shape-from-shading, intrinsic images, color constancy, illumination estimation, etc) and outperforms all previous solutions to those constituent problems.


title: A genetic algorithm-based job scheduling model for big data analytics

abstract: Big data analytics (BDA) applications are a new category of software applications that process large amounts of data using scalable parallel processing infrastructure to obtain hidden value. Hadoop is the most mature open-source big data analytics framework, which implements the MapReduce programming model to process big data with MapReduce jobs. Big data analytics jobs are often continuous and not mutually separated. The existing work mainly focuses on executing jobs in sequence, which are often inefficient and consume high energy. In this paper, we propose a genetic algorithm-based job scheduling model for big data analytics applications to improve the efficiency of big data analytics. To implement the job scheduling model, we leverage an estimation module to predict the performance of clusters when executing analytics jobs. We have evaluated the proposed job scheduling model in terms of feasibility and accuracy.


title: Solving Single-digit Sudoku Subproblems

abstract: We show that single-digit "Nishio" subproblems in n×n Sudoku puzzles may be solved in time o(2n), faster than previous solutions such as the pattern overlay method. We also show that single-digit deduction in Sudoku is NP-hard.


title: Learning Probabilistic Relational Models

abstract: A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with “flat” data representations. Thus, to apply these methods, we are forced to convert our data into a flat form, thereby losing much of the relational structure present in our database. This paper builds on the recent work on probabilistic relational models (PRMs), and describes how to learn them from databases. PRMs allow the properties of an object to depend probabilistically both on other properties of that object and on properties of related objects. Although PRMs are significantly more expressive than standard models, such as Bayesian networks, we show how to extend well-known statistical methods for learning Bayesian networks to learn these models. We describe both parameter estimation and structure learning — the automatic induction of the dependency structure in a model. Moreover, we show how the learning procedure can exploit standard database retrieval techniques for efficient learning from large datasets. We present experimental results on both real and synthetic relational databases.


title: The Pavlik harness in the treatment of congenital dislocating hip: report on a multicenter study of the European Paediatric Orthopaedic Society.

abstract: The results of functional treatment using the Pavlik harness in congenital dislocation and congenital dysplasia of the hip in children aged less than 11 months were examined by an EPOS study group. This study was conducted on 3,611 hips in 2,636 patients for a period of 1-9 years after treatment. The reduction rate was 92% in grade Tonnis 2 and 3; the healing rate was 80%. In children with dysplastic hips, the healing rate was 95.35%. Avascular necrosis of the femoral head was observed in 2.38%. The Pavlik harness is designed for outpatient treatment if the parents are compliant.


title: Fractal-Like Image Statistics in Visual Art: Similarity to Natural Scenes

abstract: Both natural scenes and visual art are often perceived as esthetically pleasing. It is therefore conceivable that the two types of visual stimuli share statistical properties. For example, natural scenes display a Fourier power spectrum that tends to fall with spatial frequency according to a power-law. This result indicates that natural scenes have fractal-like, scale-invariant properties. In the present study, we asked whether visual art displays similar statistical properties by measuring their Fourier power spectra. Our analysis was restricted to graphic art from the Western hemisphere. For comparison, we also analyzed images, which generally display relatively low or no esthetic quality (household and laboratory objects, parts of plants, and scientific illustrations). Graphic art, but not the other image categories, resembles natural scenes in showing fractal-like, scale-invariant statistics. This property is universal in our sample of graphic art; it is independent of cultural variables, such as century and country of origin, techniques used or subject matter. We speculate that both graphic art and natural scenes share statistical properties because visual art is adapted to the structure of the visual system which, in turn, is adapted to process optimally the image statistics of natural scenes.


Toward AI-enhanced Computer-supported Peer Review in Legal Education


title: Virtual clock: a new traffic control algorithm for packet switching networks

abstract: One of the challenging research issues in building high-speed packet-switched networks is how to control the transmission rate of statistical data flows. This paper describes a new traffic control algorithm, VirtualClock, for high-speed network applications. VirtualClock monitors the average transmission rate of statistical data flows and provides every flow with guaranteed throughput and low queueing delay. It provides firewall protection among individual flows, as in a TDM system, while retaining the statistical multiplexing advantages of packet switching. Simulation results show that the VirtualClock algorithm meets all its design goals.


title: International policies to reduce plastic marine pollution from single-use plastics (plastic bags and microbeads): A review

abstract: Marine plastic pollution has been a growing concern for decades. Single-use plastics (plastic bags and microbeads) are a significant source of this pollution. Although research outlining environmental, social, and economic impacts of marine plastic pollution is growing, few studies have examined policy and legislative tools to reduce plastic pollution, particularly single-use plastics (plastic bags and microbeads). This paper reviews current international market-based strategies and policies to reduce plastic bags and microbeads. While policies to reduce microbeads began in 2014, interventions for plastic bags began much earlier in 1991. However, few studies have documented or measured the effectiveness of these reduction strategies. Recommendations to further reduce single-use plastic marine pollution include: (i) research to evaluate effectiveness of bans and levies to ensure policies are having positive impacts on marine environments; and (ii) education and outreach to reduce consumption of plastic bags and microbeads at source.


title: Online Learning of Relaxed CCG Grammars for Parsing to Logical Form

abstract: We consider the problem of learning to parse sentences to lambda-calculus representations of their underlying semantics and present an algorithm that learns a weighted combinatory categorial grammar (CCG). A key idea is to introduce non-standard CCG combinators that relax certain parts of the grammar—for example allowing flexible word order, or insertion of lexical items— with learned costs. We also present a new, online algorithm for inducing a weighted CCG. Results for the approach on ATIS data show 86% F-measure in recovering fully correct semantic analyses and 95.9% F-measure by a partial-match criterion, a more than 5% improvement over the 90.3% partial-match figure reported by He and Young (2006).


Modeling Bus Communication Protocols Using Timed Colored Petri Nets — The Controller Area Network Example


title: Learning Discriminative Representation with Signed Laplacian Restricted Boltzmann Machine

abstract: We investigate the potential of a restricted Boltzmann Machine (RBM) for discriminative representation learning. By imposing the class information preservation constraints on the hidden layer of the RBM, we propose a Signed Laplacian Restricted Boltzmann Machine (SLRBM) for supervised discriminative representation learning. The model utilizes the label information and preserves the global data locality of data points simultaneously. Experimental results on the benchmark data set show the effectiveness of our method.


title: N-ary Relation Extraction using Graph State LSTM

abstract: Cross-sentence $n$-ary relation extraction detects relations among $n$ entities across multiple sentences. Typical methods formulate an input as a \textit{document graph}, integrating various intra-sentential and inter-sentential dependencies. The current state-of-the-art method splits the input graph into two DAGs, adopting a DAG-structured LSTM for each. Though being able to model rich linguistic knowledge by leveraging graph edges, important information can be lost in the splitting procedure. We propose a graph-state LSTM model, which uses a parallel state to model each word, recurrently enriching state values via message passing. Compared with DAG LSTMs, our graph LSTM keeps the original graph structure, and speeds up computation by allowing more parallelization. On a standard benchmark, our model shows the best result in the literature.


title: Networking-Enabling Enhancement for a Swarm of COTS Drones

abstract: This paper presents the augmentation of a commercial-of-the-self (COTS) multi-rotor unmanned aerial system (UAS) to extend its capabilities by introducing increased on-board processing power. The option to process sensor information on-board reduces the required bandwidth by only communicating relevant or important information and therefore increases the level of UAS autonomy. It also enables adaptive reactive abilities for a group of UAS executing a coordinated, collaborative or cooperative mission. This paper describes an effort to integrate a small board computer, more specifically, an Odroid C2 with the COTS 3DR Solo drone, and reviews the achieved enhanced capabilities of such a system. This is an extension of a traditional autopilot, as such, the original Ardupilot hardware and software is not modified in this work.


