title: Exploiting Semantic Web Knowledge Graphs in Data Mining

abstract: Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in that field are knowledge intensive and can often benefit from using additional knowledge from various sources. Therefore, many approaches have been proposed in this area that combine Semantic Web data with the data mining and knowledge discovery process. Semantic Web knowledge graphs are a backbone of many information systems that require access to structured knowledge. Such knowledge graphs contain factual knowledge about real word entities and the relations between them, which can be utilized in various natural language processing, information retrieval, and any data mining applications. Following the principles of the Semantic Web, Semantic Web knowledge graphs are publicly available as Linked Open Data. Linked Open Data is an open, interlinked collection of datasets in machine-interpretable form, covering most of the real world domains. ::: ::: In this thesis, we investigate the hypothesis if Semantic Web knowledge graphs can be exploited as background knowledge in different steps of the knowledge discovery process, and different data mining tasks. More precisely, we aim to show that Semantic Web knowledge graphs can be utilized for generating valuable data mining features that can be used in various data mining tasks. ::: ::: Identifying, collecting and integrating useful background knowledge for a given data mining application can be a tedious and time consuming task. Furthermore, most data mining tools require features in propositional form, i.e., binary, nominal or numerical features associated with an instance, while Linked Open Data sources are usually graphs by nature. Therefore, in Part I, we evaluate unsupervised feature generation strategies from types and relations in knowledge graphs, which are used in different data mining tasks, i.e., classification, regression, and outlier detection. As the number of generated features grows rapidly with the number of instances in the dataset, we provide a strategy for feature selection in hierarchical feature space, in order to select only the most informative and most representative features for a given dataset. Furthermore, we provide an end-to-end tool for mining the Web of Linked Data, which provides functionalities for each step of the knowledge discovery process, i.e., linking local data to a Semantic Web knowledge graph, integrating features from multiple knowledge graphs, feature generation and selection, and building machine learning models. However, we show that such feature generation strategies often lead to high dimensional feature vectors even after dimensionality reduction, and also, the reusability of such feature vectors across different datasets is limited. ::: ::: In Part II, we propose an approach that circumvents the shortcomings introduced with the approaches in Part I. More precisely, we develop an approach that is able to embed complete Semantic Web knowledge graphs in a low dimensional feature space, where each entity and relation in the knowledge graph is represented as a numerical vector. Projecting such latent representations of entities into a lower dimensional feature space shows that semantically similar entities appear closer to each other. We use several Semantic Web knowledge graphs to show that such latent representation of entities have high relevance for different data mining tasks. Furthermore, we show that such features can be easily reused for different datasets and different tasks. ::: ::: In Part III, we describe a list of applications that exploit Semantic Web knowledge graphs, besides the standard data mining tasks, like classification and regression. We show that the approaches developed in Part I and Part II can be used in applications in various domains. More precisely, we show that Semantic Web graphs can be exploited for analyzing statistics, building recommender systems, entity and document modeling, and taxonomy induction. ::: ::: %In Part III, we focus on semantic annotations in HTML pages, which are another realization of the Semantic Web vision. Semantic annotations are integrated into the code of HTML pages using markup languages, like Microformats, RDFa, and Microdata. While such data covers various domains and topics, and can be useful for developing various data mining applications, additional steps of cleaning and integrating the data need to be performed. In this thesis, we describe a set of approaches for processing long literals and images extracted from semantic annotations in HTML pages. We showcase the approaches in the e-commerce domain. Such approaches contribute in building and consuming Semantic Web knowledge graphs.


title: BOP: Benchmark for 6D Object Pose Estimation

abstract: We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image. The training data consists of a texture-mapped 3D object model or images of the object in known 6D poses. The benchmark comprises of: i) eight datasets in a unified format that cover different practical scenarios, including two new datasets focusing on varying lighting conditions, ii) an evaluation methodology with a pose-error function that deals with pose ambiguities, iii) a comprehensive evaluation of 15 diverse recent methods that captures the status quo of the field, and iv) an online evaluation system that is open for continuous submission of new results. The evaluation shows that methods based on point-pair features currently perform best, outperforming template matching methods, learning-based methods and methods based on 3D local features. The project website is available at this http URL.


