title: Exploring the Design Space of Graphical Passwords on Smartphones

abstract: Smartphones have emerged as a likely application area for graphical passwords, because they are easier to input on touchscreens than text passwords. Extensive research on graphical passwords and the capabilities of modern smartphones result in a complex design space for graphical password schemes on smartphones. We analyze and describe this design space in detail. In the process, we identify and highlight interrelations between usability and security characteristics, available design features, and smartphone capabilities. We further show the expressiveness and utility of the design space in the development of graphical passwords schemes by implementing five different existing graphical password schemes on one smartphone platform. We performed usability and shoulder surfing experiments with the implemented schemes to validate identified relations in the design space. From our results, we derive a number of helpful insights and guidelines for the design of graphical passwords.


title: Computer Vision for Head Pose Estimation: Review of a Competition

abstract: This paper studies the prediction of head pose from still images, and summarizes the outcome of a recently organized competition, where the task was to predict the yaw and pitch angles of an image dataset with 2790 samples with known angles. The competition received 292 entries from 52 participants, the best ones clearly exceeding the state-of-the-art accuracy. In this paper, we present the key methodologies behind selected top methods, summarize their prediction accuracy and compare with the current state of the art.


title: Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects

abstract: We present Sequential Attend, Infer, Repeat (SQAIR), an interpretable deep generative model for videos of moving objects. It can reliably discover and track objects throughout the sequence of frames, and can also generate future frames conditioning on the current frame, thereby simulating expected motion of objects. This is achieved by explicitly encoding object presence, locations and appearances in the latent variables of the model. SQAIR retains all strengths of its predecessor, Attend, Infer, Repeat (AIR, Eslami et. al., 2016), including learning in an unsupervised manner, and addresses its shortcomings. We use a moving multi-MNIST dataset to show limitations of AIR in detecting overlapping or partially occluded objects, and show how SQAIR overcomes them by leveraging temporal consistency of objects. Finally, we also apply SQAIR to real-world pedestrian CCTV data, where it learns to reliably detect, track and generate walking pedestrians with no supervision.


title: Theoretical Foundations of Deep Learning via Sparse Representations: A Multilayer Sparse Model and Its Connection to Convolutional Neural Networks

abstract: Modeling data is the way we-scientists-believe that information should be explained and handled. Indeed, models play a central role in practically every task in signal and image processing and machine learning. Sparse representation theory (we shall refer to it as Sparseland) puts forward an emerging, highly effective, and universal model. Its core idea is the description of data as a linear combination of few atoms taken from a dictionary of such fundamental elements.


Incorporating Nesterov Momentum into


title: An open-source 3D printed underactuated robotic gripper

abstract: In this paper authors present an open source low-cost basic robotic end effector platform for facilitating research on robotic grasping. The 3D design model of a three fingered underactuated robotic gripper is presented and manufactured with minimal number of 3D printed components and an off-the-shelf servomotor actuator. An underactuated finger, gear train mechanisms and an overall gripper assembly design are described in details followed by illustration and discussion of grasping of objects with various geometries. The presented open source gripper design will be released for downloading on the authors' research lab web-site www.alaris.kz and can be useful for robotic researchers as a platform to build their own robotic end effector solutions for research and educational purposes.


title: Building a Large-scale Software Programming Taxonomy from Stackoverflow.

abstract: Taxonomy is becoming indispensable to a growing number of applications in software engineering such as software repository mining and defect prediction. However, the existing related taxonomies are always manually constructed. The sizes of these taxonomies are small and their depths are limited. In order to show the full potential of taxonomies in software engineering applications, in this paper, we present the first large-scale software programming taxonomy which is more comprehensive than any existing ones. It contains 38,205 concepts and 68,098 subsumption relations. Instead of learning from a open domain, we focus on taxonomy construction from Stackoverflow which is one of the largest QA websites about software programming. We propose a machine learning based method with novel features to create a taxonomy that captures the hierarchical semantic structure of tags in Stackoverflow. This method executes iteratively to find as many relations as possible. Experimental results show that our approach achieves much better accuracy than baselines. Compared with taxonomies related to software programming which are extracted from the general-purpose taxonomies such as WikiTaxonomy, Yago Taxonomy and Schema.org, our taxonomy has the widest coverage of concepts, contains the largest number of subsumption relations, and runs up to the deepest semantic hierarchy. Keywords—Taxonomy Construction, Stackoverflow, Software Engineering


title: Freewheeling diode reverse-recovery failure modes in IGBT applications

abstract: In this paper, reverse-recovery failure modes in modern fast power diodes are investigated. By the aid of semiconductor device simulation tools, a better view is obtained for the physical process, and operating conditions at which both diode snappy recovery and dynamic avalanching occur during the recovery period in modern high-frequency power electronic applications. The work presented here confirms that the reverse-recovery process can by expressed by means of diode capacitance effects which influence the reverse-recovery characteristics. The paper also shows that the control of the carrier gradient and the remaining stored charge in the drift region during the recovery phase influence both failure modes and determine if the diode exhibits soft, snappy, or dynamic avalanche recovery characteristics.


title: Gazture: Design and Implementation of a Gaze based Gesture Control System on Tablets

abstract: We present Gazture, a light-weight gaze based real-time gesture control system on commercial tablets. Unlike existing approaches that require dedicated hardware (e.g., high resolution camera), high computation overhead (powerful CPU) or specific user behavior (keeping head steady), Gazture provides gesture recognition based on easy-to-control user gaze input with a small overhead. To achieve this goal, Gazture incorporates a two-layer structure: The first layer focuses on real-time gaze estimation with acceptable tracking accuracy while incurring a small overhead. The second layer implements a robust gesture recognition algorithm while compensating gaze estimation error. To address user posture change while using mobile device, we design a online transfer function based method to convert current eye features into corresponding eye features in reference posture, which then facilitates efficient gaze position estimation. We implement Gazture on Lenovo Tab3 8 Plus tablet with Android 6.0.1, and evaluate its performance in different scenarios. The evaluation results show that Gazture can achieve a high accuracy in gesture recognition while incurring a low overhead.


title: Security and Privacy System Requirements for Adopting Cloud Computing in Healthcare Data Sharing Scenarios.

abstract: The emerging cloud computing technology enables new essential scenarios in healthcare, in particular those of data sharing among practitioners. Nevertheless, their security and privacy concerns still impede the wide adoption of cloud computing in this area. Although there are numerous publications in the context of cloud computing in healthcare, we found no consistent typical security and privacy system requirements framework in this domain so far. Owing to the lack of those studies and preparing the ground for creating secure and privacy-friendly cloud architectures for healthcare, we survey security and privacy system requirements for cloud-based medical data sharing scenarios using two strategies. We base on a systematic design science approach following the literature-driven requirement elicitation strategy and apply an established security requirement elicitation methodology as part of the scenario-driven strategy. Finally, we evaluate and compare the two security and privacy system requirements elicitation strategies used in this paper.


title: Stateful Scalable Stream Processing at LinkedIn

abstract: Distributed stream processing systems need to support stateful processing, recover quickly from failures to resume such processing, and reprocess an entire data stream quickly. We present Apache Samza, a distributed system for stateful and fault-tolerant stream processing. Samza utilizes a partitioned local state along with a low-overhead background changelog mechanism, allowing it to scale to massive state sizes (hundreds of TB) per application. Recovery from failures is sped up by re-scheduling based on Host Affinity. In addition to processing infinite streams of events, Samza supports processing a finite dataset as a stream, from either a streaming source (e.g., Kafka), a database snapshot (e.g., Databus), or a file system (e.g. HDFS), without having to change the application code (unlike the popular Lambda-based architectures which necessitate maintenance of separate code bases for batch and stream path processing). ::: ::: Samza is currently in use at LinkedIn by hundreds of production applications with more than 10, 000 containers. Samza is an open-source Apache project adopted by many top-tier companies (e.g., LinkedIn, Uber, Netflix, TripAdvisor, etc.). Our experiments show that Samza: a) handles state efficiently, improving latency and throughput by more than 100X compared to using a remote storage; b) provides recovery time independent of state size; c) scales performance linearly with number of containers; and d) supports reprocessing of the data stream quickly and with minimal interference on real-time traffic.


title: CAAD 2018: Generating Transferable Adversarial Examples

abstract: Deep neural networks (DNNs) are vulnerable to adversarial examples, perturbations carefully crafted to fool the targeted DNN, in both the non-targeted and targeted case. In the non-targeted case, the attacker simply aims to induce misclassification. In the targeted case, the attacker aims to induce classification to a specified target class. In addition, it has been observed that strong adversarial examples can transfer to unknown models, yielding a serious security concern. The NIPS 2017 competition was organized to accelerate research in adversarial attacks and defenses, taking place in the realistic setting where submitted adversarial attacks attempt to transfer to submitted defenses. The CAAD 2018 competition took place with nearly identical rules to the NIPS 2017 one. Given the requirement that the NIPS 2017 submissions were to be open-sourced, participants in the CAAD 2018 competition were able to directly build upon previous solutions, and thus improve the state-of-the-art in this setting. Our team participated in the CAAD 2018 competition, and won 1st place in both attack subtracks, non-targeted and targeted adversarial attacks, and 3rd place in defense. We outline our solutions and development results in this article. We hope our results can inform researchers in both generating and defending against adversarial examples.


