title: Chief complaint classification with recurrent neural networks

abstract: Abstract Syndromic surveillance detects and monitors individual and population health indicators through sources such as emergency department records. Automated classification of these records can improve outbreak detection speed and diagnosis accuracy. Current syndromic systems rely on hand-coded keyword-based methods to parse written fields and may benefit from the use of modern supervised-learning classifier models. In this paper, we implement two recurrent neural network models based on long short-term memory (LSTM) and gated recurrent unit (GRU) cells and compare them to two traditional bag-of-words classifiers: multinomial naive Bayes (MNB) and a support vector machine (SVM). The MNB classifier is one of only two machine learning algorithms currently being used for syndromic surveillance. All four models are trained to predict diagnostic code groups as defined by Clinical Classification Software, first to predict from discharge diagnosis, and then from chief complaint fields. The classifiers are trained on 3.6 million de-identified emergency department records from a single United States jurisdiction. We compare performance of these models primarily using the F1 score, and we measure absolute model performance to determine which conditions are the most amenable to surveillance based on chief complaint alone. Using discharge diagnoses, the LSTM classifier performs best, though all models exhibit an F1 score above 96.00. Using chief complaints, the GRU performs best (F1 = 47.38), and MNB with bigrams performs worst (F1 = 39.40). We also note that certain syndrome types are easier to detect than others. For example, chief complaints using the GRU model predicts alcohol-related disorders well (F1 = 78.91) but predicts influenza poorly (F1 = 14.80). In all instances, the RNN models outperformed the bag-of-words classifiers suggesting deep learning models could substantially improve the automatic classification of unstructured text for syndromic surveillance.


title: Localization Using Visual Odometry and a Single Downward-Pointing Camera

abstract: Abstract Stereo imaging is a technique commonly employed for vision-basednavigation. For such applications, two images are acquired from dif-ferent vantage points and then compared using transformations to ex-tract depth information. The technique is commonly used in roboticsfor obstacle avoidance or for Simultaneous Localization And Mapping,(SLAM). Yet, the process requires a number of image processing stepsand therefore tends to be CPU-intensive, which limits the real-time datarate and use in power-limited applications. Evaluated here is a tech-nique where a monocular camera is used for vision-based odometry. Inthis work, an optical ﬂow technique with feature recognition is performedto generate odometry measurements. The visual odometry sensor mea-surements are intended to be used as control inputs or measurements ina sensor fusion algorithm using low-cost MEMS based inertial sensorsto provide improved localization information. Presented here are vi-sual odometry results which demonstrate the challenges associated withusing ground-pointing cameras for visual odometry. The focus is forrover-based robotic applications for localization within GPS-denied en-vironments.


title: Photometric stereo with near point lighting: A solution by mesh deformation

abstract: We tackle the problem of photometric stereo under near point lighting in this paper. Different from the conventional formulation of photometric stereo that assumes parallel lighting, photometric stereo under the near point lighting condition is a nonlinear problem as the local surface normals are coupled with its distance to the camera as well as the light sources. To solve this non-linear problem of PS with near point lighting, a local/global mesh deformation approach is developed in our work to determine the position and the orientation of a facet simultaneously, where each facet is corresponding to a pixel in the image captured by the camera. Unlike nonlinear optimization schemes, the mesh deformation in our approach is decoupled into an iteration of interlaced steps of local projection and global blending. Experimental results verify that our method can generate accurate estimation of surface shape under near point lighting in a few iterations. Besides, this approach is robust to errors on the positions of light sources and is easy to be implemented.


title: Scaffolding deep comprehension strategies through Point&Query, AutoTutor, and iSTART

abstract: It is well-documented that most students do not have adequate proficiencies in inquiry and metacognition, particularly at deeper levels of comprehension that require explanatory reasoning. The proficiencies are not routinely provided by teachers and normal tutors so it is worthwhile to turn to computer-based learning environments. This article describes some of our recent computer systems that were designed to facilitate explanation-centered learning through strategies of inquiry and metacognition while students learn science and technology content. Point&Query augments hypertext, hypermedia, and other learning environments with question-answer facilities that are under the learner control. AutoTutor and iSTART use animated conversational agents to scaffold strategies of inquiry, metacognition, and explanation construction. AutoTutor coaches students in generating answers to questions that require explanations (e.g., why, what-if, how) by holding a mixed-initiative dialogue in natural language. iSTART mod...


title: Summarization system evaluation revisited: N-gram graphs

abstract: This article presents a novel automatic method (AutoSummENG) for the evaluation of summarization systems, based on comparing the character n-gram graphs representation of the extracted summaries and a number of model summaries. The presented approach is language neutral, due to its statistical nature, and appears to hold a level of evaluation performance that matches and even exceeds other contemporary evaluation methods. Within this study, we measure the effectiveness of different representation methods, namely, word and character n-gram graph and histogram, different n-gram neighborhood indication methods as well as different comparison methods between the supplied representations. A theory for the a priori determination of the methods' parameters along with supporting experiments concludes the study to provide a complete alternative to existing methods concerning the automatic summary system evaluation process.


title: Image Segmentation and Shape Analysis for Road-Sign Detection

abstract: This paper proposes an automatic road-sign recognition method based on image segmentation and joint transform correlation (JTC) with the integration of shape analysis. The presented system is universal, which is able to detect traffic signs of any countries with any color and any of the existing shapes (e.g., circular, rectangular, triangular, pentagonal, and octagonal) and is invariant to transformation (e.g., translation, rotation, scale, and occlusion). The main contributions of this paper are: 1) the formulation of two new criteria for analyzing different shapes using two basic geometric properties, 2) the recategorization of the rectangular signs into diamond or nondiamond shapes based on the inclination of the four sides with the ground and 3) the employment of the distortion-invariant fringe-adjusted JTC (FJTC) technique for recognition. There are three main stages in the proposed algorithm: 1) segmentation by clustering the pixels based on the color features to find the regions of interest (ROIs); 2) traffic-sign detection by using two novel shape classification criteria, i.e., the relationship between area and perimeter and the number of sides of a given shape; and 3) recognition of the road sign using FJTC to match the unknown signs with the known reference road signs stored in the database. Experimental results on real-life images show a high success rate and a very low false hit rate and demonstrate that the proposed framework is invariant to translation, rotation, scale, and partial occlusions.


title: A weighted-resampling based transfer learning algorithm

abstract: Transfer learning has attracted more and more attention, and many scholars proposed some useful strategies. Boosting is the main strategy for transfer learning. In boosting, resampling is preferred over reweighting, and it can be applied to any base learner. In this paper, we propose a weighted-resampling method for transfer learning, called TrResampling. Firstly, resampling is applied to the data with heaven weight in the source domain, and the resampled data is used with the target data as the training data to build a classifier. Then the TrAdaBoost algorithm is used to adjust the weights of source data and target data. We discuss Decision Tree, Naive Bayes, and SVM as the base learner in TrResampling, and choose the suitable for TrResampling. In order to illustrate the performance of the proposed algorithm, we compare TrResampling with the state-of-the-art algorithm TrAdaBoost and the base learner Decision Tree, experimental results on UCI data sets indicate that TrResampling is superior to TrAdaBoost and Decision Tree on many data sets.


title: Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks

abstract: The celebrated Sequence to Sequence learning (Seq2Seq) technique and its numerous variants achieve excellent performance on many tasks. However, many machine learning tasks have inputs naturally represented as graphs; existing Seq2Seq models face a significant challenge in achieving accurate conversion from graph form to the appropriate sequence. To address this challenge, we introduce a novel general end-to-end graph-to-sequence neural encoder-decoder model that maps an input graph to a sequence of vectors and uses an attention-based LSTM method to decode the target sequence from these vectors. Our method first generates the node and graph embeddings using an improved graph-based neural network with a novel aggregation strategy to incorporate edge direction information in the node embeddings. We further introduce an attention mechanism that aligns node embeddings and the decoding sequence to better cope with large graphs. Experimental results on bAbI, Shortest Path, and Natural Language Generation tasks demonstrate that our model achieves state-of-the-art performance and significantly outperforms existing graph neural networks, Seq2Seq, and Tree2Seq models; using the proposed bi-directional node embedding aggregation strategy, the model can converge rapidly to the optimal performance.


title: Social recommendations for personalized fitness assistance

abstract: Wearable technology allows users to monitor their activity and pursue a healthy lifestyle through the use of embedded sensors. Such wearables usually connect to a mobile application that allows them to set their profile and keep track of their goals. However, due to the relatively "high maintenance" of such applications, where a significant amount of user feedback is expected, users who are very busy, or not as self-motivated, stop using them after a while. It has been shown that accountability improves commitment to an exercise routine. In this work, we present the PRO-Fit framework, a personalized fitness assistant aiming at engaging users in fitness activities, incorporating a social element. The PRO-Fit architecture collects information from activity tracking devices and automatically classifies their activity type. Moreover, the framework incorporates a social recommender system. Using collaborative filtering on user profile and activity data, PRO-Fit generates personalized fitness schedules based on their availability and wellbeing goals. We also incorporate the social network community of the application's users and identify different tie strengths based on the user's connections and location. The output of the recommendation process is twofold, as both new activities, as well as fitness buddies, are being recommended to each user.


