title: Aneka Cloud Application Platform and Its Integration with Windows Azure

abstract: Aneka is an Application Platform-as-a-Service (Aneka PaaS) for Cloud Computing. It acts as a framework for building customized applications and deploying them on either public or private Clouds. One of the key features of Aneka is its support for provisioning resources on different public Cloud providers such as Amazon EC2, Windows Azure and GoGrid. In this chapter, we will present Aneka platform and its integration with one of the public Cloud infrastructures, Windows Azure, which enables the usage of Windows Azure Compute Service as a resource provider of Aneka PaaS. The integration of the two platforms will allow users to leverage the power of Windows Azure Platform for Aneka Cloud Computing, employing a large number of compute instances to run their applications in parallel. Furthermore, customers of the Windows Azure platform can benefit from the integration with Aneka PaaS by embracing the advanced features of Aneka in terms of multiple programming models, scheduling and management services, application execution services, accounting and pricing services and dynamic provisioning services. Finally, in addition to the Windows Azure Platform we will illustrate in this chapter the integration of Aneka PaaS with other public Cloud platforms such as Amazon EC2 and GoGrid, and virtual machine management platforms such as Xen Server. The new support of provisioning resources on Windows Azure once again proves the adaptability, extensibility and flexibility of Aneka.


title: Blockchain: Data Malls, Coin Economies and Keyless Payments

abstract: We discuss several uses of blockchain (and, more generally, distributed ledger) technologies outside of cryptocurrencies with a pragmatic view. We mostly focus on three areas: the role of coin economies for what we refer to as data malls (specialized data marketplaces); data provenance (a historical record of data and its origins); and what we term keyless payments (made without having to know other users' cryptographic keys). We also discuss voting and other areas, and give a sizable list of academic and nonacademic references.


title: Impact Evaluation of a Cluster Program: An Application of Synthetic Control Methods

abstract: This paper analyzes the impact of a cluster tourism policy in the region of Colonia, Uruguay. The policy was implemented as part of an IDB-supported program. The study conducted a comparative analysis of Colonia vs. other tourism regions of the country applying a synthetic control method. This method of identifying the counterfactual is especially useful in comparative case studies where there are a limited number of control units. This is the first time that the synthetic control method hasbeen applied to a cluster policy. The estimations show a positive impact of the cluster program on the inflow of international tourists to Colonia of 30 percent in the period 2008–2015; however, no significant impact on total expenditure was found.


title: An automatic mitosis detection method for breast cancer histopathology slide images based on objective and pixel-wise textural features classification

abstract: Study of histopathological cancerous tissue is one of the most reliable ways to grade various types of cancers. The result of grading helps the physicians to diagnose and prescribe suitable prognosis. The focus of this paper is on a CAD for automatic analysis of breast cancer histopathological Images to count mitosis as an important criteria for the breast cancer grading. To achieve this aim, sets of specific digital histopathological data are used which are captured by particular microscopic scanners named as Aperio XT and Hamamatsu NanoZoomer scanners. In the proposed method, these acquired images are employed and processed based on digital image processing approaches like 2-D anisotropic diffusion as a pre-process and morphological process. For extraction of pixel-wise features from predetermined mitotic regions, an statistical approach based on color information such as maximum likelihood estimation is employed. To prevent misclassification of mitosis and non-mitosis objects, an object-wise completed local binary pattern (CLBP) is proposed to extract texture features robust against rotation and color-level changes, and finally support vector machine (SVM) is used to classify the extracted feature vectors. Having computed the evaluation criteria, our proposed method performs better f-measure (70.94% for Aperio XT scanner images and 70.11% for Hamamatsu images) among the methods proposed by other participants at ICPR2012 Mitosis detection in breast cancer histopathological images.


On-line Dialogue Policy Learning with Companion Teaching.


title: Maternal nutrition, intrauterine programming and consequential risks in the offspring

abstract: It is traditionally believed that genetic susceptibility and adult faulty lifestyle lead to type 2 diabetes, a chronic non-communicable disease. The “Developmental Origins of Health and Disease” (DOHaD) model proposes that the susceptibility to type 2 diabetes originates in the intrauterine life by environmental fetal programming, further exaggerated by rapid childhood growth, i.e. a biphasic nutritional insult. Both fetal under nutrition (sometimes manifested as low birth weight) and over nutrition (the baby of a diabetic mother) increase the risk of future diabetes. The common characteristic of these two types of babies is their high adiposity. An imbalance in nutrition seems to play an important role, and micronutrients seem particularly important. Normal to high maternal folate status coupled with low vitamin B12 status predicted higher adiposity and insulin resistance in Indian babies. Thus, 1-C (methyl) metabolism seems to play a key role in fetal programming. DOHaD represents a paradigm shift in the model for prevention of the chronic non-communicable diseases.


title: Time- and Space-Efficient Sliding Window Top-k Query Processing

abstract: A sliding window top-k (top-k/w) query monitors incoming data stream objects within a sliding window of size w to identify the k highest-ranked objects with respect to a given scoring function over time. Processing of such queries is challenging because, even when an object is not a top-k/w object at the time when it enters the processing system, it might become one in the future. Thus a set of potential top-k/w objects has to be stored in memory while its size should be minimized to efficiently cope with high data streaming rates. Existing approaches typically store top-k/w and candidate sliding window objects in a k-skyband over a two-dimensional score-time space. However, due to continuous changes of the k-skyband, its maintenance is quite costly. Probabilistic k-skyband is a novel data structure storing data stream objects from a sliding window with significant probability to become top-k/w objects in future. Continuous probabilistic k-skyband maintenance offers considerably improved runtime performance compared to k-skyband maintenance, especially for large values of k, at the expense of a small and controllable error rate. We propose two possible probabilistic k-skyband usages: (i) When it is used to process all sliding window objects, the resulting top-k/w algorithm is approximate and adequate for processing random-order data streams. (ii) When probabilistic k-skyband is used to process only a subset of most recent sliding window objects, it can improve the runtime performance of continuous k-skyband maintenance, resulting in a novel exact top-k/w algorithm. Our experimental evaluation systematically compares different top-k/w processing algorithms and shows that while competing algorithms offer either time efficiency at the expanse of space efficiency or vice-versa, our algorithms based on the probabilistic k-skyband are both time and space efficient.


title: Linguistic Individuality Transformation for Spoken Language

abstract: In text and speech, there are various features that express the individuality of the writer or speaker. In this paper, we take a step towards the creation of dialogue systems that consider this individuality by proposing a method for transforming individuality using a technique inspired by statistical machine translation (SMT). However, finding a parallel corpus with identical semantic content but different individuality is difficult, precluding the use of standard SMT techniques. Thus, in this paper, we focus on methods for creating a translation model (TM) using techniques from the paraphrasing literature and a language model (LM) by combining small amounts of individuality-rich data with larger amounts of background text. We perform an automatic and manual evaluation comparing the effectiveness of three types of TM construction techniques and find that the proposed system using a method focusing on a limited set of function words is most effective and can transform individuality to a degree that is both noticeable and identifiable.


title: Toponym Recognition in Historical Maps by Gazetteer Alignment

abstract: Historical map documents are increasingly digitized for widespread access, but most are only coarsely indexed with meta-data while the contents are largely unsearchable. We propose to increase search ability by automatically recognizing the place names in these digitized artifacts. Using a word recognition system that produces a noisy ranked list of initial hypotheses from a lexicon of viable toponyms, we form a joint probabilistic model for inferring the most likely latent alignment between image toponyms and a gazetteer of known place locations. After a robust generalized RANSAC algorithm identifies the global alignment, we rerank the toponym hypotheses by their posterior probability. Experiments demonstrate a significant boost in word recognition accuracy on a manually annotated set of 19th century U.S. state and regional maps.


title: Data conflict resolution using trust mappings

abstract: In massively collaborative projects such as scientific or community databases, users often need to agree or disagree on the content of individual data items. On the other hand, trust relationships often exist between users, allowing them to accept or reject other users' beliefs by default. As those trust relationships become complex, however, it becomes difficult to define and compute a consistent snapshot of the conflicting information. Previous solutions to a related problem, the update reconciliation problem, are dependent on the order in which the updates are processed and, therefore, do not guarantee a globally consistent snapshot. This paper proposes the first principled solution to the automatic conflict resolution problem in a community database. Our semantics is based on the certain tuples of all stable models of a logic program. While evaluating stable models in general is well known to be hard, even for very simple logic programs, we show that the conflict resolution problem admits a PTIME solution. To the best of our knowledge, ours is the first PTIME algorithm that allows conflict resolution in a principled way. We further discuss extensions to negative beliefs and prove that some of these extensions are hard. This work is done in the context of the BeliefDB project at the University of Washington, which focuses on the efficient management of conflicts in community databases.


title: Design of a hydraulic anti-lock braking system (ABS) for a motorcycle

abstract: This work presents a hydraulic anti-lock braking system (ABS) for a motorcycle. The ABS has a hydraulic modulator and an intelligent controller. The hydraulic modulator is analyzed, and then equipped on a scooter for road tests. The intelligent controller controls the hydraulic modulator by estimated vehicle velocity to calculate the slip ratio of the wheels in real time. The performance of the hydraulic modulator and intelligent controller are assessed by the hardware-in-the-loop (HIL) simulations and road tests. In HIL simulation, the ABS is tested for different initial braking velocities on roads with different adhesive coefficients. Furthermore, both HIL simulations and road tests are conducted on a one-phase pavement road and three-phase pavement road.