MicroCuckoo Hash Engine for High-Speed IP Lookup


Learned Rhetoric in Old English Poetry


title: Super-resolution algorithm for joint range-azimuth-Doppler estimation in automotive radars

abstract: The problem of localization of targets and determining their velocities by means of radar is formulated as a joint estimation problem over single, three-dimensional domain. Joint estimation algorithms remove problems with data association and allow to exploit relations between individual estimation problems. To exploit wide bandwidth of the sensing signals, a new signal model is formulated based on time-dependant steering vectors for wideband automotive LFMCW radar applications. Joint three dimensional range-azimuth-Doppler estimation is performed using MUSIC approach based on the proposed signal model. The signal model is further improved based on investigation of inter-pulse Doppler effects on the radar array. Results of proposed model are compared to narrow-band MUSIC based joint estimation approach.


title: Where2Stand: A Human Position Recommendation System for Souvenir Photography

abstract: People often take photographs at tourist sites and these pictures usually have two main elements: a person in the foreground and scenery in the background. This type of “souvenir photo” is one of the most common photos clicked by tourists. Although algorithms that aid a user-photographer in taking a well-composed picture of a scene exist [Ni et al. 2013], few studies have addressed the issue of properly positioning human subjects in photographs. In photography, the common guidelines of composing portrait images exist. However, these rules usually do not consider the background scene. Therefore, in this article, we investigate human-scenery positional relationships and construct a photographic assistance system to optimize the position of human subjects in a given background scene, thereby assisting the user in capturing high-quality souvenir photos. We collect thousands of well-composed portrait photographs to learn human-scenery aesthetic composition rules. In addition, we define a set of negative rules to exclude undesirable compositions. Recommendation results are achieved by combining the first learned positive rule with our proposed negative rules. We implement the proposed system on an Android platform in a smartphone. The system demonstrates its efficacy by producing well-composed souvenir photos.


title: FAST: a roc-based feature selection metric for small samples and imbalanced data classification problems

abstract: The class imbalance problem is encountered in a large number of practical applications of machine learning and data mining, for example, information retrieval and filtering, and the detection of credit card fraud. It has been widely realized that this imbalance raises issues that are either nonexistent or less severe compared to balanced class cases and often results in a classifier's suboptimal performance. This is even more true when the imbalanced data are also high dimensional. In such cases, feature selection methods are critical to achieve optimal performance. In this paper, we propose a new feature selection method, Feature Assessment by Sliding Thresholds (FAST), which is based on the area under a ROC curve generated by moving the decision boundary of a single feature classifier with thresholds placed using an even-bin distribution. FAST is compared to two commonly-used feature selection methods, correlation coefficient and RELevance In Estimating Features (RELIEF), for imbalanced data classification. The experimental results obtained on text mining, mass spectrometry, and microarray data sets showed that the proposed method outperformed both RELIEF and correlation methods on skewed data sets and was comparable on balanced data sets; when small number of features is preferred, the classification performance of the proposed method was significantly improved compared to correlation and RELIEF-based methods.


title: Real-Time Seamless Single Shot 6D Object Pose Prediction

abstract: We propose a single-shot approach for simultaneously detecting an object in an RGB image and predicting its 6D pose without requiring multiple stages or having to examine multiple hypotheses. Unlike a recently proposed single-shot technique for this task (Kehl et al., ICCV'17) that only predicts an approximate 6D pose that must then be refined, ours is accurate enough not to require additional post-processing. As a result, it is much faster - 50 fps on a Titan X (Pascal) GPU - and more suitable for real-time processing. The key component of our method is a new CNN architecture inspired by the YOLO network design that directly predicts the 2D image locations of the projected vertices of the object's 3D bounding box. The object's 6D pose is then estimated using a PnP algorithm. ::: For single object and multiple object pose estimation on the LINEMOD and OCCLUSION datasets, our approach substantially outperforms other recent CNN-based approaches when they are all used without post-processing. During post-processing, a pose refinement step can be used to boost the accuracy of the existing methods, but at 10 fps or less, they are much slower than our method.


title: Top leaders community detection approach in information networks

abstract: Much of the data of scientific interest, particularly when independence of data is not assumed, can be represented in the form of information networks where data nodes are joined together to form edges corresponding to some kind of associations or relationships. Such information networks abound, like protein interactions in biology, web page hyperlink connections in information retrieval on the Web, cellphone call graphs in telecommunication, co-authorships in bibliometrics, crime event connections in criminology, etc. All these networks, also known as social networks, share a common property, the formation of connected groups of information nodes, called community structures. These groups are densely connected nodes with sparse connections outside the group. Finding these communities is an important task for the discovery of underlying structures in social networks, and has recently attracted much attention in data mining research. In this paper, we present Top Leaders, a new community mining approach that, simply put, regards a community as a set of followers congregating around a potential leader. Our algorithm starts by identifying promising leaders in a given network then iteratively assembles followers to their closest leaders to form communities, and subsequently finds new leaders in each group around which to gather followers again until convergence. Our intuitions are based on proven observations in social networks and the results are very promising. Experimental results on benchmark networks verify the feasibility and eectiveness of our new community mining approach.


title: Performance evaluation of a MongoDB and hadoop platform for scientific data analysis

abstract: Scientific facilities such as the Advanced Light Source (ALS) and Joint Genome Institute and projects such as the Materials Project have an increasing need to capture, store, and analyze dynamic semi-structured data and metadata. A similar growth of semi-structured data within large Internet service providers has led to the creation of NoSQL data stores for scalable indexing and MapReduce for scalable parallel analysis. MapReduce and NoSQL stores have been applied to scientific data. Hadoop, the most popular open source implementation of MapReduce, has been evaluated, utilized and modified for addressing the needs of different scientific analysis problems. ALS and the Materials Project are using MongoDB, a document oriented NoSQL store. However, there is a limited understanding of the performance trade-offs of using these two technologies together.In this paper we evaluate the performance, scalability and fault-tolerance of using MongoDB with Hadoop, towards the goal of identifying the right software environment for scientific data analysis.


title: Modeling continuous integration practice differences in industry software development

abstract: Continuous integration is a software practice where developers integrate frequently, at least daily. While this is an ostensibly simple concept, it does leave ample room for interpretation: what is it the developers integrate with, what happens when they do, and what happens before they do? These are all open questions with regards to the details of how one implements the practice of continuous integration, and it is conceivable that not all such implementations in the industry are alike. In this paper we show through a literature review that there are differences in how the practice of continuous integration is interpreted and implemented from case to case. Based on these findings we propose a descriptive model for documenting and thereby better understanding implementations of the continuous integration practice and their differences. The application of the model to an industry software development project is then described in an illustrative case study.


title: Topology Adaptive Graph Convolutional Networks

abstract: Convolution acts as a local feature extractor in convolutional neural networks (CNNs). However, the convolution operation is not applicable when the input data is supported on an irregular graph such as with social networks, citation networks, or knowledge graphs. This paper proposes the topology adaptive graph convolutional network (TAGCN), a novel graph convolutional network that generalizes CNN architectures to graph-structured data and provides a systematic way to design a set of fixed-size learnable filters to perform convolutions on graphs. The topologies of these filters are adaptive to the topology of the graph when they scan the graph to perform convolution, replacing the square filter for the grid-structured data in traditional CNNs. The outputs are the weighted sum of these filters’ outputs, extraction of both vertex features and strength of correlation between vertices. It can be used with both directed and undirected graphs. The proposed TAGCN not only inherits the properties of convolutions in CNN for grid-structured data, but it is also consistent with convolution as defined in graph signal processing. Further, as no approximation to the convolution is needed, TAGCN exhibits better performance than existing graph-convolution-approximation methods on a number of data sets. As only the polynomials of degree two of the adjacency matrix are used, TAGCN is also computationally simpler than other recent methods.


title: Exploring the Veracity of Online Claims with BackDrop

abstract: Using the Web to assess the validity of claims presents many challenges. Whether the data comes from social networks or established media outlets, individual or institutional data publishers, one has to deal with scale and heterogeneity, as well as with incomplete, imprecise and sometimes outright false information. All of these are closely studied issues. Yet in many situations, the claims under scrutiny, and the data itself, have some inherent context-dependency making them impossible to completely disprove, or evaluate through a simple (e.g. scalar) measure. While data models used on the Web typically deal with universal knowledge, we believe the time has come to put context, such as time or provenance, at the forefront and watch knowledge through multiple lenses. We present BackDrop, an application that enables annotating knowledge and ontologies found online to explore how the veracity of claims varies with context. BackDrop comes in the form of a Web interface, in which users can interactively populate and annotate knowledge bases, and explore under which circumstances certain claims are more or less credible.


title: Know2Look: Commonsense Knowledge for Visual Search

abstract: With the rise in popularity of social media, images accompanied by contextual text form a huge section of the web. However, search and retrieval of documents are still largely dependent on solely textual cues. Although visual cues have started to gain focus, the imperfection in object/scene detection do not lead to significantly improved results. We hypothesize that the use of background commonsense knowledge on query terms can significantly aid in retrieval of documents with associated images. To this end we deploy three different modalities - text, visual cues, and commonsense knowledge pertaining to the query - as a recipe for efficient search and retrieval.


title: Searching Twitter: Separating the Tweet from the Chaff.

abstract: Within the millions of digital communications posted in online social networks, there is undoubtedly some valuable and useful information. Although a large portion of social media content is considered to be babble, research shows that people share useful links, provide recommendations to friends, answer questions, and solve problems. In this paper, we report on a qualitative investigation into the different factors that make tweets ‘useful’ and ‘not useful’ for a set of common search tasks. The investigation found 16 features that help make a tweet useful, noting that useful tweets often showed 2 or 3 of these features. ‘Not useful’ tweets, however, typically had only one of 17 clear and striking features. Further, we saw that these features can be weighted as according to different types of search tasks. Our results contribute a novel framework for extracting useful information from real-time streams of social-media content that will be used in the design of a future retrieval system.