title: Social learning platforms and the flipped classroom

abstract: This paper examines the use of social learning platforms in conjunction with the emergent pedagogy of the `flipped classroom'. In particular the attributes of the social learning platform “Edmodo” is considered alongside the changes in the way in which online learning environments are being implemented, especially within British education. Some observations are made regarding the use and usefulness of these platforms along with a consideration of the increasingly decentralized nature of education in the United Kingdom.


title: Curvature-based curb detection method in urban environments using stereo and laser

abstract: This paper addresses the problem of curb detection for ADAS or autonomous navigation in urban scenarios. The algorithm is based on clouds of 3D points. It is evaluated using 3D information from a pair of stereo cameras and a LIDAR. Curbs are detected based on road surface curvature. The curvature estimation requires a dense point cloud, therefore the density of the LIDAR cloud has been augmented using Iterative Closest Point (ICP) based on the previous scans. The proposed algorithm can deal with curbs of different curvature and heights, from as low as 3 cm, in a range up to 20 m (whenever that curbs are connected in the curvature image). The curb parameters are modeled using straight lines and compared to the ground-truth using the lateral error as the key parameter indicator. The ground-truth sequences were manually labeled on urban images from the KITTI dataset and made publicly available for the scientific community.


title: Cloudlets: at the leading edge of mobile-cloud convergence

abstract: As mobile computing and cloud computing converge, the sensing and interaction capabilities of mobile devices can be seamlessly fused with compute-intensive and data-intensive processing in the cloud. Cloudlets are important architectural components in this convergence, representing the middle tier of a mobile device — cloudlet — cloud hierarchy. We show how cloudlets enable a new genre of applications called cognitive assistance applications that augment human perception and cognition. We describe a plug-and-play architecture for cognitive assistance, and a proof of concept using Google Glass.


A Vision System for Horizon Tracking and Object Recognition for Micro Air Vehicles," presented at


title: Asynchronous exceptions in Haskell

abstract: Asynchronous exceptions, such as timeouts are important for robust, modular programs, but are extremely difficult to program with — so much so that most programming languages either heavily restrict them or ban them altogether. We extend our earlier work, in which we added synchronous exceptions to Haskell, to support asynchronous exceptions too. Our design introduces scoped combinators for blocking and unblocking asynchronous interrupts, along with a somewhat surprising semantics for operations that can suspend. Uniquely, we also give a formal semantics for our system.


title: High Capacity Associative Memories and Connection Constraints

abstract: High capacity associative neural networks can be built from networks of perceptrons, trained using simple perceptron training. Such networks perform much better than those trained using the standard Hopfield one-shot Hebbian learning. An experimental investigation into how such networks perform when the connection weights are not free to take any value is reported. The three restrictions investigated are: a symmetry constraint, a sign constraint and a dilution constraint. The selection of these constraints is motivated by both engineering and biological considerations.


Source 3 D Visualization for the RAMI 4 . 0 Reference Model


title: Compositional Falsification of Cyber-Physical Systems with Machine Learning Components

abstract: Cyber-physical systems (CPS), such as automotive systems, are starting to include sophisticated machine learning (ML) components. Their correctness, therefore, depends on properties of the inner ML modules. While learning algorithms aim to generalize from examples, they are only as good as the examples provided, and recent efforts have shown that they can produce inconsistent output under small adversarial perturbations. This raises the question: can the output from learning components can lead to a failure of the entire CPS? In this work, we address this question by formulating it as a problem of falsifying signal temporal logic (STL) specifications for CPS with ML components. We propose a compositional falsification framework where a temporal logic falsifier and a machine learning analyzer cooperate with the aim of finding falsifying executions of the considered model. The efficacy of the proposed technique is shown on an automatic emergency braking system model with a perception component based on deep neural networks.


title: Approximation Schemes for Steiner Forest on Planar Graphs and Graphs of Bounded Treewidth

abstract: We give the first polynomial-time approximation scheme (PTAS) for the Steiner forest problem on planar graphs and, more generally, on graphs of bounded genus. As a first step, we show how to build a Steiner forest spanner for such graphs. The crux of the process is a clustering procedure called prize-collecting clustering that breaks down the input instance into separate subinstances which are easier to handle; moreover, the terminals in different subinstances are far from each other. Each subinstance has a relatively inexpensive Steiner tree connecting all its terminals, and the subinstances can be solved (almost) separately. Another building block is a PTAS for Steiner forest on graphs of bounded treewidth. Surprisingly, Steiner forest is NP-hard even on graphs of treewidth 3. Therefore, our PTAS for bounded treewidth graph needs a nontrivial combination of approximation arguments and dynamic programming on the tree decomposition. We further show that Steiner forest can be solved in polynomial time for series-parallel graphs (graphs of treewidth at most two) by a novel combination of dynamic programming and minimum cut computations, completing our thorough complexity study of Steiner forest in the range of bounded treewidth graphs, planar graphs, and bounded genus graphs.


title: Sources of Constructive Social Relationships in an Urban Magnet School

abstract: This paper reports a qualitative study of a racially diverse urban middle school serving a population with preponderantly below-average achievement. The school had less conflictual relations between students and teachers and more voluntary interracial interaction than is commonly reported in such schools by the literature. The school was a magnet school and had some accompanying structural features which contributed to these results. But two influences on both student-teacher and interracial peer interactions are identified as most important to relationships in the school. These were, first, a cluster of variables consisting of curricular structure, academic reward structure, and classroom activity structure, and second, a faculty subculture that socialized teachers both to give and expect respectful behavior in interactions with students.


title: Scale-Invariant Convolutional Neural Networks

abstract: Abstract Even though convolutional neural networks (CNN) hasachieved near-human performance in various computer vi-sion tasks, its ability to tolerate scale variations is lim-ited. The popular practise is making the model biggerﬁrst, and then train it with data augmentation using ex-tensive scale-jittering. In this paper, we propose a scale-invariant convolutional neural network (SiCNN), a modeldesigned to incorporate multi-scale feature exaction andclassiﬁcation into the network structure. SiCNN uses amulti-column architecture, with each column focusing on aparticular scale. Unlike previous multi-column strategies,these columns share the same set of ﬁlter parameters by ascale transformation among them. This design deals withscale variation without blowing up the model size. Experi-mental results show that SiCNN detects features at variousscales, and the classiﬁcation result exhibits strong robust-ness against object scale variations. 1. Introduction Many classical computer vision tasks have enjoyed agreat breakthrough, primarily due to the large amount oftraining data and the application of deep convolution neu-ral networks (CNN) [8]. In the most recent ILSVRC 2014competition [11], CNN-based solutions have achieved near-human accuracies in image classiﬁcation, localization anddetection tasks [14,16].Accompanying this progress are studies trying to under-stand what CNN has learnt internally and what contributeto its success [2,13,17]. By design, layers within the net-work have progressively larger receptive ﬁeld sizes, allow-ing them to learn more complex features. Another key pointis the shift-invariance property, that a pattern in the inputcan be recognized regardless of its position [9]. Pooling lay-ers contribute resilience to slight deformation as well smallscale change [12].However, it is evident that CNN deals with shift-variancefar better than scale-invariance [3]. Not dealing with scale-invariance well poses a direct conﬂict to the design philos-ophy of CNN, in that higher layers may see and thus cap-tures features of certain plain patterns simply because theyare larger at the input, not because they are more complex.In other words, there is no alignments between in the posi-tion of a ﬁlter and the complexity it captures. What is more,there are other invariance that CNN does not deal with inter-nally. Examples include rotations and ﬂips (since featuresof natural objects are mostly symmetric).A brutal force solution would be to make the networklarger by introducing more ﬁlters to cope with scale vari-ations of the same feature, accompanied by scale-jitteringthe input images, often by order of magnitude. This is, infact, the popular practice today [1,8,14]. It is true even forproposals that directly deal with this problem. For example,[3] drives the CNN with crops of different size and positionswith three differnt scales, and then uses VLAD pooling toproduce a feature summary of the patches.We explore a radically different approach that is alsosimple. Observing that ﬁlters that detect the same patternbut with different scales bear strong relationship, we adopt amulti-column design and designate each column to special-ize on certain scales. We call our system SiCNN (Scale-invariant CNN). Unlike a conventional multi-column CNN,ﬁlters in SiCNN are strongly regulated among columns.The goal is to make the network resilient to scale variancewithout blowing up number of free parameters, and thus re-duce the need of jittering the input.We performed detailed analysis and veriﬁed that SiCNNexhibits the desired behavior. For example, the column thatdeals with larger scale is indeed activated by input patternswith the larger scaling factor, and the system as a wholebecomes less sensitive to scale variance. On unaugmentedCIFAR-10 dataset [7], our method produces the best re-1


title: Training Conditional Random Fields Using Incomplete Annotations

abstract: We address corpus building situations, where complete annotations to the whole corpus is time consuming and unrealistic. Thus, annotation is done only on crucial part of sentences, or contains unresolved label ambiguities. We propose a parameter estimation method for Conditional Random Fields (CRFs), which enables us to use such incomplete annotations. We show promising results of our method as applied to two types of NLP tasks: a domain adaptation task of a Japanese word segmentation using partial annotations, and a part-of-speech tagging task using ambiguous tags in the Penn treebank corpus.