Competition between Tax Havens : Does Proximity Matter ? ( Running head : Tax Haven Competition )


title: Competitive distribution estimation: why is Good-Turing good

abstract: Estimating distributions over large alphabets is a fundamental machine-learning tenet. Yet no method is known to estimate all distributions well. For example, add-constant estimators are nearly min-max optimal but often perform poorly in practice, and practical estimators such as absolute discounting, Jelinek-Mercer, and Good-Turing are not known to be near optimal for essentially any distribution. ::: ::: We describe the first universally near-optimal probability estimators. For every discrete distribution, they are provably nearly the best in the following two competitive ways. First they estimate every distribution nearly as well as the best estimator designed with prior knowledge of the distribution up to a permutation. Second, they estimate every distribution nearly as well as the best estimator designed with prior knowledge of the exact distribution, but as all natural estimators, restricted to assign the same probability to all symbols appearing the same number of times. ::: ::: Specifically, for distributions over k symbols and n samples, we show that for both comparisons, a simple variant of Good-Turing estimator is always within KL divergence of (3 + on(1))/n1/3 from the best estimator, and that a more involved estimator is within On(min(k/n, 1/√n)). Conversely, we show that any estimator must have a KL divergence at least Ωn(min(k/n, 1/n2/3)) over the best estimator for the first comparison, and at least Ωn(min(k/n, 1/√n)) for the second.


title: Empathy : A Social Cognitive Neuroscience Approach

abstract: There has been recent widespread interest in the neural underpinnings of the experience of empathy. In this review, we take a social cognitive neuroscience approach to understanding the existing literature on the neuroscience of empathy. A growing body of work suggests that we come to understand and share in the experiences of others by commonly recruiting the same neural structures both during our own experience and while observing others undergoing the same experience. This literature supports a simulation theory of empathy, which proposes that we understand the thoughts and feelings of others by using our own mind as a model. In contrast, theory of mind research suggests that medial prefrontal regions are critical for understanding the minds of others. In this review, we offer ideas about how to integrate these two perspectives, point out unresolved issues in the literature, and suggest avenues for future research.


title: Live API documentation

abstract: Application Programming Interfaces (APIs) provide powerful abstraction mechanisms that enable complex functionality to be used by client programs. However, this abstraction does not come for free: understanding how to use an API can be difficult. While API documentation can help, it is often insufficient on its own. Online sites like Stack Overflow and Github Gists have grown to fill the gap between traditional API documentation and more example-based resources. Unfortunately, these two important classes of documentation are independent. In this paper we describe an iterative, deductive method of linking source code examples to API documentation. We also present an implementation of this method, called Baker, that is highly precise (0.97) and supports both Java and JavaScript. Baker can be used to enhance traditional API documentation with up-to-date source code examples; it can also be used to incorporate links to the API documentation into the code snippets that use the API.


title: ScanNet: A Fast and Dense Scanning Framework for Metastastic Breast Cancer Detection from Whole-Slide Image

abstract: Lymph node metastasis is one of the most significant diagnostic indicators in breast cancer, which is traditionally observed under the microscope by pathologists. In recent years, computerized histology diagnosis has become one of the most rapidly expanding fields in medical image computing, which alleviates pathologists' workload and reduces misdiagnosis rate. However, automatic detection of lymph node metastases from whole slide images remains a challenging problem, due to the large-scale data with enormous resolutions and existence of hard mimics. In this paper, we propose a novel framework by leveraging fully convolutional networks for efficient inference to meet the speed requirement for clinical practice, while reconstructing dense predictions under different offsets for ensuring accurate detection on both micro- and macro-metastases. Incorporating with the strategies of asynchronous sample prefetching and hard negative mining, the network can be effectively trained. Extensive experiments on the benchmark dataset of 2016 Camelyon Grand Challenge corroborated the efficacy of our method. Compared with the state-of-the-art methods, our method achieved superior performance with a faster speed on the tumor localization task and surpassed human performance on the WSI classification task.


title: Product recommendation approaches: Collaborative filtering via customer lifetime value and customer demands

abstract: Recommender systems are techniques that allow companies to develop one-to-one marketing strategies and provide support in connecting with customers for e-commerce. There exist various recommendation techniques, including collaborative filtering (CF), content-based filtering, WRFM-based method, and hybrid methods. The CF method generally utilizes past purchasing preferences to determine recommendations to a target customer based on the opinions of other similar customers. The WRFM-based method makes recommendations based on weighted customer lifetime value - Recency, Frequency and Monetary. This work proposes to use customer demands derived from frequently purchased products in each industry as valuable information for making recommendations. Different from conventional CF techniques, this work uses extended preferences derived by combining customer demands and past purchasing preferences to identify similar customers. Accordingly, this work proposes several hybrid recommendation approaches that combine collaborative filtering, WRFM-based method, and extended preferences. The proposed approaches further utilize customer demands to adjust the ranking of recommended products to improve recommendation quality. The experimental results show that the proposed methods perform better than several other recommendation methods.