A Goal-Based Round-Trip Method for System Development


title: Multi-sensor simulation method for outdoor plant phenotyping based on autonomous field robots.

abstract: Based on an existing field robot for outdoor plant phenotyping a simulation environment has been built up for multi-sensor simulation. The development environment Microsoft Robotics Studio, typically used for 3D robot navigation purposes, has been adapted to this application by creating services representing sensors such as 3D-Time-ofFlight cameras, light curtains, color cameras, GPS or incremental encoders. As the real robot the virtual sensor data are stored in a data base and can be analyzed with sensor fusion algorithms. First comparisons of real and simulated data show good overall agreement, in particular an artificial plant and its corresponding virtual counterpart have been compared. There is a high potential of the new method for plant phenotyping purposes as well as for other field robot applications.


Episodic Memory Representation in a Knowledge Base, with Application to Event Monitoring and Event Detection


title: Neuroticism as a mediator of treatment response to SSRIs in major depressive disorder.

abstract: Abstract Background Serotonin function has been implicated in both major depressive disorder and neuroticism. In the current investigation, we examined the hypothesis that any change in depression severity is mediated through the reduction of neuroticism, but only for those compounds which target serotonin receptors. Methods Ninety-three outpatients in the midst of a major depressive episode received one of three antidepressant medications, classified into two broad types: selective serotonin reuptake inhibitors (SSRIs) and non-SSRIs (i.e. reversible monoamine oxidase inhibitors [RIMAs] and noradrenergic and dopaminergic reuptake blockers [NDMs]). Patients completed the Hamilton Rating Scale for Depression , Beck Depression Inventory II and Revised NEO Personality Inventory prior to and following approximately 16 weeks of treatment. Structural equation modeling was used to test two models: a mediation model, in which neuroticism change is the mechanism by which SSRIs exert a therapeutic effect upon depressive symptoms, and a complication model, in which neuroticism change is a mere epiphenomenon of depression reduction in response to SSRIs. Results The mediation model provided a good fit to the data; the complication model did not. Patients treated with SSRIs demonstrated greater neuroticism change than those treated with non-SSRIs, and greater neuroticism change was associated with greater depressive symptom change. These effects held for both self-reported and clinician-rated depressive symptom severity. Limitations Replication within a randomized control trial with multiple assessment periods is required. Conclusion Neuroticism mediates changes in depression in response to treatment with SSRIs, such that any treatment effect of SSRIs occurs through neuroticism reduction.


title: Structural Sparse Tracking

abstract: Sparse representation has been applied to visual tracking by finding the best target candidate with minimal reconstruction error by use of target templates. However, most sparse representation based trackers only consider holistic or local representations and do not make full use of the intrinsic structure among and inside target candidates, thereby making the representation less effective when similar objects appear or under occlusion. In this paper, we propose a novel Structural Sparse Tracking (SST) algorithm, which not only exploits the intrinsic relationship among target candidates and their local patches to learn their sparse representations jointly, but also preserves the spatial layout structure among the local patches inside each target candidate. We show that our SST algorithm accommodates most existing sparse trackers with the respective merits. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed SST algorithm performs favorably against several state-of-the-art methods.


title: RC-NET: A General Framework for Incorporating Knowledge into Word Representations

abstract: Representing words into vectors in continuous space can form up a potentially powerful basis to generate high-quality textual features for many text mining and natural language processing tasks. Some recent efforts, such as the skip-gram model, have attempted to learn word representations that can capture both syntactic and semantic information among text corpus. However, they still lack the capability of encoding the properties of words and the complex relationships among words very well, since text itself often contains incomplete and ambiguous information. Fortunately, knowledge graphs provide a golden mine for enhancing the quality of learned word representations. In particular, a knowledge graph, usually composed by entities (words, phrases, etc.), relations between entities, and some corresponding meta information, can supply invaluable relational knowledge that encodes the relationship between entities as well as categorical knowledge that encodes the attributes or properties of entities. Hence, in this paper, we introduce a novel framework called RC-NET to leverage both the relational and categorical knowledge to produce word representations of higher quality. Specifically, we build the relational knowledge and the categorical knowledge into two separate regularization functions, and combine both of them with the original objective function of the skip-gram model. By solving this combined optimization problem using back propagation neural networks, we can obtain word representations enhanced by the knowledge graph. Experiments on popular text mining and natural language processing tasks, including analogical reasoning, word similarity, and topic prediction, have all demonstrated that our model can significantly improve the quality of word representations.


title: Killing Two Birds with One Stone -- Querying Property Graphs using SPARQL via GREMLINATOR

abstract: Knowledge graphs have become popular over the past decade and frequently rely on the Resource Description Framework (RDF) or Property Graph (PG) databases as data models. However, the query languages for these two data models -- SPARQL for RDF and the PG traversal language Gremlin -- are lacking interoperability. We present Gremlinator, the first translator from SPARQL -- the W3C standardized language for RDF -- and Gremlin -- a popular property graph traversal language. Gremlinator translates SPARQL queries to Gremlin path traversals for executing graph pattern matching queries over graph databases. This allows a user, who is well versed in SPARQL, to access and query a wide variety of Graph Data Management Systems (DMSs) avoiding the steep learning curve for adapting to a new Graph Query Language (GQL). Gremlin is a graph computing system-agnostic traversal language (covering both OLTP graph database or OLAP graph processors), making it a desirable choice for supporting interoperability for querying Graph DMSs. Gremlinator currently supports the translation of a subset of SPARQL 1.0, specifically the SPARQL SELECT queries.


title: How do we Talk to Each other? Writing Qualitative Research for Quantitative Readers

abstract: The growth of qualitative research holds the potential for vastly enriching our understanding of phenomena in the health sciences. However, the potential of this trend is hampered by a widespread inability of quantitative and qualitative researchers to talk to each other. The authors’ concern in this area grows out of our experience reviewing small grant applications for the National Institute on Aging, where they frequently find qualitative research proposals scoring worse than do those using quantitative approaches. This article addresses practical problems in communicating qualitative research to readers whose training and experience is primarily quantitative. Two themes running through the discussion are the need for detail and the explicit tying of methodological strategies to research goals.


title: Improved Garbled Circuit: Free XOR Gates and Applications

abstract: We present a new garbled circuit construction for two-party secure function evaluation (SFE). In our one-round protocol, XOR gates are evaluated "for free", which results in the corresponding improvement over the best garbled circuit implementations (e.g. Fairplay [19]). ::: ::: We build permutation networks [26] and Universal Circuits (UC) [25] almost exclusively of XOR gates; this results in a factor of up to 4 improvement (in both computation and communication) of their SFE. We also improve integer addition and equality testing by factor of up to 2. ::: ::: We rely on the Random Oracle (RO) assumption. Our constructions are proven secure in the semi-honest model.


title: Recurrent convolutional neural network for video classification

abstract: Video classification is more difficult than image classification since additional motion feature between image frames and amount of redundancy in videos should be taken into account. In this work, we proposed a new deep learning architecture called recurrent convolutional neural network (RCNN) which combines convolution operation and recurrent links for video classification tasks. Our architecture can extract the local and dense features from image frames as well as learning the temporal features between consecutive frames. We also explore the effectiveness of sequential sampling and random sampling when training our models, and find out that random sampling is necessary for video classification. The feature maps from our learned model preserve motion from image frames, which is analogous to the persistence of vision in human visual system. We achieved 81.0% classification accuracy without optical flow and 86.3% with optical flow on the UCF-101 dataset, both are competitive to the state-of-the-art methods.


title: A design of single and multi-section microstrip directional coupler with the high directivity

abstract: In this paper, single and two-section microstrip directional couplers are implemented for realizing the high-directivity characteristics. The achievement of the high-directivity with microstrip configuration is carried out by the distributed capacitor to decrease the even and odd mode phase difference. Capacitive compensation is performed by gap coupling of open stub formed in sub-coupled line. Therefore, insertion loss and power handling capability are not affected by the gap coupling. The proposed structure is easy to fabricate and incorporate another microwave device due to planner microstrip. We designed and fabricated single and two-section directional coupler with 20 dB coupling. In spite of microstrip structure, the capacitive compensation structure shows the 30 dB (1.6 GHz-2 GHz range) and 26 dB (1 GHz-2.5 GHz) of directivity in single and two-section directional couplers, respectively. Experimental results show a good performance with excellent directivity characteristics.


title: Liquid biopsy - emergence of a new era in personalized cancer care

abstract: The most successful treatment for cancer involves identifying druggable, biological markers for targeted therapy. In the clinical setting, surgical removal of tumors is the only procedure for identifying such targetable molecules. Shed from tumor cells, these markers are also present in circulating blood, albeit in very negligible amounts. Liquid biopsy is a procedure performed on a blood sample to look for such circulating cancer markers cells or pieces of nucleic acid from the tumor. The procedure shows promise in revolutionizing personalized cancer treatments. Here we briefly review the technique, characterization, and its utilization in clinics.