title: Parallel Vectorized Algebraic AES in MATLAB for Rapid Prototyping of Encrypted Sensor Processing Algorithms and Database Analytics

abstract: The increasing use of networked sensor systems and networked databases has led to an increased interest in incorporating encryption directly into sensor algorithms and database analytics. Matlab is the dominant tool for rapid prototyping of sensor algorithms and has extensive database analytics capabilities. The advent of high level and high performance Galois Field mathematical environments allows encryption algorithms to be expressed succinctly and efficiently. This work leverages the Galois Field primitives found the Matlab Communication Toolbox to implement a mode of the Advanced Encrypted Standard (AES) based on first principals mathematics. The resulting implementation requires 100x less code than standard AES implementations and delivers speed that is effective for many design purposes. The parallel version achieves speed comparable to native OpenSSL on a single node and is sufficient for real-time prototyping of many sensor processing algorithms and database analytics.


title: Calcium cycling and signaling in cardiac myocytes.

abstract: Calcium (Ca) is a universal intracellular second messenger. In muscle, Ca is best known for its role in contractile activation. However, in recent years the critical role of Ca in other myocyte processes has become increasingly clear. This review focuses on Ca signaling in cardiac myocytes as pertaining to electrophysiology (including action potentials and arrhythmias), excitation-contraction coupling, modulation of contractile function, energy supply-demand balance (including mitochondrial function), cell death, and transcription regulation. Importantly, although such diverse Ca-dependent regulations occur simultaneously in a cell, the cell can distinguish distinct signals by local Ca or protein complexes and differential Ca signal integration.


title: Comparison of Internet addicts and non-addicts in Taiwanese high school

abstract: This study investigated the difference between Internet addicts and non-addicts in Taiwanese high schools, and focused specifically on their Internet usage patterns, and gratification and communication pleasures. A total of 1708 valid data samples of high school adolescents were collected. Among this sample, 236 subjects (13.8%) were identified as addicts using the eight-item Internet addiction Diagnostic Questionnaire designed by Young [Internet addiction survey [Online]. Available: http://www.pitt.edu/_ksy/survey.htm]. The analytical results revealed that Internet addicts spent almost twice as many hours on line on average than the non-addicts. Notably, surfing with a social/entertainment motivation and gratification was positively correlated with Internet addiction. Furthermore, Internet addicts obtained markedly higher overall PIUST scores and scored higher than non-addicts on four subscales (tolerance; compulsive use and withdrawal; related problems, including family, school, health, and other problems; interpersonal and financial problems). While Internet addicts perceived the Internet to have significantly more negative influences on daily routines, school performance, teacher and parental relation than non-addicts, both Internet addicts and non-addicts viewed Internet use as enhancing peer relations. Moreover, students with personalities characterized by dependence, shyness, depression and low self-esteem had a high tendency to become addicted.


title: A dual-metal-trench Schottky pinch-rectifier in 4H-SiC

abstract: Characteristics of high-voltage dual-metal-trench (DMT) SiC Schottky pinch-rectifiers are reported for the first time. At a reverse bias of 300 V, the reverse leakage current of the SiC DMT device is 75 times less than that of a planar device while the forward bias characteristics remain comparable to those of a planar device. In this work, 4H-SiC pinch-rectifiers have been fabricated using a small/large barrier height (Ti/Ni) DMT device structure. The DMT structure is specially designed to permit simple fabrication in SiC. The Ti Schottky contact metal serves as a self-aligned trench etch mask and only four basic fabrication steps are required.


BoosTexter: A System for Multiclass Multi-label Text Categorization


title: My data just goes everywhere: user mental models of the internet and implications for privacy and security

abstract: Many people use the Internet every day yet know little about how it really works. Prior literature diverges on how people's Internet knowledge affects their privacy and security decisions. We undertook a qualitative study to understand what people do and do not know about the Internet and how that knowledge affects their responses to privacy and security risks. Lay people, as compared to those with computer science or related backgrounds, had simpler mental models that omitted Internet levels, organizations, and entities. People with more articulated technical models perceived more privacy threats, possibly driven by their more accurate understanding of where specific risks could occur in the network. Despite these differences, we did not find a direct relationship between people's technical background and the actions they took to control their privacy or increase their security online. Consistent with other work on user knowledge and experience, our study suggests a greater emphasis on policies and systems that protect privacy and security without relying too much on users' security practices.


title: Brain magnetic resonance imaging helps to differentiate atypical multiple sclerosis with cavitary lesions and vanishing white matter disease.

abstract: BACKGROUND AND PURPOSE ::: Multiple sclerosis (MS) patients can present with atypical cavitary lesions mimicking vanishing white matter disease (VWMD). Our objective was to identify brain magnetic resonance imaging (MRI) findings that differentiate these two disorders. ::: ::: ::: METHODS ::: A cross-sectional study was performed including 14 patients with MS with cavitary lesions and 14 patients with VWMD. Two neuroradiologists retrospectively reviewed the MRI including at least T1-, T2- and fluid-attenuated inversion recovery weighted images. ::: ::: ::: RESULTS ::: The main differences included ovoid lesions perpendicular to the lateral ventricle, punctate isolated juxtacortical lesions (both 100% in MS versus 0% in VWMD) and symmetrical infratentorial hyperintensities (0% in MS versus 50% in VWMD). Other statistically significant differences included midbrain (79% in MS versus 29% in VWMD) and thalamus lesions (71% vs. 7%) as well as extensive external capsule involvement (29% vs. 86%) and extensive corpus callosum lesions (64% vs. 100%). Cavitary lesions usually had periventricular predominance in MS (36% vs. 0%) whereas they were more frequently anterior in VWMD (0% in MS versus 57% in VWMD). ::: ::: ::: CONCLUSION ::: Despite many similar MRI findings, our results suggest that a careful analysis of the morphology and the location of the lesions is helpful to differentiate these distinct disorders.


title: Back to the future: origins and directions of the “Agile Manifesto” – views of the originators

abstract: In 2001, seventeen professionals set up the manifesto for agile software development. They wanted to define values and basic principles for better software development. On top of being brought into focus, the manifesto has been widely adopted by developers, in software-developing organizations and outside the world of IT. Agile principles and their implementation in practice have paved the way for radical new and innovative ways of software and product development. In parallel, the understanding of the manifesto’s underlying principles evolved over time. This, in turn, may affect current and future applications of agile principles. This article presents results from a survey and an interview study in collaboration with the original contributors of the manifesto for agile software development. Furthermore, it comprises the results from a workshop with one of the original authors. This publication focuses on the origins of the manifesto, the contributors’ views from today’s perspective, and their outlook on future directions. We evaluated 11 responses from the survey and 14 interviews to understand the viewpoint of the contributors. They emphasize that agile methods need to be carefully selected and agile should not be seen as a silver bullet. They underline the importance of considering the variety of different practices and methods that had an influence on the manifesto. Furthermore, they mention that people should question their current understanding of “agile” and recommend reconsidering the core ideas of the manifesto.


title: VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images

abstract: Abstract Segmentation of key brain tissues from 3D medical images is of great significance for brain disease diagnosis, progression assessment and monitoring of neurologic conditions. While manual segmentation is time-consuming, laborious, and subjective, automated segmentation is quite challenging due to the complicated anatomical environment of brain and the large variations of brain tissues. We propose a novel voxelwise residual network ( VoxResNet ) with a set of effective training schemes to cope with this challenging problem. The main merit of residual learning is that it can alleviate the degradation problem when training a deep network so that the performance gains achieved by increasing the network depth can be fully leveraged. With this technique, our VoxResNet is built with 25 layers, and hence can generate more representative features to deal with the large variations of brain tissues than its rivals using hand-crafted features or shallower networks. In order to effectively train such a deep network with limited training data for brain segmentation, we seamlessly integrate multi-modality and multi-level contextual information into our network, so that the complementary information of different modalities can be harnessed and features of different scales can be exploited. Furthermore, an auto-context version of the VoxResNet is proposed by combining the low-level image appearance features, implicit shape information, and high-level context together for further improving the segmentation performance. Extensive experiments on the well-known benchmark (i.e., MRBrainS ) of brain segmentation from 3D magnetic resonance (MR) images corroborated the efficacy of the proposed VoxResNet . Our method achieved the first place in the challenge out of 37 competitors including several state-of-the-art brain segmentation methods. Our method is inherently general and can be readily applied as a powerful tool to many brain-related studies, where accurate segmentation of brain structures is critical.


title: Introduction - Platforms and Infrastructures in the Digital Age

abstract: In the last few years, leading-edge research from information systems, strategic management, and economics have separately informed our understanding of platforms and infrastructures in the digital age. Our motivation for undertaking this special issue rests in the conviction that it is significant to discuss platforms and infrastructures concomitantly, while enabling knowledge from diverse disciplines to cross-pollinate to address critical, pressing policy challenges and inform strategic thinking across both social and business spheres. In this editorial, we review key insights from the literature on digital infrastructures and platforms, present emerging research themes, highlight the contributions developed from each of the six articles in this special issue, and conclude with suggestions for further research.


title: Ant Colony Optimization for Resource-Constrained Project Scheduling with Generalized Precedence Relations

abstract: An ant colony optimization (ACO) approach for the resource-constrained project scheduling problem (RCPSP) is presented. Several new features that are interesting for ACO in general are proposed and evaluated. In particular, the use of a combination of two pheromone evaluation methods by the ants to find new solutions, a change of the influence of the heuristic on the decisions of the ants during the run of the algorithm, and the option that an elitist ant forgets the best-found solution are studied. We tested the ACO algorithm on a set of large benchmark problems from the Project Scheduling Library. Compared to several other heuristics for the RCPSP, including genetic algorithms, simulated annealing, tabu search, and different sampling methods, our algorithm performed best on average. For nearly one-third of all benchmark problems, which were not known to be solved optimally before, the algorithm was able to find new best solutions.


