title: Source camera identification using enahnced sensor pattern noise

abstract: Sensor pattern noises (SPN), extracted from digital images as device fingerprints, have been proved as an effective way for digital device identification. However, the limitation of the current method of extracting the sensor pattern noise is that the SPNs extracted from images are highly contaminated by the details from the scene and as a result the misclassification rate is high unless images of large size are used. In this work we propose a novel approach for enhancing sensor pattern noises so as to improve the performance of the identifier. The hypothesis underlying our fingerprint enhancer is that the stronger a signal component is, the less trustworthy the component should be and thus should be attenuated. An enhanced fingerprint can be obtained by assigning weighting factors inversely proportional to the magnitude of the signal components.


title: SPLAT: Semantic Pixel-Level Adaptation Transforms for Detection

abstract: Domain adaptation of visual detectors is a critical challenge, yet existing methods have overlooked pixel appearance transformations, focusing instead on bootstrapping and/or domain confusion losses. We propose a Semantic Pixel-Level Adaptation Transform (SPLAT) approach to detector adaptation that efficiently generates cross-domain image pairs. Our model uses aligned-pair and/or pseudo-label losses to adapt an object detector to the target domain, and can learn transformations with or without densely labeled data in the source (e.g. semantic segmentation annotations). Without dense labels, as is the case when only detection labels are available in the source, transformations are learned using CycleGAN alignment. Otherwise, when dense labels are available we introduce a more efficient cycle-free method, which exploits pixel-level semantic labels to condition the training of the transformation network. The end task is then trained using detection box labels from the source, potentially including labels inferred on unlabeled source data. We show both that pixel-level transforms outperform prior approaches to detector domain adaptation, and that our cycle-free method outperforms prior models for unconstrained cycle-based learning of generic transformations while running 3.8 times faster. Our combined model improves on prior detection baselines by 12.5 mAP adapting from Sim 10K to Cityscapes, recovering over 50% of the missing performance between the unadapted baseline and the labeled-target upper bound.


title: Peak-To-Average Power Ratio of Single Carrier FDMA Signals with Pulse Shaping

abstract: Single carrier frequency division multiple access (SC-FDMA), which utilizes single carrier modulation and frequency domain equalization is a technique that has similar performance and essentially the same overall complexity as those of OFDM, in which high peak-to-average power ratio (PAPR) is a major drawback. An outstanding advantage of SC-FDMA is its lower PAPR due to its single carrier structure. In this paper, we analyze the PAPR of SC-FDMA signals with pulse shaping. We analytically derive the time domain SC-FDMA signals and numerically compare PAPR characteristics using the complementary cumulative distribution function (CCDF) of PAPR. The results show that SC-FDMA signals indeed have lower PAPR compared to those of OFDMA. Comparing the two forms of SC-FDMA, we find that localized FDMA (LFDMA) has higher PAPR than interleaved FDMA (IFDMA) but somewhat lower PAPR than OFDMA. Also noticeable is the fact that pulse shaping increases PAPR.


title: Forecasting Human Dynamics from Static Images

abstract: This paper presents the first study on forecasting human dynamics from static images. The problem is to input a single RGB image and generate a sequence of upcoming human body poses in 3D. To address the problem, we propose the 3D Pose Forecasting Network (3D-PFNet). Our 3D-PFNet integrates recent advances on single-image human pose estimation and sequence prediction, and converts the 2D predictions into 3D space. We train our 3D-PFNet using a three-step training strategy to leverage a diverse source of training data, including image and video based human pose datasets and 3D motion capture (MoCap) data. We demonstrate competitive performance of our 3D-PFNet on 2D pose forecasting and 3D structure recovery through quantitative and qualitative results.


title: Exploring the Use of Text Classification in the Legal Domain

abstract: In this paper, we investigate the application of text classification methods to support law professionals. We present several experiments applying machine learning techniques to predict with high accuracy the ruling of the French Supreme Court and the law area to which a case belongs to. We also investigate the influence of the time period in which a ruling was made on the form of the case description and the extent to which we need to mask information in a full case ruling to automatically obtain training and test data that resembles case descriptions. We developed a mean probability ensemble system combining the output of multiple SVM classifiers. We report results of 98% average F1 score in predicting a case ruling, 96% F1 score for predicting the law area of a case, and 87.07% F1 score on estimating the date of a ruling.


title: The Christian Story: A Narrative Interpretation of Basic Christian Doctrine

abstract: An original work in systematic theology, The Christian Story rises from, and strives to be a resource to, the life and witness of the church and its leadership. In addition to covering the standard teachings of Christianity-the doctrines of God, creation, the fall, covenant, Christ, salvation, church and consummation-Volume 1 presents Fackre's introduction to systematic theology. This revised third edition develops in more detail the doctrine of the Trinity, takes up the issues of religious pluralism and Jewish-Christian dialogue, and offers a perspective on angelology. New appendices discuss inclusive language and describe the surge of writing in the field of systematic theology.


title: Extending the Technology Acceptance Model to Mobile Banking Adoption in Rural Zimbabwe

abstract: Improvements in wireless technologies and increased uptake of advanced mobile handsets have led to a growing trend in mobile banking activities on a global scale. This empirical study sought to investigate the applicability of the extension of the renowned framework of Technology Acceptance Model (TAM) in determining factors that influence unbanked rural communities Zimbabwe's intention to adopt mobile banking services. A self-administered questionnaire was developed and distributed in Zaka, Chiredzi, Gutu and Chivi rural districts Out of the 400 questionnaires, only 275 useable questionnaires were returned, yielding a response rate of 69%. Results were subsequently analyzed by the SPSS package. The findings indicate that the extended TAM can predict consumer intention to use mobile banking. Specifically, perceived usefulness, perceived ease of use, relative advantages, personal innovativeness and social norms have significant effect on user’s attitude thus influence the intention toward mobile banking, whilst perceived risks and costs deterred the adoption of the service. The results may provide further insights into mobile banking strategies for mobile network operators, banks and software engineers to design and implement mobile banking services to yield higher consumer acceptance amongst the unbanked rural communities in Zimbabwe.


title: Brain functional activity during gait in normal subjects: a SPECT study

abstract: The purpose of this study was to evaluate changes in brain activity during voluntary walking in normal subjects using technetium-99m-hexamethyl-propyleneamine oxime single photon emission computed tomography. This study included 14 normal subjects. Statistical parametric mapping analysis revealed that the supplementary motor area, medial primary sensorimotor area, the striatum, the cerebellar vermis and the visual cortex were activated. These results suggested that the cerebral cortices controlling motor functions, visual cortex, basal ganglia and the cerebellum might be involved in the bipedal locomotor activities in humans.


title: GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network

abstract: We propose a novel convolutional neural network for lesion detection from weak labels. Only a single, global label per image - the lesion count - is needed for training. We train a regression network with a fully convolutional architecture combined with a global pooling layer to aggregate the 3D output into a scalar indicating the lesion count. When testing on unseen images, we first run the network to estimate the number of lesions. Then we remove the global pooling layer to compute localization maps of the size of the input image. We evaluate the proposed network on the detection of enlarged perivascular spaces in the basal ganglia in MRI. Our method achieves a sensitivity of 62% with on average 1.5 false positives per image. Compared with four other approaches based on intensity thresholding, saliency and class maps, our method has a 20% higher sensitivity.


title: Handling contingency in temporal constraint networks: from consistency to controllabilities

abstract: Temporal Constraint Networks (TCN) allow one to express minimal and maximal durations between time-points. Although being used in many research areas, this model disregards the contingent nature of some constraints, whose effective duration cannot be decided by the system but is provided by the external world. We propose an extension of TCN based on the definition of the Simple Temporal Problem under Uncertainty (STPU) in which the classical network consistency property must be redefined in terms of controllability: intuitively, we would like to say that a network is controllable iff it is consistent in any situation (i.e. any assignment of the whole set of contingent intervals) that may arise in the external world. Three levels of controllability must be distinguished, namely the Strong, the Weak and the Dynamic ones. This paper provides a full characterization of those properties and their usefulness in practice, and proposes algorithms for checking them. Complexity issues and tractable equivalence clas...


title: Creating Advice-Taking Reinforcement Learners

abstract: Learning from reinforcements is a promising approach for creating intelligent agents. However, reinforcement learning usually requires a large number of training episodes. We present and evaluate a design that addresses this shortcoming by allowing a connectionist Q-learner to accept advice given, at any time and in a natural manner, by an external observer. In our approach, the advice-giver watches the learner and occasionally makes suggestions, expressed as instructions in a simple imperative programming language. Based on techniques from knowledge-based neural networks, we insert these programs directly into the agent‘s utility function. Subsequent reinforcement learning further integrates and refines the advice. We present empirical evidence that investigates several aspects of our approach and shows that, given good advice, a learner can achieve statistically significant gains in expected reward. A second experiment shows that advice improves the expected reward regardless of the stage of training at which it is given, while another study demonstrates that subsequent advice can result in further gains in reward. Finally, we present experimental results that indicate our method is more powerful than a naive technique for making use of advice.