title: Reuters Tracer: A Large Scale System of Detecting & Verifying Real-Time News Events from Twitter

abstract: News professionals are facing the challenge of discovering news from more diverse and unreliable information in the age of social media. More and more news events break on social media first and are picked up by news media subsequently. The recent Brussels attack is such an example. At Reuters, a global news agency, we have observed the necessity of providing a more effective tool that can help our journalists to quickly discover news on social media, verify them and then inform the public. In this paper, we describe Reuters Tracer, a system for sifting through all noise to detect news events on Twitter and assessing their veracity. We disclose the architecture of our system and discuss the various design strategies that facilitate the implementation of machine learning models for noise filtering and event detection. These techniques have been implemented at large scale and successfully discovered breaking news faster than traditional journalism


title: Hopfield Neural Networks for Timetabling: Formulations, Methods, and Comparative Results

abstract: This paper considers the use of discrete Hopfield neural networks for solving school timetabling problems. Two alternative formulations are provided for the problem: a standard Hopfield-Tank approach, and a more compact formulation which allows the Hopfield network to be competitive with swapping heuristics. It is demonstrated how these formulations can lead to different results. The Hopfield network dynamics are also modified to allow it to be competitive with other metaheuristics by incorporating controlled stochasticities. These modifications do not complicate the algorithm, making it possible to implement our Hopfield network in hardware. The neural network results are evaluated on benchmark data sets and are compared with results obtained using greedy search, simulated annealing and tabu search.


title: Mindfulness and emotion regulation in depression and anxiety: common and distinct mechanisms of action.

abstract: BackgroundThe current study seeks to investigate the mechanisms through which mindfulness is related to mental health in a clinical sample of adults by examining (1) whether specific cognitive emotion regulation strategies (rumination, reappraisal, worry, and nonacceptance) mediate associations between mindfulness and depression and anxiety, respectively, and (2) whether these emotion regulation strategies operate uniquely or transdiagnostically in relation to depression and anxiety. MethodsParticipants were 187 adults seeking treatment at a mood and anxiety disorders clinic in Connecticut. Participants completed a battery of self-report measures that included assessments of depression and anxiety (Mood and Anxiety Symptom Questionnaire), and emotion regulation (Ruminative Response Scale, Penn State Worry Questionnaire, Emotion Regulation Questionnaire, Difficulties in Emotion Regulation Scale). ResultsSimple mediation analyses indicated that rumination and worry significantly mediated associations between mindfulness and anxiety symptoms, whereas rumination and reappraisal significantly mediated associations between mindfulness and depressive symptoms. Multiple mediation analyses showed that worry significantly mediated associations between mindfulness and anxiety symptoms and rumination and reappraisal significantly mediated associations between mindfulness and depressive symptoms. ConclusionsFindings suggest that mindfulness operates through distinct and common mechanisms depending on clinical context. Depression and Anxiety 30: 654-661, 2013. (C) 2013 Wiley Periodicals, Inc.


title: A low loss self-packaged quasi-lumped-element high pass filter using SISL technology

abstract: This paper presents a low loss self-packaged quasi-lumped-element high pass filter (HPF) based on substrate integrated suspended line (SISL) technology. The HPF is composed of double metal layer spiral inductors and interdigital capacitors. And the dielectric loss for the filter elements is further reduced through hollowing the support substrate. The HPF also has advantages of self-packaging, low loss, compact size, and low cost by using SISL technology.


title: Predictive Direct Power Control of Three-Phase Pulsewidth Modulation (PWM) Rectifier Using Space-Vector Modulation (SVM)

abstract: In this paper, we present a direct power control (DPC) of three-phase pulsewidth modulation rectifier with constant switching frequency using space-vector modulation (SVM). The developed DPC scheme is based on the predictive control strategy to achieve direct control of instantaneous active and reactive power of the converter. For this purpose, at the beginning of each switching period, the required rectifier average voltage vector allowing the cancellation of active and reactive power tracking errors, at the end of the switching period, is calculated by means of predictive control algorithm in the sense of deadbeat control. The main advantages of the proposed control, compared to the works published in this subject, are that no need to use predefined switching table and voltage vector or virtual flux position, PI-based active and reactive power control loops are not necessary and constant-switching frequency. The proposed predictive direct power control was tested both in simulations and experimentally and compared with DPC using switching table. Results have proved excellent performance, and verify the validity of the proposed DPC scheme, which is much better than conventional DPC using switching table.


title: Graph Visualization and Navigation in Information Visualization: A Survey

abstract: This is a survey on graph visualization and navigation techniques, as used in information visualization. Graphs appear in numerous applications such as Web browsing, state-transition diagrams, and data structures. The ability to visualize and to navigate in these potentially large, abstract graphs is often a crucial part of an application. Information visualization has specific requirements, which means that this survey approaches the results of traditional graph drawing from a different perspective.


title: Brain plasticity: ‘visual’ acuity of blind persons via the tongue

abstract: Abstract The ‘visual’ acuity of blind persons perceiving information through a newly developed human–machine interface, with an array of electrical stimulators on the tongue, has been quantified using a standard Ophthalmological test (Snellen Tumbling E). Acuity without training averaged 20/860. This doubled with 9 h of training. The interface may lead to practical devices for persons with sensory loss such as blindness, and offers a means of exploring late brain plasticity.


Acoustic modeling with deep neural networks using raw time signal for LVCSR.


title: Addressing the anti-vaccination movement and the role of HCWs.

abstract: Over the last two decades, growing numbers of parents in the industrialized world are choosing not to have their children vaccinated. Trying to explain why this is occurring, public health commentators refer to the activities of an anti-vaccination movement. The aim of this paper is to review the literature about the anti-vaccination movements and to highlight the knowledge and the skills needed for HCWs to fight against their ideas. The main theoretical structures of anti-vaccination ideology in the 19th and 20th centuries are: vaccines cause idiopathic illness; opponents against vaccines accused vaccine partisans to be afraid of the "search after truth," they fear unveiling errors; the vaccination law not only insults every subject of the realm, but also it insults every human being; vaccine immunity is temporary; an alternative healthy lifestyle, personal hygiene and diet stop diseases. Proponents against vaccination now have additional means to communicate their positions to the general public, the Internet in particular. Doctors and HCWs constantly have to face parents and patients who search information about vaccination. A lot of these people have previously found data about vaccinations from a lot of sources, such as papers, media or in websites and in these sources most contents come from anti-vaccine movements. For these reasons doctors and HCWs need to have updated knowledge about the vaccinations and to know the contents proposed by vaccine sceptics. Educating the general public cannot be fully effective unless there is a corresponding provision, enthusiasm and commitment by trained HCWs.


title: TorWard: Discovery, Blocking, and Traceback of Malicious Traffic Over Tor

abstract: Tor is a popular low-latency anonymous communication system. It is, however, currently abused in various ways. Tor exit routers are frequently troubled by administrative and legal complaints. To gain an insight into such abuse, we designed and implemented a novel system, TorWard, for the discovery and the systematic study of malicious traffic over Tor. The system can avoid legal and administrative complaints, and allows the investigation to be performed in a sensitive environment such as a university campus. An intrusion detection system (IDS) is used to discover and classify malicious traffic. We performed comprehensive analysis and extensive real-world experiments to validate the feasibility and the effectiveness of TorWard. Our results show that around 10% Tor traffic can trigger IDS alerts. Malicious traffic includes P2P traffic, malware traffic (e.g., botnet traffic), denial-of-service attack traffic, spam, and others. Around 200 known malwares have been identified. To mitigate the abuse of Tor, we implemented a defense system, which processes IDS alerts, tears down, and blocks suspect connections. To facilitate forensic traceback of malicious traffic, we implemented a dual-tone multi-frequency signaling-based approach to correlate botnet traffic at Tor entry routers and that at exit routers. We carried out theoretical analysis and extensive real-world experiments to validate the feasibility and the effectiveness of TorWard for discovery, blocking, and traceback of malicious traffic.


title: Meaning in Life and Mortality

abstract: Objectives. The purpose of this exploratory study was to see if meaning in life is associated with mortality in old age. Methods. Interviews were conducted with a nationwide sample of older adults ( N = 1,361). Data were collected on meaning in life, mortality, and select control measures. Results. Three main fi ndings emerged from this study. First, the data suggest that older people with a strong sense of meaning in life are less likely to die over the study follow-up period than those who do not have a strong sense of meaning. Second, the fi ndings indicate that the effect of meaning on mortality can be attributed to the potentially important indirect effect that operates through health. Third, further analysis revealed that one dimension of meaning — having a strong sense of purpose in life — has a stronger relationship with mortality than other facets of meaning. The main study fi ndings were observed after the effects of attendance at religious services and emotional support were controlled statistically. Discussion. If the results from this study can be replicated, then interventions should be designed to help older people fi nd a greater sense of purpose in life.