title: Commercialization and implementation of biocontrol.

abstract: Although the number of biocontrol products is increasing, these products still represent only about 1% of agricultural chemical sales. Yet these are important contributions because biocontrol agents offer disease management alternatives with different mechanisms of action than chemical pesticides. Trends in research include the increased use of biorational screening processes to identify microorganisms with potential for biocontrol, increased testing under semicommercial and commercial production conditions, increased emphasis on combining biocontrol strains with each other and with other control methods, integrating biocontrol into an overall system.


title: HiNextApp: A Context-Aware and Adaptive Framework for App Prediction in Mobile Systems

abstract: A variety of applications (App) installed on mobile systems such as smartphones enrich our lives, but make it more difficult to the system management. For example, finding the specific Apps becomes more inconvenient due to more Apps installed on smartphones, and App response time could become longer because of the gap between more, larger Apps and limited memory capacity. Recent work has proposed several methods of predicting next used Apps (here in after appprediction) to solve the issues, but faces the problems of the low prediction accuracy and high training costs. Especially, applying app-prediction to memory management (such as LMK) and App prelaunching has high requirements for the prediction accuracy and training costs. In this paper, we propose an app-prediction framework, named HiNextApp, to improve the app-prediction accuracy and reduce training costs in mobile systems. HiNextApp is based on contextual information, and can adjust the size of prediction periods adaptively. The framework mainly consists of two parts: non-uniform bayes model and an elastic algorithm. The experimental results show that HiNextApp can effectively improve the prediction accuracy and reduce training times. Besides, compared with traditional bayes model, the overhead of our framework is relatively low.


title: Knowledge Discovery in Data Science KDD meets Big Data

abstract: Cross-Industry Standard process model (CRISP-DM) was developed in the late 90s by a consortium of industry participants to facilitate the end-to-end data mining process for Knowledge Discovery in Databases (KDD). While there have been efforts to better integrate with management and software development practices, there are no extensions to handle the new activities involved in using big data technologies. Data Science Edge (DSE) is an enhanced process model to accommodate big data technologies and data science activities. In recognition of the changes, the author promotes the use of a new term, Knowledge Discovery in Data Science (KDDS) as a call for the community to develop a new industry standard data science process model.


title: An Approach for Named Entity Disambiguation with Knowledge Graph

abstract: Entity disambiguation has always been a key issue in the field of semantic analysis, question answering and recommendation system. The existing approaches for entity disambiguation are based on similarity calculation. The similarity is calculated by considering the similarity of entity context, or the correlation between entities. These similarity calculation approaches are used to calculate the entity similarity at paragraph and document level. If the similarity of entities in short text with limited context is needed, the existing methods are not applicable. Therefore, we proposed a disambiguation method based on semantic similarity of ambiguous word. Firstly, according to the entities in the context of ambiguous word, a classifier is constructed to predict the classification of the ambiguous word, and a list of candidate entities is obtained according to the classification. Then, the contextual Resource Description Framework(RDF) triples related to ambiguous words in Knowledge Graph are mapped to the same vector space with the RDF triples related to the entities in the candidate entity table. Finally, semantic similarity is obtained according to the cosine similarity, and the top-k similarity is selected. In this paper, the validity of this method for entity disambiguation is evaluated by data sets. The results show that the proposed method is superior to the existing context similarity baseline. In addition, the improved method is suitable for the most of Knowledge Graph.


title: FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media.

abstract: Social media has become a popular means for people to consume news. Meanwhile, it also enables the wide dissemination of fake news, i.e., news with intentionally false information, which brings significant negative effects to the society. Thus, fake news detection is attracting increasing attention. However, fake news detection is a non-trivial task, which requires multi-source information such as news content, social context, and dynamic information. First, fake news is written to fool people, which makes it difficult to detect fake news simply based on news contents. In addition to news contents, we need to explore social contexts such as user engagements and social behaviors. For example, a credible user's comment that "this is a fake news" is a strong signal for detecting fake news. Second, dynamic information such as how fake news and true news propagate and how users' opinions toward news pieces are very important for extracting useful patterns for (early) fake news detection and intervention. Thus, comprehensive datasets which contain news content, social context, and dynamic information could facilitate fake news propagation, detection, and mitigation; while to the best of our knowledge, existing datasets only contains one or two aspects. Therefore, in this paper, to facilitate fake news related researches, we provide a fake news data repository FakeNewsNet, which contains two comprehensive datasets that includes news content, social context, and dynamic information. We present a comprehensive description of datasets collection, demonstrate an exploratory analysis of this data repository from different perspectives, and discuss the benefits of FakeNewsNet for potential applications on fake news study on social media.


title: Localizing Moments in Video with Temporal Language

abstract: Localizing moments in a longer video via natural language queries is a new, challenging task at the intersection of language and video understanding. Though moment localization with natural language is similar to other language and vision tasks like natural language object retrieval in images, moment localization offers an interesting opportunity to model temporal dependencies and reasoning in text. We propose a new model that explicitly reasons about different temporal segments in a video, and shows that temporal context is important for localizing phrases which include temporal language. To benchmark whether our model, and other recent video localization models, can effectively reason about temporal language, we collect the novel TEMPOral reasoning in video and language (TEMPO) dataset. Our dataset consists of two parts: a dataset with real videos and template sentences (TEMPO - Template Language) which allows for controlled studies on temporal language, and a human language dataset which consists of temporal sentences annotated by humans (TEMPO - Human Language).


Optimizing Interventions via Offline Policy Evaluation: Studies in Citizen Science


title: Are spatial and global constraints really necessary for segmentation?

abstract: Many state-of-the-art segmentation algorithms rely on Markov or Conditional Random Field models designed to enforce spatial and global consistency constraints. This is often accomplished by introducing additional latent variables to the model, which can greatly increase its complexity. As a result, estimating the model parameters or computing the best maximum a posteriori (MAP) assignment becomes a computationally expensive task. In a series of experiments on the PASCAL and the MSRC datasets, we were unable to find evidence of a significant performance increase attributed to the introduction of such constraints. On the contrary, we found that similar levels of performance can be achieved using a much simpler design that essentially ignores these constraints. This more simple approach makes use of the same local and global features to leverage evidence from the image, but instead directly biases the preferences of individual pixels. While our investigation does not prove that spatial and consistency constraints are not useful in principle, it points to the conclusion that they should be validated in a larger context.


title: Perceived health status in urban minority young adolescents.

abstract: Purpose: To describe perceptions of health status among a sample of urban minority adolescents and the contribution of demographics intrinsic motivation general self-efficacy risk taking and stressful life experiences on the adolescent’s perception of health status. Method: Correlational design. A total of 71 adolescents were studied using the Adolescent Health Chart for perceived health status the Health Self-Determinism Index for Children the Self-Efficacy Scale the Risk Taking Instrument and the Life Events Checklist. Results: There were no statistically significant effects of demographics on perceived health status. Scores of Perceived Health Status correlated with scores of self-efficacy (r= 0.56; p< .0001) risk-taking (r= 0.39; p< .001) negative events (r= 0.32; p< .01) and violent events (r= -0.41; p< .001). Clinical Implications: The study findings suggest that self-efficacy risk taking and life events are predictive of perceived health status in urban minority adolescents. The results contribute to the present body of knowledge about patterns of adolescent health as perceived by the adolescent. In addition to expanding the understanding of the minority adolescent experience in relation to health promotion attributes and health compromising behaviors the results identify antecedents that are predictive of improved perceived health status for the urban adolescent. (authors)


title: A strategy for comparing alternative software development life cycle models

abstract: It is difficult to compare and contrast models of software development because their proponents often use different terminology, and the models often have little in common except their beginnings (marked by a recognition that a problem exists) and ends (marked by the existence of a software solution). A framework is provided that can serve: as a basis for analyzing the similarities and differences among alternate life-cycle models; as a tool for software engineering researchers to help describe the probable impacts of a life-cycle mode; and as a means to help software practitioners decide on an appropriate life-cycle model to utilize on a particular project or in a particular application area. >


title: Single Lens Stereo with a Plenoptic Camera

abstract: Ordinary cameras gather light across the area of their lens aperture, and the light striking a given subregion of the aperture is structured somewhat differently than the light striking an adjacent subregion. By analyzing this optical structure, one can infer the depths of the objects in the scene, i.e. one can achieve single lens stereo. The authors describe a camera for performing this analysis. It incorporates a single main lens along with a lenticular array placed at the sensor plane. The resulting plenoptic camera provides information about how the scene would look when viewed from a continuum of possible viewpoints bounded by the main lens aperture. Deriving depth information is simpler than in a binocular stereo system because the correspondence problem is minimized. The camera extracts information about both horizontal and vertical parallax, which improves the reliability of the depth estimates. >


title: Comparison of Four Approaches to Automatic Language Identification of Telephone Speech

abstract: Abstruct- We have compared the performance of four approaches for automatic language identification of speech utterances: Gaussian mixture model (GMM) classification; single-language phone recognition followed by languagedependent, interpolated n-gram language modeling (PRLM); parallel PRLM, which uses multiple single-language phone recognizers, each trained in a different language; and languagedependent parallel phone recognition (PPR). These approaches, which span a wide range of training requirements and levels of recognition complexity, were evaluated with the Oregon Graduate Institute Multi-Language Telephone Speech Corpus. Systems containing phone recognizers performed better than the simpler GMM classifier. The top-performing system was parallel PRLM, which exhibited an error rate of 2% for 45-s utterances and 5% for 10-s utterances in two-language, closed-set, forcedchoice classification. The error rate for 11-language, closed-set, forced-choice classification was 11 % for 45-s utterances and 21% for 10-s utterances.