Leveraging coreference to identify arms in medical abstracts: An experimental study


title: Geodesic Convolutional Neural Networks on Riemannian Manifolds

abstract: Feature descriptors play a crucial role in a wide range of geometry analysis and processing applications, including shape correspondence, retrieval, and segmentation. In this paper, we introduce Geodesic Convolutional Neural Networks (GCNN), a generalization of the convolutional networks (CNN) paradigm to non-Euclidean manifolds. Our construction is based on a local geodesic system of polar coordinates to extract "patches", which are then passed through a cascade of filters and linear and non-linear operators. The coefficients of the filters and linear combination weights are optimization variables that are learned to minimize a task-specific cost function. We use GCNN to learn invariant shape features, allowing to achieve state-of-the-art performance in problems such as shape description, retrieval, and correspondence.


title: Personality traits and concern for privacy: an empirical study in the context of location-based services

abstract: For more than a century, concern for privacy (CFP) has co-evolved with advances in information technology. The CFP refers to the anxious sense of interest that a person has because of various types of threats to the person's state of being free from intrusion. Research studies have validated this concept and identified its consequences. For example, research has shown that the CFP can have a negative influence on the adoption of information technology; but little is known about factors likely to influence such concern. This paper attempts to fill that gap. Because privacy is said to be a part of a more general ‘right to one's personality’, we consider the so-called ‘Big Five’ personality traits (agreeableness, extraversion, emotional stability, openness to experience, and conscientiousness) as factors that can influence privacy concerns. Protection motivation theory helps us to explain this influence in the context of an emerging pervasive technology: location-based services. Using a survey-based approach, we find that agreeableness, conscientiousness, and openness to experience each affect the CFP. These results have implications for the adoption, the design, and the marketing of highly personalized new technologies.


title: Vallecular Rupture With Cervical Spine Fracture After a Failed Hanging Suicide Attempt

abstract: Hanging is a common method of suicide. We present a case of vallecular rupture and cervical spine fracture without an external wound after a failed hanging suicide attempt. Surgical treatment involved posterior fusion of C2 to 3, followed by repair of the vallecular rupture via an external approach. The patient recovered with no residual physical or mental sequelae. Language: en


title: Ultrasound for fetal assessment in early pregnancy

abstract: BACKGROUND ::: Advantages of early pregnancy ultrasound screening are thought to be more accurate calculation of gestational age, earlier identification of multiple pregnancies, and diagnosis of non-viable pregnancies and certain fetal malformations. ::: ::: ::: OBJECTIVES ::: The objective of this review was to assess the use of routine (screening) ultrasound compared with the selective use of ultrasound in early pregnancy (ie before 24 weeks). ::: ::: ::: SEARCH STRATEGY ::: The Cochrane Pregnancy and Childbirth Group trials register and the Cochrane Controlled Trials Register (up to July 1998) were searched. ::: ::: ::: SELECTION CRITERIA ::: Adequately controlled trials of routine ultrasound imaging in early pregnancy. ::: ::: ::: DATA COLLECTION AND ANALYSIS ::: One reviewer assessed trial quality and extracted data. Study authors were contacted for additional information. ::: ::: ::: MAIN RESULTS ::: Nine trials were included. The quality of the trials was generally good. Routine ultrasound examination was associated with earlier detection of multiple pregnancies (twins undiagnosed at 26 weeks, odds ratio 0.08, 95% confidence interval 0.04 to 0.16) and reduced rates of induction of labour for post-term pregnancy (odds ratio 0. 61, 95% confidence interval 0.52 to 0.72). There were no differences detected for substantive clinical outcomes such as perinatal mortality (odds ratio 0.86, 95% confidence interval 0.67 to 1.12). Where detection of fetal abnormality was a specific aim of the examination, the number of terminations of pregnancy for fetal anomaly increased. ::: ::: ::: REVIEWER'S CONCLUSIONS ::: Routine ultrasound in early pregnancy appears to enable better gestational age assessment, earlier detection of multiple pregnancies and earlier detection of clinically unsuspected fetal malformation at a time when termination of pregnancy is possible. However the benefits for other substantive outcomes are less clear.


title: A Novel Bus-Aggregation-Based Structure-Preserving Power System Equivalent

abstract: The challenges power systems engineers are facing today require the development of new system planning tools for analyzing generation and environmental policy options in the transmission-constrained electricity market. The requirements of a power-system equivalent to be used with such tools are very different from those assumed in the traditional network reduction process. To solve this issue, a novel structure-preserving network equivalent is proposed in this paper for modeling large power systems in the context of analyzing policy options and emissions. In the proposed method, a power system is first clustered into zones based on the similarity of the power transfer distribution factors (PTDFs); network reduction is achieved by aggregating buses (generators/loads) on a zonal basis and modeling inter-zonal transactions (power flows) using equivalent transmission lines. The proposed equivalent is superior to existing bus-aggregation-based equivalents in its accuracy under both the base case and change-case operating conditions. In addition, the method is more computationally efficient than other bus aggregation methods proposed heretofore. This paper also examined several classic clustering techniques and identified their performance and computational efficiencies when applied to very large power systems. The proposed network equivalencing approach is tested on an illustrative six-bus system as well as the 62,000-bus and 80,000-branch Eastern Interconnection (EI).