Twin Clouds: An Architecture for Secure Cloud Computing


title: Resolving data duplication, inaccuracy and inconsistency issues using Master Data Management

abstract: The management of scattered datasets over multiple data sources has led to data quality issues in an organization. Master Data Management (MDM) has been used to resolve this issue by providing “a single reference of truth” to reduce data redundancy in an organization. To the best of our knowledge, there is lack of study reviewing the progress of MDM research. Therefore, this paper intends to fill in the gap by conducting a systematic literature review to summarize the progress of MDM research domain. We also synthesize the data quality issues on multiple data sources management and how MDM tends to resolve them. We strategized our literature methods through relevant keywords searching from nine (9) databases including journals, proceedings, books, book chapters and industry research. The strategy has shown seven hundred and seventy-seven (777) articles were found during the initial searching stage and three hundred and forty-seven (347) relevant articles were filtered out for the analysis. The review shows that currently, MDM research has received a slope of enlightenment hence it still relevant to be explored. MDM is not just about a technology, it is an approach through a combination of processes, data governance, and technical implementation to resolve data quality issues on multiple data sources management such as duplication, inaccuracy and inconsistency of information.


title: A portable specification of zero-overhead looping control hardware applied to embedded processors

abstract: Looping operations impose a significant bottleneck to achieving better computational efficiency for embedded applications. To confront this problem on embedded RISC processors, an architectural modification involving the integration of a zero-overhead loop controller (ZOLC) has been suggested, supporting arbitrary loop structures with multiple-entry and multiple-exit nodes. In this paper, a graph formalism is introduced for representing the loop structure of application programs, which can assist in ZOLC code synthesis. Also, a portable description of a ZOLC component is given in detail, which can be exploited in the scope of RTL synthesis, compiler optimizations or assembly level transformations for enabling its utilization. This description is designed to be easily retargetable to single-issue RISC processors, requiring only minimal effort for this task.


title: Simultaneous tracking and shape estimation with laser scanners

abstract: Advanced driver assistance systems and the environment perception for autonomous vehicles will benefit from systems robustly tracking objects while simultaneously estimating their shape. Unlike many recent approaches that represent object shapes by approximated models such as boxes or ellipses, this paper proposes an algorithm that estimates a free-formed shape derived from raw laser measurements. For that purpose local occupancy grid maps are used to model arbitrary object shapes. Beside shape estimation the algorithm keeps a stable reference point on the object. This will be important to avoid apparent motion if the observable part of an object contour changes. The algorithm is part of a perception system and is tested with two 4-layer laser scanners.


title: phishGILLNET—phishing detection methodology using probabilistic latent semantic analysis, AdaBoost, and co-training

abstract: Identity theft is one of the most profitable crimes committed by felons. In the cyber space, this is commonly achieved using phishing. We propose here robust server side methodology to detect phishing attacks, called phishGILLNET, which incorporates the power of natural language processing and machine learning techniques. phishGILLNET is a multi-layered approach to detect phishing attacks. The first layer (phishGILLNET1) employs Probabilistic Latent Semantic Analysis (PLSA) to build a topic model. The topic model handles synonym (multiple words with similar meaning), polysemy (words with multiple meanings), and other linguistic variations found in phishing. Intentional misspelled words found in phishing are handled using Levenshtein editing and Google APIs for correction. Based on term document frequency matrix as input PLSA finds phishing and non-phishing topics using tempered expectation maximization. The performance of phishGILLNET1 is evaluated using PLSA fold in technique and the classification is achieved using Fisher similarity. The second layer of phishGILLNET (phishGILLNET2) employs AdaBoost to build a robust classifier. Using probability distributions of the best PLSA topics as features the classifier is built using AdaBoost. The third layer (phishGILLNET3) further expands phishGILLNET2 by building a classifier from labeled and unlabeled examples by employing Co-Training. Experiments were conducted using one of the largest public corpus of email data containing 400,000 emails. Results show that phishGILLNET3 outperforms state of the art phishing detection methods and achieves F-measure of 100%. Moreover, phishGILLNET3 requires only a small percentage (10%) of data be annotated thus saving significant time, labor, and avoiding errors incurred in human annotation.


Microplastics and Human Health: Our Great Future to Think About Now


title: Capturing braided hairstyles

abstract: From fishtail to princess braids, these intricately woven structures define an important and popular class of hairstyle, frequently used for digital characters in computer graphics. In addition to the challenges created by the infinite range of styles, existing modeling and capture techniques are particularly constrained by the geometric and topological complexities. We propose a data-driven method to automatically reconstruct braided hairstyles from input data obtained from a single consumer RGB-D camera. Our approach covers the large variation of repetitive braid structures using a family of compact procedural braid models. From these models, we produce a database of braid patches and use a robust random sampling approach for data fitting. We then recover the input braid structures using a multi-label optimization algorithm and synthesize the intertwining hair strands of the braids. We demonstrate that a minimal capture equipment is sufficient to effectively capture a wide range of complex braids with distinct shapes and structures.


title: Ontology and NLP support for building disaster knowledge base

abstract: The data from pre, post and during disasters play a very important and vital role while immediate decision making in emergency management. Data availability, analysis, and exchange build up a critical knowledge base that helps in taking the right decision at the right time. Voluminous data is available through various sources in heterogeneous formats. It is a very big challenge for utilization of such massive heterogeneous data in disaster management situations. Disaster data is widely available in various documents and formats. Past disasters, anticipated disasters and during the disasters, a large amount of data is getting exchanged in various documents between different agencies. Effective techniques to extract and gather the data from heterogeneous documents helps in right data extraction and build up a knowledge base to formulate ease of analysis and modeling. Ontology based semantic technology is a powerful mechanism that supports information integration, exchange, share, reuse and build a knowledge base from heterogeneous information sources. The current research work focuses on three aspects of knowledge base system for effective disaster management. The current research work implemented a mechanism to extract relevant information from semi-structured and structured heterogeneous documents using NLP, representing extracted information in homogeneous and machine understandable format using RDF and map the RDF triples to appropriate concepts of the disaster management domain ontologies. This is very novel and effective means of data usage mechanism to help and guide policy makers, disaster mitigation agencies and society at large.