title: Aggregating Local Deep Features for Image Retrieval

abstract: Several recent works have shown that image descriptors produced by deep convolutional neural networks provide state-of-the-art performance for image classification and retrieval problems. It has also been shown that the activations from the convolutional layers can be interpreted as local features describing particular image regions. These local features can be aggregated using aggregation approaches developed for local features (e.g. Fisher vectors), thus providing new powerful global descriptors. In this paper we investigate possible ways to aggregate local deep features to produce compact global descriptors for image retrieval. First, we show that deep features and traditional hand-engineered features have quite different distributions of pairwise similarities, hence existing aggregation methods have to be carefully re-evaluated. Such re-evaluation reveals that in contrast to shallow features, the simple aggregation method based on sum pooling provides arguably the best performance for deep convolutional features. This method is efficient, has few parameters, and bears little risk of overfitting when e.g. learning the PCA matrix. Overall, the new compact global descriptor improves the state-of-the-art on four common benchmarks considerably.


title: Clustering and projected clustering with adaptive neighbors

abstract: Many clustering methods partition the data groups based on the input data similarity matrix. Thus, the clustering results highly depend on the data similarity learning. Because the similarity measurement and data clustering are often conducted in two separated steps, the learned data similarity may not be the optimal one for data clustering and lead to the suboptimal results. In this paper, we propose a novel clustering model to learn the data similarity matrix and clustering structure simultaneously. Our new model learns the data similarity matrix by assigning the adaptive and optimal neighbors for each data point based on the local distances. Meanwhile, the new rank constraint is imposed to the Laplacian matrix of the data similarity matrix, such that the connected components in the resulted similarity matrix are exactly equal to the cluster number. We derive an efficient algorithm to optimize the proposed challenging problem, and show the theoretical analysis on the connections between our method and the K-means clustering, and spectral clustering. We also further extend the new clustering model for the projected clustering to handle the high-dimensional data. Extensive empirical results on both synthetic data and real-world benchmark data sets show that our new clustering methods consistently outperforms the related clustering approaches.


Working Paper : Designing a Data Governance Framework


title: An Automotive Onboard 3.3-kW Battery Charger for PHEV Application

abstract: An onboard charger is responsible for charging the battery pack in a plug-in hybrid electric vehicle (PHEV). In this paper, a 3.3-kW two-stage battery charger design is presented for a PHEV application. The objective of the design is to achieve high efficiency, which is critical to minimize the charger size, charging time, and the amount and cost of electricity drawn from the utility. The operation of the charger power converter configuration is provided in addition to a detailed design procedure. The mechanical packaging design and key experimental results are provided to verify the suitability of the proposed charger power architecture.


title: Chlorella vulgaris Modulates Immunomyelopoietic Activity and Enhances the Resistance of Tumor-Bearing Mice

abstract: We studied the effects of Chlorella vulgaris (CV) on the interaction between stromal and hematopoietic stem cells in normal and Ehrlich ascites tumor (EAT)-bearing mice. Long-term bone marrow culture (LTBMC), cytokine production, spleen mononuclear cells (SMC) proliferation (SCP), colony stimulating activity (CSA), and NK cells activity were evaluated. In tumor bearers, reduced capacity of stromal cell layer to support the growth and differentiation of granulocyte-macrophage progenitor cells (CFU-GM), concomitantly to decreased numbers of total nonadherent cells in LTBMC and reduced local production of IL-6 and IL-1α, were observed. Presence of the tumor has not altered the number of stromal adherent cells. CV treatment restored the ability of stromal cells from EAT-bearing mice to produce IL-6 and IL-1α, which was consistent with increased number of nonadherent cells and higher ability to display CFU-GM in vitro. EAT growth increased SCP, serum CSA, and IL-10 production and concurrently depressed NK cell...


title: Autonomous coupled oscillators with hyperbolic strange attractors

abstract: Abstract We propose several examples of smooth low-order autonomous dynamical systems which have apparently uniformly hyperbolic attractors. The general idea is based on the use of coupled self-sustained oscillators where, due to certain amplitude nonlinearities, successive epochs of damped and excited oscillations alternate. Because of additional, phase sensitive coupling terms in the equations, the transfer of excitation from one oscillator to another is accompanied by a phase transformation corresponding to some chaotic map (in particular, an expanding circle map or Anosov map of a torus). The first example we construct is a minimal model possessing an attractor of the Smale–Williams type. It is a four-dimensional system composed of two oscillators. The underlying amplitude equations are similar to those of the predator-pray model. The other three examples are systems of three coupled oscillators with a heteroclinic cycle. This scheme presents more variability for the phase manipulations: in the six-dimensional system not only the Smale–Williams attractor, but also an attractor with Arnold cat map dynamics near a two-dimensional toral surface, and a hyperchaotic attractor with two positive Lyapunov exponents, are realized.


title: Improving Massive MIMO Belief Propagation Detector with Deep Neural Network

abstract: In this paper, deep neural network (DNN) is utilized to improve the belief propagation (BP) detection for massive multiple-input multiple-output (MIMO) systems. A neural network architecture suitable for detection task is firstly introduced by unfolding BP algorithms. DNN MIMO detectors are then proposed based on two modified BP detectors, damped BP and max-sum BP. The correction factors in these algorithms are optimized through deep learning techniques, aiming at improved detection performance. Numerical results are presented to demonstrate the performance of the DNN detectors in comparison with various BP modifications. The neural network is trained once and can be used for multiple online detections. The results show that, compared to other state-of-the-art detectors, the DNN detectors can achieve lower bit error rate (BER) with improved robustness against various antenna configurations and channel conditions at the same level of complexity.


title: Flexible Ambiguity Resolution and Incompleteness Detection in Requirements Descriptions via an Indicator-Based Configuration of Text Analysis Pipelines

abstract: Natural language software requirements descriptions enable end users to formulate their wishes and expectations for a future software product without much prior knowledge in requirements engineering. However, these descriptions are susceptible to linguistic inaccuracies such as ambiguities and incompleteness that can harm the development process. There is a number of software solutions that can detect deficits in requirements descriptions and partially solve them, but they are often hard to use and not suitable for end users. For this reason, we develop a software system that helps end-users to create unambiguous and complete requirements descriptions by combining existing expert tools and controlling them using automatic compensation strategies. In order to recognize the necessity of individual compensation methods in the descriptions, we have developed linguistic indicators, which we present in this paper. Based on these indicators, the whole text analysis pipeline is ad-hoc configured and thus adapted to the individual circumstances of a requirements description.


title: Positioning and Driving Control of Fork-type Automatic Guided Vehicle With Laser Navigation

abstract: We designed and implemented a fork-type automatic guided vehicle (AGV) with a laser guidance system. Most previous AGVs have used two types of guidance systems: magnetgyro and wire guidance. However, these guidance systems have high costs, are difficult to maintain with changes in the operating environment, and can drive only a pre-determined path with installed sensors. A laser guidance system was developed for addressing these issues, but limitations including slow response time and low accuracy remain. We present a laser guidance system and control system for AGVs with laser navigation. For analyzing the performance of the proposed system, we designed and built a fork-type AGV, and performed repetitions of our experiments under the same working conditions. The results show an average positioning error of 51.76 mm between the simulated driving path and the driving path of the actual fork-type AGV. Consequently, we verified that the proposed method is effective and suitable for use in actual AGVs.


title: Fine-grained classification of identity document types with only one example

abstract: In this paper, we tackle the task of recognizing types of partly very similar identity documents using state-of-the-art visual recognition approaches. Given a scanned document, the goal is to identify the country of issue, the type of document, and its version. Whereas recognizing the individual parts of a document with known standardized layout can be done reliably, identifying the type of a document and therefore also its layout is a challenging problem due to the large variety of documents. In our paper, we develop and evaluate different techniques for this application including feature representations based on recent achievements with convolutional neural networks. On a dataset with 74 different classes and using only one training image per class, our best approach achieves a mean class-wise accuracy of 97.7%.


title: The roles of habit and web site quality in e-commerce

abstract: Previous research has primarily examined consumers' perceived usefulness of web sites and trust in the web retailer as two major predictors of web site use and e-commerce adoption. While the consumers' repeated behavior in the past (i.e., habit) may contribute to continuance behavior, it has not been investigated. This article includes habit as a primary construct along with perceived usefulness and trust to predict and explain consumers' continued behavior of using a B2C web site. Additionally, included are several web quality measures as antecedents to trust and perceived usefulness. The research model is evaluated using structural equation modeling. Results show that consumers' behavioral intentions to continue using a B2C web site are determined by all three key drivers: perceived usefulness, trust, and habit. Furthermore, not all dimensions of web quality have a significant effect on perceived usefulness and trust.


title: Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening

abstract: Reliable verification of image quality of retinal screening images is a prerequisite for the development of automatic screening systems for diabetic retinopathy. A system is presented that can automatically determine whether the quality of a retinal screening image is sufficient for automatic analysis. The system is based on the assumption that an image of sufficient quality should contain particular image structures according to a certain pre-defined distribution. We cluster filterbank response vectors to obtain a compact representation of the image structures found within an image. Using this compact representation together with raw histograms of the R, G, and B color planes, a statistical classifier is trained to distinguish normal from low quality images. The presented system does not require any previous segmentation of the image in contrast with previous work. The system was evaluated on a large, representative set of 1000 images obtained in a screening program. The proposed method, using different feature sets and classifiers, was compared with the ratings of a second human observer. The best system, based on a Support Vector Machine, has performance close to optimal with an area under the ROC curve of 0.9968.