title: Genome Instability Mediates the Loss of Key Traits by Acinetobacter baylyi ADP1 during Laboratory Evolution

abstract: Acinetobacter baylyi ADP1 has the potential to be a versatile bacterial host for synthetic biology because it is naturally transformable. To examine the genetic reliability of this desirable trait and to understand the potential stability of other engineered capabilities, we propagated ADP1 for 1,000 generations of growth in rich nutrient broth and analyzed the genetic changes that evolved by whole-genome sequencing. Substantially reduced transformability and increased cellular aggregation evolved during the experiment. New insertions of IS1236 transposable elements and IS1236-mediated deletions led to these phenotypes in most cases and were common overall among the selected mutations. We also observed a 49-kb deletion of a prophage region that removed an integration site, which has been used for genome engineering, from every evolved genome. The comparatively low rates of these three classes of mutations in lineages that were propagated with reduced selection for 7,500 generations indicate that they increase ADP1 fitness under common laboratory growth conditions. Our results suggest that eliminating transposable elements and other genetic failure modes that affect key organismal traits is essential for improving the reliability of metabolic engineering and genome editing in undomesticated microbial hosts, such as Acinetobacter baylyi ADP1.


title: Geogames: Designing Location-Based Games from Classic Board Games

abstract: The traditional image of interactive entertainment - games that reduce players' physical involvement to moving a joy stick - is obsolete. Games researchers and designers are already integrating complex movement into games. Location-based games involve body movements beyond figural space - that is, beyond the space of computer screens and small 3D objects. They focus on locomotion in vista space, typically a single room or sports field, or in environmental space, such as a neighborhood or city. Several location-based games have been designed for environmental space using localization technologies such as GPS. Some are adaptations of popular computer games, and others are rather straightforward chase games. A third type of game - what we call challenging location-based games - combines the intellectual and strategic appeal of classic board games with the real-time locomotion and physical involvement of location-based games. Furthermore, a straightforward approach to the spatial mapping of classic board games doesn't work. Instead, we developed the Geogames framework, which uses search techniques to identify a single temporal parameter for balancing sportive versus strategic elements


title: Diversify to survive: making passwords stronger with adaptive policies

abstract: Password-composition policies are intended to increase resistance to guessing attacks by requiring certain features (e.g., a minimum length and the inclusion of a digit). Sadly, they often result in users' passwords exhibiting new, yet still predictable, patterns. In this paper, we investigate the usability and security of adaptive password-composition policies, which dynamically change password requirements over time as users create new passwords. We conduct a 2,619- participant between-subjects online experiment to evaluate the strength and usability of passwords created with two adaptive password policies. We also design and test a feedback system that guides users to successfully create a password conforming to these policies. We find that a well-configured, structure-based adaptive password policy can significantly increase password strength with little to no decrease in usability. We discuss how system administrators can use these results to improve password diversity.


title: Deep Visual Attributes vs. Hand-Crafted Audio Features on Multidomain Speech Emotion Recognition

abstract: Emotion recognition from speech may play a crucial role in many applications related to human–computer interaction or understanding the affective state of users in certain tasks, where other modalities such as video or physiological parameters are unavailable. In general, a human’s emotions may be recognized using several modalities such as analyzing facial expressions, speech, physiological parameters (e.g., electroencephalograms, electrocardiograms) etc. However, measuring of these modalities may be difficult, obtrusive or require expensive hardware. In that context, speech may be the best alternative modality in many practical applications. In this work we present an approach that uses a Convolutional Neural Network (CNN) functioning as a visual feature extractor and trained using raw speech information. In contrast to traditional machine learning approaches, CNNs are responsible for identifying the important features of the input thus, making the need of hand-crafted feature engineering optional in many tasks. In this paper no extra features are required other than the spectrogram representations and hand-crafted features were only extracted for validation purposes of our method. Moreover, it does not require any linguistic model and is not specific to any particular language. We compare the proposed approach using cross-language datasets and demonstrate that it is able to provide superior results vs. traditional ones that use hand-crafted features.


title: Ropossum: an authoring tool for designing, optimizing and solving cut the rope levels

abstract: We present a demonstration of Ropossum, an authoring tool for the generation and testing of levels of the physics-based game, Cut the Rope. Ropossum integrates many features: (1) automatic design of complete solvable content, (2) incorporation of designer's input through the creation of complete or partial designs, (3) automatic check for playability and (4) optimization of a given design based on playability. The system includes a physics engine to simulate the game and an evolutionary framework to evolve content as well as an AI reasoning agent to check for playability. The system is optimised to allow on-line feedback and realtime interaction.


title: Modelling performance & resource management in kubernetes

abstract: Containers are rapidly replacing Virtual Machines (VMs) as the compute instance of choice in cloud-based deployments. The significantly lower overhead of deploying containers (compared to VMs) has often been cited as one reason for this. We analyse performance of the Kubernetes system and develop a Reference net-based model of resource management within this system. Our model is characterised using real data from a Kubernetes deployment, and can be used as a basis to design scalable applications that make use of Kubernetes.