title: Linear Regression for Face Recognition

abstract: In this paper, we present a novel approach of face identification by formulating the pattern recognition problem in terms of linear regression. Using a fundamental concept that patterns from a single-object class lie on a linear subspace, we develop a linear model representing a probe image as a linear combination of class-specific galleries. The inverse problem is solved using the least-squares method and the decision is ruled in favor of the class with the minimum reconstruction error. The proposed Linear Regression Classification (LRC) algorithm falls in the category of nearest subspace classification. The algorithm is extensively evaluated on several standard databases under a number of exemplary evaluation protocols reported in the face recognition literature. A comparative study with state-of-the-art algorithms clearly reflects the efficacy of the proposed approach. For the problem of contiguous occlusion, we propose a Modular LRC approach, introducing a novel Distance-based Evidence Fusion (DEF) algorithm. The proposed methodology achieves the best results ever reported for the challenging problem of scarf occlusion.


title: Cys-Oxidation of Protein Tyrosine Phosphatases: Its Role in Regulation of Signal Transduction and Its Involvement in Human Cancers

abstract: Reactive oxygen species (ROS) are currently viewed as secondary messengers that control signal transduction through the post-translational modifications of Cys-oxidation in targeting proteins. The physiological levels of ROS are generated in response to stimulation induced by extracellular ligands. Due to the labile feature of ROS, the effect of protein oxidation is normally transient. Such a character is essential for the precise control of redox-dependent signaling homeostasis. Nevertheless, studies have shown that under pathological conditions, such as certain types of cancer, the cellular ROS are produced in a ligand-independent manner. Interestingly, the generation of ROS is required for the development of transformed phenotype and tumors, suggesting that the constitutive oxidation of signaling modulators might contribute to the uncontrolled signaling cascades, further leading to pathological consequences. Emerging evidence has revealed that protein tyrosine phosphatases (PTPs), which regulate tyrosine phosphorylation-dependent signal transduction through tyrosine dephosphorylation of substrates, are highly susceptible to oxidation. Interestingly, oxidation of catalytic Cys residue results in enzymatically inactivated form of PTPs, concomitant with an enhanced tyrosine phosphorylation level in cells. The primary focus of this review is to comprehend recent studies that greatly advanced our understanding towards redox-dependent regulation of cell signaling through Cys oxidation of endogenous PTPs. The rational implication of this mechanism in contributing to the development and maintenance of cell transformation is discussed.


title: Prediction of the sound pressure level of blanking noise during sheet-metal blanking using servo press with counterforce

abstract: In this research, a prediction model of the sound pressure level during blanking using servo press with counterforce was established based on the acoustic theory as well as the vibration equation of punch in the course of breakthrough process. The theoretical values of the sound pressure level of blanking noise were calculated based on the developed prediction model. The variation of the sound pressure level of blanking noise with different parameters was also investigated and compared with the experimental results. It reveals that the blanking noise increases with the shear strength and the thickness of sheet metal, and further the blanking speed. The relative error between the experimental results and the theoretical ones determined based on the prediction model is less than 1.7%, and the application example of the prediction model in batch production is given. The prediction model is thus proved to be promising and efficient for noise prediction during blanking using servo press with counterforce.


title: Forward Actor-Critic for Nonlinear Function Approximation in Reinforcement Learning

abstract: Multi-step methods are important in reinforcement learn- ing (RL). Eligibility traces, the usual way of handling them, works well with linear function approximators. Recently, van Seijen (2016) had introduced a delayed learning approach, without eligibility traces, for handling the multi-step λ-return with nonlinear function approximators. However, this was limited to action-value methods. In this paper, we extend this approach to handle n-step returns, generalize this approach to policy gradient methods and empirically study the effect of such delayed updates in control tasks. Specifically, we introduce two novel forward actor- critic methods and empirically investigate our proposed methods with the conventional actor-critic method on mountain car and pole-balancing tasks. From our experiments, we observe that forward actor-critic dramatically outperforms the conventional actor-critic in these standard control tasks. Notably, this forward actor-critic method has produced a new class of multi-step RL algorithms without eligibility traces.


title: Detection of Intelligent Mobile Target in a Mobile Sensor Network

abstract: We study the problem of a mobile target (the mouse) trying to evade detection by one or more mobile sensors (we call such a sensor a cat) in a closed network area. We view our problem as a game between two players: the mouse, and the collection of cats forming a single (meta-)player. The game ends when the mouse falls within the sensing range of one or more cats. A cat tries to determine its optimal strategy to minimize the worst case expected detection time of the mouse. The mouse tries to determine an optimal counter movement strategy to maximize the expected detection time. We divide the problem into two cases based on the relative sensing capabilities of the cats and the mouse. When the mouse has a sensing range smaller than or equal to the cats', we develop a dynamic programming solution for the mouse's optimal strategy, assuming high level information about the cats' movement model. We discuss how the cats' chosen movement model will affect its presence matrix in the network, and hence its payoff in the game. When the mouse has a larger sensing range than the cats, we show how the mouse can determine its optimal movement strategy based on local observations of the cats' movements. We further present a coordination protocol for the cats to collaboratively catch the mouse by: 1) forming opportunistically a cohort to limit the mouse's degree of freedom in escaping detection; and 2) minimizing the overlap in the spatial coverage of the cohort's members. Extensive experimental results verify and illustrate the analytical results, and evaluate the game's payoffs as a function of several important system parameters.


title: Large‐scale sparse functional networks from resting state fMRI

abstract: Abstract Delineation of large-scale functional networks (FNs) from resting state functional MRI data has become a standard tool to explore the functional brain organization in neuroscience. However, existing methods sacrifice subject specific variation in order to maintain the across-subject correspondence necessary for group-level analyses. In order to obtain subject specific FNs that are comparable across subjects, existing brain decomposition techniques typically adopt heuristic strategies or assume a specific statistical distribution for the FNs across subjects, and therefore might yield biased results. Here we present a novel data-driven method for detecting subject specific FNs while establishing group level correspondence. Our method simultaneously computes subject specific FNs for a group of subjects regularized by group sparsity, to generate subject specific FNs that are spatially sparse and share common spatial patterns across subjects. Our method is built upon non-negative matrix decomposition techniques, enhanced by a data locality regularization term that makes the decomposition robust to imaging noise and improves spatial smoothness and functional coherences of the subject specific FNs. Our method also adopts automatic relevance determination techniques to eliminate redundant FNs in order to generate a compact set of informative sparse FNs. We have validated our method based on simulated, task fMRI, and resting state fMRI datasets. The experimental results have demonstrated our method could obtain subject specific, sparse, non-negative FNs with improved functional coherence, providing enhanced ability for characterizing the functional brain of individual subjects.


title: The Collateral Channel : How Real Estate Shocks affect Corporate Investment ∗ Job Market Paper

abstract: What is the impact of real estate prices on corporate investment? In the presence of financing frictions, firms use pledgeable assets as collateral to finance new projects. Through this collateral channel, shocks to the value of real estate can have a large impact on aggregate investment. To compute the sensitivity of investment to collateral value, we use local variations in real estate prices as shocks to the collateral value of firms that own real estate. Over the 1993-2007 period, the representative US corporation invests $0.06 out of each $1 of collateral.


title: Prequential AUC: properties of the area under the ROC curve for data streams with concept drift

abstract: Modern data-driven systems often require classifiers capable of dealing with streaming imbalanced data and concept changes. The assessment of learning algorithms in such scenarios is still a challenge, as existing online evaluation measures focus on efficiency, but are susceptible to class ratio changes over time. In case of static data, the area under the receiver operating characteristics curve, or simply AUC, is a popular measure for evaluating classifiers both on balanced and imbalanced class distributions. However, the characteristics of AUC calculated on time-changing data streams have not been studied. This paper analyzes the properties of our recent proposal, an incremental algorithm that uses a sorted tree structure with a sliding window to compute AUC with forgetting. The resulting evaluation measure, called prequential AUC, is studied in terms of: visualization over time, processing speed, differences compared to AUC calculated on blocks of examples, and consistency with AUC calculated traditionally. Simulation results show that the proposed measure is statistically consistent with AUC computed traditionally on streams without drift and comparably fast to existing evaluation procedures. Finally, experiments on real-world and synthetic data showcase characteristic properties of prequential AUC compared to classification accuracy, G-mean, Kappa, Kappa M, and recall when used to evaluate classifiers on imbalanced streams with various difficulty factors.