title: Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos

abstract: Explainability and interpretability are two critical aspects of decision support systems. Within computer vision, they are critical in certain tasks related to human behavior analysis such as in health care applications. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of computer vision with an emphasis on looking at people tasks. Specifically, we review and study those mechanisms in the context of first impressions analysis. To the best of our knowledge, this is the first effort in this direction. Additionally, we describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, the evaluation protocol, and summarize the results of the challenge. Finally, derived from our study, we outline research opportunities that we foresee will be decisive in the near future for the development of the explainable computer vision field.


title: The 2017 DAVIS Challenge on Video Object Segmentation

abstract: We present the 2017 DAVIS Challenge, a public competition specifically designed for the task of video object segmentation. Following the footsteps of other successful initiatives, such as ILSVRC and PASCAL VOC, which established the avenue of research in the fields of scene classification and semantic segmentation, the DAVIS Challenge comprises a dataset, an evaluation methodology, and a public competition with a dedicated workshop co-located with CVPR 2017. The DAVIS Challenge follows up on the recent publication of DAVIS (Densely-Annotated VIdeo Segmentation), which has fostered the development of several novel state-of-the-art video object segmentation techniques. In this paper we describe the scope of the benchmark, highlight the main characteristics of the dataset and define the evaluation metrics of the competition.


title: A Decision Fusion Model for 3D Detection of Autonomous Driving

abstract: This paper proposes a multimodal fusion model for 3D car detection inputting both point clouds and RGB images and generates the corresponding 3D bounding boxes. Our model is composed of two subnetworks: one is point-based method and another is multi-view based method, which is then combined by a decision fusion model. This decision model can absorb the advantages of these two sub-networks and restrict their shortcomings effectively. Experiments on the KITTI 3D car detection benchmark show that our work can achieve state of the art performance.


title: The effects of tree-view based presentation adaptation on mobile web browsing

abstract: Accessing the Web from mobile handheld devices has become increasingly common. However, accomplishing that task remains challenging mainly due to the physical constraints of handheld devices and the static presentation of Web pages. Adapting the presentation of Web pages is, therefore, critical to enabling effective mobile Web browsing and information searching. Based on cognitive fit theory and information foraging theory, we propose a novel hybrid approach to adapting Web page presentation that integrates three types of adaptation techniques, namely tree-view, hierarchical text summarization, and colored keyword highlighting. By following the design science research framework, we implemented the proposed approach on handheld devices and empirically evaluated the effects of presentation adaptation on mobile Web browsing. The results show that presentation adaptation significantly improves user performance and perception of mobile Web browsing. We also discover that the positive impact of presentation adaptation is moderated by the complexity of an information search task. The findings have significant theoretical and practical implications for the design and implementation of mobile Web applications.


title: TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network

abstract: In this work, we present the Text Conditioned Auxiliary Classifier Generative Adversarial Network, (TAC-GAN) a text to image Generative Adversarial Network (GAN) for synthesizing images from their text descriptions. Former approaches have tried to condition the generative process on the textual data; but allying it to the usage of class information, known to diversify the generated samples and improve their structural coherence, has not been explored. We trained the presented TAC-GAN model on the Oxford-102 dataset of flowers, and evaluated the discriminability of the generated images with Inception-Score, as well as their diversity using the Multi-Scale Structural Similarity Index (MS-SSIM). Our approach outperforms the state-of-the-art models, i.e., its inception score is 3.45, corresponding to a relative increase of 7.8% compared to the recently introduced StackGan. A comparison of the mean MS-SSIM scores of the training and generated samples per class shows that our approach is able to generate highly diverse images with an average MS-SSIM of 0.14 over all generated classes.


title: Role-based access control on the web

abstract: Current approaches to access control on the Web servers do not scale to enterprise-wide systems because they are mostly based on individual user identities. Hence we were motivated by the need to manage and enforce the strong and efficient RBAC access control technology in large-scale Web environments. To satisfy this requirement, we identify two different architectures for RBAC on the Web, called user-pull and server-pull . To demonstrate feasibility, we implement each architecture by integrating and extending well-known technologies such as cookies, X.509, SSL, and LDAP, providing compatibility with current web technologies. We describe the technologies we use to implement RBAC on the Web in different architectures. Based on our experience, we also compare the tradeoffs of the different approaches.


Semantic Web-Based Seismic Disaster Management Expert System


title: An Adaptive Routing Algorithm: Enhanced Confidence-Based Q Routing Algorithm in Network Traffic

abstract: Confidence-based Q (CQ) Routing Algorithm is an adaptive network routing algorithm. CQ Routing Algorithm evaluates how confidence value (C value) can be used to improve the quality of exploration in Q Routing Algorithm for adaptive packet routing in communication networks. However, the C value incompletely evaluates how closely the Q value represents the current condition of the network in a given length of time, which is measured in term of estimated delivery time for a packet to arrive at its destination. In this paper, an Enhanced Confidence-based Q (ECQ) Routing Algorithm is proposed. The Variable of Decay Constant and Update All Q value approaches are introduced for updating the C values of non-selected Q values. Using these C values would make those nonselected Q values more competitive in order to achieve updated and more reliable values. The quality of exploration in CQ Routing Algorithm would be improved. The performance of ECQ and CQ Routing Algorithms are compared to prove this improvement. ECQ and CQ Routing Algorithms are tested on an irregular 6 x 6 nodes network grid.


Classifying Processes and Basic Formal Ontology


title: Need accurate user behaviour?: pay attention to groups!

abstract: In this paper, we show that characterizing user behaviour from location or smartphone usage traces, without accounting for the interaction of individuals in physical-world groups, can lead to erroneous results. We conducted one of the largest studies in the UbiComp domain thus far, involving indoor location traces of more than 6,000 users, collected over a 4-month period at our university campus, and further studied fine-grained App usage of a subset of 156 Android users. We apply a state-of-the-art group detection algorithm to annotate such location traces with group vs. individual context, and then show that individuals vs. groups exhibit significant differences along three behavioural traits: (1) the mobility pattern, (2) the responsiveness to calls / SMSs and (3) application usage. We show that these significant differences are robust to underlying errors in the group detection technique and that the use of such group context leads to behavioural results that differ from those reported in prior popular work.


title: Silence speaks volumes : The effectiveness of reticence in comparison to apology and denial for responding to integrity-and competence-based trust violations

abstract: Prior research on responses to trust violations has focused primarily on the effects of apology and denial. The authors extended this research by studying another type of verbal response that is often used to respond to trust violations but has not been considered in the trust literature: reticence. An accused party may use reticence in a sincere and even legitimate attempt to persuade a trustor to withhold judgment. Yet, by considering information diagnosticity and belief formation mechanisms through which verbal responses influence trust, the authors argue that reticence is a suboptimal response because it combines the least effective elements of apology and denial. Specifically, reticence is a suboptimal response to an integrity violation because, like apology, it fails to address guilt. And reticence is a suboptimal response to a competence violation because, like denial, it fails to signal redemption. Results from 2 laboratory studies, simulating different contexts and using research participants from 2 different countries, provide support for the prediction. The results offer important implications for those who might use reticence to respond to a perceived trust violation and also for those who must judge another's reticence.


title: Internet-of-Things Security and Vulnerabilities: Taxonomy, Challenges, and Practice

abstract: Recent years have seen rapid development and deployment of Internet-of-Things (IoT) applications in a diversity of application domains. This has resulted in creation of new applications (e.g., vehicle networking, smart grid, and wearables) as well as advancement, consolidation, and transformation of various traditional domains (e.g., medical and automotive). One upshot of this scale and diversity of applications is the emergence of new and critical threats to security and privacy: it is getting increasingly easier for an adversary to break into an application, make it unusable, or steal sensitive information and data. This paper provides a summary of IoT security attacks and develops a taxonomy and classification based on the application domain and underlying system architecture. We also discuss some key characteristics of IoT that make it difficult to develop robust security architectures for IoT applications.