title: Grey system theory based parallel combination forecast method and its application

abstract: Due to the complexity and importance of crop yield forecasting, many works have been done during the past decades. In this paper, a reasonable division of influencing respects of crop yield per unit area is presented, and a novel intelligent parallel combination model consisting of a GM(1,1) model from the grey system theory and a intelligent evolution algorithm is referred to put into practice as well, which is a comprehensive reflection of both social production levels and environmental factors in the crop yield forecasting results, with respective individual models' merits. In the mentioned model, errors of each individual forecasting model (GM(1,1) models both for the social influence forecasting and for the environmental influence forecasting) are put into an intelligent evolution model to determine the own weights in the combination model. A experiment based on the observed historical data from Shulehe basin in Gansu province in Northwestern of China is carried out to verify the reasonability and validation of the combination model, which shows that the novel model will yield lower error level than that of original individual models with less risky in practice, and is relatively more intuitive and more feasible than some other conventional approaches applying agronomy analysis as well.


title: Learning deep representations for ground-to-aerial geolocalization

abstract: The recent availability of geo-tagged images and rich geospatial data has inspired a number of algorithms for image based geolocalization. Most approaches predict the location of a query image by matching to ground-level images with known locations (e.g., street-view data). However, most of the Earth does not have ground-level reference photos available. Fortunately, more complete coverage is provided by oblique aerial or “bird's eye” imagery. In this work, we localize a ground-level query image by matching it to a reference database of aerial imagery. We use publicly available data to build a dataset of 78K aligned crossview image pairs. The primary challenge for this task is that traditional computer vision approaches cannot handle the wide baseline and appearance variation of these cross-view pairs. We use our dataset to learn a feature representation in which matching views are near one another and mismatched views are far apart. Our proposed approach, Where-CNN, is inspired by deep learning success in face verification and achieves significant improvements over traditional hand-crafted features and existing deep features learned from other large-scale databases. We show the effectiveness of Where-CNN in finding matches between street view and aerial view imagery and demonstrate the ability of our learned features to generalize to novel locations.


title: New Complexity Results about Nash Equilibria

abstract: We provide a single reduction that demonstrates that in normal-form games: (1) it is -complete to determine whether Nash equilibria with certain natural properties exist (these results are similar to those obtained by Gilboa and Zemel [Gilboa, I., Zemel, E., 1989. Nash and correlated equilibria: Some complexity considerations. Games Econ. Behav. 1, 80-93]), (2) more significantly, the problems of maximizing certain properties of a Nash equilibrium are inapproximable (unless ), and (3) it is -hard to count the Nash equilibria. We also show that determining whether a pure-strategy Bayes-Nash equilibrium exists in a Bayesian game is -complete, and that determining whether a pure-strategy Nash equilibrium exists in a Markov (stochastic) game is -hard even if the game is unobserved (and that this remains -hard if the game has finite length). All of our hardness results hold even if there are only two players and the game is symmetric.


title: Anonymous Walk Embeddings

abstract: The task of representing entire graphs has seen a surge of prominent results, mainly due to learning convolutional neural networks (CNNs) on graph-structured data. While CNNs demonstrate state-of-the-art performance in graph classification task, such methods are supervised and therefore steer away from the original problem of network representation in task-agnostic manner. Here, we coherently propose an approach for embedding entire graphs and show that our feature representations with SVM classifier increase classification accuracy of CNN algorithms and traditional graph kernels. For this we describe a recently discovered graph object, anonymous walk, on which we design task-independent algorithms for learning graph representations in explicit and distributed way. Overall, our work represents a new scalable unsupervised learning of state-of-the-art representations of entire graphs.


title: A secure credit card protocol over NFC

abstract: NFC ("Near Field Communication") is a short-range wireless communication channel. The current NFC credit card protocol allows a contactless credit card to communicate wirelessly with a Point-of-Sale in order to perform a purchase. This protocol is vulnerable to four common attacks: eavesdropping, skimming, relay attacks, and compromised Points-of-Sale. The attacker's objective is twofold: stealing sensitive information, and performing unauthorized. We use stepwise refinement to design a secure NFC credit card protocol which defends against all four of these attacks. The resulting protocol does not use heavyweight cryptographic operations, instead using only inexpensive primitives such as pre-computed hashes, indexing, and XOR operations. Moreover, it explores the lower-bound of computation required on the card to mount an effective defense against these four classes of attacks. As such, the energy and computational requirements of the credit card in our protocol are kept to a minimum.