Trusting Digital Technologies Correctly


title: High speed pipelined 64-point FFT processor based on Radix-22 for wireless LAN

abstract: This Paper presents high Speed pipeline 64-point FFT processor based on Radix-22 for wireless LAN communication systems. This method uses Radix-2 butterfly structure and Radix-22 CFA algorithm. Radix-2 butterfly's complexity is very low and Radix-22 CFA algorithm reduces number of twiddle factors compared to Radix-4 and Radix-2. An efficient VHDL code has been written, synthesized successfully using XST of Xilinx ISE 14.1 and simulated using ModelSim PE Student Edition 10.4a. Also MATLAB code has been written and simulated with MATLAB R2012a tool. The computation speed of proposed design is observed to be 158.96 MHz after the synthesis process and SQNR 37.02dB for 64 point.


title: HA2lloc: Hardware-Assisted Secure Allocator

abstract: With ever-increasing complexity of software systems, the number of reported security issues increases as well. Among them, memory corruption attacks are a prevalent vector used against today's software stacks. These attacks are repeatedly leveraged to compromise common application software, such as web browsers or document viewers. However, previous work to mitigate memory corruption attacks either suffer from high overhead or can be bypassed by a knowledgeable attacker. In this work, we introduce HA2lloc, a hardware-assisted allocator that is capable of leveraging an extended memory management unit to detect memory errors in the heap. We also perform some preliminary testing using HA2lloc in a simulation environment and find that the approach is capable of detecting and preventing common memory vulnerabilities.


title: Semi-supervised Zero-Shot Learning by a Clustering-based Approach

abstract: In some of object recognition problems, labeled data may not be available for all categories. Zero-shot learning utilizes auxiliary information (also called signatures) describing each category in order to find a classifier that can recognize samples from categories with no labeled instance. In this paper, we propose a novel semi-supervised zero-shot learning method that works on an embedding space corresponding to abstract deep visual features. We seek a linear transformation on signatures to map them onto the visual features, such that the mapped signatures of the seen classes are close to labeled samples of the corresponding classes and unlabeled data are also close to the mapped signatures of one of the unseen classes. ::: We use the idea that the rich deep visual features provide a representation space in which samples of each class are usually condensed in a cluster. The effectiveness of the proposed method is demonstrated through extensive experiments on four public benchmarks improving the state-of-the-art prediction accuracy on three of them.


title: Location Systems for Ubiquitous Computing

abstract: This survey and taxonomy of location systems for mobile-computing applications describes a spectrum of current products and explores the latest in the field. To make sense of this domain, we have developed a taxonomy to help developers of location-aware applications better evaluate their options when choosing a location-sensing system. The taxonomy may also aid researchers in identifying opportunities for new location-sensing techniques.


title: Analyzing (social media) networks with NodeXL

abstract: We present NodeXL, an extendible toolkit for network overview, discovery and exploration implemented as an add-in to the Microsoft Excel 2007 spreadsheet software. We demonstrate NodeXL data analysis and visualization features with a social media data sample drawn from an enterprise intranet social network. A sequence of NodeXL operations from data import to computation of network statistics and refinement of network visualization through sorting, filtering, and clustering functions is described. These operations reveal sociologically relevant differences in the patterns of interconnection among employee participants in the social media space. The tool and method can be broadly applied.


title: MAFIA: a maximal frequent itemset algorithm

abstract: We present a new algorithm for mining maximal frequent itemsets from a transactional database. The search strategy of the algorithm integrates a depth-first traversal of the itemset lattice with effective pruning mechanisms that significantly improve mining performance. Our implementation for support counting combines a vertical bitmap representation of the data with an efficient bitmap compression scheme. In a thorough experimental analysis, we isolate the effects of individual components of MAFIA including search space pruning techniques and adaptive compression. We also compare our performance with previous work by running tests on very different types of data sets. Our experiments show that MAFIA performs best when mining long itemsets and outperforms other algorithms on dense data by a factor of three to 30.


title: Question Generation Shared Task and Evaluation Challenge - Status Report

abstract: The First Shared Task Evaluation Challenge on Question Generation took place in 2010 as part of the 3rd workshop on Question Generation. The campaign included two tasks: Question Generation from Sentences and Question Generation from Paragraphs. This status report briefly summarizes the motivation, tasks and results. Lessons learned relevant to future QG-STECs are also offered.


title: Behavioral Interpretations of Intrinsic Connectivity Networks

abstract: An increasingly large number of neuroimaging studies have investigated functionally connected networks during rest, providing insight into human brain architecture. Assessment of the functional qualities of resting state networks has been limited by the task-independent state, which results in an inability to relate these networks to specific mental functions. However, it was recently demonstrated that similar brain networks can be extracted from resting state data and data extracted from thousands of task-based neuroimaging experiments archived in the BrainMap database. Here, we present a full functional explication of these intrinsic connectivity networks at a standard low order decomposition using a neuroinformatics approach based on the BrainMap behavioral taxonomy as well as a stratified, data-driven ordering of cognitive processes. Our results serve as a resource for functional interpretations of brain networks in resting state studies and future investigations into mental operations and the tasks that drive them.


title: Challenges of implementing e-learning in Kenya: A case of Kenyan public universities

abstract: In this paper, we discuss the challenges experienced by Kenyan public universities in implementation of e-learning and recommend possible solutions towards its successful implementation. In the last few years, most Kenyan public universities have adopted e-learning as a new approach to teaching and learning. However, the implementation challenges faced by these universities have continued to impact negatively on its effective utilization. This paper presents the findings from a survey of 148 staff of three Kenyan public universities who are currently using e-learning in blended mode approach. The purpose of this study was to investigate the challenges hindering the implementation of e-learning in Kenyan public universities. Data was collected through questionnaires, in-depth interviews and document analysis. The findings reveal that e-learning comes with some challenges that must be addressed by Kenyan public universities before successful implementation can be realized. However, the benefits and opportunities presented by e-learning far outweigh the challenges. The paper finally recommends some possible solutions that public universities could embrace towards successful implementation of e-learning.


title: User privacy-preserving identity data dependencies

abstract: Identity Federation technologies have enabled users to leverage their relationships with an Identity Provider (IdP) into a Service Provider's (SP) domain. They allow user-initiated and IdP-controlled sharing of authentication information, attributes and authorization policies, allowing users to get benefits like Single Sign On (SSO) and attribute linking across the different domains. Federation-based Identity Services have enabled a standardized mechanism of sharing a particular type of user identity information with interested SPs. Yet, with increasing focus on composite as well as personalized user experiences, different types of User Identity Data need to be used together. In this paper, we argue that there is a lack of standardized mechanisms for resolution and ownership, when it comes to data associations across different Identity Providers. Additionally, users have different privacy requirements for these different kinds of interacting identity information and need mechanisms to enforce them. We propose a solution which allows users to define privacy-preserving data dependencies between their different Identity information. Thus, a query for a particular user information, would honor and traverse its associated data dependencies, possibly triggering user-defined policies, to come up with a resultant set of identity information.


title: Think Locally, Act Globally: Highly Balanced Graph Partitioning

abstract: We present a novel local improvement scheme for graph partitions that allows to enforce strict balance constraints. Using negative cycle detection algorithms this scheme combines local searches that individually violate the balance constraint into a more global feasible improvement. We combine this technique with an algorithm to balance unbalanced solutions and integrate it into a parallel multi-level evolutionary algorithm, KaFFPaE, to tackle the problem. Overall, we obtain a system that is fast on the one hand and on the other hand is able to improve or reproduce many of the best known perfectly balanced partitioning results reported in the Walshaw benchmark.


title: Combing Context and Commonsense Knowledge Through Neural Networks for Solving Winograd Schema Problems.

abstract: This paper proposes a general framework to combine context and commonsense knowledge for solving the Winograd Schema (WS) and Pronoun Disambiguation Problems (PDP). In the proposed framework, commonsense knowledge bases (e.g. cause-effect word pairs) are quantized as knowledge constraints. The constraints guide us to learn knowledge enhanced embeddings (KEE) from large text corpus. Based on the pre-trained KEE models, this paper proposes two methods to solve the WS and PDP problems. The first method is an unsupervised method, which represents all the pronouns and candidate mentions in continuous vector spaces based on their contexts and calculates the semantic similarities between all the possible word pairs. The pronoun disambiguation procedure could then be implemented by comparing the semantic similarities between the pronoun (to be resolved) and all the candidate mentions. The second method is a supervised method, which extracts features for all the pronouns and candidate mentions and solves the WS problems by training a typical mention pair classification model. Similar to the first method, the features used in the second method are also extracted based on the KEE models. Experiments conducted on the available PDP and WS test sets show that, these two methods both achieve consistent improvements over the baseline systems. The best performance reaches 62\% in accuracy on the PDP test set of the first Winograd Schema Challenge.


title: A Methodology for Realizing High Efficiency Class-J in a Linear and Broadband PA

abstract: The design and implementation of a class-J mode RF power amplifier is described. The experimental results indicate the class-J mode's potential in achieving high efficiency across extensive bandwidth, while maintaining predistortable levels of linearity. A commercially available 10 W GaN (gallium nitride) high electron mobility transistor device was used in this investigation, together with a combination of high power waveform measurements, active harmonic load-pull and theoretical analysis of the class-J mode. Targeting a working bandwidth of 1.5-2.5 GHz an initial power amplifier (PA) design was based on basic class-J theory and computer-aided design simulation. This realized a 50% bandwidth with measured drain efficiency of 60%-70%. A second PA design iteration has realized near-rated output power of 39 dBm and improved efficiency beyond the original 2.5 GHz target, hence extending efficient PA operation across a bandwidth of 1.4-2.6 GHz, centered at 2 GHz. This second iteration made extensive use of active harmonic load-pull and waveform measurements, and incorporated a novel design methodology for achieving predistortable linearity. The class-J amplifier has been found to be more realizable than conventional class-AB modes, with a better compromise between power and efficiency tradeoffs over a substantial RF bandwidth.


title: Comparing User Simulation Models For Dialog Strategy Learning

abstract: This paper explores what kind of user simulation model is suitable for developing a training corpus for using Markov Decision Processes (MDPs) to automatically learn dialog strategies. Our results suggest that with sparse training data, a model that aims to randomly explore more dialog state spaces with certain constraints actually performs at the same or better than a more complex model that simulates realistic user behaviors in a statistical way.