Sarcasm Detection in Product Reviews using Sentence Scale Sentiment Change with Recurrent Neural Networks Submitted in Partial Fulfillment of the Requirements for CS 886


title: 1 An Inside Look at Botnets

abstract: The continued growth and diversification of the Internet has been accompanied by an increasing prevalence of attacks and intrusions [40]. It can be argued, however, that a significant change in motivation for malicious activity has taken place over the past several years: from vandalism and recognition in the hacker community, to attacks and intrusions for financial gain. This shift has been marked by a growing sophistication in the tools and methods used to conduct attacks, thereby escalating the network security arms race.


title: Autonomous Driving - 5 Years after the Urban Challenge: The Anticipatory Vehicle as a Cyber-Physical System

abstract: In November 2007 the international competition DARPA Urban Challenge took place on the former George Airforce Base in Victorville, California to significantly promote the research and development on autonomously driving vehicles for urban environments. In the final race only eleven out of initially 89 competitors participated and"Boss"from Carnegie Mellon University succeeded. This paper summarizes results of the research carried out by all finalists within the last five years after the competition and provides an outlook where further investigation especially for software engineering is now necessary to achieve the goal of driving safely and reliably through urban environments with an anticipatory vehicle for the mass-market.


title: The Shattered Gradients Problem: If resnets are the answer, then what is the question?

abstract: A long-standing obstacle to progress in deep learning is the problem of vanishing and exploding gradients. Although, the problem has largely been overcome via carefully constructed initializations and batch normalization, architectures incorporating skip-connections such as highway and resnets perform much better than standard feedforward architectures despite well-chosen initialization and batch normalization. In this paper, we identify the shattered gradients problem. Specifically, we show that the correlation between gradients in standard feedforward networks decays exponentially with depth resulting in gradients that resemble white noise whereas, in contrast, the gradients in architectures with skip-connections are far more resistant to shattering, decaying sublinearly. Detailed empirical evidence is presented in support of the analysis, on both fully-connected networks and convnets. Finally, we present a new "looks linear" (LL) initialization that prevents shattering, with preliminary experiments showing the new initialization allows to train very deep networks without the addition of skip-connections.


title: Low-Loss Patterned Ground Shield Interconnect Transmission Lines in Advanced IC Processes

abstract: In this paper, we provide an extensive experimental and theoretical study of the benefits of patterned ground shield interconnect transmission lines over more conventional layouts in advanced integrated-circuit processes. As part of this experimental work, we present the first comparative study taken on truly differential transmission line test structures. Our experimental results obtained on transmission lines with patterned ground shields are compared against a predictive compact equivalent-circuit model. This model employs exact closed-form expressions for the inductances, and describes key performance figures such as characteristic impedance and attenuation loss with excellent accuracy


title: A prediction- and cost function-based algorithm for robust autonomous freeway driving

abstract: In this paper, a prediction- and cost function-based algorithm (PCB) is proposed to implement robust freeway driving in autonomous vehicles. A prediction engine is built to predict the future microscopic traffic scenarios. With the help of a human-understandable and representative cost function library, the predicted traffic scenarios are evaluated and the best control strategy is selected based on the lowest cost. The prediction- and cost function-based algorithm is verified using the simulator of the autonomous vehicle Boss from the DARPA Urban Challenge 2007. The results of both case tests and statistical tests using PCB show enhanced performance of the autonomous vehicle in performing distance keeping, lane selecting and merging on freeways.


title: Replacing suffix trees with enhanced suffix arrays

abstract: The suffix tree is one of the most important data structures in string processing and comparative genomics. However, the space consumption of the suffix tree is a bottleneck in large scale applications such as genome analysis. In this article, we will overcome-this obstacle. We will show how every algorithm that uses a suffix tree as data structure can systematically be replaced with an algorithm that uses an enhanced suffix array and solves the same problem in the same time complexity. The generic name enhanced suffix array stands for data structures consisting of the suffix array and additional tables. Our new algorithms are not only more space efficient than previous ones, but they are also faster and easier to implement.


title: Trichoscopy Tips.

abstract: Although trichoscopy has only recently been introduced in the assessment of hair and scalp disorders, it is already considered an essential diagnostic tool by many experts in the field. This article discusses topics that are often a source of doubt among clinicians, and tips that may help dermatologists better perform trichoscopy. Subjects include fundamental concepts needed to start performing the method, basic trichoscopic structures, tips to identify a scarring condition and to approach a patient with a receding hairline, features that are unique to children, those that are typically seen in the dark scalp, and pitfalls of the technique.


title: Inhibition of Lipid Peroxidation of Kiwicha (Amaranthus caudatus) Hydrolyzed Protein Using Zebrafish Larvae and Embryos

abstract: Amaranth protein concentrate (APC) was hydrolyzed under in vitro gastrointestinal conditions. APC proteins were partially degraded by pepsin at pHs 1.2, 2.0, and 3.2. During the intestinal phase (pepsin/pancreatin enzymes at pH 7.0), no polypeptide bands were observed in the gel, suggesting the susceptibility of amaranth proteins to the action of digestive enzymes. The potent in vitro inhibition of lipid peroxidation, shown by the gastric and intestinal digests, was confirmed in the zebrafish larvae, with a 72.86% reduction in oxidation of lipids in the presence of the gastric hydrolysate at pH 2.0, compared to a 95.72% reduction in the presence of the gastrointestinal digest. APC digests were capable of reducing reactive oxygen species (ROS) production in the zebrafish embryo model with a value of fluorescence of 52.5% for the gastric hydrolysate, and 48.4% for the intestinal hydrolysate.


title: Beware What You Inject: Complications of Injectables—Dermal Fillers

abstract: Cosmetic filler injections are most popular among the antiaging procedures. As in any other cosmetic injectable procedures, complications are likely to occur even under experienced hands. However, it is the duty of the aesthetic physician to identify and manage these potential complications. The objective of this symposia is to help the aesthetic physician to identify these complications early and manage them appropriately to improve outcomes.


title: Stiffening in Soft Robotics: A Review of the State of the Art

abstract: The need for building robots with soft materials emerged recently from considerations of the limitations of service robots in negotiating natural environments, from observation of the role of compliance in animals and plants [1], and even from the role attributed to the physical body in movement control and intelligence, in the so-called embodied intelligence or morphological computation paradigm [2]-[4]. The wide spread of soft robotics relies on numerous investigations of diverse materials and technologies for actuation and sensing, and on research of control techniques, all of which can serve the purpose of building robots with high deformability and compliance. But the core challenge of soft robotics research is, in fact, the variability and controllability of such deformability and compliance.


title: Dynamic load-balancing using prediction in a parallel object-oriented system

abstract: In this paper, a replication-based parallel object model will be presented first, where object replication is used to improve the performance of load-balancing and to reduce the cost of object migration. After that, a threshold-based dynamic load-balancing strategy, that makes use of the object replication, will be introduced. The paper will then focus on a performance prediction model that is used in the decision making of the dynamic load-balancing strategy. The prediction model monitors the runtime behavior of an invoked method and estimates the execution time of its subsequent invocations. It helps the dynamic load-balancing strategy to make wiser decisions on whether or not to migrate objects in order to achieve better performance. A detailed simulation system is constructed to evaluate the performance of the proposed dynamic load-balancing strategy and the prediction model. Experimental results of the simulation will also be discussed in the paper.


Control of Mechanical Systems With Rolling Constraints: Application to Dynamic Control of Mobile Robots


title: Brain tumor segmentation from MRI data sets using region growing approach

abstract: Brain segmentation is an important part of medical image processing. Most commonly, it aims at locating various lesions and pathologies inside the human brain. In this paper, a new brain segmentation algorithm is proposed. The method is seeded region growing approach which was developed to segment the area of the brain affected by a tumor. The proposed algorithm was described. Results of testing the developed method on real MRI data set are presented and discussed.


title: An Investigation of Hedonic and Utilitarian Value on User Satisfaction and Loyalty for Mobile Music Streaming Application

abstract: Online music streaming service is becoming a popular way to play the music. Using a mobile device to listen to music is a very common phenomenon. Music Streaming Service Providers developed their respective application of mobile to increase the number of users. This study proposed a framework to examine the role of utilitarian and hedonic values in user loyalty and satisfaction. Finally, we expect our result of this study can help music streaming service providers to understand which value or factor can influence users behavioral intention and provide recommendations for improvement.


title: Nano-probing station incorporating MEMS probes for 1D device RF on-wafer characterization

abstract: A microwave nano-probing station incorporating home-made MEMS coplanar waveguide (CPW) probes was built inside a scanning electron microscope. The instrumentation proposed is able to measure accurately the guided complex reflection of 1D devices embedded in dedicated CPW micro-structures. As a demonstration, RF impedance characterization of an Indium Arsenide nanowire is exemplary shown up to 6 GHz. Next, optimization of the MEMS probe assembly is experimentally verified by establishing the measurement uncertainty up to 18 GHz.


title: End-to-End Blind Image Quality Assessment Using Deep Neural Networks

abstract: We propose a multi-task end-to-end optimized deep neural network (MEON) for blind image quality assessment (BIQA). MEON consists of two sub-networks—a distortion identification network and a quality prediction network—sharing the early layers. Unlike traditional methods used for training multi-task networks, our training process is performed in two steps. In the first step, we train a distortion type identification sub-network, for which large-scale training samples are readily available. In the second step, starting from the pre-trained early layers and the outputs of the first sub-network, we train a quality prediction sub-network using a variant of the stochastic gradient descent method. Different from most deep neural networks, we choose biologically inspired generalized divisive normalization (GDN) instead of rectified linear unit as the activation function. We empirically demonstrate that GDN is effective at reducing model parameters/layers while achieving similar quality prediction performance. With modest model complexity, the proposed MEON index achieves state-of-the-art performance on four publicly available benchmarks. Moreover, we demonstrate the strong competitiveness of MEON against state-of-the-art BIQA models using the group maximum differentiation competition methodology.