title: FlyAR: Augmented Reality Supported Micro Aerial Vehicle Navigation

abstract: Micro aerial vehicles equipped with high-resolution cameras can be used to create aerial reconstructions of an area of interest. In that context automatic flight path planning and autonomous flying is often applied but so far cannot fully replace the human in the loop, supervising the flight on-site to assure that there are no collisions with obstacles. Unfortunately, this workflow yields several issues, such as the need to mentally transfer the aerial vehicle's position between 2D map positions and the physical environment, and the complicated depth perception of objects flying in the distance. Augmented Reality can address these issues by bringing the flight planning process on-site and visualizing the spatial relationship between the planned or current positions of the vehicle and the physical environment. In this paper, we present Augmented Reality supported navigation and flight planning of micro aerial vehicles by augmenting the user's view with relevant information for flight planning and live feedback for flight supervision. Furthermore, we introduce additional depth hints supporting the user in understanding the spatial relationship of virtual waypoints in the physical world and investigate the effect of these visualization techniques on the spatial understanding.


title: Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization

abstract: We introduce a proximal version of the stochastic dual coordinate ascent method and show how to accelerate the method using an inner-outer iteration procedure. We analyze the runtime of the framework and obtain rates that improve state-of-the-art results for various key machine learning optimization problems including SVM, logistic regression, ridge regression, Lasso, and multiclass SVM. Experiments validate our theoretical findings.


title: TAPAS: Two-pass Approximate Adaptive Sampling for Softmax

abstract: TAPAS is a novel adaptive sampling method for the softmax model. It uses a two pass sampling strategy where the examples used to approximate the gradient of the partition function are first sampled according to a squashed population distribution and then resampled adaptively using the context and current model. We describe an efficient distributed implementation of TAPAS. We show, on both synthetic data and a large real dataset, that TAPAS has low computational overhead and works well for minimizing the rank loss for multi-class classification problems with a very large label space.


title: ADsafety: Type-Based Verification of JavaScript Sandboxing

abstract: Web sites routinely incorporate JavaScript programs from several sources into a single page. These sources must be protected from one another, which requires robust sandboxing. The many entry-points of sandboxes and the subtleties of JavaScript demand robust verification of the actual sandbox source. We use a novel type system for JavaScript to encode and verify sandboxing properties. The resulting verifier is lightweight and efficient, and operates on actual source. We demonstrate the effectiveness of our technique by applying it to ADsafe, which revealed several bugs and other weaknesses.


title: The Authority of"Fair"in Machine Learning

abstract: In this paper, we argue for the adoption of a normative definition of fairness within the machine learning community. After characterizing this definition, we review the current literature of Fair ML in light of its implications. We end by suggesting ways to incorporate a broader community and generate further debate around how to decide what is fair in ML.


title: Electro-hydraulic power steering system modelling for parameter fault detection based on model reference adaptive frame

abstract: A simple and efficient fault detection and parameter monitoring algorithm for electro-hydraulic power steering (EHPS) system is proposed using model reference adaptive system (MRAS) without any additional sensor. In a faulty situation where the hydraulic system is disabled, assisted power is provided by an electric motor. Therefore, in low speed maneuvers, during sudden direction changes and parking, some parameters of an electric motor in EHPS are affected. These changes threaten the vehicle's driving reliability and performance of EHPS. In this study, an EHPS system with rotor flux estimation based adaptive driver torque control principle has been presented. The motor winding temperature is obtained for monitoring purpose with the help of MRAS stator resistance estimation. The proposed parameter estimation method for EHPS has been simulated by using AMESim and MATLAB/Simulink cosimulation model. Simulation results demonstrate the feasibility and effectiveness of the proposed method.


title: Design of Miniaturized Dual-Band Microstrip Antenna for WLAN Application

abstract: Wireless local area network (WLAN) is a technology that combines computer network with wireless communication technology. The 2.4 GHz and 5 GHz frequency bands in the Industrial Scientific Medical (ISM) band can be used in the WLAN environment. Because of the development of wireless communication technology and the use of the frequency bands without the need for authorization, the application of WLAN is becoming more and more extensive. As the key part of the WLAN system, the antenna must also be adapted to the development of WLAN communication technology. This paper designs two new dual-frequency microstrip antennas with the use of electromagnetic simulation software—High Frequency Structure Simulator (HFSS). The two antennas adopt ordinary FR4 material as a dielectric substrate, with the advantages of low cost and small size. The first antenna adopts microstrip line feeding, and the antenna radiation patch is composed of a folded T-shaped radiating dipole which reduces the antenna size, and two symmetrical rectangular patches located on both sides of the T-shaped radiating patch. The second antenna is a microstrip patch antenna fed by coaxial line, and the size of the antenna is diminished by opening a stepped groove on the two edges of the patch and a folded slot inside the patch. Simulation experiments prove that the two designed antennas have a higher gain and a favourable transmission characteristic in the working frequency range, which is in accordance with the requirements of WLAN communication.


title: The neural mechanisms of top-down attentional control

abstract: Selective visual attention involves dynamic interplay between attentional control systems and sensory brain structures. We used event-related functional magnetic resonance imaging (fMRI) during a cued spatialattention task to dissociate brain activity related to attentional control from that related to selective processing of target stimuli. Distinct networks were engaged by attention-directing cues versus subsequent targets. Superior frontal, inferior parietal and superior temporal cortex were selectively activated by cues, indicating that these structures are part of a network for voluntary attentional control. This control biased activity in multiple visual cortical areas, resulting in selective sensory processing of relevant visual targets.


title: Common Subspace Learning via Cross-Domain Extreme Learning Machine

abstract: Extreme learning machine (ELM) is proposed for solving a single-layer feed-forward network (SLFN) with fast learning speed and has been confirmed to be effective and efficient for pattern classification and regression in different fields. ELM originally focuses on the supervised, semi-supervised, and unsupervised learning problems, but just in the single domain. To our best knowledge, ELM with cross-domain learning capability in subspace learning has not been exploited very well. Inspired by a cognitive-based extreme learning machine technique (Cognit Comput. 6:376–390, 1; Cognit Comput. 7:263–278, 2.), this paper proposes a unified subspace transfer framework called cross-domain extreme learning machine (CdELM), which aims at learning a common (shared) subspace across domains. Three merits of the proposed CdELM are included: (1) A cross-domain subspace shared by source and target domains is achieved based on domain adaptation; (2) ELM is well exploited in the cross-domain shared subspace learning framework, and a new perspective is brought for ELM theory in heterogeneous data analysis; (3) the proposed method is a subspace learning framework and can be combined with different classifiers in recognition phase, such as ELM, SVM, nearest neighbor, etc. Experiments on our electronic nose olfaction datasets demonstrate that the proposed CdELM method significantly outperforms other compared methods.