title: Fingerprint attack against touch-enabled devices

abstract: Oily residues left by tapping fingers on a touch screen may breach user privacy. In this paper, we introduce the fingerprint attack against touch-enabled devices. We dust the touch screen surface to reveal fingerprints, and use an iPhone camera to carefully photograph fingerprints while striving to remove the virtual image of the phone from the fingerprint image. We then sharpen the fingerprints in an image via various image processing techniques and design effective algorithms to automatically map fingerprints to a keypad in order to infer tapped passwords. Extensive experiments were conducted on iPad, iPhone and Android phone and the results show that the fingerprint attack is effective and efficient in inferring passwords from fingerprint images. To the best of our knowledge, we are the first using fingerprint powder on touch screen and inferring passwords from fingerprints. Video at http://www.youtube.com/watch?v=vRUbJIcV9vg shows the dusting process on iPhone and video at http://www.youtube.com/watch?v=6jS6KroER3Y shows the dusting process on iPad. After dusting, password characters for login are clearly disclosed.


title: Intent-Aware Diversification Using a Constrained PLSA

abstract: The intent-aware diversification framework was introduced initially in information retrieval and adopted to the context of recommender systems in the work of Vargas et al. The framework considers a set of aspects associated with items to be recommended. For instance, aspects may correspond to genres in movie recommendations. The framework depends on input aspect model consisting of item selection or relevance probabilities, given an aspect, and user intents, in the form of probabilities that the user is interested in each aspect. In this paper, we examine a number of input aspect models and evaluate the impact that different models have on the framework. In particular, we propose a constrained PLSA model that allows for interpretable output, in terms of known aspects, while achieving greater performance that the explicit co-occurrence counting method used in previous work. We evaluate the proposed models using a well-known MovieLens dataset for which item genres are available.


title: Implementing a Collaborative Protocol in a Sepsis Intervention Program: Lessons Learned

abstract: The objective of this prospective cohort study was to see the effect of the implementation of a Sepsis Intervention Program on the standard processes of patient care using a collaborative approach between the Emergency Department (ED) and Medical Intensive Care Unit (MICU). This was performed in a large urban tertiary-care hospital, with no previous experience utilizing a specific intervention program as routine care for septic shock and which has services and resources commonly available in most hospitals. The study included 106 patients who presented to the ED with severe sepsis or septic shock. Eighty-seven of those patients met the inclusion criteria for complete data analysis. The ED and MICU staff underwent a 3-month training period followed by implementation of a protocol for sepsis intervention program over 6 months. In the first 6 months of the program's implementation, 106 patients were admitted to the ED with severe sepsis and septic shock. During this time, the ED attempted to initiate the sepsis intervention protocol in 76% of the 87 septic patients who met the inclusion criteria. This was assessed by documentation of a central venous catheter insertion for continuous SvO(2) monitoring in a patient with sepsis or septic shock. However, only 48% of the eligible patients completed the early goal-directed therapy (EGDT) protocol. Our data showed that the in-hospital mortality rate was 30.5% for the 87 septic shock patients with a mean APACHE II score of 29. This was very similar to a landmark study of EGDT (30.5% mortality with mean APACHE II of 21.5). Data collected on processes of care showed improvements in time to fluid administration, central venous access insertion, antibiotic administration, vasopressor administration, and time to MICU transfer from ED arrival in our patients enrolled in the protocol versus those who were not. Further review of our performance data showed that processes of care improved steadily the longer the protocol was in effect, although this was not statistically significant. There was no improvement in secondary outcomes, including total length of hospital stay, MICU days, and mortality. Implementation of a sepsis intervention program as a standard of care in a typical hospital protocol leads to improvements in processes of care. However, despite a collaborative approach, the sepsis intervention program was underutilized with only 48% of the patients completing the sepsis intervention protocol.


title: The prediction of professional behaviour

abstract: Objective The purpose of this study was to establish outcome measures for professionalism in medical students and to identify predictors of these outcomes. ::: ::: Design Retrospective cohort study. ::: ::: Setting A US medical school. ::: ::: Participants All students entering in 1995 and graduating within 5 years. ::: ::: Measures Outcome measures included review board identification of professionalism problems and clerkship evaluations for items pertaining to professionalism. Pre-clinical predictor variables included material from the admissions application, completion of required course evaluations, students' self-reporting of immunisation compliance, students' performance on standardised patient (SP) exercises, and students' self-assessed performance on SP exercises. ::: ::: Results The outcome measures of clerkship professionalism scores were found to be highly reliable (alpha 0.88–0.96). No data from the admissions material was found to be predictive of professional behaviour in the clinical years. Using multivariate regression, failing to complete required course evaluations (B = 0.23) and failing to report immunisation compliance (B = 0.29) were significant predictors of unprofessional behaviour found by the review board in subsequent years. Immunisation non-compliance predicted low overall clerkship professional evaluation scores (B = − 0.34). Student self-assessment accuracy (SP score minus self-assessed score) (B = 0.03) and immunisation non-compliance (B = 0.54) predicted the internal medicine clerkship professionalism score. ::: ::: Conclusions This study identifies a set of reliable, context-bound outcome measures in professionalism. Although we searched for predictors of behaviour in the admissions application and other domains commonly felt to be predictive of professionalism, we found significant predictors only in domains where students had had opportunities to demonstrate conscientious behaviour or humility in self-assessment.


title: Sleeping beauties in meme diffusion

abstract: A sleeping beauty in diffusion indicates that the information, can be ideas or innovations, will experience a hibernation before a sudden spike of popularity and it is widely found in citation history of scientific publications. However, in this study, we demonstrate that the sleeping beauty is an interesting and unexceptional phenomenon in information diffusion and even more inspiring, there exist two consecutive sleeping beauties in the entire lifetime of propagation, suggesting that the information, including scientific topics, search queries or Wikipedia entries, which we call memes, will go unnoticed for a period and suddenly attracts some attention, and then it falls asleep again and later wakes up with another unexpected popularity peak. Further explorations on this phenomenon show that intervals between two wake ups follow an exponential distribution and the second wake up generally reaches its peak at a higher velocity. In addition, higher volume of the first wake up will lead to even much higher popularity of the second wake up with great odds. Taking these findings into consideration, an upgraded Bass model is presented to well describe the diffusion dynamics of memes on different media. Our results can help understand the common mechanism behind propagation of different memes and are instructive to locate the tipping point in marketing or find innovative publications in science.


title: Revisiting Simple Neural Networks for Learning Representations of Knowledge Graphs

abstract: We address the problem of learning vector representations for entities and relations in Knowledge Graphs (KGs) for Knowledge Base Completion (KBC). This problem has received significant attention in the past few years and multiple methods have been proposed. Most of the existing methods in the literature use a predefined characteristic scoring function for evaluating the correctness of KG triples. These scoring functions distinguish correct triples (high score) from incorrect ones (low score). However, their performance vary across different datasets. In this work, we demonstrate that a simple neural network based score function can consistently achieve near start-of-the-art performance on multiple datasets. We also quantitatively demonstrate biases in standard benchmark datasets, and highlight the need to perform evaluation spanning various datasets.


title: Robotics, Vision and Control: Fundamental Algorithms in MATLAB

abstract: The practice of robotics and computer vision both involve the application of computational algorithms to data. Over the fairly recent history of the fields of robotics and computer vision a very large body of algorithms has been developed. However this body of knowledge is something of a barrier for anybody entering the field, or even looking to see if they want to enter the field What is the right algorithm for a particular problem?, and importantly, How can I try it out without spending days coding and debugging it from the original research papers? The author has maintained two open-source MATLAB Toolboxes for more than 10 years: one forrobotics and one for vision. The key strength of the Toolboxes provide a set of tools that allow the user to work with real problems, not trivial examples. For the student the book makes the algorithms accessible, the Toolbox code can be read to gain understanding, and the examples illustrate how it can be used instant gratification in just a couple of lines of MATLAB code. The code can also be the starting point for new work, for researchers or students, by writing programs based on Toolbox functions, or modifying the Toolbox code itself. The purpose of this book is to expand on the tutorial material provided with the toolboxes, add many more examples, and to weave this into a narrative that covers robotics and computer vision separately and together. The author shows how complex problems can be decomposed and solved using just a few simple lines of code, and hopefully to inspire up and coming researchers. The topics covered are guided by the real problems observed over many years as a practitioner of both robotics and computer vision. It is written in a light but informative style, it is easy to read and absorb, and includes a lot of Matlab examples and figures. The book is a real walk through the fundamentals of robot kinematics, dynamics and joint level control, then camera models, image processing, feature extraction and epipolar geometry, and bring it all together in avisual servo system. Additional material is provided athttp://www.petercorke.com/RVC


title: Sentiment analysis of Bengali comments with Word2Vec and sentiment information of words

abstract: The vector representation of Bengali words using word2vec model (Mikolov et al. (2013)) plays an important role in Bengali sentiment classification. It is observed that the words that are from same context stay closer in the vector space of word2vec model and they are more similar than other words. In this article, a new approach of sentiment classification of Bengali comments with word2vec and Sentiment extraction of words are presented. Combining the results of word2vec word co-occurrence score with the sentiment polarity score of the words, the accuracy obtained is 75.5%.