General Image Quality Equation; GIQE version 5


title: Enhanced terahertz detection via ErAs:GaAs nanoisland superlattices

abstract: We demonstrate enhanced terahertz detection using photoconductive antennas based on self-assembled ErAs:GaAs nanoisland superlattices. Three detectors are compared; one each fabricated on low-temperature grown GaAs, radiation-damaged silicon-on-sapphire, and an ErAs:GaAs superlattice. The ErAs:GaAs based detector shows a strong enhancement in terahertz detection efficiency with respect to incident optical power, though optical saturation occurs more rapidly. Detected terahertz bandwidth and signal-to-noise ratios are simultaneously maintained or improved.


title: Here, there, anywhere: remote usability testing that works

abstract: Usability testing is regarded as a key element in user-centered design. Several studies from the Standish Group have shown that usability testing increases the chance of developing usable software. Companies are faced with many challenges: their customers demand usable products at reasonable costs and the customer base is distributed and diverse. Unfortunately, usability testing is often perceived as impractical due to the remote and distributed location of users, limited access to representative users, or a work context that is difficult to reproduce in a laboratory setting. Additionally, for some companies, the cost of transporting users or developers to remote locations can be prohibitive. Interest in remote usability testing has grown in response to these concerns. As the user advocate, the Information Technology student needs to be provided not only with hands-on experience in usability testing, but also with its nuances. These include an understanding of the wide range of formats and options available such as remote usability testing. Moreover, a remote format may be a solution for universities that do not have the resources to set up formal laboratories. There is a need to understand how best to facilitate remote testing in a classroom environment. The focus of our study was to identify appropriate tools and define methodologies for efficient and effective remote testing environments. We investigated commercial tools to determine their usefulness and cost-effectiveness for the classroom and then conducted an empirical study to compare traditional and remote usability testing of a web site using one of these tools. This paper will report on advantages and disadvantages of various remote testing tools, modifications to procedures and protocols of traditional testing, and the project findings, including usability problem identification, which establish the effectiveness of remote usability testing. Recommendations will be made for providing a credible environment for remote testing.


title: Building Attack Scenarios through Integration of Complementary Alert Correlation Method

abstract: Several alert correlation methods were proposed in the past several years to construct high-level attack scenarios from low-level intrusion alerts reported by intrusion detection systems (IDSs). These correlation methods have different strengths and limitations; none of them clearly dominate the others. However, all of these methods depend heavily on the underlying IDSs, and perform poorly when the IDSs miss critical attacks. In order to improve the performance of intrusion alert correlation and reduce the impact of missed attacks, this paper presents a series of techniques to integrate two complementary types of alert correlation methods: (1) those based on the similarity between alert attributes, and (2) those based on prerequisites and consequences of attacks. In particular, this paper presents techniques to hypothesize and reason about attacks possibly missed by IDSs based on the indirect causal relationship between intrusion alerts and the constraints they must satisfy. This paper also discusses additional techniques to validate the hypothesized attacks through raw audit data and to consolidate the hypothesized attacks to generate concise attack scenarios. The experimental results in this paper demonstrate the potential of these techniques in building high-level attack scenarios and reasoning about possibly missed attacks.


Chapter to appear in Social Psychology and Leadership


Analysis of Fraud Detection Mechanism in Health Insurance Using Statistical Data Mining Techniques


title: An emergent functional parcellation of the temporal cortex

abstract: Abstract The temporal lobe has been associated with various cognitive functions which include memory, auditory cognition and semantics. However, at a higher level of conceptualisation, all of the functions associated with the temporal lobe can be considered as lying along one major axis; from modality-specific to modality-general processing. This paper used a spectral reordering technique on resting-state and task-based functional data to extract the major organisational axis of the temporal lobe in a bottom-up, data-driven fashion. Independent parcellations were performed on resting-state scans from 71 participants and active semantic task scans from 23 participants acquired using dual echo gradient echo planar imaging in order to preserve signal in inferior temporal cortex. The resulting organisational axis was consistent (over dataset and hemisphere) and progressed from superior temporal gyrus and posterior inferior temporal cortex to ventrolateral anterior temporal cortex. A hard parcellation separated a posterior (superior temporal and posterior fusiform and inferior temporal gyri) and an anterior cluster (ventrolateral anterior temporal lobe). The functional connectivity of the hard clusters supported the hypothesis that the connectivity gradient separated modality-specific and modality-general regions. This hypothesis was then directly tested by performing a VOI analysis upon an independent semantic task-based data set including auditory and visually presented stimuli. This confirmed that the ventrolateral anterior aspects of the temporal lobe are associated with modality-general processes whilst posterior and superior aspects are specific to certain modalities, with the posterior inferior subregions involved in visual processes and superior regions involved in audition.


title: Investigating the association between social interactions and personality states dynamics

abstract: The recent personality psychology literature has coined the name of personality states to refer to states having the same behavioural, affective and cognitive content (described by adjectives) as the corresponding trait, but for a shorter duration. The variability in personality states may be the reaction to specific characteristics of situations. The aim of our study is to investigate whether specific situational factors, that is, different configurations of face-to-face interactions, are predictors of variability of personality states in a work environment. The obtained results provide evidence that within-person variability in personality is associated with variation in face-to-face interactions. Interestingly, the effects differ by type and level of the personality states: adaptation effects for Agreeableness and Emotional Stability, whereby the personality states of an individual trigger similar states in other people interacting with them and complementarity effects for Openness to Experience, whereby the personality states of an individual trigger opposite states in other people interacting with them. Overall, these findings encourage further research to characterize face-to-face and social interactions in terms of their relevance to personality states.


title: Liquid-Claim Production, Risk Management, and Bank Capital Structure: Why High Leverage is Optimal for Banks

abstract: Liquidity production is a central function of banks. High leverage is optimal for banks in a model that has just enough frictions for banks to have a meaningful role in liquid-claim production. The model has a market premium for (socially valuable) safe/liquid debt, but no taxes or other traditional motives to lever up. Because only safe debt commands a liquidity premium, banks with risky assets use risk management to maximize their capacity to include such debt in the capital structure. The model can explain why banks have higher leverage than most operating firms, why risk management is central to banks׳ operating policies, why bank leverage increased over the last 150 years or so, and why leverage limits for regulated banks impede their ability to compete with unregulated shadow banks.


A Model-Based Sensor Database for Internet of Things


title: Basic aspects of food preservation by hurdle technology.

abstract: Hurdle technology is used in industrialized as well as in developing countries for the gentle but effective preservation of foods. Previously hurdle technology, i.e., a combination of preservation methods, was used empirically without much knowledge of the governing principles. Since about 20 years the intelligent application of hurdle technology became more prevalent, because the principles of major preservative factors for foods (e.g., temperature, pH, a(w), Eh, competitive flora), and their interactions, became better known. Recently, the influence of food preservation methods on the physiology and behaviour of microorganisms in foods, i.e. their homeostasis, metabolic exhaustion, stress reactions, are taken into account, and the novel concept of multitarget food preservation emerged. In the present contribution a brief introduction is given on the potential hurdles for foods, the hurdle effect, and the hurdle technology. However, emphasis is placed on the homeostasis, metabolic exhaustion, and stress reactions of microorganisms related to hurdle technology, and the prospects of the future goal of a multitarget preservation of foods.


title: BigDansing: A System for Big Data Cleansing

abstract: Data cleansing approaches have usually focused on detecting and fixing errors with little attention to scaling to big datasets. This presents a serious impediment since data cleansing often involves costly computations such as enumerating pairs of tuples, handling inequality joins, and dealing with user-defined functions. In this paper, we present BigDansing, a Big Data Cleansing system to tackle efficiency, scalability, and ease-of-use issues in data cleansing. The system can run on top of most common general purpose data processing platforms, ranging from DBMSs to MapReduce-like frameworks. A user-friendly programming interface allows users to express data quality rules both declaratively and procedurally, with no requirement of being aware of the underlying distributed platform. BigDansing takes these rules into a series of transformations that enable distributed computations and several optimizations, such as shared scans and specialized joins operators. Experimental results on both synthetic and real datasets show that BigDansing outperforms existing baseline systems up to more than two orders of magnitude without sacrificing the quality provided by the repair algorithms.


title: RANDOM WALK IN SHORTCUT MODELS

abstract: Earlier studies of a parametrized class of models whose fractal dimension transitions from one to two indicated that the transition occurs infinitely sharply at the parameter value p=0, as the system size increases to infinity. We study a random walk process which is sensitive to dimension, and we find the same sharp transition at p=0. We use the tool of rescaled range analysis to analyze the drift velocity of the random walk process.


title: DataSite: Proactive visual data exploration with computation of insight-based recommendations

abstract: Effective data analysis ideally requires the analyst to have high expertise as well as high knowledge of the data. Even with such familiarity, manually pursuing all potential hypotheses and explori...