title: The Effects of Personal Susceptibility and Social Support on Internet Addiction: an Application of Adler’s Theory of Individual Psychology

abstract: The aim of the present study was to examine the addictive effects of personal susceptibility and social support from family, friends, and significant other on Internet addiction. In this study, 207 medical students completed the UCLA Loneliness Scale, the Academic Expectations Stress Inventory, the Multidimensional Scale of Perceived Social Support, and the Young’s Diagnostic Questionnaire for Internet addiction. Participants were recruited via simple random sampling technique. Personal susceptibility variables such as loneliness, academic stress due to other expectations, and academic stress due to self-expectations were significantly positively correlated to, and all domains of social support were significantly negatively correlated to, Internet addiction. After adjusting for demographic variables, social support emerged as a significant predictor of Internet addiction in the hierarchical regression analysis. In support of Adler’s individual psychology theory, the present findings suggest that social support from family is a valuable adjunct to prevention and intervention programs aimed at alleviating Internet addiction in medical students.


title: Turning programs against each other: high coverage fuzz-testing using binary-code mutation and dynamic slicing

abstract: Mutation-based fuzzing is a popular and widely employed black-box testing technique for finding security and robustness bugs in software. It owes much of its success to its simplicity; a well-formed seed input is mutated, e.g. through random bit-flipping, to produce test inputs. While reducing the need for human effort, and enabling security testing even of closed-source programs with undocumented input formats, the simplicity of mutation-based fuzzing comes at the cost of poor code coverage. Often millions of iterations are needed, and the results are highly dependent on configuration parameters and the choice of seed inputs. In this paper we propose a novel method for automated generation of high-coverage test cases for robustness testing. Our method is based on the observation that, even for closed-source programs with proprietary input formats, an implementation that can generate well-formed inputs to the program is typically available. By systematically mutating the program code of such generating programs, we leverage information about the input format encoded in the generating program to produce high-coverage test inputs, capable of reaching deep states in the program under test. Our method works entirely at the machine-code level, enabling use-cases similar to traditional black-box fuzzing. We have implemented the method in our tool MutaGen, and evaluated it on 7 popular Linux programs. We found that, for most programs, our method improves code coverage by one order of magnitude or more, compared to two well-known mutation-based fuzzers. We also found a total of 8 unique bugs.


title: The effect of intergenerational programs on the mental health of elderly adults

abstract: Objectives: The aim of this study was to clarify the effect of an intergenerational program on elderly persons’ symptoms of depressive mood and in improving their sense of coherence, which is an element for successful coping with stressors. Method: We evaluated an intervention research project (Research of Productivity by Intergenerational Sympathy [REPRINTS]), in which volunteers >65 years old read picture books to children in a school setting. The intervention group (REPRINTS) was recruited through intensive weekly training seminars for three months. The no-contact control group members were also recreated to participate in health checks and surveys for data collection purposes. Eventually, 26 participants in the intervention group and 54 in the control group were included for data analysis. Results: The age or gender was not significantly different between the intervention and control groups. A two-way repeated-measures ANOVA shows a time × group significant interaction effects. Analyses of the simple main effects showed that sense of meaningfulness significantly increased for members of the intervention group at all terms, with no changes in the control group over time. Multiple mediation analysis revealed that participation in the intergenerational program was associated with a sense of manageability which was also significantly related to depressive mood. Conclusion: Intergenerational programs could serve as key health promoters among elderly people by decreasing the risk of social isolation and loneliness due to the greater sense of meaningfulness. However, given our limited sample size, generalizability was restricted and studies with larger cohorts are required to further validate our findings.


title: Changes of trace minerals (serum iron, zinc, copper and magnesium) in thalassemia.

abstract: We have determined serum Zn, Fe, Cu and Mg in 42 patients aging from 3 months to 22 years with homozygous beta-thalassemia and thalassemia intermedia and in 36 control subjects of the same ages. Serum zinc was significantly decreased. Serum copper and iron were increased, but magnesium was found to be at normal levels.


title: Dual current control scheme for PWM converter under unbalanced input voltage conditions

abstract: Voltage unbalance in a three-phase system causes performance deterioration of PWM power converters by producing 120 Hz voltage ripples in the DC link and by increasing the reactive power. To eliminate the DC link voltage ripple and the DC component of the reactive power, both positive- and negative-sequence currents should be controlled simultaneously, according to the paper by Rioual et al (1996). The authors used two synchronous reference frames: a positive-sequence current regulated by a proportional integral (PI) controller in a positive synchronous reference frame (SRF); and a negative-sequence current regulated by a PI controller in a negative SRF. In the positive SRF, which rotates counterclockwise, the positive sequence appears as DC, while the negative sequence appears as 120 Hz. In contrast, in the negative SRF, which rotates clockwise, the negative sequence appears as DC, while the positive sequence appears as 120 Hz. By deleting 120 Hz components using a notch filter in each SRF, one can measure positive- and negative-sequence currents separately, and use them for constructing two feedback controllers. Since the negative-sequence current is also controlled in its own SRF by a DC command, this approach yields better performance without increasing the control gain. Note that, since the controller is implemented by a software routine in the digital signal professor chip, using two SRFs does not require additional hardware. The authors demonstrated the effectiveness of the proposed control scheme by using computer simulation and experiments.


title: Function and Regulation of Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) / CRISPR Associated (Cas) Systems

abstract: Phages are the most abundant biological entities on earth and pose a constant challenge to their bacterial hosts. Thus, bacteria have evolved numerous ‘innate’ mechanisms of defense against phage, such as abortive infection or restriction/modification systems. In contrast, the clustered regularly interspaced short palindromic repeats (CRISPR) systems provide acquired, yet heritable, sequence-specific ‘adaptive’ immunity against phage and other horizontally-acquired elements, such as plasmids. Resistance is acquired following viral infection or plasmid uptake when a short sequence of the foreign genome is added to the CRISPR array. CRISPRs are then transcribed and processed, generally by CRISPR associated (Cas) proteins, into short interfering RNAs (crRNAs), which form part of a ribonucleoprotein complex. This complex guides the crRNA to the complementary invading nucleic acid and targets this for degradation. Recently, there have been rapid advances in our understanding of CRISPR/Cas systems. In this review, we will present the current model(s) of the molecular events involved in both the acquisition of immunity and interference stages and will also address recent progress in our knowledge of the regulation of CRISPR/Cas systems.