title: Here Is Your Fingerprint!: Actual Risk versus User Perception of Latent Fingerprints and Smudges Remaining on Smartphones

abstract: A small touch sensor employed in smartphones can only capture a partial limited portion of the full fingerprint, and so it is more vulnerable to fingerprint spoofing attacks that leverage a user's firm impression. However, it is still unknown whether daily smudges remaining on the smartphone surface can be exploited to circumvent the small touch sensor. In this paper, we first study how to exploit the fingerprint smudges left on the smartphone surface in daily use, and present the so-called fingerprint SCRAP attack, which uses smudges remaining on the home button and touch screen to reconstruct an image of the enrolled fingerprint in good quality. We conduct an experimental study to show the actual risk regarding this attack. We collect 403 latent fingerprints from the smudges left on the touch screens (361) and home buttons (42) by seven users in six conditions (tapping, passcode-typing, text-typing, facebook, in-pocket, wiping). Using them, we perform our attack and evaluate the results in comparison with the firmly impressed fingerprints. The study results indicate that our attack is actual risk to the small touch sensors. We then investigate the user's touch behavior and perception gap. We conduct in-person surveys involving 82 participants, and ask about their touch behaviors and also their risk perception regarding the latent fingerprints. The survey results show that the fingers most frequently used on a touch screen and a home button are the same, and the user's risk perception is very low. We finally discuss mitigation methods and future directions.


title: Real-Time ROS Extension on Transparent CPU/GPU Coordination Mechanism

abstract: Robot Operating System (ROS) promotes fault isolation, faster development, modularity, and core reusability and is therefore widely studied and used as the de facto standard for autonomous driving systems. Graphics processing units (GPUs) also facilitate high-performance computing and are therefore used for autonomous driving. As the requirements for real-time processing increase, methods for satisfying real-time constraints for ROS and GPUs are being developed. Unfortunately, scheduling algorithms specifying ROS's transportation (publish/subscribe) model, which can have execution order restrictions, are not being investigated, leading to the introduction of waiting time and degrading the responsiveness of the entire system. Furthermore, GPU tasks on ROS are also affected by the ROS transportation model, because central processing unit (CPU) time is occupied when GPU functions are launched. This paper proposes a loadable kernel module framework, called real-time ROS extension on transparent CPU/GPU coordination mechanism (ROSCH-G), for scheduling ROS in a heterogeneous environment without modifying the OS kernel and device drivers and then evaluates it experimentally. ROSCH-G provides a scheduling algorithm that considers ROS's execution order restrictions and a CPU/GPU coordination mechanism. Experimental results demonstrate that the proposed algorithm reduces the deadline miss rate and, compared with previous studies, makes effective use of the benefits of parallel processing. In addition, the results for the coordination mechanism demonstrate that ROSCH-G can schedule multiple GPU applications successfully.


title: Blog site search using resource selection

abstract: A blog site consists of many individual blog postings. Current blog search services focus on retrieving postings but there is also a need to identify relevant blog sites. Blog site search is similar to resource selection in distributed information retrieval, in that the target is to find relevant collections of documents. We introduce resource selection techniques for blog site search and evaluate their performance. Further, we propose a "diversity factor" that measures the topic diversity of each blog site. Our results show that the appropriate combination of the resource selection techniques and the diversity factor can achieve significant improvements in retrieval performance compared to baselines. We also report results using these techniques on the TREC blog distillation task.


title: Scene Recognition and Object Detection in a Unified Convolutional Neural Network on a Mobile Manipulator

abstract: Environment understanding, object detection and recognition are crucial skills for robots operating in the real world. In this paper, we propose a Convolutional Neural Network with multi-task objectives: object detection and scene classification in one unified architecture. The proposed network reasons globally about an image to understand the scene, hypothesize object locations, and encodes global scene features with regional object features to improve object recognition. We evaluate our network on the standard SUN RGBD dataset. Experiments show that our approach outperforms state-of-the-arts. Network predictions are further transformed into continuous robot beliefs to ensure temporal coherence and extended to 3D space for robotics applications. We embed the whole framework in Robot Operating System, and evaluate its performance on a real robot for semantic mapping and grasp detection.


title: Deep Recurrent Neural Network for Intrusion Detection in SDN-based Networks

abstract: Software Defined Networking (SDN) has emerged as a key enabler for future agile Internet architecture. Nevertheless, the flexibility provided by SDN architecture manifests several new design issues in terms of network security. These issues must be addressed in a unified way to strengthen overall network security for future SDN deployments. Consequently, in this paper, we propose a Gated Recurrent Unit Recurrent Neural Network (GRU-RNN) enabled intrusion detection systems for SDNs. The proposed approach is tested using the NSL-KDD dataset, and we achieve an accuracy of 89% with only six raw features. Our experiment results also show that the proposed GRU-RNN does not deteriorate the network performance. Through extensive experiments, we conclude that the proposed approach exhibits a strong potential for intrusion detection in the SDN environments.


Face Verification from Depth using Privileged Information


Analytics As A Service: Cloud Computing and the Transformation of Business Analytics Business Models and Ecosystems


title: Cold-Start Collaborative Filtering

abstract: Collaborative Filtering (CF) is a technique to generate personalised recommendations for a user from a collection of correlated preferences in the past. In general, the effectiveness of CF greatly depends on the amount of available information about the target user and the target item. The cold-start problem, which describes the difficulty of making recommendations when the users or the items are new, remains a great challenge for CF. Traditionally, this problem is tackled by resorting to an additional interview process to establish the user (item) profile before making any recommendations. During this process the user's information need is not addressed. In this thesis, however, we argue that recommendations would be preferably provided right from the beginning. And the goal of solving the cold-start problem should be maximising the overall recommendation utility during all interactions with the recommender system. In other words, we should not distinguish between the informationgathering and recommendation-making phases, but seamlessly integrate them together. This mechanism naturally addresses the cold-start problem as any user (item) can immediately receive sequential recommendations without providing extra information beforehand. This thesis solves the cold-start problem in an interactive setting by focusing on four interconnected aspects. First, we consider a continuous sequential recommendation process with CF and relate it to the exploitation-exploration (EE) trade-off. By employing probabilistic matrix factorization, we obtain a structured decision space and are thus able to leverage several EE algorithms, such as Thompson sampling and upper confidence bounds, to select items. Second, we extend the sequential recommendation process to a batch mode where multiple recommendations are made at each interaction stage. We specifically discuss the case of two consecutive interaction stages, and model it with the partially observable Markov decision process (POMDP) to obtain its exact theoretical solution. Through an indepth analysis of the POMDP value iteration solution, we identify that an exact solution can be abstracted as selecting users (items) that are not only highly relevant to the target according to the initial-stage information, but also highly correlated with other potential users (items) for the next stage. Third, we consider the intra-stage recommendation optimisation and focus on the problem of personalised item diversification. We reformulate the latent factor models using the mean-variance analysis from the portfolio theory in economics. The resulting portfolio ranking algorithm naturally captures the user's interest range and the uncertainty of the user preference by employing the variance of the learned user latent factors, leading to a diversified item list adapted to the individual user. And, finally, we relate the diversification algorithm back to the interactive process by considering inter-stage joint portfolio diversification, where the recommendations are optimised jointly with the user's past preference records. The pdf version of my thesis is available for download at http://discovery.ucl.ac.uk/1474118/.


title: Student Perceptions of Flipped Learning

abstract: Flipped learning has been the subject of signicant hype and attention but descriptions of the development and the evaluation of this pedagogical model are lacking. Flipped learning is an inverted teaching approach where students learn the basics via short videos at home, then come to class to complete challenges and clarify any misunderstandings. This paper describes how an IT unit was delivered using the ipped learning approach. A survey was used to determine how students perceived ipped learning. Students were generally positive about the approach, particularly the convenience and exibility of the ipped videos. Although face to face teaching time was reduced in this ipped learning implementation, students felt that they interacted more with their instructors and peers. Students felt strongly positive to walkthroughs and were mixed as to the need for the instructors face. Signicant eorts to produce high quality and engaging videos were made, but the survey suggested that students learnt the most during tutorial time. The relative importance of interactive tutorials is congruent with a large body of research and pedagogical approaches advocating the importance of active student-centred learning.


title: Rogowski Coil: Exceptional Current Measurement Tool For Almost Any Application

abstract: This paper provides the basic concept and the applications of Rogowski coils in today's transforming electrical industry. This type of current transformer belongs to the family of Non conventional Current Transformer (NCIT). We will recall the basic concepts, the calculations and the real benefits of the Rogowski coil compared to a conventional current transformer, and how the design was improved by the various implementation that were made in the 1990's and early 2000's. We will emphases the inherent advantages of Rogowski coils such as noise reduction and extended measuring ranges resulting from digital integration. Industrial applications are discussed within the gas insulated substations (GIS). This is the GIS that is industrially manufactured today. A brief description of the electronic supplied to the NCIT is described. The new laboratories applications is presented for the measurement of pre-arc and post-arc current after interruption of short circuit current in circuit breakers.


title: Shock Graphs and Shape Matching

abstract: We have been developing a theory for the generic representation of 2-D shape, where structural descriptions are derived from the shocks (singularities) of a curve evolution process, acting on bounding contours. We now apply the theory to the problem of shape matching. The shocks are organized into a directed, acyclic shock graph, and complexity is managed by attending to the most significant (central) shape components first. The space of all such graphs is highly structured and can be characterized by the rules of a shock graph grammar. The grammar permits a reduction of a shock graph to a unique rooted shock tree. We introduce a novel tree matching algorithm which finds the best set of corresponding nodes between two shock trees in polynomial time. Using a diverse database of shapes, we demonstrate our system's performance under articulation, occlusion, and moderate changes in viewpoint.