Deep Neural Networks versus Naive Bayes Classifiers for Identifying Informative Tweets during Disasters


title: Thrombus Detection from Echocardiographic Images Using Image Processing Techniques.

abstract: Image processing is a powerful tool for increasing the reliability and reproducibility of disease diagnostics. In the hands of pathologists, image processing provides quantitative data from histological images which supplement the qualitative data currently used by specialists. This paper presents the method for analysing digitized images of echocardiography and detect some of the basic diseases which can cause the Heart Attack. This paper presents the method for detecting the diseases like thrombus related to heart. Methods presented in this paper for detection of thrombus include steps like noise removal, feature extraction, segmentation and thrombus detection. At first the speckle noise is been removed using Lee Filter. Image is segmented using active contour method to detect LV (Left Ventricular). From LV chamber thrombus is detected using operations like thresholding, intersection, morphological opening, finding largest connected component and edge detection.


title: Detecting Risks in the Banking System by Sentiment Analysis

abstract: In November 2014, the European Central Bank (ECB) started to directly supervise the largest banks in the Eurozone via the Single Supervisory Mechanism (SSM). While supervisory risk assessments are usually based on quantitative data and surveys, this work explores whether sentiment analysis is capable of measuring a bank’s attitude and opinions towards risk by analyzing text data. For realizing this study, a collection consisting of more than 500 CEO letters and outlook sections extracted from bank annual reports is built up. Based on these data, two distinct experiments are conducted. The evaluations find promising opportunities, but also limitations for risk sentiment analysis in banking supervision. At the level of individual banks, predictions are relatively inaccurate. In contrast, the analysis of aggregated figures revealed strong and significant correlations between uncertainty or negativity in textual disclosures and the quantitative risk indicator’s future evolution. Risk sentiment analysis should therefore rather be used for macroprudential analyses than for assessments of individual banks.


title: Robust ground surface map generation using vehicle-mounted stereo camera

abstract: We propose a robust method for incrementally estimating a regular-grid ground surface map from stereo image sequences captured by nearly front-looking vehicle-mounted stereo cameras. The method simultaneously estimates a camera ego-motion and vertex heights of a regular mesh, which is composed of piecewise triangular patches drawn on a level plane in the ground coordinate system, by minimizing pixel value differences over the ground surface. The method combinationally uses feature-based approach and pixel-based approach for robustly estimating ego-motion parameters. We also show that this combination is beneficial for removing outlier pixels, which mainly represent the edge of the self-shadow area on the ground surface. The validity of the proposed method is demonstrated through experiments using real images.


Art, Emergence, and the Computational Sublime


42 UCL Depthmap 7: From Isovist Analysis to Generic Spatial Network Analysis


title: Interferon gamma modifies fibronectin and laminin synthesis in human neuroblastoma cell lines.

abstract: Six human neuroblastoma (NB) cell lines of different type: three neuronal, one mixed and two epithelial, were studied for their responsiveness to interferon gamma (IFNgamma). The effects of IFNgamma on cell growth rate and morphology were first evaluated: the replication was significantly inhibited in five out of six NB, while a neural morphological maturation was evident only in the three neural NB. Whether IFNgamma could also control the synthesis of two extracellular matrix glycoproteins, often involved in NB differentiation, laminin (LM) and fibronectin (FN), was then analyzed. Both glycoproteins increased after IFNgamma treatment in all neural and in the mixed NB, while they slightly decreased in one and did not change significantly in the other epithelial NB. Therefore, IFNgamma determines the up-regulation of FN and LM, while inducing NB cell growth inhibition and neural differentiation. These data support the hypothesis that the expression of the two extracellular matrix glycoproteins is controlled by IFNgamma during the complex event of NB terminal maturation.


title: Dirt Cheap Web-Scale Parallel Text from the Common Crawl

abstract: Parallel text is the fuel that drives modern machine translation systems. The Web is a comprehensive source of preexisting parallel text, but crawling the entire web is impossible for all but the largest companies. We bring web-scale parallel text to the masses by mining the Common Crawl, a public Web crawl hosted on Amazon’s Elastic Cloud. Starting from nothing more than a set of common two-letter language codes, our open-source extension of the STRAND algorithm mined 32 terabytes of the crawl in just under a day, at a cost of about $500. Our large-scale experiment uncovers large amounts of parallel text in dozens of language pairs across a variety of domains and genres, some previously unavailable in curated datasets. Even with minimal cleaning and filtering, the resulting data boosts translation performance across the board for five different language pairs in the news domain, and on open domain test sets we see improvements of up to 5 BLEU. We make our code and data available for other researchers seeking to mine this rich new data resource. 1


Structural and Dielectric Investigations of Cerium Stabilized Zirconia ( Zr 1x Ce x O 2 ( x = 0-0 . 05 ) ) Nanocrystals Blended by Wet Chemical Method


title: Chapter 8 Swarm-Based Morphogenetic Artificial Life

abstract: We present a swarm-based framework for designing and implementing morphogenetic artifacts that can grow, self-organize and self-repair in a fully decentralized manner. Artifacts are realized as swarms of multiple types of very simple, kinetically interacting particles with no elaborate sensing, computation, or communication capabilities. The proposed framework is based on our earlier work, Swarm Chemistry, a computational model of particle swarms where mobile particles with different kinetic properties interact with each other to produce dynamic structures and behaviors spontaneously. The features of emergent patterns are implicitly encoded through interactive evolutionary design methods into a set of kinetic parameter values, called a recipe. In this chapter, we summarize several extensions of the model for morphogenetic engineering and demonstrate a variety of morphogenetic processes that can be achieved by using simple particles with minimal capability. Specifically, we show (1) diversity of self-organizing patterns that can be generated by simple particle swarms in our framework, (2) robustness of those patterns against external perturbations, (3) growth and self-assembly by local information transmission between particles and their stochastic differentiation, and (4) self-repair by stochastic re-differentiation of particles.


title: Active Classification: Theory and Application to Underwater Inspection

abstract: We discuss the problem in which an autonomous vehicle must classify an object based on multiple views. We focus on the active classification setting, where the vehicle controls which views to select to best perform the classification. The problem is formulated as an extension to Bayesian active learning, and we show connections to recent theoretical guarantees in this area. We formally analyze the benefit of acting adaptively as new information becomes available. The analysis leads to a probabilistic algorithm for determining the best views to observe based on information theoretic costs. We validate our approach in two ways, both related to underwater inspection: 3D polyhedra recognition in synthetic depth maps and ship hull inspection with imaging sonar. These tasks encompass both the planning and recognition aspects of the active classification problem. The results demonstrate that actively planning for informative views can reduce the number of necessary views by up to 80 % when compared to passive methods.


title: MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices

abstract: Deep neural networks have been widely used in numerous computer vision applications, particularly in face recognition. However, deploying deep neural network face recognition on mobile devices has recently become a trend but still limited since most high-accuracy deep models are both time and GPU consumption in the inference stage. Therefore, developing a lightweight deep neural network is one of the most practical solutions to deploy face recognition on mobile devices. Such the lightweight deep neural network requires efficient memory with small number of weights representation and low cost operators. In this paper, a novel deep neural network named MobiFace, a simple but effective approach, is proposed for productively deploying face recognition on mobile devices. The experimental results have shown that our lightweight MobiFace is able to achieve high performance with 99.73% on LFW database and 91.3% on large-scale challenging Megaface database. It is also eventually competitive against large-scale deep-networks face recognition while significant reducing computational time and memory consumption.


A Deep Relevance Model for Zero-Shot Document Filtering


title: Low-Complexity Map Channel Estimation for Mobile MIMO-OFDM Systems

abstract: This paper presents a reduced-complexity maximum a posteriori probability (MAP) channel estimator with iterative data detection for orthogonal frequency division multiplexing (OFDM) systems over mobile multiple-input multiple- output channels. The optimal MAP estimator needs to invert an NNT x NNT data-dependent matrix each in OFDM symbol interval, where N is the number of subcarriers and NT is the number of transmit antennas. We derive an expectation maximization (EM) algorithm with low-rank approximation to avoid inverting large-size matrices, and thus drastically reduce the receiver complexity. In the iterative process, channel parameters are initially obtained by a least square (LS) estimator for temporary symbol decisions. Then, inter-carrier interference (ICI) due to fast fading is approximated and canceled. Finally, the temporary symbol decisions and the ICI-canceled received signals are processed by the EM-based MAP estimator to refine the channel state information for improved detection. The proposed scheme achieves about 2 dB gain over the LS scheme in channels with medium to high normalized Doppler shifts.


title: Discovering objects and their relations from entangled scene representations

abstract: Our world can be succinctly and compactly described as structured scenes of objects and relations. A typical room, for example, contains salient objects such as tables, chairs and books, and these objects typically relate to each other by virtue of their correlated features, such as position, function and shape. Humans exploit knowledge of objects and their relations for learning a wide spectrum of tasks, and more generally when learning the structure underlying observed data. In this work, we introduce relation networks (RNs) - a general purpose neural network architecture for object-relation reasoning. We show that RNs are capable of learning object relations from scene description data. Furthermore, we show that RNs can act as a bottleneck that induces the factorization of objects from entangled scene description inputs, and from distributed deep representations of scene images provided by a variational autoencoder. The model can also be used in conjunction with differentiable memory mechanisms for implicit relation discovery in one-shot learning tasks. Our results suggest that relation networks are a powerful architecture for solving a variety of problems that require object relation reasoning.