Voltage source inverters for grid connected photovoltaic systems


title: DiagDroid: Android performance diagnosis via anatomizing asynchronous executions

abstract: Rapid UI responsiveness is a key consideration to Android app developers. However, the complicated concurrency model of Android makes it hard for developers to understand and further diagnose the UI performance. This paper presents DiagDroid, a tool specifically designed for Android UI performance diagnosis. The key notion of DiagDroid is that UI-triggered asynchronous executions contribute to the UI performance, and hence their performance and their runtime dependency should be properly captured to facilitate performance diagnosis. However, there are tremendous ways to start asynchronous executions, posing a great challenge to profiling such executions and their runtime dependency. To this end, we properly abstract five categories of asynchronous executions as the building basis. As a result, they can be tracked and profiled based on the specifics of each category with a dynamic instrumentation approach carefully tailored for Android. DiagDroid can then accordingly profile the asynchronous executions in a task granularity, equipping it with low-overhead and high compatibility merits. The tool is successfully applied in diagnosing 33 real-world open-source apps, and we find 14 of them contain 27 performance issues. It shows the effectiveness of our tool in Android UI performance diagnosis. The tool is open-source released online.


title: A Low-Latency Library in FPGA Hardware for High-Frequency Trading (HFT)

abstract: Current High-Frequency Trading (HFT) platforms are typically implemented in software on computers with high-performance network adapters. The high and unpredictable latency of these systems has led the trading world to explore alternative "hybrid" architectures with hardware acceleration. In this paper, we survey existing solutions and describe how FPGAs are being used in electronic trading to approach the goal of zero latency. We present an FPGA IP library which implements networking, I/O, memory interfaces and financial protocol parsers. The library provides pre-built infrastructure which accelerates the development and verification of new financial applications. We have developed an example financial application using the IP library on a custom 1U FPGA appliance. The application sustains 10Gb/s Ethernet line rate with a fixed end-to-end latency of 1µs -- up to two orders of magnitude lower than comparable software implementations.


title: Semantic representation and enrichment of information retrieval experimental data

abstract: Experimental evaluation carried out in international large-scale campaigns is a fundamental pillar of the scientific and technological advancement of information retrieval (IR) systems. Such evaluation activities produce a large quantity of scientific and experimental data, which are the foundation for all the subsequent scientific production and development of new systems. In this work, we discuss how to semantically annotate and interlink this data, with the goal of enhancing their interpretation, sharing, and reuse. We discuss the underlying evaluation workflow and propose a resource description framework model for those workflow parts. We use expertise retrieval as a case study to demonstrate the benefits of our semantic representation approach. We employ this model as a means for exposing experimental data as linked open data (LOD) on the Web and as a basis for enriching and automatically connecting this data with expertise topics and expert profiles. In this context, a topic-centric approach for expert search is proposed, addressing the extraction of expertise topics, their semantic grounding with the LOD cloud, and their connection to IR experimental data. Several methods for expert profiling and expert finding are analysed and evaluated. Our results show that it is possible to construct expert profiles starting from automatically extracted expertise topics and that topic-centric approaches outperform state-of-the-art language modelling approaches for expert finding.


title: Joint Inference for Fine-grained Opinion Extraction

abstract: This paper addresses the task of finegrained opinion extraction ‐ the identification of opinion-related entities: the opinion expressions, the opinion holders, and the targets of the opinions, and the relations between opinion expressions and their targets and holders. Most existing approaches tackle the extraction of opinion entities and opinion relations in a pipelined manner, where the interdependencies among different extraction stages are not captured. We propose a joint inference model that leverages knowledge from predictors that optimize subtasks of opinion extraction, and seeks a globally optimal solution. Experimental results demonstrate that our joint inference approach significantly outperforms traditional pipeline methods and baselines that tackle subtasks in isolation for the problem of opinion extraction.


title: Geometric Rectification of Camera-Captured Document Images

abstract: Compared to typical scanners, handheld cameras offer convenient, flexible, portable, and noncontact image capture, which enables many new applications and breathes new life into existing ones. However, camera-captured documents may suffer from distortions caused by a nonplanar document shape and perspective projection, which lead to the failure of current optical character recognition (OCR) technologies. We present a geometric rectification framework for restoring the frontal-flat view of a document from a single camera-captured image. Our approach estimates the 3D document shape from texture flow information obtained directly from the image without requiring additional 3D/metric data or prior camera calibration. Our framework provides a unified solution for both planar and curved documents and can be applied in many, especially mobile, camera-based document analysis applications. Experiments show that our method produces results that are significantly more OCR compatible than the original images.


Design of A Room Temperature And Humidity Controller Using Fuzzy Logic


title: Enhancing Android permission through usage control: a BYOD use-case

abstract: The Bring Your Own Device (BYOD) paradigm, where the employees of a company install an application on their mobile devices to access company privileged information, is becoming very popular in the business environment. In order to perform their tasks, BYOD applications typically require a large set of rights which, in Android mobile devices, must be statically granted in order to have the application installed. However, this access control model is too coarse grained for the BYOD scenario, because employees would like to have a finer control on the rights granted to such applications, for instance to protect their privacy when they are not on duty. To address this issue, we propose to enhance the Android permission system through a Usage Control-based framework enabling employees to write policies which are continuously enforced while BYOD applications are running. This framework acts as a dynamic permission manager, where usage control policies grants, revokes and restores permissions to running applications on the base of mutable attributes describing the current context. Context is observed by using Android device standard APIs to monitor attributes such as mobile device location, WiFi status, battery level, current date and time, and so on. External trusted attribute providers can also be exploited.


title: Phylogenetic Analysis in the Anomaly Zone

abstract: The concatenation method has been widely used as a means of combining data to estimate phylogenetic trees (Huelsenbeck et al. 1996a, 1996b; Glazko and Nei 2003). However, simulation studies have shown that the maximum likelihood (ML) estimate of the species tree for concatenated sequences may be statistically incon sistent if the gene trees are highly heterogeneous (Ko laczkowski and Thornton 2004; Kubatko and Degnan 2007). Recently, Degnan and Rosenberg (2006) defined an "anomaly zone"?a set of short internal branches in species trees that will generate gene trees that are discor dant with the species tree more often than gene trees that are concordant. Kubatko and Degnan (2007) went on to show that when DNA sequences are generated from gene trees simulated from species trees in the anomaly zone, as well as from species trees slightly outside this zone but still with short internal branches, the ML esti mate of the species tree for the concatenated sequences can be inconsistent, resulting in increasing certainty in the wrong species tree. These studies were all performed with a molecular clock on rooted gene and species trees within the variation realized in stochastic simulations of DNA sequences under the Jukes and Cantor (1969) model of nucleotide substitution. They applied the ML method with a clock to recover phylogenetic trees from their simulated concatenated data sets.


title: Hydrodynamic analysis of a SWATH planing USV based on CFD

abstract: As small waterplane area twin hull (SWATH) has excellent seakeeping performance and low wave resistance, this structure has been applied to unmanned surface vehicle (USV). However, with the increase of speed, fluid resistances will increase exponentially because of its large wet surface. A way to improve this situation is to reduce the depth of draft at high speed, so that all or a part of the volume of the submerged bodies is above the surface of water. Based on this idea, a new type of SWATH USV is designed in this paper. The two submerged bodies of the USV have a planing boat shape. At high speed, hydrodynamic lift force generated by the submerged bodies of the USV can push most part of the submerged bodies out of water, which can greatly reduce the resistance of the USV. In this paper, computational fluid dynamic (CFD) method is used to analyze the attitude and resistance of the USV at different speed, showing that the designed USV can achieve the expected movement characteristics and low resistance at high speed.