Introduction to Matrix Analytic Methods in Stochastic Modeling


title: SCRUM: An extension pattern language for hyperproductive software development

abstract: Novel spiro[dibenz(b,f)oxepin-piperidine]s and methods of preparing same are described. These compounds are useful as analgetics, tranquilizers and anticonvulsants.


title: Fidelity as a criterion for practicing and evaluating narrative inquiry

abstract: “Fidelity” is presented as a criterion for practicing and evaluating narrative inquiry, linking narrative inquiry to both social science and art. “Fidelity” is contrasted with “truth” and characterized as moral in character. “Fidelity” is further characterized as a “betweenness,” construed as both intersubjective (obligations between teller and receiver) and as a resonance between the story told and the social and cultural context of a story. Storytelling as an arena of purposeful reconstruction of events on the part of both the teller and the narrative inquirer links narrative inquiry to art making. Using Ricoeur's work on emplotment and Langer's work in aesthetic philosophy, a criterion of “believability” is established. The narrative is believable when it can be credited with conveying, convincingly, that the events occurred and were felt in ways the narrator is asserting. Dilemmas with achieving “fidelity” and aesthetics in narrative inquiry are investigated.


title: Combining feature extraction and expansion to improve classification based similarity learning

abstract: Abstract Metric learning has been shown to outperform standard classification based similarity learning in a number of different contexts. In this paper, we show that the performance of classification similarity learning strongly depends on the data format used to learn the model. We then present an Enriched Classification Similarity Learning method that follows a hybrid approach that combines both feature extraction and feature expansion. In particular, we propose a data transformation and the use of a set of standard distances to supplement the information provided by the feature vectors of the training samples. The method is compared to state-of-the-art feature extraction and metric learning approaches, using linear learning algorithms in both a classification and a regression experimental setting. Results obtained show comparable performances in favor of the method proposed.


title: Asthma and chronic obstructive pulmonary disease (COPD) : aspects of treatment with inhaled corticosteroids of patients with mild signs or a clinical diagnosis

abstract: Treatment of patients with (mild signs of) asthma or COPD with inhaled corticosteroids was the central theme of the research in this thesis. Part One of the thesis contains original data from the DIMCA study, a large general-practice-based study on Detection, Intervention, and Monitoring of patients with mild signs of COPD and Asthma. Several aspects of early intervention with the relatively new inhaled corticosteroid fluticasone propionate {Flixotide©, 500 microgram daily) in patients with mild signs of asthma or COPD without a clear diagnosis were investigated, such as 'fear of corticosteroids' (Chapter 2), clinical effects (Chapter 3) and compliance of treatment (Chapter 4). The main conclusion of this part of the thesis was that the experimental treatment with fluticasone propionate (500 microgram daily) did not improve lung function in subjects with mild signs of COPD during two years of treatment or patients with mild signs of asthma during one year of treatment. In Part Two related questions are presented concerning the dosage and duration of treatment with inhaled corticosteroids in patient with diagnosed mild asthma (Chapter 5 and 6) and in patients with diagnosed moderate to severe COPD (Chapter 7 and 8). This resulted in the following main conclusion: in contrast to the results of recent large international trials in our study high dosages of inhaled corticosteroids (1500/1600 microgram daily) during two years of study seemed to improve lung function a little in patients with moderate COPD


title: Design of RKE System Based on KEELOQ Encryption Technology

abstract: KEELOQ code hopping technology is a nonlinear anti-encryption algorithm designed for secure Remote Keyless Entry (RKE) systems. It combines a 66-bits transmission length, so as to prevent the system from code predicting, code grabbing and code scanning. Because of its feature of high-security, it effectively overcomes the disadvantages of the traditional fixed code technology. This paper researched on KEELOQ encryption algorithm and presented a designed RKE system based on KEELOQ technology. When the software and hardware were designed, the whole RKE system was afterwards made as a prototype for the functionality tests. The experimental results showed that the design achieved the anticipative effects.


title: A Review of Theoretical and Practical Challenges of Trusted Autonomy in Big Data

abstract: Despite the advances made in artificial intelligence, software agents, and robotics, there is little we see today that we can truly call a fully autonomous system. We conjecture that the main inhibitor for advancing autonomy is lack of trust. Trusted autonomy is the scientific and engineering field to establish the foundations and ground work for developing trusted autonomous systems (robotics and software agents) that can be used in our daily life, and can be integrated with humans seamlessly, naturally, and efficiently. In this paper, we review this literature to reveal opportunities for researchers and practitioners to work on topics that can create a leap forward in advancing the field of trusted autonomy. We focus this paper on the trust component as the uniting technology between humans and machines. Our inquiry into this topic revolves around three subtopics: 1) reviewing and positioning the trust modeling literature for the purpose of trusted autonomy; 2) reviewing a critical subset of sensor technologies that allow a machine to sense human states; and 3) distilling some critical questions for advancing the field of trusted autonomy. The inquiry is augmented with conceptual models that we propose along the way by recompiling and reshaping the literature into forms that enable trusted autonomous systems to become a reality. This paper offers a vision for a Trusted Cyborg Swarm, an extension of our previous Cognitive Cyber Symbiosis concept, whereby humans and machines meld together in a harmonious, seamless, and coordinated manner.


title: New literacy, new audiences : social media and cultural institutions

abstract: This paper reports on a major Australian research project which examines whether the evolution in digital content creation and social media can create a new audience of active cultural participants. The project draws together experts from major Australian museums, libraries and screen centres to examine the evolution in digital contentcreation and social media. It explores whether organizations can become active in content generation ('new literacy'), and thereby be linked into new modes of distribution, calling into being 'new audiences'. The paper presents interim findings of the project, describing the theories and methodologies developed to investigate the rise of social media and, more broadly, digital content creation, within cultural institutions.


title: Cube2Video: Navigate Between Cubic Panoramas in Real-Time

abstract: Online virtual navigation systems enable users to hop from one 360° panorama to another, which belong to a sparse point-to-point collection, resulting in a less pleasant viewing experience. In this paper, we present a novel method, namely Cube2Video, to support navigating between cubic panoramas in a video-viewing mode. Our method circumvents the intrinsic challenge of cubic panoramas, i.e., the discontinuities between cube faces, in an efficient way. The proposed method extends the matching-triangulation-interpolation procedure with special considerations of the spherical domain. A triangle-to-triangle homography-based warping is developed to achieve physically plausible and visually pleasant interpolation results. The temporal smoothness of the synthesized video sequence is improved by means of a compensation transformation. As experimental results demonstrate, our method can synthesize pleasant video sequences in real time, thus mimicking walking or driving navigation.


title: A Sequential Matching Framework for Multi-Turn Response Selection in Retrieval-Based Chatbots

abstract: We study the problem of response selection for multi-turn conversation in retrieval-based chatbots. The task requires matching a response candidate with a conversation context, whose challenges include how to recognize important parts of the context, and how to model the relationships among utterances in the context. Existing matching methods may lose important information in contexts as we can interpret them with a unified framework in which contexts are transformed to fixed-length vectors without any interaction with responses before matching. The analysis motivates us to propose a new matching framework that can sufficiently carry the important information in contexts to matching and model the relationships among utterances at the same time. The new framework, which we call a sequential matching framework (SMF), lets each utterance in a context interacts with a response candidate at the first step and transforms the pair to a matching vector. The matching vectors are then accumulated following the order of the utterances in the context with a recurrent neural network (RNN) which models the relationships among the utterances. The context-response matching is finally calculated with the hidden states of the RNN. Under SMF, we propose a sequential convolutional network and sequential attention network and conduct experiments on two public data sets to test their performance. Experimental results show that both models can significantly outperform the state-of-the-art matching methods. We also show that the models are interpretable with visualizations that provide us insights on how they capture and leverage the important information in contexts for matching.


title: iVector-based prosodic system for language identification

abstract: Prosody is the part of speech where rhythm, stress, and intonation are reflected. In language identification tasks, these characteristics are assumed to be language dependent, and thus the language can be identified from them. In this paper, an automatic language recognition system that extracts prosody information from speech and makes decisions about the language with a generative classifier based on iVectors is built. The system is tested on the NIST LRE09 dataset. The results are still not comparable to state-of-the-art acoustic and phonotactic systems. However, they are promising and the fusion of the new approach with an iVector-based acoustic system is found to bring further improvements over the latter.


title: Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples

abstract: In computer-aided diagnosis (CAD), machine learning techniques have been widely applied to learn a hypothesis from diagnosed samples to assist the medical experts in making a diagnosis. To learn a well-performed hypothesis, a large amount of diagnosed samples are required. Although the samples can be easily collected from routine medical examinations, it is usually impossible for medical experts to make a diagnosis for each of the collected samples. If a hypothesis could be learned in the presence of a large amount of undiagnosed samples, the heavy burden on the medical experts could be released. In this paper, a new semisupervised learning algorithm named Co-Forest is proposed. It extends the co-training paradigm by using a well-known ensemble method named Random Forest, which enables Co-Forest to estimate the labeling confidence of undiagnosed samples and easily produce the final hypothesis. Experiments on benchmark data sets verify the effectiveness of the proposed algorithm. Case studies on three medical data sets and a successful application to microcalcification detection for breast cancer diagnosis show that undiagnosed samples are helpful in building CAD systems, and Co-Forest is able to enhance the performance of the hypothesis that is learned on only a small amount of diagnosed samples by utilizing the available undiagnosed samples.