title: Randomized instruction set emulation

abstract: Injecting binary code into a running program is a common form of attack. Most defenses employ a “guard the doors” approach, blocking known mechanisms of code injection. Randomized instruction set emulation (RISE) is a complementary method of defense, one that performs a hidden randomization of an application's machine code. If foreign binary code is injected into a program running under RISE, it will not be executable because it will not know the proper randomization. The paper describes and analyzes RISE, describing a proof-of-concept implementation built on the open-source Valgrind IA32-to-IA32 translator. The prototype effectively disrupts binary code injection attacks, without requiring recompilation, linking, or access to application source code. Under RISE, injected code (attacks) essentially executes random code sequences. Empirical studies and a theoretical model are reported which treat the effects of executing random code on two different architectures (IA32 and PowerPC). The paper discusses possible extensions and applications of the RISE technique in other contexts.


title: Spectral basis neural networks for real-time travel time forecasting

abstract: This paper examines how real-time information gathered as part of intelligent transportation systems can be used to predict link travel times for one through five time periods ahead (of 5-min duration). The study employed a spectral basis artificial neural network (SNN) that utilizes a sinusoidal transformation technique to increase the linear separability of the input features. Link travel times from Houston that had been collected as part of the automatic vehicle identification system of the TranStar system were used as a test bed. It was found that the SNN outperformed a conventional artificial neural network and gave similar results to that of modular neural networks. However, the SNN requires significantly less effort on the part of the modeler than modular neural networks. The results, of the best SNN were compared with conventional link travel time prediction techniques including a Kalman filtering model, exponential smoothing model, historical profile, and real-time profile. It was found that the SNN gave the best overall results.


title: Recurrent Attentional Networks for Saliency Detection

abstract: Convolutional-deconvolution networks can be adopted to perform end-to-end saliency detection. But, they do not work well with objects of multiple scales. To overcome such a limitation, in this work, we propose a recurrent attentional convolutional-deconvolution network (RACDNN). Using spatial transformer and recurrent network units, RACDNN is able to iteratively attend to selected image sub-regions to perform saliency refinement progressively. Besides tackling the scale problem, RACDNN can also learn context-aware features from past iterations to enhance saliency refinement in future iterations. Experiments on several challenging saliency detection datasets validate the effectiveness of RACDNN, and show that RACDNN outperforms state-of-the-art saliency detection methods.


title: GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descent

abstract: In this paper, we present GossipGraD - a gossip communication protocol based Stochastic Gradient Descent (SGD) algorithm for scaling Deep Learning (DL) algorithms on large-scale systems. The salient features of GossipGraD are: 1) reduction in overall communication complexity from {\Theta}(log(p)) for p compute nodes in well-studied SGD to O(1), 2) model diffusion such that compute nodes exchange their updates (gradients) indirectly after every log(p) steps, 3) rotation of communication partners for facilitating direct diffusion of gradients, 4) asynchronous distributed shuffle of samples during the feedforward phase in SGD to prevent over-fitting, 5) asynchronous communication of gradients for further reducing the communication cost of SGD and GossipGraD. We implement GossipGraD for GPU and CPU clusters and use NVIDIA GPUs (Pascal P100) connected with InfiniBand, and Intel Knights Landing (KNL) connected with Aries network. We evaluate GossipGraD using well-studied dataset ImageNet-1K (~250GB), and widely studied neural network topologies such as GoogLeNet and ResNet50 (current winner of ImageNet Large Scale Visualization Research Challenge (ILSVRC)). Our performance evaluation using both KNL and Pascal GPUs indicates that GossipGraD can achieve perfect efficiency for these datasets and their associated neural network topologies. Specifically, for ResNet50, GossipGraD is able to achieve ~100% compute efficiency using 128 NVIDIA Pascal P100 GPUs - while matching the top-1 classification accuracy published in literature.


title: Generating Diverse and Accurate Visual Captions by Comparative Adversarial Learning

abstract: We study how to generate captions that are not only accurate in describing an image but also discriminative across different images. The problem is both fundamental and interesting, as most machine-generated captions, despite phenomenal research progresses in the past several years, are expressed in a very monotonic and featureless format. While such captions are normally accurate, they often lack important characteristics in human languages - distinctiveness for each caption and diversity for different images. To address this problem, we propose a novel conditional generative adversarial network for generating diverse captions across images. Instead of estimating the quality of a caption solely on one image, the proposed comparative adversarial learning framework better assesses the quality of captions by comparing a set of captions within the image-caption joint space. By contrasting with human-written captions and image-mismatched captions, the caption generator effectively exploits the inherent characteristics of human languages, and generates more discriminative captions. We show that our proposed network is capable of producing accurate and diverse captions across images.


title: Ghosts in the Machine. Interoceptive Modeling for Chronic Pain Treatment

abstract: Pain is a complex and multidimensional perception, embodied in our daily experiences through interoceptive appraisal processes. The article reviews the recent literature about interoception along with predictive coding theories and tries to explain a missing link between the sense of the physiological condition of the entire body and the perception of pain in chronic conditions, which are characterized by interoceptive deficits. Understanding chronic pain from an interoceptive point of view allows us to better comprehend the multidimensional nature of this specific organic information, integrating the input of several sources from Gifford’s Mature Organism Model to Melzack’s neuromatrix. The article proposes the concept of residual interoceptive images (ghosts), to explain the diffuse multilevel nature of chronic pain perceptions. Lastly, we introduce a treatment concept, forged upon the possibility to modify the interoceptive chronic representation of pain through external input in a process that we call interoceptive modelling, with the ultimate goal of reducing pain in chronic subjects.


title: An evaluation framework for 360-degree video compression

abstract: 360-degree video is emerging as a new way of offering immersive visual experience. 360-degree video can be viewed on dedicated head mounted devices as well as on conventional 2D displays. Due to increased resolution to support wide field of view, efficient compression of 360-degree video becomes crucial. Whereas it is of significant interest to evaluate how different projection formats impact the compression efficiency of 360-degree video, a main technical challenge is that the input video is captured in a given native projection format, and that the native format has an obvious edge over other projection formats. In this paper, an evaluation framework is proposed to reduce the bias towards the native projection format when comparing different projection formats and their impact on 360-degree video compression. Additionally, a quality metric called area weighted spherical PSNR (AW-SPSNR) is proposed for objective 360-degree video quality evaluation. The proposed evaluation framework was included in the common test conditions defined for the exploration work of 360-degree video compression technologies under the Joint Video Exploration Team (JVET).


title: Interactive Character Animation Using Simulated Physics: A State-of-the-Art Review

abstract: Physics simulation offers the possibility of truly responsive and realistic animation. Despite wide adoption of physics simulation for the animation of passive phenomena, such as fluids, cloths and rag-doll characters, commercial applications still resort to kinematics-based approaches for the animation of actively controlled characters. However, following a renewed interest in the use of physics simulation for interactive character animation, many recent publications demonstrate tremendous improvements in robustness, visual quality and usability. We present a structured review of over two decades of research on physics-based character animation, as well as point out various open research areas and possible future directions. © 2012 Wiley Periodicals, Inc.


title: A State-of-the-Art Survey on Real-Time Issues in Embedded Systems Virtualization

abstract: Virtualization has gained great acceptance in the server and cloud computing arena. In recent years, it has also been widely applied to real-time embedded systems with stringent timing constraints. We present a comprehensive survey on real-time issues in virtualization for embedded systems, covering popular virtualization systems including KVM, Xen, L4 and others.


title: Rebar automatically counting on the product line

abstract: In order to realize the automation of rebar counting on the production line, this paper analyzes the advantages and the disadvantages of existing solutions. Combining their dominances, this paper presents a new solution. This solution captures the radius of normal rebar according to maximal internal tangent circle algorithm. With the radius, modified gradient Hough circle transform and a series of corresponding operators are used so as to locate rebar. In the end, this paper proposes a set of tracing and counting algorithm which is simple and effective. Hundreds of experiments proved that the failure rate of this solution is extremely low. With elaborate parameters, the failure rate of this system can be reduced to 0.01 percent while producing small diameter rebar in quick pace.


title: Design of Resistive Synaptic Array for Implementing On-Chip Sparse Learning

abstract: The resistive cross-point array architecture has been proposed for on-chip implementation of weighted sum and weight update operations in neuro-inspired learning algorithms. However, several limiting factors potentially hamper the learning accuracy, including the nonlinearity and device variations in weight update, and the read noise, limited ON/OFF weight ratio and array parasitics in weighted sum. With unsupervised sparse coding as a case study algorithm, this paper employs device-algorithm co-design methodologies to quantify and mitigate the impact of these non-ideal properties on the accuracy. Our analysis shows that the realistic properties in weight update are tolerable, while those in weighted sum are detrimental to the accuracy. With calibration of realistic synaptic behaviors from experimental data, our study shows that the recognition accuracy of MNIST handwriting digits degrades from ∼96 to ∼30 percent. The strategies to mitigate this accuracy loss include 1) redundant cells to alleviate the impact of device variations; 2) a dummy column to eliminate the off-state current; and 3) selector and larger wire width to reduce IR drop along interconnects. The selector also reduces the leakage power in weight update. With improved properties by these strategies, the accuracy increases back to ∼95 percent, enabling reliable integration of realistic synaptic devices in neuromorphic systems.


title: A review of waste management practices and their impact on human health.