title: Decentralized Computation Offloading and Resource Allocation in Heterogeneous Networks with Mobile Edge Computing

abstract: We consider a heterogeneous network with mobile edge computing, where a user can offload its computation to one among multiple servers. In particular, we minimize the system-wide computation overhead by jointly optimizing the individual computation decisions, transmit power of the users, and computation resource at the servers. The crux of the problem lies in the combinatorial nature of multi-user offloading decisions, the complexity of the optimization objective, and the existence of inter-cell interference. Then, we decompose the underlying problem into two subproblems: i) the offloading decision, which includes two phases of user association and subchannel assignment, and ii) joint resource allocation, which can be further decomposed into the problems of transmit power and computation resource allocation. To enable distributed computation offloading, we sequentially apply a many-to-one matching game for user association and a one-to-one matching game for subchannel assignment. Moreover, the transmit power of offloading users is found using a bisection method with approximate inter-cell interference, and the computation resources allocated to offloading users is achieved via the duality approach. The proposed algorithm is shown to converge and is stable. Finally, we provide simulations to validate the performance of the proposed algorithm as well as comparisons with the existing frameworks.


title: Arterial Tortuosity: An Imaging Biomarker of Childhood Stroke Pathogenesis?

abstract: Background and Purpose— Arteriopathy is the leading cause of childhood arterial ischemic stroke. Mechanisms are poorly understood but may include inherent abnormalities of arterial structure. Extracranial dissection is associated with connective tissue disorders in adult stroke. Focal cerebral arteriopathy is a common syndrome where pathophysiology is unknown but may include intracranial dissection or transient cerebral arteriopathy. We aimed to quantify cerebral arterial tortuosity in childhood arterial ischemic stroke, hypothesizing increased tortuosity in dissection. ::: ::: Methods— Children (1 month to 18 years) with arterial ischemic stroke were recruited within the Vascular Effects of Infection in Pediatric Stroke (VIPS) study with controls from the Calgary Pediatric Stroke Program. Objective, multi-investigator review defined diagnostic categories. A validated imaging software method calculated the mean arterial tortuosity of the major cerebral arteries using 3-dimensional time-of-flight magnetic resonance angiographic source images. Tortuosity of unaffected vessels was compared between children with dissection, transient cerebral arteriopathy, meningitis, moyamoya, cardioembolic strokes, and controls (ANOVA and post hoc Tukey). Trauma-related versus spontaneous dissection was compared (Student t test). ::: ::: Results— One hundred fifteen children were studied (median, 6.8 years; 43% women). Age and sex were similar across groups. Tortuosity means and variances were consistent with validation studies. Tortuosity in controls (1.346±0.074; n=15) was comparable with moyamoya (1.324±0.038; n=15; P =0.998), meningitis (1.348±0.052; n=11; P =0.989), and cardioembolic (1.379±0.056; n=27; P =0.190) cases. Tortuosity was higher in both extracranial dissection (1.404±0.084; n=22; P =0.021) and transient cerebral arteriopathy (1.390±0.040; n=27; P =0.001) children. Tortuosity was not different between traumatic versus spontaneous dissections ( P =0.70). ::: ::: Conclusions— In children with dissection and transient cerebral arteriopathy, cerebral arteries demonstrate increased tortuosity. Quantified arterial tortuosity may represent a clinically relevant imaging biomarker of vascular biology in pediatric stroke.


title: Open University Learning Analytics dataset

abstract: Learning Analytics focuses on the collection and analysis of learners’ data to improve their learning experience by providing informed guidance and to optimise learning materials. To support the research in this area we have developed a dataset, containing data from courses presented at the Open University (OU). What makes the dataset unique is the fact that it contains demographic data together with aggregated clickstream data of students’ interactions in the Virtual Learning Environment (VLE). This enables the analysis of student behaviour, represented by their actions. The dataset contains the information about 22 courses, 32,593 students, their assessment results, and logs of their interactions with the VLE represented by daily summaries of student clicks (10,655,280 entries). The dataset is freely available at https://analyse.kmi.open.ac.uk/open_dataset under a CC-BY 4.0 license.


title: Modeling snapback of LVTSCR devices for ESD circuit simulation using advanced BJT and MOS models

abstract: SCRs have been playing an increasingly significant role in ESD protection for CMOS technologies. A major challenge is to develop effective compact simulation models for these devices valid under ESD stress conditions. A simple macro modeling approach is presented for SPICE simulation of LVTSCR devices. The method uses advanced standard BJT and MOS models such as BSIM4 and Mextram. The simulation results have been verified using VFTLP and standard TLP measurements. The method provides a practical simulation tool for ESD protection circuits using LVTSCRs.