title: Digital implementation of adaptive synchronous rectifier (SR) driving scheme for LLC resonant converters

abstract: In this paper, an adaptive synchronous rectifier (SR) driving scheme for the LLC resonant converters using the ripple counter concept is proposed, along with two methods of implementation. With the proposed scheme, the SR drain to source voltage is sensed to detect the body diode conduction, based on which the SR on-time can be well tuned to eliminate the body diode conduction. One proposed implementation tunes the SR on-time every switching cycle based on the ripple detection; another proposed implementation tunes the SR on-time every nth switching cycle (n = 1, 2, 3 ...) based on the ripple counter, which is suitable for the high frequency LLC converters. The proposed SR driving scheme has the simple implementation, requires only low-cost digital controllers and occupies very few controller resources. More importantly, since the digital controllers have already been widely adopted in the control of the LLC converters, the proposed adaptive SR driving method can be embedded into these digital controllers with little extra cost. Furthermore, how to integrate the proposed SR driving method with closed-loop control is explained in details. Experimental results are demonstrated on a 130kHz LLC converter with 100MHz microcontroller (MCU) and a 500kHz LLC converter with a 60MHz MCU and a ripple counter.


title: Reliability study of Au-In transient liquid phase bonding for SiC power semiconductor packaging

abstract: Transient liquid phase (TLP) bonding is a promising advanced die-attach technique for wide-bandgap power semiconductor and high-temperature packaging. TLP bonding advances modern soldering techniques by raising the melting point to over 500 °C without detrimental high-lead materials. The bond also has greater reliability and rigidity due in part to a bonding temperature of 200 °C that drastically lowers the peak bond stresses. Furthermore, the thermal conductivity is fractionally increased 67 % while the bond thickness is substantially reduced, lowering the thermal resistance by an order of magnitude or more. It is observed that Au-In TLP bonds exude excellent electrical reliability against thermal cycling degradation if designed properly as experimentally confirmed in this work.


title: Causal Models, Unobserved Variables, and Experimental Data

abstract: While techniques of causal modeling have been employed in the analysis of nonexperimental data, they have been largely ignored by social scientists who deal with experimental data. This paper discusses ways in which causal models may be fruitfully used in the analysis of experimental data, and illustrates their uses. Central to the paper's exposition is the representation of social science experiments in terms of causal processes involving unobserved variables. Within this framework, two key experimental issues are addressed: the evaluation of manipulated independent variables and the evaluation of indicators of dependent variables. Costner's (1971) suggestions regarding techniques for examining rival interpretations of experimental results are also discussed. This type of analysis of experimental data permits us to take explicit account of error in the induction and measurement of independent and dependent variables, thereby yielding better estimates of true causal parameters. Data from an experimental i...


title: designing good mapreduce Algorithms An introduction to designing algorithms for the MapReduce framework for parallel processing of big data

abstract: An introduction to designing algorithms for the MapReduce framework for parallel processing of big data.


Improving the use of instruction cache-locking methods in . . .


title: Effects of Duration of Immersion in a Virtual Reality Environment on Postural Stability

abstract: Few studies have been carried out to examine the relation between postural stability and subjective reports or feelings of motion sickness. Two views seem to exist on the relation between immersion in a virtual reality (VR) environment and subjective feelings of motion sickness. One predicts that the immersion induces both postural instability and motion sickness. Another view is that preimmersion postural instability predisposes people to motion sickness. However, these views are not supported by empirical research. Longer immersions in a VR environment may induce higher levels of postural instability and symptoms of motion sickness. In this study, effects of long-hours immersion in a VR environment on postural stability were examined to approach the underlying mechanism of postural instability and motion sickness using force platform measurement and self-reported questionnaire on motion sickness. As a result, it was suggested that longer immersion in a VR environment induced postural instability and sym...


title: LTE in unlicensed spectrum: are we there yet?

abstract: In this work, we explore the potential and impact of unlicensed LTE on WiFi in unlicensed spectrum. Our experiments demonstrate that the large asymmetry in the channel access methodologies employed by WiFi and LTE (carrier sensing/notification in WiFi, energy sensing alone in LTE-U), can result LTE-U completely blocking WiFi transmissions, and causing significant degradation to either technologies from collisions. We address this critical sensing asymmetry with U ltron , a LTE-WiFi co-existence solution that integrates WiFi's carrier sensing and notification mechanisms into LTE, without any modifications to the LTE PHY standard. Ultron operates at the LTE base station and consists of two key components: (a) WiFi embedding that embeds appropriate data into the LTE-subframes through an intelligent reverse-engineering of the LTE PHY, so as to realize a WiFi PLCP preamble-header transmission over the air directly using the LTE PHY; and (b) scalable WiFi sensing that employs a single WiFi interface and maximizes its carrier sensing benefits to all the unlicensed channels operating at the LTE node. Our evaluations demonstrate that U ltron can increase the WiFi and LTE throughput by 5x and 6x respectively, resulting from a sharp reduction in LTE-WiFi interference.


title: Multiple People Tracking by Lifted Multicut and Person Re-identification

abstract: Tracking multiple persons in a monocular video of a crowded scene is a challenging task. Humans can master it even if they loose track of a person locally by re-identifying the same person based on their appearance. Care must be taken across long distances, as similar-looking persons need not be identical. In this work, we propose a novel graph-based formulation that links and clusters person hypotheses over time by solving an instance of a minimum cost lifted multicut problem. Our model generalizes previous works by introducing a mechanism for adding long-range attractive connections between nodes in the graph without modifying the original set of feasible solutions. This allows us to reward tracks that assign detections of similar appearance to the same person in a way that does not introduce implausible solutions. To effectively match hypotheses over longer temporal gaps we develop new deep architectures for re-identification of people. They combine holistic representations extracted with deep networks and body pose layout obtained with a state-of-the-art pose estimation model. We demonstrate the effectiveness of our formulation by reporting a new state-of-the-art for the MOT16 benchmark. The code and pre-trained models are publicly available.


title: IoT architecture to enable intercommunication through REST API and UPnP using IP, ZigBee and arduino

abstract: To provide better life quality for mankind through the use of electrical and electronic devices that surround the world, controlling and communication between these equipments must occur and it must happen in an automatic and transparent way. This paper proposes a model based on usual technology to enable the event reading and the controlling of electrical devices, already existent and later developed, through a RESTful API or UPnP technology in order to allow the easy development of applications that can effectively improve life by transparent interaction with regular people. Furthermore, it will be proposed two direct applications of these APIs and it will be shown how to extend it.


title: ISSUES AND CHALLENGES OF AGILE SOFTWARE DEVELOPMENT WITH SCRUM

abstract: Agile software development methods have been developed and evolved since early 1990s. Due to the short development life cycle through an iterative and incremental process, the agile methods have been used widely in business sectors where requirements are relatively unstable. This paper explains the differences between traditional software development methods and agile software development methods, and introduces the characteristics of one of the popular agile methods, Scrum. Finally, the paper illustrates issues and challenges discovered through an in-depth case study in a company which has employed Scrum for many projects. The insights presented in the paper can be used in organizations that are in the process of agile software development using Scrum.


title: Active articulation model estimation through interactive perception

abstract: We introduce a particle filter-based approach to representing and actively reducing uncertainty over articulated motion models. The presented method provides a probabilistic model that integrates visual observations with feedback from manipulation actions to best characterize a distribution of possible articulation models. We evaluate several action selection methods to efficiently reduce the uncertainty about the articulation model. The full system is experimentally evaluated using a PR2 mobile manipulator. Our experiments demonstrate that the proposed system allows for intelligent reasoning about sparse, noisy data in a number of common manipulation scenarios.


title: INVITED PAPER Special Section on Recent Progress in Microwave and Millimeter-Wave Technologies Compact MIC Magic-T and the Integration with Planar Array Antenna

abstract: SUMMARY This paper describes very compact MIC magic-Ts and their integration with planar array antennas to realize the advanced antenna modules. The orthogonal transmission modes are effectively used to arrange the preferable port layout of magic-Ts. This flexible port layout of magic-Ts is a practical feature for integration with planar array antennas. The integration of magic-Ts and planar array antennas can easily create advanced functions. A couple of array antennas based on the integration advantages are introduced to materialize this technical concept. This integration approach is of big worth to originate various kinds of advanced


title: A discussion on the validation tests employed to compare human action recognition methods using the MSR Action3D dataset

abstract: This paper aims to determine which is the best human action recognition method based on features extracted from RGB-D devices, such as the Microsoft Kinect. A review of all the papers that make reference to MSR Action3D, the most used dataset that includes depth information acquired from a RGB-D device, has been performed. We found that the validation method used by each work differs from the others. So, a direct comparison among works cannot be made. However, almost all the works present their results comparing them without taking into account this issue. Therefore, we present different rankings according to the methodology used for the validation in orden to clarify the existing confusion.