title: Improving Context Aware Language Models

abstract: Increased adaptability of RNN language models leads to improved predictions that benefit many applications. However, current methods do not take full advantage of the RNN structure. We show that the most widely-used approach to adaptation (concatenating the context with the word embedding at the input to the recurrent layer) is outperformed by a model that has some low-cost improvements: adaptation of both the hidden and output layers. and a feature hashing bias term to capture context idiosyncrasies. Experiments on language modeling and classification tasks using three different corpora demonstrate the advantages of the proposed techniques.


title: Quantum computation in brain microtubules? The Penrose-Hameroff 'Orch OR' model of consciousness

abstract: Potential features of quantum computation could explain enigmatic aspects of consciousness. The Penrose{Hamero model (orchestrated objective reduction: ‘Orch OR’) suggests that quantum superposition and a form of quantum computation occur in microtubules|cylindrical protein lattices of the cell cytoskeleton within the brain’s neurons. Microtubules couple to and regulate neural-level synaptic functions, and they may be ideal quantum computers because of dynamical lattice structure, quantum-level subunit states and intermittent isolation from environmental interactions. In addition to its biological setting, the Orch OR proposal diers in an essential way from technologically envisioned quantum computers in which collapse, or reduction to classical output states, is caused by environmental decoherence (hence introducing randomness). In the Orch OR proposal, reduction of microtubule quantum superposition to classical output states occurs by an objective factor|Roger Penrose’s quantum gravity threshold stemming from instability in Planck-scale separations (superpositions) in spacetime geometry. Output states following Penrose’s objective reduction are neither totally deterministic nor random, but influenced by a non-computable factor ingrained in fundamental spacetime. Taking a modern panpsychist view in which protoconscious experience and Platonic values are embedded in Planck-scale spin networks, the Orch OR model portrays consciousness as brain activities linked to fundamental ripples in spacetime geometry.


title: Design Techniques for Wideband Discrete-Time Delta-Sigma ADCs With Extra Loop Delay

abstract: Novel implementation techniques, such as the use of direct-charge-transfer stage, noise coupling, and dynamic element matching can improve the performance of wideband ΔΣ ADCs. However, they introduce extra loop delay, which compromises the low-distortion property and even the loop stability. This paper shows how the addition of independent feedback and feed-forward branches to the loop filter can compensate the extra loop delay, and restore the desired signal and noise transfer functions. The design methodology is then generalized for different kinds of ΔΣ ADCs, and the low-distortion property is analyzed. Two wideband delta-sigma ADCs have been designed and simulated to verify the theory.


title: Stealthy Domain Generation Algorithms

abstract: Botnets are groups of compromised computers that botmasters (botherders) use to launch attacks over the Internet. To avoid detection, botnets use DNS fast flux to change the mapping between IP addresses and domain names periodically. Domain generation algorithms (DGAs) are employed to generate a large number of domain names. Detection techniques have been proposed to identify malicious domain names generated by DGAs. Three metrics, Kullback–Leibler (KL) distance, Edit distance (ED), and Jaccard index (JI), are used to detect botnet domains with up to 100% detection rate and 2.5% false-positive rate. In this paper, we propose two DGAs that use hidden Markov models (HMMs) and probabilistic context-free grammars (PCFGs), respectively. Experiment results show that DGA detection metrics (KL, JI, and ED) and detection systems (BotDigger and Pleiades) have difficulty detecting domain names generated using the proposed approaches. Game theory is used to optimize strategies for both botmasters and security personnel. Results show that, to optimize DGA detection, security personnel should use the ED detection technique with probability 0.78 and JI detection with probability 0.22, and botmasters should choose the HMM-based DGA with probability 0.67 and PCFG-based DGA with probability 0.33.


title: Information extraction - a text mining approach

abstract: Text mining concerns looking for patterns in unstructured text. The related task of information extraction (IE) is about locating specific items in natural-language documents. This paper presents a framework for text mining, called DISCOTEX (discovery from text extraction), using a learned information extraction system to transform text into more structured data which is then mined for interesting relationships. The initial version of DISCOTEX integrates an IE module acquired by an IE learning system, and a standard rule induction module. In addition, rules mined from a database extracted from a corpus of texts are used to predict additional information to extract from future documents, thereby improving the recall of the underlying extraction system. Encouraging results are presented on applying these techniques to a corpus of computer job announcement postings from an Internet newsgroup.


title: Comparison and evaluation of pedestrian motion models for vehicle safety systems

abstract: In this paper the influence of different information on the performance of pedestrian motion models is evaluated. For this, besides of dynamic and physiological constraints, additional information as head orientation, typical pedestrian motion behavior and environment informations are used. A vehicle sensor system, consisting of a 12Mpx camera and Lidar provides the pedestrian and environment information in the considered test cases. Based on this, four pedestrian models, each based on different information, are used to predict the future position of the pedestrian. The predicted area is presented by a probability grid. The results show the performance of the models for evaluation parameters as prediction error, standard deviation and collision probability. Earlier works show, that the models have different performance depending on the prediction time. So a new introduced system fuses the probability map of each model to achieve a continuous prediction performance.


title: Learning to Rank for Information Retrieval and Natural Language Processing

abstract: Learning to rank refers to machine learning techniques for training the model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on the problem recently and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, existing approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings. Ranking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation and ranking aggregation, including training and testing, evaluation, feature creation, and major approaches. Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting SVM, Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include PRank, OC SVM, Ranking SVM, IR SVM, GBRank, RankNet, LambdaRank, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, Borda Count, Markov Chain, and CRanking. The author explains several example applications of learning to rank including web search, collaborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation. A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed. Table of Contents: Introduction / Learning for Ranking Creation / Learning for Ranking Aggregation / Methods of Learning to Rank / Applications of Learning to Rank / Theory of Learning to Rank / Ongoing and Future Work


title: Implementation of $2^{n}\,- \,2^{k}\,- \,1$ Modulo Adder Based RFID Mutual Authentication Protocol

abstract: In wireless communication, secure transmission and reception of data are the major concern today. In recent years, Radio Frequency Identification (RFID) has played a vital role in the security system for secured data communication. The main challenge in RFID-based security system is to design a more secure, better area and power efficient encoder architecture for tag-reader mutual authentication protocol. This paper proposes new and efficient encoder architecture for RFID mutual authentication protocol which utilizes $2^{n}\,- \,2^{k}\,- \,1$ modulo adder for achieving higher security. The proposed architecture is described in Verilog hardware description language and the functionalities are verified and synthesized using Altera Quartus II tool. The architecture is also synthesized using Cadence RTL compiler for 180 and 90 nm technology. Experimental results of the proposed scheme give better performances in terms of area, power, and delay when compared with existing mutual authentication schemes. In addition to that, the architecture is realized as hardware in Altera DE2 Cyclone II (EP2C35F672C6) field programmable gate array and real time verification has been carried out using Logic Analyzer 16851A. Finally, formal security analysis has been performed using the Burrows–Abadi–Needham (BAN) logic to show that the proposed protocol is secure.