Social Spider Algorithm Approach for Clustering


title: Video Super-Resolution via Deep Draft-Ensemble Learning

abstract: We propose a new direction for fast video super-resolution (VideoSR) via a SR draft ensemble, which is defined as the set of high-resolution patch candidates before final image deconvolution. Our method contains two main components -- i.e., SR draft ensemble generation and its optimal reconstruction. The first component is to renovate traditional feedforward reconstruction pipeline and greatly enhance its ability to compute different super resolution results considering large motion variation and possible errors arising in this process. Then we combine SR drafts through the nonlinear process in a deep convolutional neural network (CNN). We analyze why this framework is proposed and explain its unique advantages compared to previous iterative methods to update different modules in passes. Promising experimental results are shown on natural video sequences.


title: Content based video retrieval on mobile devices: How much content is enough?

abstract: In the field of content based video retrieval, recent work has focused on creation and matching of signatures as an effective method for video search, copy or “near-duplicate” detection. In this paper, we address the question of how much content is enough to initiate a content based video retrieval query from a mobile device. We extract and utilize the motion vectors from an HEVC-encoded bitstream and use them as the content-dependent features to create video signatures. We propose an energy function to quantify the mount of motion information contained in video. Our experimental results exhibit that our proposed approach is able to generate a signature robust to common signal processing techniques and that our proposed energy function can be efficiently used to select the length of a video clip used to query the video database from a mobile device.


title: Cosimulation of ansys simplerer and MATLAB/Simulink

abstract: The cosimulation concept of Ansys Simplorer and MATLAB/Simulink program environments is presented on a case study of an electric drive. General program descriptions are given along with the step-by-step procedure of cosimulation preparation. Results, obtained by simulation in Simplorer and cosimulation are practically identical and confirm suitability of both analysis approaches. Comments and discussion on user experience are given, focusing on usability of both programs. Simulink has a modern design and a huge component library, while Simplorer is more appropriate for circuit design. Concerning plotting of results and data post-processing, Simulink and MATLAB are superior. Cosimulation concept has strong advantages over using each program individually.


Primal-Dual Interior-Point Technique for Optimisation of 330kV Power System on One Variable


title: Predictive control and stabilization of nonholonomic formations with integrated spline-path planning

abstract: A path planning in the space of multinominals integrated into a model predictive control mechanism for driving formations of autonomous mobile robots is presented in this paper. The proposed approach is designed to stabilize the formations in desired shapes, and to navigate the group into a final position in a partly known environment with dynamic obstacles. In addition, the system provides inter-vehicle coordination and collision avoidance in the event of failure of a team member. The method is aimed at reaching the final position of the formation in the desired shape, but it enables to change temporarily this shape if it is enforced by the environment (in narrow corridors, on response to an impending collision with obstacles and faulty team members, etc.). This autonomous emergent behaviour increases the robustness of the system and its usability. It enables a proper compromise to be found between the formation driving requirement and the effort to fulfil the mission objective, i.e., to move the group from the current state into the required position. In this paper, the convergence of the method and the requirements for stability are shown on the basis of the results of the Lyapunov theorems of stability. These theoretical achievements imply constraints on the applicability of the system, which are verified in numerical simulations and in various tests with real autonomous robots. The performances of the entire system and of independent sub-systems in various formation driving scenarios are also shown in these tests. Spline path planning integrated in model predictive control of formations is proposed.Autonomous change of shape of formations enforced by environment is enabled.Inter-vehicle coordination and collision avoidance after a failure of a team member.Decomposition of the control system into two planning loops with different rates.Convergence and stability of the method shown on the basis of Lyapunov theorems.


title: Deep Supervised and Contractive Neural Network for SAR Image Classification

abstract: The classification of a synthetic aperture radar (SAR) image is a significant yet challenging task, due to the presence of speckle noises and the absence of effective feature representation. Inspired by deep learning technology, a novel deep supervised and contractive neural network (DSCNN) for SAR image classification is proposed to overcome these problems. In order to extract spatial features, a multiscale patch-based feature extraction model that consists of gray level-gradient co-occurrence matrix, Gabor, and histogram of oriented gradient descriptors is developed to obtain primitive features from the SAR image. Then, to get discriminative representation of initial features, the DSCNN network that comprises four layers of supervised and contractive autoencoders is proposed to optimize features for classification. The supervised penalty of the DSCNN can capture the relevant information between features and labels, and the contractive restriction aims to enhance the locally invariant and robustness of the encoding representation. Consequently, the DSCNN is able to produce effective representation of sample features and provide superb predictions of the class labels. Moreover, to restrain the influence of speckle noises, a graph-cut-based spatial regularization is adopted after classification to suppress misclassified pixels and smooth the results. Experiments on three SAR data sets demonstrate that the proposed method is able to yield superior classification performance compared with some related approaches.


title: C2PS: A Digital Twin Architecture Reference Model for the Cloud-Based Cyber-Physical Systems

abstract: Cyber-physical system (CPS) is a new trend in the Internet-of-Things related research works, where physical systems act as the sensors to collect real-world information and communicate them to the computation modules (i.e. cyber layer), which further analyze and notify the findings to the corresponding physical systems through a feedback loop. Contemporary researchers recommend integrating cloud technologies in the CPS cyber layer to ensure the scalability of storage, computation, and cross domain communication capabilities. Though there exist a few descriptive models of the cloud-based CPS architecture, it is important to analytically describe the key CPS properties: computation, control, and communication. In this paper, we present a digital twin architecture reference model for the cloud-based CPS, C2PS, where we analytically describe the key properties of the C2PS. The model helps in identifying various degrees of basic and hybrid computation-interaction modes in this paradigm. We have designed C2PS smart interaction controller using a Bayesian belief network, so that the system dynamically considers current contexts. The composition of fuzzy rule base with the Bayes network further enables the system with reconfiguration capability. We also describe analytically, how C2PS subsystem communications can generate even more complex system-of-systems. Later, we present a telematics-based prototype driving assistance application for the vehicular domain of C2PS, VCPS, to demonstrate the efficacy of the architecture reference model.