title: The development and malleability of executive control abilities

abstract: Executive control (EC) generally refers to the regulation of mental activity. It plays a crucial role in complex cognition, and EC skills predict high-level abilities including language processing, memory, and problem solving, as well as practically relevant outcomes like scholastic achievement. Executive control develops relatively late in ontogeny, and many sub-groups of developmental populations demonstrate an exaggeratedly poor ability to control cognition even alongside the normal protracted growth of EC skills. Given the value of EC to human performance, researchers have sought means to improve it through targeted training; indeed, accumulating evidence suggests that regulatory processes are malleable through experience and practice. Nonetheless, there is a need to understand both whether specific populations might particularly benefit from training, and what cortical mechanisms engage during performance of the tasks used in the training protocols. This contribution has two parts: in Part I, we review EC development and intervention work in select populations. Although promising, the mixed results in this early field make it difficult to draw strong conclusions. To guide future studies, in Part II, we discuss training studies that have included a neuroimaging component—a relatively new enterprise that also has not yet yielded a consistent pattern of results post-training, preventing broad conclusions. We therefore suggest that recent developments in neuroimaging (e.g., multivariate and connectivity approaches) may be useful to advance our understanding of the neural mechanisms underlying the malleability of executive control and brain plasticity. In conjunction with behavioral data, these methods may further inform our understanding of the brain-behavior relationship and the extent to which EC is dynamic and malleable, guiding the development of future, targeted interventions to promote executive functioning in both healthy and atypical populations.


title: Narcissism and Romantic Attraction

abstract: A model of narcissism and romantic attraction predicts that narcissists will be attracted to admiring individuals and highly positive individuals and relatively less attracted to individuals who offer the potential for emotional intimacy. Five studies supported this model. Narcissists, compared with nonnarcissists, preferred more self-oriented (i.e., highly positive) and less other-oriented (i.e., caring) qualities in an ideal romantic partner (Study 1). Narcissists were also relatively more attracted to admiring and highly positive hypothetical targets and less attracted to caring targets (Studies 2 and 3). Indeed, narcissists displayed a preference for highly positive-noncaring targets compared with caring but not highly positive targets (Study 4). Finally, mediational analyses demonstrated that narcissists' romantic attraction is, in part, the result of a strategy for enhancing self-esteem (Study 5). From the perspective of the modern observer, the ancient Greek myth of Narcissus can be viewed as a tale of romantic attraction. The handsome youth Narcissus wanders the world refusing romantic offers from a variety of eligible others, none of whom he deems worthy of his love. Eventually, he finds himself in a dark wood staring into a pool of water. It is here that he discovers the face of the person he loves staring back at him. This person he so adores is himself. Narcissus's attraction freezes him on the spot; he dies, and eventually turns into a flower. The story of Narcissus yields interesting insights into the nature of narcissism and romantic attraction. The myth suggests that, in the domain of narcissism, romantic attraction is bound up with a focus on the self and a lack of attention to others. The former is evident in Narcissus's gaze at his own reflection, and the latter is evident in his refusal to form relationships with those who desire him. What drives this pattern of romantic attraction in narcissists? The myth of Narcissus suggests that self-love, which in modern psychological terms may be translated as self-enhancem ent (i.e., the desire to maintain or increase the positivity of the selfconcept), underlies narcissists' romantic attraction. The goal of the present research was to understand the relation between narcissism and romantic attraction. Arguably, this brief exposition of the myth of Narcissus reveals a theme central to the romantic life of narcissists, elements of which are an attention to the self, a relative lack of intimate contact with others, and a strategy for self-esteem regulation. I trace this theme through the


title: BEME Guide No. 1: Best Evidence Medical Education

abstract: There is a need to move from opinion-based education to evidence-based education. Best evidence medical education (BEME) is the implementation, by teachers in their practice, of methods and approaches to education based on the best evidence available. It involves a professional judgement by the teacher about his/her teaching taking into account a number of factors-the QUESTS dimensions. The Quality of the research evidence available-how reliable is the evidence? the Utility of the evidence-can the methods be transferred and adopted without modification, the Extent of the evidence, the Strength of the evidence, the Target or outcomes measured-how valid is the evidence? and the Setting or context-how relevant is the evidence? The evidence available can be graded on each of the six dimensions. In the ideal situation the evidence is high on all six dimensions, but this is rarely found. Usually the evidence may be good in some respects, but poor in others.The teacher has to balance the different dimensions and come to a decision on a course of action based on his or her professional judgement.The QUESTS dimensions highlight a number of tensions with regard to the evidence in medical education: quality vs. relevance; quality vs. validity; and utility vs. the setting or context. The different dimensions reflect the nature of research and innovation. Best Evidence Medical Education encourages a culture or ethos in which decision making takes place in this context.


title: Top Management Human Capital, Inventor Mobility, and Corporate Innovation

abstract: We analyze the effect of the human capital or “quality” of the top management of a firm on its innovation activities. We extract a “management quality factor” using common factor analysis on various individual proxies for the quality of a firm's management team, such as management team size, fraction of managers with MBAs, the average employment- and education-based connections of each manager in the management team, fraction of members with prior work experience in a top management position, the average number of prior board positions that each manager serves on, and the fraction of managers with doctoral degrees. We find that firms with higher quality management teams not only invest more in innovation (as measured by R&D expenditures), but also have a greater quantity and quality of innovation, as measured by the number of patents and citations per patent, respectively. We control for the endogenous matching of higher quality managers and higher quality firms using an instrumental variable analysis where we use a function of the number of top managers who faced Vietnam War era drafts (and therefore had an incentive to go to graduate school to get a draft deferment) as an instrument for top management human capital. We also show that an important channel through which higher management quality firms achieve greater innovation success is by hiring a larger number of inventors (controlling for R&D expenditures), and also by hiring higher quality inventors (as measured by their prior citations per patent record). Finally, we show that firms with higher quality top management teams are able to develop a larger number of both exploratory and exploitative innovations.


title: Artificial Intelligence and Systems Theory: Applied to Cooperative Robots

abstract: This paper describes an approach to the design of a population of cooperative robots based on concepts borrowed from Systems Theory and Artificial Intelligence. The research has been developed under the SocRob project, carried out by the Intelligent Systems Laboratory at the Institute for Systems and Robotics - Instituto Superior Tecnico (ISR/IST) in Lisbon. The acronym of the project stands both for “Society of Robots” and “Soccer Robots”, the case study where we are testing our population of robots. Designing soccer robots is a very challenging problem, where the robots must act not only to shoot a ball towards the goal, but also to detect and avoid static (walls, stopped robots) and dynamic (moving robots) obstacles. Furthermore, they must cooperate to defeat an opposing team. Our past and current research in soccer robotics includes cooperative sensor fusion for world modeling, object recognition and tracking, robot navigation, multi-robot distributed task planning and coordination, including cooperat...


title: Chemotherapy-induced Spontaneous Pneumothorax: Case Series

abstract: We present 2 patients who developed spontaneous pneumothorax (SP) following rapid regression of lymphoma and rhabdomyosarcoma with lung metastases. Case 1, a 43-year old man was admitted to our hospital with dyspnea 10 days before admission. He denied any recent trauma or previous treatment for lung tuberculosis. Three weeks prior to admission, he received first cycle of CHOP for non-Hodgkin’s lymphoma stage II BE. Chest X-ray consistent with right pneumothorax. After treatment with chest tube drainage for about 1 month, the patient recovered and chemotherapy could be continued without further complications. Case 2, a 35- year old man was admitted to other hospital with dyspnea and chest pain on day 4 after second cycle of systemic combined chemotherapy for rhabdomyosarcoma stage IV (lung metastases) with doxorubicin, ifosfamide, mesna, and dacarbazine. Chest X-ray showed hydropneumothorax on right and left lung. After treatment with chest tube drainage about 2 weeks, the patient recovered and chemotherapy could be continued without further complications. The mechanism of pneumothorax following chemotherapy is not clearly understood yet, however, several hypotheses have been considered: 1) the rupture of a subpleural bulla after chemotherapy; 2) the rupture of an emphysematous bulla in an over expanded portion of the lung which is partially obstructed by a neoplasm; 3) tumor lyses or necrosis due to cytotoxic chemotherapy directly induces the formation of fistula. Dyspnea and chest pain suddenly appear during successful chemotherapy for metastatic chemosensitive tumors should alert the physician to the possibility of SP. The treatment is directed toward lung re-expansion. Chemotherapy induced pneumothorax should be considered as oncologic emergency.


title: Simurg: An Extendable Multilingual Corpus for Abstractive Single Document Summarization

abstract: Abstractive single document summarization is considered as a challenging problem in the field of artificial intelligence and natural language processing. Meanwhile and specifically in the last two years, several deep learning summarization approaches were proposed that once again attracted the attention of researchers to this field. It is a well-known issue that deep learning approaches do not work well with small amounts of data. With some exceptions, this is, unfortunately, the case for most of the datasets available for the summarization task. Besides this problem, it should be considered that phonetic, morphological, semantic and syntactic features of the language are constantly changing over the time and unfortunately most of the summarization corpora are constructed from old resources. Another problem is the language of the corpora. Not only in the summarization field, but also in other fields of natural language processing, most of the corpora are only available in English. In addition to the above problems, license terms, and fees of the corpora are obstacles that prevent many academics and specifically non-academics from accessing these data. This work describes an open source framework to create an extendable multilingual corpus for abstractive single document summarization that addresses the above-mentioned problems. We describe a tool consisted of a scalable crawler and a centralized key-value store database to construct a corpus of an arbitrary size using a news aggregator service.


title: Detection And Classification Of Various Image Operations Using Deep Learning Technology

abstract: As one of the main medium for information transmission, the digital imagecan be easily tampered during transmission. It is becoming more and more important to identifywhether the given image is an original image or a processed image. In this paper, we propose acompact universal feature based on spatial domain in virtue of some latest image forensic methods and design a multi-class classification scheme using the deep learning technique, to identify and furthermore classify the various normal image operations. According to the experimental results, the proposed method can well detect and classify the multiple common image post-processing operations. And the comparison with the existing feature shows the better performance of the proposed method.