Taming App Delivery via Streaming


title: Tor: The Second-Generation Onion Router (2014 DRAFT v1)

abstract: THIS IS A DRAFT. IT IS NOT FINISHED. We present Tor, a circuit-based low-latency anonymous communication service. This Onion Routing system addresses limitations in the earlier design by adding perfect forward secrecy, congestion control, directory servers, integrity checking, configurable exit policies, anticensorship features, guard nodes, application- and user-selectable stream isolation, and a practical design for location-hidden services via rendezvous points. Tor is deployed on the real-world Internet, requires no special privileges or kernel modifications, requires little synchronization or coordination between nodes, and provides a reasonable tradeoff between anonymity, usability, and efficiency. An earlier paper in 2004 described Tor’s original design; here we explain Tor’s current design as of late 2012, and describe our experiences with an international network of approximately 3000 nodes and 500000 users. We close with a list of open problems in anonymous communication.


title: Receiver-driven bandwidth sharing for TCP

abstract: Applications using TCP, such as Web-browsers, ftp, and various P2P programs, dominate most of the Internet traffic today. In many cases the last-hop access links are bottlenecks due to their limited bandwidth capability with users running many simultaneous network applications. Standard TCP shares bottleneck link capacity according to connection round-trip time (RTT), and may result in a bandwidth partition which does not necessarily coincide with the user's desires. We present a receiver-based control system for allocating bandwidth among TCP flows according to user preferences. Our system does not require any changes to network infrastructure, and works with standard TCP senders. NS-2 simulations, as well as actual Internet experiments, show that our system achieves desired bandwidth allocation in a wide variety of scenarios including interfering cross-traffic. We also demonstrate the viability of our system in multimedia streaming applications over TCP.


title: Bank of One: Empirical Analysis of Peer-to-Peer Financial Marketplaces

abstract: Peer to peer financial marketplaces provide a platform for individual lenders and borrowers to interact and transact. These marketplaces disintermediate the traditional financial services business models. In this exploratory paper we study the operation and effectiveness of one such marketplace: Prosper.com. We analyze six months of lender, borrower and loan repayment data to answer preliminary research questions about lender behavior, market effectiveness and antecedents of loan default. We show that lenders mostly behave rationally and charge appropriate risk premiums for antecedents of loan default. We also show that there are mismatches between risk premiums charged and relative importance of factors that drive loan default. We then explore the dynamic process of lenders adjusting their lending strategies to reduce these mismatches. We analyze the effectiveness of the group reputation used in the marketplace and show that it is not effective in promoting good borrower behavior. Our analysis provides a base for future research in this exciting and evolving context. Our results provide directions for practice applications as well as future research in design of financial marketplaces, investing and risk mitigation strategies and improving the effectiveness of peer-to-peer financial marketplaces.


title: Zero-Knowledge Proofs of Knowledge Without Interaction (Extended Abstract)

abstract: A zero-knowledge proof system of knowledge is a protocol between two parties called the prover and the verifier. The prover wants to convince the verifier that he “knows” the proof of a given theorem without revealing any additional information. This is different from a zero-knowledge proof system of membership where the prover convinces the verifier only of the veridicity of the statement. Zero-knowledge proofs of knowledge are very useful tools in the design of secure protocols. Though, the concept of a proof of knowledge is a very subtle one and great care is needed to obtain a satisfying formalization. In this paper, we investigate the concept of a zero-knowledge proof of knowledge in the noninteractive model of [5, 61. Here, the prover and the verifier share a short random string and the only communication allowed is from the prover to the verifier. Although this is a simpler model than the interactive one, still formalizing zeroknowledge proofs of knowledge is a delicate task. The main results of the paper are the following e We present formal definitions for the concept of non-interactive zero-knowledge proofs


title: Model-Based Face De-Identification

abstract: Advances in camera and computing equipment hardware in recent years have made it increasingly simple to capture and store extensive amounts of video data. This, among other things, creates ample opportunities for the sharing of video sequences. In order to protect the privacy of subjects visible in the scene, automated methods to de-identify the images, particularly the face region, are necessary. So far the majority of privacy protection schemes currently used in practice rely on ad-hoc methods such as pixelation or blurring of the face. In this paper we show in extensive experiments that pixelation and blurring offers very poor privacy protection while significantly distorting the data. We then introduce a novel framework for de-identifying facial images. Our algorithm combines a model-based face image parameterization with a formal privacy protection model. In experiments on two large-scale data sets we demonstrate privacy protection and preservation of data utility.


title: Learning Models for Actions and Person-Object Interactions with Transfer to Question Answering

abstract: This paper proposes deep convolutional network models that utilize local and global context to make human activity label predictions in still images, achieving state-of-the-art performance on two recent datasets with hundreds of labels each. We use multiple instance learning to handle the lack of supervision on the level of individual person instances, and weighted loss to handle unbalanced training data. Further, we show how specialized features trained on these datasets can be used to improve accuracy on the Visual Question Answering (VQA) task, in the form of multiple choice fill-in-the-blank questions (Visual Madlibs). Specifically, we tackle two types of questions on person activity and person-object relationship and show improvements over generic features trained on the ImageNet classification task


title: Supporting scenario-based requirements engineering

abstract: Scenarios have been advocated as a means of improving requirements engineering yet few methods or tools exist to support scenario based RE. The paper reports a method and software assistant tool for scenario based RE that integrates with use case approaches to object oriented development. The method and operation of the tool are illustrated with a financial system case study. Scenarios are used to represent paths of possible behavior through a use case, and these are investigated to elaborate requirements. The method commences by acquisition and modeling of a use case. The use case is then compared with a library of abstract models that represent different application classes. Each model is associated with a set of generic requirements for its class, hence, by identifying the class(es) to which the use case belongs, generic requirements can be reused. Scenario paths are automatically generated from use cases, then exception types are applied to normal event sequences to suggest possible abnormal events resulting from human error. Generic requirements are also attached to exceptions to suggest possible ways of dealing with human error and other types of system failure. Scenarios are validated by rule based frames which detect problematic event patterns. The tool suggests appropriate generic requirements to deal with the problems encountered. The paper concludes with a review of related work and a discussion of the prospects for scenario based RE methods and tools.


title: Classification Algorithm for Feature Extraction using Linear Discriminant Analysis and Cross-correlation on ECG Signals

abstract: This paper develops a novel framework for feature extraction based on a combination of Linear Discriminant Analysis and cross-correlation. Multiple Electrocardiogram (ECG) signals, acquired from the human heart in different states such as in fear, during exercise, etc. are used for simulations. The ECG signals are composed of P, Q, R, S and T waves. They are characterized by several parameters and the important information relies on its HRV (Heart Rate Variability). Human interpretation of such signals requires experience and incorrect readings could result in potentially life threatening and even fatal consequences. Thus a proper interpretation of ECG signals is of paramount importance. This work focuses on designing a machine based classification algorithm for ECG signals. The proposed algorithm filters the ECG signals to reduce the effects of noise. It then uses the Fourier transform to transform the signals into the frequency domain for analysis. The frequency domain signal is then cross correlated with predefined classes of ECG signals, in a manner similar to pattern recognition. The correlated co-efficients generated are then thresholded. Moreover Linear Discriminant Analysis is also applied. Linear Discriminant Analysis makes classes of different multiple ECG signals. LDA makes classes on the basis of mean, global mean, mean subtraction, transpose, covariance, probability and frequencies. And also setting thresholds for the classes. The distributed space area is divided into regions corresponding to each of the classes. Each region associated with a class is defined by its thresholds. So it is useful in distinguishing ECG signals from each other. And pedantic details from LDA (Linear Discriminant Analysis) output graph can be easily taken in account rapidly. The output generated after applying cross-correlation and LDA displays either normal, fear, smoking or exercise ECG signal. As a result, the system can help clinically on large scale by providing reliable and accurate classification in a fast and computationally efficient manner. The doctors can use this system by gaining more efficiency. As very few errors are involved in it, showing accuracy between 90% - 95%.


title: Analysis, design, and performance evaluation of asymmetrical half-bridge flyback converter for universal-line-voltage-range applications

abstract: The asymmetrical half-bridge (AHB) flyback converter is an attractive topology for operation at higher switching frequencies because it can operate with zero-voltage switching of the primary-side switches and zero-current switching of the secondary-side rectifier. In this paper, a detailed analysis and design procedure of the AHB flyback converter for the universal-line-voltage-range applications is presented. The performance of the AHB flyback converter is evaluated by loss analysis based on the simulation waveforms obtained in Simplis and experimentally verified on a laboratory prototype of a 65-W (19.5-V, 3.33-A) universal-line-voltage-range adapter.


title: A new proof of the weak pigeonhole principle

abstract: The exact complexity of the weak pigeonhole principle is an old and fundamental problem in proof complexity. Using a diagonalization argument, J. B. Paris et al. (J. Symbolic Logic 53 (1988), 1235-1244) showed how to prove the weak pigeonhole principle with bounded-depth, quasipolynomialsize proofs. Their argument was further refined by J. Krajicek (J. Symbolic Logic 59 (1994), 73-86). In this paper, we present a new proof: we show that the weak pigeonhole principle has quasipolynomial-size LK proofs where every formula consists of a single AND/OR of polylog fan-in. Our proof is conceptually simpler than previous arguments, and is optimal with respect to depth.


title: A Low-Profile Dual-Polarized High-Isolation MIMO Antenna Arrays for Wideband Base-Station Applications

abstract: A low-profile dual-polarized high-isolation multiple input multiple output (MIMO) antenna array for wideband base-station applications is presented in this paper. The proposed dual-polarized antenna element has the advantage of lower profile ( $0.067\lambda $ ) by utilizing artificial magnetic conductor structure. The antenna array consisting of four elements is working within the frequency band from 2.4 to 3 GHz. Furthermore, decoupling branches among the elements are introduced to improve the isolation by about 10 dB. Both simulation and measured results indicate that the proposed dual-polarized antenna element has a good isolation over 28 dB. Moreover, the beamwidth of the antenna array can be effectively broadened by the adjustment of phase distributions of corresponding artificial material plane. Finally, a larger MIMO system is also investigated, and the simulation and measured results prove that dual-polarized dipole antenna MIMO array has good system performance.


title: Forward-Backward Activation Algorithm for Hierarchical Hidden Markov Models

abstract: Hierarchical Hidden Markov Models (HHMMs) are sophisticated stochastic models that enable us to capture a hierarchical context characterization of sequence data. However, existing HHMM parameter estimation methods require large computations of time complexity O(TN2D) at least for model inference, where D is the depth of the hierarchy, N is the number of states in each level, and T is the sequence length. In this paper, we propose a new inference method of HHMMs for which the time complexity is O(TND+1). A key idea of our algorithm is application of the forward-backward algorithm to state activation probabilities. The notion of a state activation, which offers a simple formalization of the hierarchical transition behavior of HHMMs, enables us to conduct model inference efficiently. We present some experiments to demonstrate that our proposed method works more efficiently to estimate HHMM parameters than do some existing methods such as the flattening method and Gibbs sampling method.