abstract: This work reviews (i) the most recent information on waste arisings and waste disposal options in the world, in the European Union (EU), in Organisation for Economic Co-operation and Development (OEDC) countries, and in some developing countries (notably China) and (ii) the potential direct and indirect impact of waste management activities on health. Though the main focus is primarily on municipal solid waste (MSW), exposure to bioaerosols from composting facilities and to pathogens from sewage treatment plants are considered. The reported effects of radioactive waste are also briefly reviewed. Hundreds of epidemiological studies reported on the incidence of a wide range of possible illnesses on employees of waste facilities and on the resident population. The main conclusion of the overall assessment of the literature is that the evidence of adverse health outcomes for the general population living near landfill sites, incinerators, composting facilities and nuclear installations is usually insufficient and inconclusive. There is convincing evidence of a high risk of gastrointestinal problems associated with pathogens originating at sewage treatment plants. In order to improve the quality and usefulness of epidemiological studies applied to populations residing in areas where waste management facilities are located or planned, preference should be given to prospective cohort studies of sufficient statistical power, with access to direct human exposure measurements, and supported by data on health effect biomarkers and susceptibility biomarkers.


title: How to Estimate and Interpret Various Effect Sizes.

abstract: The present article presents a tutorial on how to estimate and interpret various effect sizes. The 5th edition of the Publication Manual of the American Psychological Association (2001) described the failure to report effect sizes as a “defect” (p. 5), and 23 journals have published author guidelines requiring effect size reporting. Although dozens of effect size statistics have been available for some time, many researchers were trained at a time when effect sizes were not emphasized, or perhaps even taught. Consequently, some readers may appreciate a review of how to estimate and interpret various effect sizes. In addition to the tutorial, the authors recommend effect size interpretations that emphasize direct and explicit comparisons of effects in a new study with those reported in the prior related literature, with a focus on evaluating result replicability. For decades, statistical significance has been the norm for evaluating results. In fact, little change has occurred since Carver (1993) noted: “A quick perusal of research journals, educational and psychological statistic textbooks, and doctoral dissertations will confirm that tests of statistical significance continue to dominate the interpretation of quantitative data in social science research” (p. 294). Although statistical significance “evaluates the probability or likelihood of the sample results, given the sample size, and assuming that the sample came from a population in which the null hypothesis is exactly true” (Thompson, 2003, p. 7), statistical


title: A 0.6–3.8 GHz GaN Power Amplifier Designed Through a Simple Strategy

abstract: This letter presents the design strategy for an ultra-wideband, high-efficiency hybrid power amplifier based on a commercial GaN-HEMT. The measurement results demonstrate a state-of-the-art fractional bandwidth of 145.5%, with saturated output power higher than 10 W from 0.6 to 3.8 GHz and power added efficiency exceeding 46% in the whole band, thus covering most of the mobile frequencies and making this device suitable for small-base station applications. The simple design approach exploits a $N$ -section transformer, and allows for a priori estimation of the bandwidth: in the proposed case, a good agreement between estimated and measured bandwidth is obtained.


title: Effects of low birth weight on time to BCG vaccination in an urban poor settlement in Nairobi, Kenya: an observational cohort study

abstract: The World Health Organization recommends Bacillus Calmette-Guérin (BCG) vaccination against tuberculosis be given at birth. However, in many developing countries, pre-term and low birth weight infants get vaccinated only after they gain the desired weight. In Kenya, the ministry of health recommends pre-term and low birth weight infants to be immunized at the time of discharge from hospital irrespective of their weight. This paper seeks to understand the effects of birth weight on timing of BCG vaccine. The study was conducted in two Nairobi urban informal settlements, Korogocho and Viwandani which hosts the Nairobi Urban Health and Demographic Surveillance system. All infants born in the study area since September 2006 were included in the study. Data on immunization history and birth weight of the infant were recorded from child’s clinic card. Follow up visits were done every four months to update immunization status of the child. A total of 3,602 infants were included in this analysis. Log normal accelerated failure time parametric model was used to assess the association between low birth weight infants and time to BCG immunization. In total, 229 (6.4%) infants were low birth weight. About 16.6% of the low birth weight infants weighed less than 2000 grams and 83.4% weighed between 2000 and 2490 grams. Results showed that, 60% of the low birth weight infants received BCG vaccine after more than five weeks of life. Private health facilities were less likely to administer a BCG vaccine on time compared to public health facilities. The effects of low birth weight on females was 0.60 and 0.97-times that of males for infants weighing 2000–2499 grams and for infants weighing <2000 grams respectively. The effect of low birth weight among infants born in public health facilities was 1.52 and 3.94-times that of infants delivered in private health facilities for infants weighing 2000–2499 grams and those weighing < 2000 grams respectively. Low birth weight infants received BCG immunization late compared to normal birth weight infants. Low birth weight infants delivered in public health facilities were more likely to be immunized much later compared to private health facilities.


title: Brain regions with mirror properties: A meta-analysis of 125 human fMRI studies

abstract: Mirror neurons in macaque area F5 fire when an animal performs an action, such as a mouth or limb movement, and also when the animal passively observes an identical or similar action performed by another individual. Brain-imaging studies in humans conducted over the last 20 years have repeatedly attempted to reveal analogous brain regions with mirror properties in humans, with broad and often speculative claims about their functional significance across a range of cognitive domains, from language to social cognition. Despite such concerted efforts, the likely neural substrates of these mirror regions have remained controversial, and indeed the very existence of a distinct subcategory of human neurons with mirroring properties has been questioned. Here we used activation likelihood estimation (ALE), to provide a quantitative index of the consistency of patterns of fMRI activity measured in human studies of action observation and action execution. From an initial sample of more than 300 published works, data from 125 papers met our strict inclusion and exclusion criteria. The analysis revealed 14 separate clusters in which activation has been consistently attributed to brain regions with mirror properties, encompassing 9 different Brodmann areas. These clusters were located in areas purported to show mirroring properties in the macaque, such as the inferior parietal lobule, inferior frontal gyrus and the adjacent ventral premotor cortex, but surprisingly also in regions such as the primary visual cortex, cerebellum and parts of the limbic system. Our findings suggest a core network of human brain regions that possess mirror properties associated with action observation and execution, with additional areas recruited during tasks that engage non-motor functions, such as auditory, somatosensory and affective components.


title: Gamification as a Disruptive Factor in Software Process Improvement Initiatives

abstract: For any Software Process Improvement (SPI) initiative to succeed human factors, in particular, motivation and commitment of the people involved should be kept in mind. In fact, Organizational Change Management (OCM) has been identified as an essential knowledge area for any SPI initiative. However, enough attention is still not given to the human factors and therefore, the high degree of failures in the SPI initiatives is directly linked to a lack of commitment and motivation. Gamification discipline allows us to define mechanisms that drive people's motivation and commitment towards the development of tasks in order to encourage and accelerate the acceptance of an SPI initiative. In this paper, a gamification framework oriented to both organization needs and software practitioners groups involved in an SPI initiative is defined. This framework tries to take advantage of the transverse nature of gamification in order to apply its Critical Success Factors (CSF) to the organizational change management of an SPI. Gamification framework guidelines have been validated by some qualitative methods. Results show some limitations that threaten the reliability of this validation. These require further empirical validation of a software organization.


title: Sketch retrieval via local dense stroke features

abstract: Sketch retrieval aims at retrieving the most similar sketches from a large database based on one hand-drawn query. Successful retrieval hinges on an effective representation of sketch images and an efficient search method. In this paper, we propose a representation scheme which takes sketch strokes into account with local features, thereby facilitating efficient retrieval with codebooks. Stroke features are detected via densely sampled points on stroke lines with crucial corners as anchor points, from which local gradients are enhanced and described by a quantized histogram of gradients. A codebook is organized in a hierarchical vocabulary tree, which maintains structural information of visual words and enables efficient retrieval in sub-linear time. Experimental results on three data sets demonstrate the merits of the proposed algorithm for effective and efficient sketch retrieval. Display Omitted


title: Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors

abstract: Solving the cold-start problem in review spam detection is an urgent and significant task. It can help the on-line review websites to relieve the damage of spammers in time, but has never been investigated by previous work. This paper proposes a novel neural network model to detect review spam for the cold-start problem, by learning to represent the new reviewers’ review with jointly embedded textual and behavioral information. Experimental results prove the proposed model achieves an effective performance and possesses preferable domain-adaptability. It is also applicable to a large-scale dataset in an unsupervised way.


title: Quantum Chemical Study of the Initial Step of Ozone Addition to the Double Bond of Ethylene

abstract: The mechanisms of the initial step in chemical reaction between ozone and ethylene were studied by multireference perturbation theory methods (MRMP2, CASPT2, NEVPT2, and CIPT2) and density functional theory (OPW91, OPBE, and OTPSS functionals). Two possible reaction channels were considered: concerted addition through the symmetric transition state (Criegee mechanism) and stepwise addition by the biradical mechanism (DeMore mechanism). Predicted structures of intermediates and transition states, the energies of elementary steps, and activation barriers were reported. For the rate-determining steps of both mechanisms, the full geometry optimization of stationary points was performed at the CASPT2/cc-pVDZ theory level, and the potential energy surface profiles were constructed at the MRMP2/cc-pVTZ, NEVPT2/cc-pVDZ, and CIPT2/cc-pVDZ theory levels. The rate constants and their ratio for reaction channels calculated for both mechanisms demonstrate that the Criegee mechanism is predominant for this reaction. These results are also in agreement with the experimental data and previous computational results. The structure of DeMore prereactive complex is reported here for the first time at the CCSD(T)/cc-pVTZ and CASPT2/cc-pVDZ levels. Relative stability of the complexes and activation energies were refined by single-point energy calculations at the CCSD(T)-F12/VTZ-F12 level. The IR shifts of ozone bands due to formation of complexes are presented and discussed.


title: Directional Resolution: The Davis-Putnam Procedure, Revisited

abstract: Abstract The paper presents algorithm directional resolution, a variation on the original Davis-Putnam algorithm, and analyzes its worst-case behavior as a function of the topological structure of the theories. The notions of induced width and diversity are shown to play a key role in bounding the complexity of the procedure. The importance of our analysis lies in highlighting structure-based tractable classes of satisfiability and in providing theoretical guarantees on the time and space complexity of the algorithm. Contrary to previous assessments, we show that for many theories directional resolution could be an effective procedure. Our empirical tests confirm theoretical prediction, showing that on problems with special structures, like chains, directional resolution greatly outperforms one of the most effective satisfiability algorithm known to date, namely the popular Davis-Putnam procedure.