title: Communities' Detection in Social Networks: State of the art and perspectives

abstract: Nowadays, social networks have been widely used by different people for different purposes in the world. The discovering of communities and clustering is a widespread subject in the space of social networks analysis. Many interesting solutions have been proposed in the literature, such as the algorithms of Newman and Girvan. However, most solutions have common problems: the stability and the community structures quality. In this paper, we give a state of the art, from 1970 until 2018 on the approaches as well as the algorithms of communities' detection in social networks. This study aims to gives a comparison between the different proposed architectures;


title: Deep Learning Models for the Prediction of Rainfall

abstract: Rainfall is one of the major source of freshwater for all the organism around the world. Rainfall prediction model provides the information regarding various climatological variables on the amount of rainfall. In recent days, Deep Learning enabled the self-learning data labels which allows to create a data-driven model for a time series dataset. It allows to make the anomaly/change detection from the time series data and also predicts the future event's data with respect to the events occurred in the past. This paper deals with obtaining models of the rainfall precipitation by using Deep Learning Architectures (LSTM and ConvNet) and determining the better architecture with RMSE of LSTM as 2.55 and RMSE of ConvNet as 2.44 claiming that for any time series dataset, Deep Learning models will be effective and efficient for the modellers.


title: A LSTM Based Model for Personalized Context-Aware Citation Recommendation

abstract: The rapid growth of scientific papers makes it difficult to find relevant and appropriate citations. Context-aware citation recommendation aims to overcome this problem by providing a list of scientific papers given a short passage of text. In this paper, we propose a long–short-term memory (LSTM)-based model for context-aware citation recommendation, which first learns the distributed representations of the citation contexts and the scientific papers separately based on LSTM, and then measures the relevance based on the learned distributed representation of citation contexts and the scientific papers. Finally, the scientific papers with high relevance scores are selected as the recommendation list. In particular, we try to incorporate author information, venue information, and content information in scientific paper distributed vector representation. Furthermore, we integrate author information of the given context in citation context distributed vector representation. Thus, the proposed model makes personalized context-aware citation recommendation possible, which is a new issue that few papers addressed in the past. When conducting experiments on the ACL Anthology Network and DBLP data sets, the results demonstrate the proposed LSTM-based model for context-aware citation recommendation is able to achieve considerable improvement over previous context-aware citation recommendation approaches. The personalized recommendation approach is also competitive with the non-personalized recommendation approach.


title: Hallucinating Point Cloud into 3D Sculptural Object

abstract: Our team of artists and machine learning researchers designed a creative algorithm that can generate authentic sculptural artworks. These artworks do not mimic any given forms and cannot be easily categorized into the dataset categories. Our approach extends DeepDream from images to 3D point clouds. The proposed algorithm, Amalgamated DeepDream (ADD), leverages the properties of point clouds to create objects with better quality than the naive extension. ADD presents promise for the creativity of machines, the kind of creativity that pushes artists to explore novel methods or materials and to create new genres instead of creating variations of existing forms or styles within one genre. For example, from Realism to Abstract Expressionism, or to Minimalism. Lastly, we present the sculptures that are 3D printed based on the point clouds created by ADD.


title: Hackers vs. Testers: A Comparison of Software Vulnerability Discovery Processes

abstract: Identifying security vulnerabilities in software is a critical task that requires significant human effort. Currently, vulnerability discovery is often the responsibility of software testers before release and white-hat hackers (often within bug bounty programs) afterward. This arrangement can be ad-hoc and far from ideal; for example, if testers could identify more vulnerabilities, software would be more secure at release time. Thus far, however, the processes used by each group — and how they compare to and interact with each other — have not been well studied. This paper takes a first step toward better understanding, and eventually improving, this ecosystem: we report on a semi-structured interview study (n=25) with both testers and hackers, focusing on how each group finds vulnerabilities, how they develop their skills, and the challenges they face. The results suggest that hackers and testers follow similar processes, but get different results due largely to differing experiences and therefore different underlying knowledge of security concepts. Based on these results, we provide recommendations to support improved security training for testers, better communication between hackers and developers, and smarter bug bounty policies to motivate hacker participation.


title: Learning End-to-End Goal-Oriented Dialog with Multiple Answers

abstract: In a dialog, there can be multiple valid next utterances at any point. The present end-to-end neural methods for dialog do not take this into account. They learn with the assumption that at any time there is only one correct next utterance. In this work, we focus on this problem in the goal-oriented dialog setting where there are different paths to reach a goal. We propose a new method, that uses a combination of supervised learning and reinforcement learning approaches to address this issue. We also propose a new and more effective testbed, permuted-bAbI dialog tasks, by introducing multiple valid next utterances to the original-bAbI dialog tasks, which allows evaluation of goal-oriented dialog systems in a more realistic setting. We show that there is a significant drop in performance of existing end-to-end neural methods from 81.5% per-dialog accuracy on original-bAbI dialog tasks to 30.3% on permuted-bAbI dialog tasks. We also show that our proposed method improves the performance and achieves 47.3% per-dialog accuracy on permuted-bAbI dialog tasks.


DNN-based speech bandwidth expansion and its application to adding high-frequency missing features for automatic speech recognition of narrowband speech


title: Learning Visual Attributes

abstract: We present a probabilistic generative model of visual attributes, together with an efficient learning algorithm. Attributes are visual qualities of objects, such as 'red', 'striped', or 'spotted'. The model sees attributes as patterns of image segments, repeatedly sharing some characteristic properties. These can be any combination of appearance, shape, or the layout of segments within the pattern. Moreover, attributes with general appearance are taken into account, such as the pattern of alternation of any two colors which is characteristic for stripes. To enable learning from unsegmented training images, the model is learnt discriminatively, by optimizing a likelihood ratio. ::: ::: As demonstrated in the experimental evaluation, our model can learn in a weakly supervised setting and encompasses a broad range of attributes. We show that attributes can be learnt starting from a text query to Google image search, and can then be used to recognize the attribute and determine its spatial extent in novel real-world images.


title: A review and quantitative comparison of methods for kinect calibration

abstract: To utilize the full potential of RGB-D devices, calibration must be performed to determine the intrinsic and extrinsic parameters of the color and depth sensors and to reduce lens and depth distortion. After doing so, the depth pixels can be mapped to color pixels and both data streams can be simultaneously utilized. This work presents an overview and quantitative comparison of RGB-D calibration techniques and examines how the resolution and number of images affects calibration.


title: A Real-Time Scratchpad-Centric OS for Multi-Core Embedded Systems

abstract: Multi-core processors have replaced single-core systems in almost every segment of the industry. Unfortunately, their increased complexity often causes a loss of temporal predictability which represents a key requirement for hard real-time systems. Major sources of unpredictability are the shared low level resources, such as the memory hierarchy and the I/O subsystem. In this paper, we approach the problem of shared resource arbitration at an OS-level and propose a novel scratchpad-centric OS design for multi-core platforms. In the proposed OS, the predictable usage of shared resources across multiple cores represents a central design-time goal. Hence, we show (i) how contention-free execution of real-time tasks can be achieved on scratchpad-based architectures, and (ii) how a separation of application logic and I/O perations in the time domain can be enforced. To validate the proposed design, we implemented the proposed OS using a commercial-off-the-shelf (COTS) platform. Experiments show that this novel design delivers predictable temporal behavior to hard real-time tasks, and it improves performance up to 2.1x compared to traditional approaches.


title: The Bias in Small RNA Profiles Between Symptomless Dendrobium and Severe Symptom Ascocenda Orchids Infected Long-Term with Cymbidium Mosaic Virus

abstract: Perennial orchids worldwide are frequently infected with Cymbidium mosaic virus (CymMV). To date, no studies have described non-coding small RNA (sRNA) responses in the CymMV long-term-infected orchids. In this study, sRNA profiles of microRNAs (miRNA) and small virus-derived interfering RNAs (siRNA) in symptomless, long-term CymMV-infected Dendrobium and severe symptom, long-term CymMV-infected Ascocenda were identified in four sRNA libraries of virus-free and CymMV-infected orchids by Illumina high-throughput sequencing. The miRNA analysis revealed that the infected orchids had unique sRNA profiles that differed in size distribution, specially the presence of 21 nt sequences in severe symptom Ascocenda. Gel blot analysis showed a change in the miR528 expression which was at high level in virus free Ascocenda, on the other hand, only detected at low level in severe symptom Ascocenda. This expression alteration was also detected with the miR156 expression level in Dendrobium. Moreover, the characterization in polarity of CymMV-derived siRNAs in both infected orchids demonstrated that antisense polarity siRNAs accumulated in severe symptom Ascocenda, and the symptomless Dendrobium contained more sense polarity siRNAs. Here, we showed that miRNA expression was altered after long-term CymMV infection. Furthermore, a different pattern of siRNAs was observed between two orchid genera infected with the same virus and depended on the symptom severity.