title: Proof of Luck: an Efficient Blockchain Consensus Protocol

abstract: In the paper, we present designs for multiple blockchain consensus primitives and a novel blockchain system, all based on the use of trusted execution environments (TEEs), such as Intel SGX-enabled CPUs. First, we show how using TEEs for existing proof of work schemes can make mining equitably distributed by preventing the use of ASICs. Next, we extend the design with proof of time and proof of ownership consensus primitives to make mining energy- and time-efficient. Further improving on these designs, we present a blockchain using a proof of luck consensus protocol. Our proof of luck blockchain uses a TEE platform's random number generation to choose a consensus leader, which offers low-latency transaction validation, deterministic confirmation time, negligible energy consumption, and equitably distributed mining. Lastly, we discuss a potential protection against up to a constant number of compromised TEEs.


title: Traditional usage, phytochemistry and pharmacology of the South African medicinal plant Boophone disticha (L.f.) Herb. (Amaryllidaceae).

abstract: Ethnopharmacological relevance ::: Boophone disticha is the most common member of the South African Amaryllidaceae used extensively in traditional medicine of the various indigenous population groups, including the Sotho, Xhosa and Zulu as well as the San. This survey was carried out to identify and highlight areas relevant to the traditional usage of Boophone disticha. Pharmacological aspects were examined with the purpose of reconciling these with the traditional usage of the plant. In relation to phytochemical make-up, particular attention was paid on how its alkaloid constitution might corroborate the various biological effects manifested by the plant.


title: Tile Based HEVC Video for Head Mounted Displays

abstract: 360° Video services with resolutions of UHD and beyond for Virtual Reality head mounted displays are a challenging task due to limits of video decoders in constrained end devices. Adaptivity to the current user viewport is a promising approach but incurs significant encoding overhead when encoding per user or set of viewports. A more efficient way to achieve viewport adaptive streaming is to facilitate motion-constrained HEVC tiles. Original content resolution within the user viewport is preserved while content currently not presented to the user is delivered in lower resolution. A lightweight aggregation of varying resolution tiles into a single HEVC bitstream can be carried out on-the-fly and allows usage of a single decoder instance on the end device.


title: The Effectiveness of a Psychoeducation Intervention delivered via WhatsApp for mothers of children with Autism Spectrum Disorder (ASD) in the Kingdom of Saudi Arabia: A randomized controlled trial

abstract: Background ::: Mothers of children with autism spectrum disorder (ASD) report high levels of stress and lower levels of well-being than parents of typically developing children. Current interventions for ASD typically focus on working with the child rather than delivering strategies to help support parents. ::: ::: Objective ::: To evaluate the effectiveness of a psychoeducation intervention developed to support mothers of children with ASD in Saudi Arabia. ::: ::: Method ::: Sixty-two mothers (23–52 years) of children (26–78 months) were recruited to a multisite randomized controlled trials of the intervention. The intervention consisted of one face-to-face session (60 min) and four virtual sessions (30 min each) delivered using WhatsApp. Parenting stress was the primary outcome, with secondary outcomes focusing on maternal depression, anxiety, and happiness, and child behaviour problems and ASD symptoms. Data were collected at baseline T1, immediately postintervention T2 and 8-week follow-up T3. ::: ::: Results ::: One-way analysis of covariance was used at T2 and T3 with T1 scores entered as a covariate. Improvements were found at T2 for stress (F = 234.34, p = .00, and d = −1.52) and depression (F = 195.70, p = .00, and d = −2.14) but not anxiety, and these results were maintained at T3. Changes in child behaviour problems were limited to improvements in hyperactivity at T2 (F = 133.66, p = .00, and d = −1.54). Although changes in stress and depression were statistically significant, change to clinically normal levels was limited to depression. None of the participants had recovered after the intervention (Parent Stress Index Short Form stress scores), whereas 23 mothers (71.87%) in the intervention group had recovered at T2 and 22 (68.75%) at T3 (Hospital Anxiety and Depression Scale depression scores). ::: ::: Conclusion ::: This intervention with WhatsApp support is beneficial but may need to be augmented with other forms of support for mothers of children with ASD including more condensed sessions on stress and interventions targeting anxiety.


title: Fuzzy logic control in regenerative braking system for electric vehicle

abstract: In this paper a regenerative braking energy recovery control strategy for electric vehicle (EV) based on fuzzy logic control is proposed. By analyzing the characteristic and the influence factors of the regenerative braking, the sugeno fuzzy logic controller (FLC) is established. Under assurance of braking safety and stability, the strategy distributes regenerative braking force and frictional braking force reasonably during braking, to make use of regenerative braking characteristic of motor as much as possible, so that much more kinetic energy can be converted into electric energy and stored in the battery. Model based on fuzzy logic control was established under the Matlab/Simulink environment and embedded into simulation software ADVISOR. Simulation experiments with different driving cycles were carried out, and the feasibility and effectiveness of fuzzy logic control strategy is tested with the state of charge (SOC) and braking energy recovery.


title: The CanMEDS initiative: implementing an outcomes-based framework of physician competencies.

abstract: Background: Outcomes-based education in the health professions has emerged as a priority for curriculum planners striving to align with societal needs. However, many struggle with effective methods of implementing such an approach. In this narrative, we describe the lessons learned from the implementation of a national, needs-based, outcome-oriented, competency framework called the CanMEDS initiative of The Royal College of Physicians and Surgeons of Canada. Methods: We developed a framework of physician competencies organized around seven physician ‘‘Roles’’: Medical Expert, Communicator, Collaborator, Manager, Health Advocate, Scholar, and Professional. A systematic implementation plan involved: the development of standards for curriculum and assessment, faculty development, educational research and resources, and outreach. Lessons learned: Implementing this competency framework has resulted in successes, challenges, resistance to change, and a list of essential ingredients for outcomes-based medical education. Conclusions: A multifaceted implementation strategy has enabled this large-scale curriculum change for outcomes-based education.


title: Balancing and translation control of a ball segway that a human can ride

abstract: A ball segway, a ballbot-type robot that can carry a person is developed. The robot is of a sufficient size and has a car-like structure mounted on a single spherical tire with a seat that a human can ride. Based on the kinematic model of the ball segway, the full 3D dynamic model is simplified and decoupled into two orthogonal 2D dynamic models, where one is related sagittal motion and the other is related frontal motion, based on Lagrange equation and decoupling method. After linearizing dynamic model, two-loop control system is applied to each plane for control balancing, station keeping and transferring itself from point to point. Balancing and transferring performance of the ball segway is evaluated by experiments.


title: Asset Allocation with a High Dimensional Latent Factor Stochastic Volatility Model

abstract: We investigate the implications of time-varying expected return and volatility on asset allocation in a high dimensional setting. We propose a dynamic factor multivariate stochastic volatility (DFMSV) model that allows the first two moments of returns to vary over time for a large number of assets. We then evaluate the economic significance of the DFMSV model by examining the performance of various dynamic portfolio strategies chosen by mean-variance investors in a universe of 36 stocks. We find that the DFMSV dynamic strategies significantly outperform various benchmark strategies out of sample. This outperformance is robust to different performance measures, investor's objective functions, time periods, and assets. Copyright 2006, Oxford University Press.


title: Ultra broadband transmission line transformers - planar realization principles

abstract: Three Guanella-type transmission line transformers (TLT) are presented: a coiled TLT on a GaAs substrate, a straight ferriteless TLT on a multilayer PCB and a straight hybrid TLT that employs semi-rigid coaxial cables and a ferrite. All three devices have 4:1 impedance transformation ratio, matching 12.5 /spl Omega/ to 50 /spl Omega/. Extremely broadband operation is achieved. A detailed description of the devices and their operational principle is given. General aspects of the design of TLT are discussed.


title: The epidemiology of eating disorders in six European countries: Results of the ESEMeD-WMH project

abstract: Few data are available to estimate the prevalence of eating disorders (EDs) and their correlates in the community. This paper reports data on EDs obtained in the framework of the ESEMeD project, aimed at investigating the prevalence of non-psychotic mental disorders in six European countries (Belgium, France, Germany, Italy, the Netherlands and Spain), using a new version of the Composite International Diagnostic Interview. The ESEMeD study was a general population cross-sectional household survey. In total, 21,425 respondents aged 18 or older provided data for the project between January 2001 and August 2003. A subsample (N = 4139) underwent a detailed investigation on EDs. Lifetime estimated prevalence of anorexia nervosa, bulimia nervosa, binge eating disorder, sub-threshold binge eating disorder, and any binge eating were 0.48%, 0.51%, 1.12%, 0.72%, and 2.15%, respectively, and they were 3–8 times higher among women for all EDs. However, since people under 18 were excluded from this study, our prevalence should be taken as lower-bound estimate of real frequencies. Indeed, cumulative lifetime prevalence analysis showed that the majority of eating disorders had their initial onset between 10 and 20 years of age. Role impairment and comorbidity with other mental disorders were highly common, yet only small proportions of patients with a lifetime diagnosis of EDs requested medical treatment. It still has to be proven whether early diagnostic identification and access to specialized care can reduce the burden caused by these disorders.