An Object Oriented Role-based Access Control Model for Secure Domain Environments


title: A novel modular dual-active-bridge (MDAB) dc-dc converter with dc fault ride-through capability for battery energy storage systems

abstract: This paper proposes a novel modular dual-active-bridge (DAB) dc-dc converter for spilt-battery energy storage system (BESS) in medium-voltage dc (MVDC) grid application. Compared to modular cascaded DAB converter, the proposed topology has current-fed DAB characteristics, with directly dc current control to achieve dc fault ride-through capability. In addition, the proposed converter exhibits favorite features of DAB converter such as galvanic isolation, soft-switching condition and small passive components. The operating principle is described in details, and the corresponding control system is developed to realize the power flow and balancing control. A case study of 500 kW, 20 kHz BESS based on proposed converter is simulated for validation.


title: A review of plants used in divination in southern Africa and their psychoactive effects

abstract: Numerous indigenous healing traditions around the world employ plants with psychoactive effects to facilitate divination and other spiritual healing rituals. Southern Africa has thus far been considered to have relatively few psychoactive plant species of cultural importance, and little has been published on the subject. This paper reports on 85 species of plants that are used for divination by southern Bantu-speaking people. Of these, 39 species (45 %) have other reported psychoactive uses, and a number have established hallucinogenic activity. These findings indicate that psychoactive plants have an important role in traditional healing practices in southern Africa.


title: Democratizing Production-Scale Distributed Deep Learning

abstract: The interest and demand for training deep neural networks have been experiencing rapid growth, spanning a wide range of applications in both academia and industry. However, training them distributed and at scale remains difficult due to the complex ecosystem of tools and hardware involved. One consequence is that the responsibility of orchestrating these complex components is often left to one-off scripts and glue code customized for specific problems. To address these restrictions, we introduce \emph{Alchemist} - an internal service built at Apple from the ground up for \emph{easy}, \emph{fast}, and \emph{scalable} distributed training. We discuss its design, implementation, and examples of running different flavors of distributed training. We also present case studies of its internal adoption in the development of autonomous systems, where training times have been reduced by 10x to keep up with the ever-growing data collection.


title: Communication with Aboveground Devices in Wireless Underground Sensor Networks: An Empirical Study

abstract: Wireless Underground Sensor Networks (WUSNs) consist of wirelessly connected underground sensor nodes that communicate untethered through soil. WUSNs have the potential to impact a wide variety of novel applications including intelligent irrigation, environment monitoring, border patrol, and assisted navigation. Although its deployment is mainly based on underground sensor nodes, a WUSN still requires aboveground devices for data retrieval, management, and relay functionalities. Therefore, the characterization of the bi-directional communication between a buried node and an aboveground device is essential for the realization of WUSNs. In this work, empirical evaluations of underground-to- aboveground (UG2AG) and aboveground-to-underground (AG2UG) communication are presented. More specifically, testbed experiments have been conducted with commodity sensor motes in a real-life agricultural field. The results highlight the asymmetry between UG2AG and AG2UG communication with distinct behaviors for different burial depths. To combat the adverse effects of the change in wavelength in soil, an ultra wideband antenna scheme is deployed, which increases the communication range by more than 350% compared to the original antennas. The results also reveal that a 21% increase in the soil moisture decreases the communication range by more than 70%. To the best of our knowledge, this is the first empirical study that highlights the effects of the antenna design, burial depth, and soil moisture on both UG2AG and AG2UG communication performance. These results have a significant impact on the development of multi-hop networking protocols for WUSNs.


title: Simulation of SVPWM based FOC of CSI fed induction motor drive

abstract: The application of current source inverters (CSI)in induction motor (IM) drives offers a number of advantages, including voltage boosting capability, natural shoot-through short-circuit protection and generation of sinusoidal voltages. In this paper, an attempt to model the CSI fed IM drive is presented. The mathematical model takes into account of the inverter, and induction motor dynamics and is established in the stationary reference frame. For controlling the drive speed, a direct field-oriented control (FOC) is proposed. To counter the effects of torque pulsations at very low speeds and the rotor resistance variation, a slip angle compensation loop is included in the control law formulation. Analytical expressions for CSI fed IM with Direct FOC are derived and validated using MATLAB/SIMULINK.


title: PosDB: A Distributed Column-Store Engine

abstract: In this paper we present a novel disk-based distributed column-store, describe its architecture and discuss a number of technical solutions. Our system is essentially a query engine which was written completely from scratch. It is aimed for shared-nothing environments and supports different forms of parallel query processing.


title: How Important Is a Neuron?

abstract: The problem of attributing a deep network's prediction to its \emph{input/base} features is well-studied. We introduce the notion of \emph{conductance} to extend the notion of attribution to the understanding the importance of \emph{hidden} units. ::: Informally, the conductance of a hidden unit of a deep network is the \emph{flow} of attribution via this hidden unit. We use conductance to understand the importance of a hidden unit to the prediction for a specific input, or over a set of inputs. We evaluate the effectiveness of conductance in multiple ways, including theoretical properties, ablation studies, and a feature selection task. The empirical evaluations are done using the Inception network over ImageNet data, and a sentiment analysis network over reviews. In both cases, we demonstrate the effectiveness of conductance in identifying interesting insights about the internal workings of these networks.


title: IEEE Recommended Practice for Speech Quality Measurements

abstract: The IEEE Recommended Practice for Speech Quality Measurement is the product of roughly six years of study, discussion, writing, and rewriting by a diligent team of scientists representing a broad range of disciplines and research institutions. We will attempt to recognize here all those who participated, ranging from the dogged individuals who were with the committee for its entire life to those who participated in at least one meeting.


title: Spectral redemption: clustering sparse networks

abstract: Spectral algorithms are classic approaches to clustering and community detection in networks. However, for sparse networks the standard versions of these algorithms are suboptimal, in some cases completely failing to detect communities even when other algorithms such as belief propagation can do so. Here, we present a class of spectral algorithms based on a nonbacktracking walk on the directed edges of the graph. The spectrum of this operator is much better-behaved than that of the adjacency matrix or other commonly used matrices, maintaining a strong separation between the bulk eigenvalues and the eigenvalues relevant to community structure even in the sparse case. We show that our algorithm is optimal for graphs generated by the stochastic block model, detecting communities all of the way down to the theoretical limit. We also show the spectrum of the nonbacktracking operator for some real-world networks, illustrating its advantages over traditional spectral clustering.


title: Statistical analysis plan for the POLAR-RCT: The Prophylactic hypOthermia trial to Lessen trAumatic bRain injury-Randomised Controlled Trial

abstract: BACKGROUND ::: The Prophylactic hypOthermia to Lessen trAumatic bRain injury-Randomised Controlled Trial (POLAR-RCT) will evaluate whether early and sustained prophylactic hypothermia delivered to patients with severe traumatic brain injury improves patient-centred outcomes. ::: ::: ::: METHODS ::: The POLAR-RCT is a multicentre, randomised, parallel group, phase III trial of early, prophylactic cooling in critically ill patients with severe traumatic brain injury, conducted in Australia, New Zealand, France, Switzerland, Saudi Arabia and Qatar. A total of 511 patients aged 18-60 years have been enrolled with severe acute traumatic brain injury. The trial intervention of early and sustained prophylactic hypothermia to 33 °C for 72 h will be compared to standard normothermia maintained at a core temperature of 37 °C. The primary outcome is the proportion of favourable neurological outcomes, comprising good recovery or moderate disability, observed at six months following randomisation utilising a midpoint dichotomisation of the Extended Glasgow Outcome Scale (GOSE). Secondary outcomes, also assessed at six months following randomisation, include the probability of an equal or greater GOSE level, mortality, the proportions of patients with haemorrhage or infection, as well as assessment of quality of life and health economic outcomes. The planned sample size will allow 80% power to detect a 30% relative risk increase from 50% to 65% (equivalent to a 15% absolute risk increase) in favourable neurological outcome at a two-sided alpha of 0.05. ::: ::: ::: DISCUSSION ::: Consistent with international guidelines, a detailed and prospective analysis plan has been developed for the POLAR-RCT. This plan specifies the statistical models for evaluation of primary and secondary outcomes, as well as defining covariates for adjusted analyses and methods for exploratory analyses. Application of this statistical analysis plan to the forthcoming POLAR-RCT trial will facilitate unbiased analyses of these important clinical data. ::: ::: ::: TRIAL REGISTRATION ::: ClinicalTrials.gov, NCT00987688 (first posted 1 October 2009); Australian New Zealand Clinical Trials Registry, ACTRN12609000764235 . Registered on 3 September 2009.