title: Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm

abstract: The current paper presents a novel texture-based method for detecting texts in images. A support vector machine (SVM) is used to analyze the textural properties of texts. No external texture feature extraction module is used, but rather the intensities of the raw pixels that make up the textural pattern are fed directly to the SVM, which works well even in high-dimensional spaces. Next, text regions are identified by applying a continuously adaptive mean shift algorithm (CAMSHIFT) to the results of the texture analysis. The combination of CAMSHIFT and SVMs produces both robust and efficient text detection, as time-consuming texture analyses for less relevant pixels are restricted, leaving only a small part of the input image to be texture-analyzed.


title: Implementation of Four-Switch Brushless DC Motor Drive based on TMS320LF2407 DSP

abstract: Brushless DC (BLDC) motor is attracting much interest due to good performance and ease of control for many applications. Moreover, reducing of the drive components is more attractive for low cost applications. This paper presents the design and implementation of a reduced parts BLDC motor drive using the TMS320LF2407A digital signal processor (DSP) produced by Texas instruments. Part reducing is achieved by reducing the number of power switches. After analyzing the performance of the proposed BLDC motor drive, the hardware and software details of the system are explored. Finally, an experimental system included by a BLDC motor, DSP control board, inverters, and rectifier is set up to validate the theoretical results.


title: Transmission line model of RGW slot antenna covered with superstrate at 60 GHz

abstract: An accurate and fast analytical formulation for the radiation fields of a slot antenna covered with a superstrate is proposed. The analytical formulation is based on the transmission line analogy in conjunction with the reciprocity theorem. The slot antenna working at 60 GHz is formed in the top plate of a microstrip ridge gap waveguide (RGW) structure. The proposed analytical formulation is validated by comparison with full-wave simulations. The proposed analytical solution requires only 7 % of the time required by full-wave analysis.


title: Interleaved LLC Resonant Converter With Hybrid Rectifier and Variable-Frequency Plus Phase-Shift Control for Wide Output Voltage Range Applications

abstract: A family of two-phase interleaved LLC (iLLC) resonant converter with hybrid rectifier is proposed for wide output voltage range applications. The primary sides of the two LLC converters are in parallel, and the connection of the secondary windings in the two LLC converters can be regulated by the hybrid rectifier according to the output voltage. Variable frequency control is employed to regulate the output voltage and the secondary windings are in series when the output voltage is high. Fixed-frequency phase-shift control is adopted to regulate the configuration of the secondary windings as well as the output voltage when the output voltage is low. The output voltage range is extended by adaptively changing the configuration of the hybrid rectifier, which results in reduced switching frequency range, circulating current, and conduction losses of the LLC resonant tank. Zero voltage switching and zero current switching are achieved for all the active switches and diodes, respectively, within the entire operation range. The operation principles are analyzed and a 3.5 kW prototype with 400 V input voltage and 150–500 V output voltage is built and tested to evaluate the feasibility of the proposed method.


title: Meckel-Gruber syndrome: Antenatal diagnosis and ethical perspectives.

abstract: Meckel-Gruber syndrome (MGS) is an autosomal recessive disorder characterized by occipital encephalocele, polycystic kidneys and variable other congenital malformations. We report on a Sudanese patient with MGS diagnosed by antenatal ultrasound scan. Pregnancy was terminated at 25 weeks of gestation.


Human-inthe-Loop Parsing


title: Open Information Extraction Using Wikipedia

abstract: Information-extraction (IE) systems seek to distill semantic relations from natural-language text, but most systems use supervised learning of relation-specific examples and are thus limited by the availability of training data. Open IE systems such as TextRunner, on the other hand, aim to handle the unbounded number of relations found on the Web. But how well can these open systems perform? ::: ::: This paper presents WOE, an open IE system which improves dramatically on TextRunner's precision and recall. The key to WOE's performance is a novel form of self-supervised learning for open extractors -- using heuristic matches between Wikipedia infobox attribute values and corresponding sentences to construct training data. Like TextRunner, WOE's extractor eschews lexicalized features and handles an unbounded set of semantic relations. WOE can operate in two modes: when restricted to POS tag features, it runs as quickly as TextRunner, but when set to use dependency-parse features its precision and recall rise even higher.


title: TinyOS : An Operating System for Sensor Networks

abstract: We present TinyOS, a flexible, application-specific operating system for sensor networks, which form a core component of ambient intelligence systems. Sensor networks consist of (potentially) thousands of tiny, low-power nodes, each of which execute concurrent, reactive programs that must operate with severe memory and power constraints. The sensor network challenges of limited resources, event-centric concurrent applications, and low-power operation drive the design of TinyOS. Our solution combines flexible, fine-grain components with an execution model that supports complex yet safe concurrent operations. TinyOS meets these challenges well and has become the platform of choice for sensor network research; it is in use by over a hundred groups worldwide, and supports a broad range of applications and research topics. We provide a qualitative and quantitative evaluation of the system, showing that it supports complex, concurrent programs with very low memory requirements (many applications fit within 16KB of memory, and the core OS is 400 bytes) and efficient, low-power operation.We present our experiences with TinyOS as a platform for sensor network innovation and applications.


title: Design of a compact planar inverted-F antenna (PIFA) for RFID applications

abstract: This paper presents the design of a compact planar inverted-F antenna for Radio Frequency identification (RFID). The antenna has a reading range more than 5 m with resonant frequency 915 MHz. The antenna is designed as two parallel patches. The dimensions of the lower patch control the resonant frequency and the reading range. The paper presents how to choose the dimensions of each of the patches and the shorting wall to control the resonant frequencies and the gain of the antenna. A design and simulation of PIFA based on this geometry for RFID technology is also presented.


title: Accurate 3D face reconstruction from weakly calibrated wide baseline images with profile contours

abstract: We propose a method to generate a highly accurate 3D face model from a set of wide-baseline images in a weakly calibrated setup. Our approach is purely data driven, and produces faithful 3D models without any pre-defined models, unlike other statistical model-based approaches. Our results do not rely upon a critical initialization step nor parameters for optimization steps. We process 5 images (including profile views), infer the accurate poses of cameras in all views, and then infer a dense 3D face model. The quality of 3D face models depends on the accuracy of estimated head-camera motion. First, we propose to use an iterative bundle adjustment approach to remove outliers in corresponding points. Contours in the profile views are matched to provide reliable correspondences that link two opposite side of views together. For dense reconstruction, we propose to use a face-specific cylindrical representation which allows us to solve a global optimization problem for N-view dense aggregation. Profile contours are used once again to provide constraints in the optimization step. Experimental results using synthetic and real images show that our method provides accurate and stable reconstruction results on wide-baseline images. We compare our method with state of the art methods, and show that it provides significantly better results in terms of both accuracy and efficiency.


title: Control of the single-phase three-leg AC/AC converter

abstract: This paper investigates the control of a single-phase three-leg ac/ac reversible converter in which a leg is shared by both the grid and the load side. Pulsewidth-modulation (PWM) techniques based on scalar and vector approaches are developed, introducing the concept of local and general apportioning factor and, also, a complete equivalence between scalar and space vector PWM. A hysteresis current controller capable of taking into account the shared leg is developed and a zero current error linear controller is presented. Furthermore, a control strategy to obtain maximum utilization of the dc-bus voltage is proposed. In addition, several relevant characteristics of the converter are addressed, such as voltage rating, harmonic distortion, shared leg and capacitor currents, and power rating. The converter is compared to four-leg and two-leg converters. Experimental results are presented.


title: Wideband Circularly Polarized Dielectric Bird-Nest Antenna With Conical Radiation Pattern

abstract: A wideband circularly polarized (CP) antenna with a conical radiation pattern is investigated. It consists of a feeding probe and parasitic dielectric parallelepiped elements that surround the probe. Since the structure of the antenna looks like a bird nest, it is named as bird-nest antenna. The probe, which protrudes from a circular ground plane, operates in its fundamental monopole mode that generates omnidirectional linearly polarized (LP) fields. The dielectric parallelepipeds constitute a wave polarizer that converts omnidirectional LP fields of the probe into omnidirectional CP fields. To verify the design, a prototype operating in C band was fabricated and measured. The reflection coefficient, axial ratio (AR), radiation pattern, and antenna gain are studied, and reasonable agreement between the measured and simulated results is observed. The prototype has a 10-dB impedance bandwidth of 41.0% and a 3-dB AR bandwidth of as wide as 54.9%. A parametric study was carried out to characterize the proposed antenna. Also, a design guideline is given to facilitate designs of the antenna.


Persuasion in business negotiations


Distributed Learning-to-Rank on Streaming Data using Alternating Direction Method of Multipliers


title: Dielectric properties of soils in the 0.3-1.3-GHz range

abstract: In 1985, the authors reported the development of a semiempirical dielectric model for soils, covering the frequency range between 1.4 and 18 GHz. The model provides expressions for the real and imaginary parts of the relative dielectric constant of a soil medium in terms of the soil's textural composition (sand, silt, and clay fractions), the bulk density and volumetric moisture content of the soil, and the dielectric constant of water at the specified microwave frequency and physical temperature. This communication provides similar expressions for the 0.3-1.3-GHz range. Upon comparing experimental results measured in this study with predictions based on the semiempirical model, it was found that the model underpredicts the real part of the dielectric constant for high-moisture cases and underestimates the imaginary part for all soils and moisture conditions. A small linear adjustment has been introduced to correct the expression for the real part and a new equation was generated for the effective conductivity to correct the expression for the imaginary part. In addition, dielectric measurements were made to evaluate the dependence of the dielectric constant on clay type. The results show significant variations for the real part and large variations for the imaginary part among soils with the same clay fractions but with clays of different specific surface areas. >