title: Exploring OpenStreetMap Capability for Road Perception

abstract: Drivable space detection or road perception is one of the most important tasks for autonomous driving. Sensorbased vision/laser systems may have limited performance in bad illumination/weather conditions, a prior knowledge of the road from the map data is expected to improve the effectiveness. This paper is to employ the map information extracted from the OpenStreetMap (OSM) data, and explore its capability for road perception. The OSM data can be used to render virtual street views, and further refined to provide the prior road mask. The OSM masks can be also combine with image processing and Lidar point clouding approaches to characterize the drivable space. Using a Fully Convolutional Neural Network (FCNN), the OSM availability for deep learning methods is also discussed.


title: Survey of Information Retrieval Techniques for Web using NLP

abstract: with data each passing day. There needs to be efficient development in the area of information retrieval so the required data can be fetched accurately and efficiently. In this paper the two promising areas Natural Language Processing and Web technologies which can be combined together to enable the enterprise to combine the unstructured and structured data in ways that was not handled efficiently by traditional tools. The better understanding of web information can be done by integrating NLP and Web portal. The paper also explores various techniques used both areas. Also the NLP frameworks which can be used for the future work in this area.


title: Performative Verbs and Performative Acts

abstract: Searle (1989) posits a set of adequacy criteria for any account of the meaning and use of performative verbs, such as order or promise. Central among them are: (a) performative utterances are performances of the act named by the performative verb; (b) performative utterances are self-verifying; (c) per- formative utterances achieve (a) and (b) in virtue of their literal meaning. He then argues that the fundamental problem with assertoric accounts of performa- tives is that they fail (b), and hence (a), because being committed to having an intention does not guarantee having that intention. Relying on a uniform mean- ing for verbs on their reportative and performative uses, we propose an assertoric analysis of performative utterances that does not require an actual intention for deriving (b), and hence can meet (a) and (c). Explicit performative utterances are those whose illocutionary force is made explicit by the verbs appearing in them (Austin 1962): (1) I (hereby) promise you to be there at five. (is a promise)


AutoNet: Knowledge Graphs for Occasions Object Recognition


Mixed Methods Research to Improve Course Design for Preservice Teachers


title: A novel co/cross-polarizing chipless RFID tags for high coding capacity and robust detection

abstract: The aim of this work is to introduce a novel tag design enhancing the coding efficiency for the Frequency Coded (FC) chipless RFID system. The proposed approach relies on exploiting the backscattered co/cross-polarized signals from a tag excited with a linear-polarized wave. Consequently, the tag signature is encoded into Notch/Peak (N/P) format in two orthogonal planes. Furthermore, the introduced tag design exhibits narrow resonance bandwidth ensuring best utilization of the operating frequency range and sufficient coding capacity with compact tag size. Hence, 80-bit coding capacity is reached employing physical amplitude On Off-N/P (OO-N/P) modulation and N/P-Position (N/P-P) modulation principles. The presence or absence of a resonator resembles the OO-N/P modulation while the shift in the frequency position imitates the (N/P-P) modulation. In addition, the depolarization property increases the probability of tag detection in a real-world heavily dense multi-path environment. An asymmetry crossed dipole structure is employed to facilitate coding in both divergent polarities separately. The manufactured tags are verified in indoor real-world environment to proof the robustness of the introduced tag design and encoding algorithm. In the full paper, various parameters such as reading range, tag miniaturization and angular stability will be investigated.


title: Can Movies and Books Collaborate? Cross-Domain Collaborative Filtering for Sparsity Reduction

abstract: The sparsity problem in collaborative filtering (CF) is a major bottleneck for most CF methods. In this paper, we consider a novel approach for alleviating the sparsity problem in CF by transferring useritem rating patterns from a dense auxiliary rating matrix in other domains (e.g., a popular movie rating website) to a sparse rating matrix in a target domain (e.g., a new book rating website). We do not require that the users and items in the two domains be identical or even overlap. Based on the limited ratings in the target matrix, we establish a bridge between the two rating matrices at a cluster-level of user-item rating patterns in order to transfer more useful knowledge from the auxiliary task domain. We first compress the ratings in the auxiliary rating matrix into an informative and yet compact cluster-level rating pattern representation referred to as a codebook. Then, we propose an efficient algorithm for reconstructing the target rating matrix by expanding the codebook. We perform extensive empirical tests to show that our method is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary tasks, as compared to many state-of-the-art CF methods.


title: The overview of reviews: unique challenges and opportunities when research syntheses are the principal elements of new integrative scholarship.

abstract: In the past two decades, a new form of scholarship has appeared in which researchers present an overview of previously conducted research syntheses on the same topic. In these efforts, research syntheses are the principal units of evidence. Overviews of reviews introduce unique problems that require unique solutions. This article describes what methods overviewers have developed or have adopted from other forms of scholarship. These methods concern how to (a) define the broader problem space of an overview, (b) conduct literature searches that specifically look for research syntheses, (c) address the overlap in evidence in related reviews, (d) evaluate the quality of both primary research and research syntheses, (e) integrate the outcomes of research syntheses, especially when they produce discordant results, (f) conduct a second-order meta-analysis, and (g) present findings. The limitations of overviews are also discussed, especially with regard to the age of the included evidence.


title: A Logical Approach to Factoring Belief Networks

abstract: We have recently proposed a tractable logical form, known as deterministic, decomposable negation normal form (d-DNNF). We have shown that d-DNNF supports a number of logical operations in polynomial time, including clausal entailment, model counting, model enumeration, model minimization, and probabilistic equivalence testing. In this paper, we discuss another major application of this logical form: the implementation of multi-linear functions (of exponential size) using arithmetic circuits (that are not necessarily exponential). Specifically, we show that each multi–linear function can be encoded using a propositional theory, and that each d-DNNF of the theory corresponds to an arithmetic circuit that implements the encoded multi–linear function. We discuss the application of these results to factoring belief networks, which can be viewed as multi–linear functions as has been shown recently. We discuss the merits of the proposed approach for factoring belief networks, and present experimental results showing how it can handle efficiently belief networks that are intractable to structure–based methods for probabilistic inference.


title: Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings

abstract: Conventional feature-based and model-based gaze estimation methods have proven to perform well in settings with controlled illumination and specialized cameras. In unconstrained real-world settings, however, such methods are surpassed by recent appearance-based methods due to difficulties in modeling factors such as illumination changes and other visual artifacts. We present a novel learning-based method for eye region landmark localization that enables conventional methods to be competitive to latest appearance-based methods. Despite having been trained exclusively on synthetic data, our method exceeds the state of the art for iris localization and eye shape registration on real-world imagery. We then use the detected landmarks as input to iterative model-fitting and lightweight learning-based gaze estimation methods. Our approach outperforms existing model-fitting and appearance-based methods in the context of person-independent and personalized gaze estimation.


title: RelationNet: Learning Deep-Aligned Representation for Semantic Image Segmentation

abstract: Semantic image segmentation, which assigns labels in pixel level, plays a central role in image understanding. Recent approaches have attempted to harness the capabilities of deep learning. However, one central problem of these methods is that deep convolutional neural network gives little consideration to the correlation among pixels. To handle this issue, in this paper, we propose a novel deep neural network named RelationNet, which utilizes CNN and RNN to aggregate context information. Besides, a spatial correlation loss is applied to train RelationNet to align features of spatial pixels belonging to same category. Importantly, since it is expensive to obtain pixel-wise annotations, we exploit a new training method to combine the coarsely and finely labeled data. Experiments show the detailed improvements of each proposal. Experimental results demonstrate the effectiveness of our proposed method to the problem of semantic image segmentation, which obtains state-of-the-art performance on the Cityscapes benchmark and Pascal Context dataset.


title: GNA: Reconfigurable and Efficient Architecture for Generative Network Acceleration

abstract: Generative networks have become ubiquitous in image generation applications like image super-resolution, image to image translation, and text to image synthesis. They are usually composed of convolutional (CONV) layers, convolution-based residual blocks, and deconvolutional (DeCONV) layers. Previous works on neural network acceleration focus too much on optimizing CONV layers computation such as data-reuse or parallel computation, but have low processing element (PE) utilization in computing residual blocks and DeCONV layers: residual blocks require very high memory bandwidth when performing elementwise additions on residual paths; DeCONV layers have imbalanced operation counts for different outputs. In this paper, we propose a dual convolution mapping method for CONV and DeCONV layers to make full use of the available PE resources. A cross-layer scheduling method is also proposed to avoid extra off-chip memory access in residual block processing. Precision-adaptive PEs and buffer bandwidth reconfiguration are used to support flexible bitwidths for both inputs and weights in deep neural networks. We implement a generative network accelerator (GNA) based on intra-PE processing, inter-PE processing, and cross-layer scheduling techniques. Owing to the proposed optimization techniques, GNA achieves energy efficiency of 2.05 TOPS/W with 61% higher PE utilization than traditional methods in generative network acceleration.


title: Active Learning for Real Time Detection of Polyps in Videocolonoscopy

abstract: Abstract In this paper a method to perform real-time detection of polyps in videocolonoscopy is introduced. Polyps are at the origins of colorectal cancer which is one of the deadliest diseases in the world. Many methods to improve detection of polyps have been proposed so far. However performance of these methods strongly depends on the available computational resources and, until now, are not able to perform real-time detection during a standard examination. The proposed method, based on active learning, is able to solve these issues. Most precisely, this approach allows us to detect approximately 90% of polyps on a freely available database introduced to the community in 2012, for a F2 score of 65%, and matches real-time constraint by making possible the analysis of a frame in only 0.023s (average value) on a standard computer not necessarily dedicated to that kind of application.