title: The SMARTER pilot study: Testing feasibility of real-time feedback for dietary self-monitoring

abstract: Abstract Self-monitoring (SM) of food intake is central to weight loss treatment. Technology makes it possible to reinforce this behavior change strategy by providing real-time feedback (FB) tailored to the diary entry. To test the feasibility of providing 1–4 daily FB messages tailored to dietary recordings via a smartphone, we conducted a 12-week pilot randomized clinical trial in Pittsburgh, PA in US in 2015. We compared 3 groups: SM using the Lose It! smartphone app (Group 1); SM + FB (Group 2); and SM + FB + attending three in-person group sessions (Group 3). The sample (N = 39) was mostly white and female with a mean body mass index of 33.76 kg/m 2 . Adherence to dietary SM was recorded daily, weight was assessed at baseline and 12 weeks. The mean percentage of days adherent to dietary SM was similar among Groups 1, 2, and 3 (p = 0.66) at 53.50% vs. 55.86% vs. 65.33%, respectively. At 12 weeks, all groups had a significant percent weight loss (p


title: Fast-join: An efficient method for fuzzy token matching based string similarity join

abstract: String similarity join that finds similar string pairs between two string sets is an essential operation in many applications, and has attracted significant attention recently in the database community. A significant challenge in similarity join is to implement an effective fuzzy match operation to find all similar string pairs which may not match exactly. In this paper, we propose a new similarity metrics, called “fuzzy token matching based similarity”, which extends token-based similarity functions (e.g., Jaccard similarity and Cosine similarity) by allowing fuzzy match between two tokens. We study the problem of similarity join using this new similarity metrics and present a signature-based method to address this problem. We propose new signature schemes and develop effective pruning techniques to improve the performance. Experimental results show that our approach achieves high efficiency and result quality, and significantly outperforms state-of-the-art methods.


title: Digital transmission over cross-coupled linear channels

abstract: For a multiuser data communications system operating over a mutually cross-coupled linear channel with additive noise sources, we determine the following: (1) a linear cross-coupled receiver processor (filter) that yields the least-mean-squared error between the desired outputs and the actual outputs, and (2) a cross-coupled transmitting filter that optimally distributes the total available power among the different users, as well as the total available frequency spectrum. The structure of the optimizing filters is similar to the known 2 × 2 case encountered in problems associated with digital transmission over dually polarized radio channels.


title: Overview of the crowdsourcing process

abstract: A decade ago, the crowdsourcing term was first coined and used to represent a method for expressing the wisdom of the crowd in accomplishing two types of tasks. One type includes tasks that need human intelligence rather than machines, and the other type covers those tasks that can be accomplished with a higher time and cost efficiency using the crowd rather than employing experts. The crowdsourcing process contains five modules: The first is designing incentives to mobilize the crowd to do the required task. This step is followed by four modules for collecting and assuring quality and then verifying and aggregating the received information. The verification and quality control can be done for the tasks, collected data and the participants by having more participants answer the same question or accepting answers only from experts to avoid errors from unreliable participants. Methods of discovering topic experts are utilized to discover reliable candidates in the crowd who have relevant experience in the discussed topic. Expert discovery reduces the number of needed participants per question which reduces the overall cost. This work summarizes and reviews the methods used to accomplish each processing step. Yet, choosing a specific method remains application dependent.


title: The Benefits of Facebook “Friends:” Social Capital and College Students’ Use of Online Social Network Sites

abstract: This study examines the relationship between use of Facebook, a popular online social network site, and the formation and maintenance of social capital. In addition to assessing bonding and bridging social capital, we explore a dimension of social capital that assesses one’s ability to stay connected with members of a previously inhabited community, which we call maintained social capital. Regression analyses conducted on results from a survey of undergraduate students (N = 286) suggest a strong association between use of Facebook and the three types of social capital, with the strongest relationship being to bridging social capital. In addition, Facebook usage was found to interact with measures of psychological well-being, suggesting that it might provide greater benefits for users experiencing low self-esteem and low life satisfaction.


title: Evaluating the effect of centralization on routing convergence on a hybrid BGP-SDN emulation framework

abstract: A lot of applications depend on reliable and stable Internet connectivity. These characteristics are crucial for mission-critical services such as telemedical applications. An important factor that can affect connection availability is the convergence time of BGP, the de-facto inter-domain routing (IDR) protocol in the Internet. After a routing change, it may take several minutes until the network converges and BGP routing becomes stable again. Kotronis et al propose a novel Internet routing approach based on SDN principles that combines several Autonomous Systems (AS) into groups, called clusters, and introduces a logically centralized routing decision process for the cluster participants. One of the goals of this concept is to stabilize the IDR system and bring down its convergence time. However, testing whether such approaches can improve on BGP problems requires hybrid SDN and BGP experimentation tools that can emulate multiple ASes. Presently, there is a lack of an easy to use public tool for this purpose. This work fills this gap by building a suitable emulation framework and evaluating the effect that a proof-of-concept IDR controller has on IDR convergence time.


title: The Design Space of Augmented and Virtual Reality Applications for Assistive Environments in Manufacturing: A Visual Approach

abstract: Research on how to take advantage of Augmented Reality and Virtual Reality applications and technologies in the domain of manufacturing has brought forward a great number of concepts, prototypes, and working systems. Although comprehensive surveys have taken account of the state of the art, the design space of industrial augmented and virtual reality keeps diversifying. We propose a visual approach towards assessing this space and present an interactive, community-driven tool which supports interested researchers and practitioners in gaining an overview of the aforementioned design space. Using such a framework we collected and classified relevant publications in terms of application areas and technology platforms. This tool shall facilitate initial research activities as well as the identification of research opportunities. Thus, we lay the groundwork, forthcoming workshops and discussions shall address the refinement.


title: Ego-lane estimation for lane-level navigation in urban scenarios

abstract: Future lane-precise navigation systems will recommend lane changes to drivers if needed. To achieve this, robust lane-level localization on a navigable map is essential. We propose an ego-lane estimation algorithm to robustly determine the ego-lane in urban scenarios based on a particle filter approach. The method only requires sensors available in a current production car, i.e. visual lane-marking detection, radar, and GPS, and a digital map describing road geometry and topology. Extensive experimental validation has shown an error rate of less than 0.75% with an availability of 95% of the total time and below 0.4% at 96% availability in situations most relevant for navigation. The influence of the used sensors has been evaluated.


title: Election Forecasts With Twitter: How 140 Characters Reflect the Political Landscape

abstract: This study investigates whether microblogging messages on Twitter validly mirror the political landscape off-line and can be used to predict election results. In the context of the 2009 German federal election, we conducted a sentiment analysis of over 100,000 messages containing a reference to either a political party or a politician. Our results show that Twitter is used extensively for political deliberation and that the mere number of party mentions accurately reflects the election result. The tweets' sentiment (e.g., positive and negative emotions associated with a politician) corresponds closely to voters' political preferences. In addition, party sentiment profiles reflect the similarity of political positions between parties. We derive suggestions for further research and discuss the use of microblogging services to aggregate dispersed information.


Addressing challenges in the clinical applications associated with CRISPR/Cas9 technology and ethical questions to prevent its misuse


title: The student activity meter for awareness and self-reflection

abstract: Visualization of user actions can be used in Technology Enhanced Learning to increase awareness for learners and teachers and to support self-reflection. In this paper, we present our Student Activity Meter that visualizes learner actions. We present four design iterations and results of both quantitative and qualitative evaluation studies in real-world settings that assess the usability, use and usefulness of different visualizations. Results indicate that our tool is useful for a variety of teacher and learner needs, including awareness of time spent and resource use. Tools like SAM can also be deployed in other settings that require awareness and self-reflection, e.g. in personal informatics and health monitoring, where motivated users will value the flexible mechanisms to analyze trending data.


title: Bilateral blue noise sampling

abstract: Blue noise sampling is an important component in many graphics applications, but existing techniques consider mainly the spatial positions of samples, making them less effective when handling problems with non-spatial features. Examples include biological distribution in which plant spacing is influenced by non-positional factors such as tree type and size, photon mapping in which photon flux and direction are not a direct function of the attached surface, and point cloud sampling in which the underlying surface is unknown a priori. These scenarios can benefit from blue noise sample distributions, but cannot be adequately handled by prior art. Inspired by bilateral filtering, we propose a bilateral blue noise sampling strategy. Our key idea is a general formulation to modulate the traditional sample distance measures, which are determined by sample position in spatial domain, with a similarity measure that considers arbitrary per sample attributes. This modulation leads to the notion of bilateral blue noise whose properties are influenced by not only the uniformity of the sample positions but also the similarity of the sample attributes. We describe how to incorporate our modulation into various sample analysis and synthesis methods, and demonstrate applications in object distribution, photon density estimation, and point cloud sub-sampling.


title: Timing Recovery in Digital Synchronous Data Receivers

abstract: A new class of fast-converging timing recovery methods for synchronous digital data receivers is investigated. Starting with a worst-case timing offset, convergence with random binary data will typically occur within 10-20 symbols. The input signal is sampled at the baud rate; these samples are then processed to derive a suitable control signal to adjust the timing phase. A general method is outlined to obtain near-minimum-variance estimates of the timing offset with respect to a given steady-state sampling criterion. Although we make certain independence assumptions between successive samples and postulate ideal decisions to obtain convenient analytical results, our simulations with a decision-directed reference and baud-to-baud adjustments yield very similar results. Convergence is exponential, and for small loop gains the residual jitter is proportional and convergence time is inversely proportional to the loop gain. The proposed algorithms are simple and economic to implement. They apply to binary or multilevel PAM signals as well as to partial response signals.