title: A Sentence Is Worth a Thousand Pixels

abstract: We are interested in holistic scene understanding where images are accompanied with text in the form of complex sentential descriptions. We propose a holistic conditional random field model for semantic parsing which reasons jointly about which objects are present in the scene, their spatial extent as well as semantic segmentation, and employs text as well as image information as input. We automatically parse the sentences and extract objects and their relationships, and incorporate them into the model, both via potentials as well as by re-ranking candidate detections. We demonstrate the effectiveness of our approach in the challenging UIUC sentences dataset and show segmentation improvements of 12.5% over the visual only model and detection improvements of 5% AP over deformable part-based models.


title: The callosal angle measured on MRI as a predictor of outcome in idiopathic normal-pressure hydrocephalus

abstract: OBJECT ::: Different neuroimaging biomarkers have been studied to find a tool for prediction of response to CSF shunting in idiopathic normal-pressure hydrocephalus (iNPH). The callosal angle (CA) has been described as useful in discriminating iNPH from ventricular dilation secondary to atrophy. However, the usefulness of the CA as a prognostic tool for the selection of shunt candidates among patients with iNPH is unclear. The aim of this study was to compare the CA in shunt responders with that in nonresponders and clarify whether the CA can serve as a predictor of the outcome. ::: ::: ::: METHODS ::: Preoperative MRI brain scans were evaluated in 109 patients who had undergone shunt surgery for iNPH during 2006-2010. Multiplanar reconstruction was performed interactively to obtain a coronal image through the posterior commissure, perpendicular to the anterior-posterior commissure plane. The CA was measured as the angle between the lateral ventricles on the coronal image. The patients were examined clinically before surgery and at 12 months postoperatively. ::: ::: ::: RESULTS ::: Shunt responders had a significantly smaller mean preoperative CA compared with nonresponders: 59° (95% CI 56°-63°) versus 68° (95% CI 61°-75°) (p < 0.05). A CA cutoff value of 63° showed the best prognostic accuracy. ::: ::: ::: CONCLUSIONS ::: The preoperative CA is smaller in patients whose condition improves after shunt surgery and may be a useful tool in the selection of shunt candidates among patients with iNPH.


title: A Distributed Solution for Winograd Schema Challenge

abstract: This paper presents a new distributed representation based approach to capture commonsense knowledge. Currently, we focus on meanings of verbs and related dependency relations. Built upon such dependency relations, a set of vector space models are developed to capture the meaning of the verb and transfer it to related ambiguous pronouns. To demonstrate the capability of this data-driven approach, we do not incorporate any human knowledge. Among all the vector models without human knowledge, we can achieve the best performance on all of the verb similarity datasets. We can also achieve superior performance on a subset of Winograd Schema challenge dataset, compared with other existing pronoun co-reference and embedding models.


title: Amazon Aurora: On Avoiding Distributed Consensus for I/Os, Commits, and Membership Changes

abstract: Amazon Aurora is a high-throughput cloud-native relational database offered as part of Amazon Web Services (AWS). One of the more novel differences between Aurora and other relational databases is how it pushes redo processing to a multi-tenant scale-out storage service, purpose-built for Aurora. Doing so reduces networking traffic, avoids checkpoints and crash recovery, enables failovers to replicas without loss of data, and enables fault-tolerant storage that heals without database involvement. Traditional implementations that leverage distributed storage would use distributed consensus algorithms for commits, reads, replication, and membership changes and amplify cost of underlying storage. In this paper, we describe how Aurora avoids distributed consensus under most circumstances by establishing invariants and leveraging local transient state. Doing so improves performance, reduces variability, and lowers costs.


title: Numerical calibration for 3-axis accelerometers and magnetometers

abstract: Magnetometers and accelerometers are sensors that are now integrated in objects of everyday life like automotive applications, mobile phones and so on. Some applications need information of acceleration and attitude with a high accuracy. For example, MEMS magnetometers and accelerometers can be integrated in embedded like mobile phones and GPS receivers. The parameters of such sensors must be precisely estimated to avoid drift and biased values. Thus, calibration is an important step to correctly use these sensors and get the expected measurements. This paper presents the theoretical and experimental steps of a method to compute gains, bias and non orthogonality factors of magnetometer and accelerometer sensors. This method of calibration can be used for automatic calibration in embedded systems. The calibration procedure involves arbitrary rotations of the sensors platform and a visual 2D projection of measurements.


title: Policy Gradient Methods for Reinforcement Learning with Function Approximation and Action-Dependent Baselines

abstract: We show how an action-dependent baseline can be used by the policy gradient theorem using function approximation, originally presented with action-independent baselines by (Sutton et al. 2000).


title: Optimisation of the superplastic forming of a dental implant for bone augmentation using finite element simulations

abstract: Abstract Objectives. The phenomenon of superplasticity has made it possible to form complex shapes that require extremely high degrees of ductility in titanium alloy with minimal internal stresses. Combined with the use of an investment casting material as the die material, which makes possible the forming of re-entrant angles, it is possible to produce membranes for ridge augmentation. The aim is to characterise the metal alloy sheet and simulate the superplastic forming process in three dimensions to produce process parameters, namely gas pressure as a function of time, to accurately adapt the titanium sheet to the bone surface. Method. The surface of the die was digitised using a 3D laser scanning system (UBM-Keyence LC2450). Ti-6Al-4V sheet of 140 mm diameter was modelled using a grid of triangular membrane elements. This mesh was automatically refined during the simulations. Finite element simulation was carried out using the Superflag software program (University of Wales Swansea) Three different options for gas pressure control were adopted, namely, target flow stress, target strain rate and target energy dissipation. The pressure cycles produced from the simulation were used to form titanium alloy sheet at 900 °C using argon gas. The deformed regions of the formed sheet were then examined to determine the regions of contact with the die and to characterise surface damage. Results. Comparison of the simulations with experiment showed that there was good agreement between simulated and experimental thickness distributions in most parts of the sheet that were examined. Interrupted tests showed that in the intermediate positions of the forming sheet the simulations were slightly ahead of the experiment. The target stress option was found to produce the best degree of adaptation and the sheet formed using this cycle showed good surface quality, whereas in highly deformed regions using the other target options, the sheet was found to have formed microcracks. The use of a solid lubricant on the surface of the forming sheet was not found to have a significant influence on the adaptation of the titanium alloy sheet except in areas of high deformation where the sheet perforated. Significance. The finite element membrane formulation is well adapted to the superplastic forming of a ridge augmentation membrane prosthesis. The simulation accurately describes the evolution of the shape of the prosthesis and its thickness distribution with time, which allows the manufacturer to select an appropriate initial thickness of titanium alloy sheet prior to attempting to form the component. The investment dies are found to have sufficient strength to withstand the forming operation if a suitable orientation of the titanium sheet with respect to the die is adopted. A metal surface of good quality can be produced in the formed prosthesis using the appropriate gas control option.


title: Poincar\'e Embeddings for Learning Hierarchical Representations

abstract: Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs. However, while complex symbolic datasets often exhibit a latent hierarchical structure, state-of-the-art methods typically learn embeddings in Euclidean vector spaces, which do not account for this property. For this purpose, we introduce a new approach for learning hierarchical representations of symbolic data by embedding them into hyperbolic space -- or more precisely into an n-dimensional Poincare ball. Due to the underlying hyperbolic geometry, this allows us to learn parsimonious representations of symbolic data by simultaneously capturing hierarchy and similarity. We introduce an efficient algorithm to learn the embeddings based on Riemannian optimization and show experimentally that Poincare embeddings outperform Euclidean embeddings significantly on data with latent hierarchies, both in terms of representation capacity and in terms of generalization ability.