Mining Association Rules from NoSQL data bases using MapReduce Fuzzy Association Rule Mining Algorithm


title: Retrieval models for question and answer archives

abstract: Retrieval in a question and answer archive involves finding good answers for a user's question. In contrast to typical document retrieval, a retrieval model for this task can exploit question similarity as well as ranking the associated answers. In this paper, we propose a retrieval model that combines a translation-based language model for the question part with a query likelihood approach for the answer part. The proposed model incorporates word-to-word translation probabilities learned through exploiting different sources of information. Experiments show that the proposed translation based language model for the question part outperforms baseline methods significantly. By combining with the query likelihood language model for the answer part, substantial additional effectiveness improvements are obtained.


title: Immersing Web3D Furniture into Real Interior Images

abstract: Platforms for interior DIY(Do It Yourself) design should hold sufficient realistic sense and light manual operation in interior modeling, besides more flexibility and adaptation of online editing are also essential. But current pure Web3D or pure image based platforms are hardly meet those goals. Therefore this paper presents a lightweight and immersive solutions by editing virtual 3D furniture into captured 2D interior pictures interactively, placing them at the optimal location automatically and rendering them in real time to consistently harmonize them with real interior pictures in terms of geometric layout and lighting visual effects. Our contributions consist in: (1)cuboid modeling from camera captured interior pictures interactively without loss of realistic sense and with light manual operation; (2)lightweight automatic furniture arrangement method with enough flexibility and adaptation for online editing; (3)lightweight IBL(Image Based Lighting) and PBR(Physic Based Rendering) to make virtual furniture immerse into real interior image more visually authentic. Compared with those existing online systems, this solution can provide low-cost, convenient, pervasive online services for interior DIY design over mobile Internet.


title: OCNet: Object Context Network for Scene Parsing

abstract: In this paper, we address the problem of scene parsing with deep learning and focus on the context aggregation strategy for robust segmentation. Motivated by that the label of a pixel is the category of the object that the pixel belongs to, we introduce an \emph{object context pooling (OCP)} scheme, which represents each pixel by exploiting the set of pixels that belong to the same object category with such a pixel, and we call the set of pixels as object context. ::: Our implementation, inspired by the self-attention approach, consists of two steps: (i) compute the similarities between each pixel and all the pixels, forming a so-called object context map for each pixel served as a surrogate for the true object context, and (ii) represent the pixel by aggregating the features of all the pixels weighted by the similarities. The resulting representation is more robust compared to existing context aggregation schemes, e.g., pyramid pooling modules (PPM) in PSPNet and atrous spatial pyramid pooling (ASPP), which do not differentiate the context pixels belonging to the same object category or not, making the reliability of contextually aggregated representations limited. We empirically demonstrate our approach and two pyramid extensions with state-of-the-art performance on three semantic segmentation benchmarks: Cityscapes, ADE20K and LIP. Code has been made available at: this https URL.


title: A framework of code reuse in open source software

abstract: Recently, adopting open source software into software development has become a growing trend in the IT software industry. Unlike the general commercial software, open source software leverages the capability of the communities to improve the quality, but also reduce the cost of software development. This paper aims at analyzing the influence factors of applying open source software in software reuse. We begin by proposing our framework for enhancing the open source software usage and then set out to determine the influence factors of software reuse. A reuse system based on our proposed framework is implemented. For the assessment, a questionnaire survey and analysis of 20 software projects are carried out to evaluate our approach. There are 50 experienced corresponding project managers and developers participating in our experiments. The assessment shows that reuse factors of projects managers and developers influence the OSS reuse.


title: Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking

abstract: Methods that learn representations of nodes in a graph play a critical role in network analysis since they enable many downstream learning tasks. We propose Graph2Gauss - an approach that can efficiently learn versatile node embeddings on large scale (attributed) graphs that show strong performance on tasks such as link prediction and node classification. Unlike most approaches that represent nodes as point vectors in a low-dimensional continuous space, we embed each node as a Gaussian distribution, allowing us to capture uncertainty about the representation. Furthermore, we propose an unsupervised method that handles inductive learning scenarios and is applicable to different types of graphs: plain/attributed, directed/undirected. By leveraging both the network structure and the associated node attributes, we are able to generalize to unseen nodes without additional training. To learn the embeddings we adopt a personalized ranking formulation w.r.t. the node distances that exploits the natural ordering of the nodes imposed by the network structure. Experiments on real world networks demonstrate the high performance of our approach, outperforming state-of-the-art network embedding methods on several different tasks. Additionally, we demonstrate the benefits of modeling uncertainty - by analyzing it we can estimate neighborhood diversity and detect the intrinsic latent dimensionality of a graph.


title: The RC5 Encryption Algorithm

abstract: This document describes the RC5 encryption algorithm, a fast symmetric block cipher suitable for hardware or software implementations. A novel feature of RC5 is the heavy use of data-dependent rotations. RC5 has a variable word size, a variable number of rounds, and a variable-length secret key. The encryption and decryption algorithms are exceptionally simple.


title: An Empirical Study of Cross-Platform Mobile Development in Industry

abstract: The purpose of this study is to report on the industry’s perspectives and opinions on cross-platform mobile development, with an emphasis on the popularity, adoption, and arising issues related to the use of technical development frameworks and tools. We designed and conducted an online survey questionnaire, for which 101 participants were recruited from various developer-oriented online forums and websites. A total of five questions are reported in this study, of which two employed a Likert scale instrument, while three were based on multiple choice. In terms of technical frameworks, we find that PhoneGap, the Ionic Framework, and React Native were the most popular in use, both in hobby projects and in professional settings. The participants report an awareness of trade-offs when embracing cross-platform technologies and consider penalties in performance and user experience to be expected. This is also in line with what is reported in academic research. We find patterns in the reported perceived issues which match both older and newer research, thus rendering the findings a point of departure for further endevours.


title: Experimental Studies on the Duration of Life. XIV. The Comparative Mortality of Certain Lower Organisms

abstract: T HE first complete life tables based upon comprehensive and accurate observations for any other organism than man, were, so far as we know, those published in the first of this series of Studies by Pearl and Parker (30) in i91i for normal wild-type Drosophila melanogaster, and for its mutant vestigial. Since that time partial or complete life tables for Drosophila have been published by Pearl and Parker (3i) for line-bred strains; (32.) showing effect of successive etherizations; (33) demonstrating influence of density of population on duration of life; (34) influence of ventilation and of feeding embryonic juice; (35) re-computed life tables; (36) for duration of life in the complete absence of food. Pearl, Parker and Gonzalez (37) have published life tables on the Mendelian inheritance of duration of life in Drosophila, and on the effect of surgically removing the wings upon duration of life. Gonzalez (iL) computed Drosophila life tables showing the relation of certain mutant genes to duration of life; Pearl, Miner and Parker (29) on the effect of density of population; Alpatov and Pearl (X) on the effect of temperature; Alpatov (i) on the effects of feeding; and Steinfeld (43) on the effects of a sterile environment. For other non-human forms the following life tables have been published: for the saturniid moth Telea polyphemus a graph of the ungraduated survivorship line by Pearl and Parker (35) based on data published by Philip and N. Rau (40); for the rotifer Proales decipiens by Pearl and Doering (26) based on data published by Noyes (i9); and for mice by Greenwood (I4) based on data from Leonard Hill and J. A. Murray. Data on life duration without the computation of life tables have also been given by Hase (i6) for the common Hydra fusca; by Rau (39) for the roach Blatta orientalis; and by the Szabos (45) for the slug Agriolimax agrestis. Recently Gardner and Hurst have published life tables for the domestic fowl (io). Certain of these data, not previously graduated, have been fitted with the following equations:


title: Scalable matrix inversion using MapReduce

abstract: Matrix operations are a fundamental building block of many computational tasks in fields as diverse as scientific computing, machine learning, and data mining. Matrix inversion is an important matrix operation, but it is difficult to implement in today's popular parallel dataflow programming systems, such as MapReduce. The reason is that each element in the inverse of a matrix depends on multiple elements in the input matrix, so the computation is not easily partitionable. In this paper, we present a scalable and efficient technique for matrix inversion in MapReduce. Our technique relies on computing the LU decomposition of the input matrix and using that decomposition to compute the required matrix inverse. We present a technique for computing the LU decomposition and the matrix inverse using a pipeline of MapReduce jobs. We also present optimizations of this technique in the context of Hadoop. To the best of our knowledge, our technique is the first matrix inversion technique using MapReduce. We show experimentally that our technique has good scalability, enabling us to invert a 10^5 x 10^5 matrix in 5 hours on Amazon EC2. We also show that our technique outperforms ScaLAPACK, a state-of-the-art linear algebra package that uses MPI.