The Collaborative ESL Classroom: Perspectives and Techniques.


title: High voltage SiC super-cascode power switch parameter optimization for loss reduction

abstract: A method for voltage balancing capacitor selection of high voltage silicon carbide (SiC) Junction Gate Field Effect Transistor (JEFTs) based super-cascode power switch (SCPS) is presented in this paper. The proposed method can effectively reduce the device switching loss as well as thermal stress on critical components caused by repetitive avalanche. The basic operation principle of SCPS is shown at the beginning of the paper with critical modes. Then, equations to describe the voltage distribution among all the SiC JEFTs have been derived. Based on the derived equations, a sizing method of the SCPS voltage balancing capacitors is proposed to accelerate the device switching speed and minimize the avalanche time of its internal diodes. Compared with no voltage balancing capacitor case, the turn-on loss of the SCPS with sized capacitors has been reduced to 9.7%. The turn-off loss has been reduced to 48.4%.


title: Dropout algorithms for recurrent neural networks

abstract: In the last decade, hardware advancements have allowed for neural networks to become much larger in size. Dropout is a popular deep learning technique which has shown to improve the performance of large neural networks. Recurrent neural networks are powerful networks specialised at solving problems which use time series data. Three different approaches to incorporating Dropout with recurrent neural networks have been suggested. However, these approaches have not been evaluated under identical experimental conditions. This article investigates the performance of these Dropout approaches using a 2D physics simulation benchmark. After applying statistical tests it was found that using Dropout did improve network performance on the benchmark. However, contrary to the literature, the Dropout approach which was expected to perform poorly, performed well, and the approach which was expected to perform well, performed poorly.


title: Design and fabrication of a shape-morphing soft pneumatic actuator: Soft robotic pad

abstract: Silicone-based soft pneumatic actuator (SPA) is one of the key interests in soft robotic research. Currently, most of the SPAs bear a similar one-dimensional rod-like shape, regardless of their design and fabrication. There are few prototypes of SPAs with two-dimensional initial shapes, however, they are basically the linear combination of several one-dimensional SPAs. This paper presents a new class of silicone-based SPA named soft robotic pad (SRP). The SRP is shaped into a soft pad with a single flat air chamber and can generate different kinds of motions according to the internal constraint matrix within the SRP. In this paper, the design and fabrication of our SRPs are elaborated in detail, followed by the preliminary characterization of the different SRP prototypes. Although the current design is not optimized, our SRP prototypes prove the feasibility of the design and fabrication of a 2D shape-morphing SPA. With subsequent optimization, the SRP can work robustly in various real-world applications as strong flat artificial muscles for human joint rehabilitation, or as fins in marine robots.


title: Image Signature: Highlighting Sparse Salient Regions

abstract: We introduce a simple image descriptor referred to as the image signature. We show, within the theoretical framework of sparse signal mixing, that this quantity spatially approximates the foreground of an image. We experimentally investigate whether this approximate foreground overlaps with visually conspicuous image locations by developing a saliency algorithm based on the image signature. This saliency algorithm predicts human fixation points best among competitors on the Bruce and Tsotsos [1] benchmark data set and does so in much shorter running time. In a related experiment, we demonstrate with a change blindness data set that the distance between images induced by the image signature is closer to human perceptual distance than can be achieved using other saliency algorithms, pixel-wise, or GIST [2] descriptor methods.


title: Anatomy of secondary features in keystroke dynamics - achieving more with less

abstract: Keystroke dynamics is an effective behavioral biometric for user authentication at a computer terminal. While many distinctive features have been used for the analysis of acquired user patterns and verification of users transparently, a group of features such as Shift and Comma has always been overlooked and treated as noise. In this paper, we define these normally ignored features as secondary features and investigate their effectiveness in user verification/authentication. By evaluating all the available secondary features, we have found that they contain valuable information that is characteristic of individuals. With a limited number of secondary features, we achieved a promising Equal Error Rate (EER) of 2.94% and Area Under the ROC Curve (AUC) of 0.9940 for classification on a publicly available data set. Surprisingly, this result compares well with the results obtained from primary features by other researchers and we are able to achieve quality results with fewer data records, indicating a reduced training time in comparison.


title: Probabilistic Neural Network with Complex Exponential Activation Functions in Image Recognition using Deep Learning Framework

abstract: If the training dataset is not very large, image recognition is usually implemented with the transfer learning methods. In these methods the features are extracted using a deep convolutional neural network, which was preliminarily trained with an external very-large dataset. In this paper we consider the nonparametric classification of extracted feature vectors with the probabilistic neural network (PNN). The number of neurons at the pattern layer of the PNN is equal to the database size, which causes the low recognition performance and high memory space complexity of this network. We propose to overcome these drawbacks by replacing the exponential activation function in the Gaussian Parzen kernel to the complex exponential functions in the Fej\'er kernel. We demonstrate that in this case it is possible to implement the network with the number of neurons in the pattern layer proportional to the cubic root of the database size. Thus, the proposed modification of the PNN makes it possible to significantly decrease runtime and memory complexities without loosing its main advantages, namely, extremely fast training procedure and the convergence to the optimal Bayesian decision. An experimental study in visual object category classification and unconstrained face recognition with contemporary deep neural networks have shown, that our approach obtains very efficient and rather accurate decisions for the small training sample in comparison with the well-known classifiers.


title: Continuous Online Sequence Learning with an Unsupervised Neural Network Model

abstract: The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) sequence memory is recently proposed as a theoretical framework for sequence learning in the cortex. In this paper, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variable-order temporal sequences using an unsupervised Hebbian-like learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM sequence memory with other sequence learning algorithms, including statistical methods: autoregressive integrated moving average (ARIMA), feedforward neural networks: online sequential extreme learning machine (ELM), and recurrent neural networks: long short-term memory (LSTM) and echo-state networks (ESN), on sequence prediction problems with both artificial and real-world data. The HTM model achieves comparable accuracy to other state-of-the-art algorithms. The model also exhibits properties that are critical for sequence learning, including continuous online learning, the ability to handle multiple predictions and branching sequences with high order statistics, robustness to sensor noise and fault tolerance, and good performance without task-specific hyper- parameters tuning. Therefore the HTM sequence memory not only advances our understanding of how the brain may solve the sequence learning problem, but is also applicable to a wide range of real-world problems such as discrete and continuous sequence prediction, anomaly detection, and sequence classification.