title: V-Clip: fast and robust polyhedral collision detection

abstract: This article presents the Voronoi-clip, or V-Clip, collision detection alogrithm for polyhedral objects specified by a boundary representation. V-Clip tracks the closest pair of features between convex polyhedra, using an approach reminiscent of the Lin-Canny closest features algorithm. V-Clip is an improvement over the latter in several respects. Coding complexity is reduced, and robustness is significantly improved; the implementation has no numerical tolerances and does not exhibit cycling problems. The algorithm also handles penetrating polyhedra, and can therefore be used to detect collisions between nonvconvex polyhedra described as hierarchies of convex pieces. The article presents the theoretical principles of V-Clip, and gives a pseudocode description of the algorithm. It also documents various test that compare V-Clip, Lin-Canny, and the Enhanced GJK algorithm, a simplex-based algorithm that is widely used for the same application. The results show V-Clip to be a strong contender in this field, comparing favorably with the other algorithms in most of the tests, in term of both performance and robustness.


Text Classification from Labeled and Unlabeled Documents using EM


title: Dense Monocular Depth Estimation in Complex Dynamic Scenes

abstract: We present an approach to dense depth estimation from a single monocular camera that is moving through a dynamic scene. The approach produces a dense depth map from two consecutive frames. Moving objects are reconstructed along with the surrounding environment. We provide a novel motion segmentation algorithm that segments the optical flow field into a set of motion models, each with its own epipolar geometry. We then show that the scene can be reconstructed based on these motion models by optimizing a convex program. The optimization jointly reasons about the scales of different objects and assembles the scene in a common coordinate frame, determined up to a global scale. Experimental results demonstrate that the presented approach outperforms prior methods for monocular depth estimation in dynamic scenes.


title: Global war on drugs is a major factor driving HIV pandemic, report warns.

abstract: Major reforms of the global drug prohibition regime are urgently required to halt the spread of HIV infection, according to the Global Commission on Drug Policy. ::: ::: The commission, a high level think tank that includes six former presidents, warns that the global war on drugs is driving the HIV pandemic among people who use drugs and their sexual partners. ::: ::: In a report released last week in London, the commission explains how “repressive drug law enforcement practices force drug users away from public health services and into hidden environments where HIV risk becomes markedly elevated,” whilst “mass incarceration of non-violent drug offenders also plays a …


title: Extracting Argument and Domain Words for Identifying Argument Components in Texts

abstract: Argument mining studies in natural language text often use lexical (e.g. n-grams) and syntactic (e.g. grammatical production rules) features with all possible values. In prior work on a corpus of academic essays, we demonstrated that such large and sparse feature spaces can cause difficulty for feature selection and proposed a method to design a more compact feature space. The proposed feature design is based on post-processing a topic model to extract argument and domain words. In this paper we investigate the generality of this approach, by applying our methodology to a new corpus of persuasive essays. Our experiments show that replacing n-grams and syntactic rules with features and constraints using extracted argument and domain words significantly improves argument mining performance for persuasive essays.


title: Nutritional management of search and rescue dogs

abstract: Dogs used for search and rescue (SAR) may experience continuous micro-traumas that predispose them to skeletal disorders. The aim of the present study was to evaluate the effect of diet on osteo-articular apparatus in healthy SAR dogs. A total of sixteen SAR dogs were divided into two groups (low supplementation (LS) and high supplementation (HS)) and were fed for 3 months with two experimental diets, characterised by the same protein and energy density, but different in n-3 PUFA (6·2 v. 8·4 % of metabolisable energy), chondroitin sulfate (219·8 v. 989·0 mg/kg DM) and glucosamine (769·2 v. 1318·7 mg/kg DM) in the LS and HS groups, respectively. At recruitment all dogs showed no joint inflammation signs, except four that showed mild symptoms. Haematology and serum biochemistry were performed every 30 d. Joint status was scored by physical and lameness evaluations. The sampling effect analysis showed potential beneficial effects by a decrease in a specific marker of membrane integrity (creatine kinase; CK). Comparing groups, glucose was significantly higher and CK was significantly lower in the HS group; however, in both cases the levels of these parameters fell in the normal range. At the end of the experiment, erythrocytes, Hb and packed cell volume were significantly higher in the HS compared with the LS group. These could result in an improvement in dogs' performance, even if this aspect was not investigated in the present study. Concerning joint evaluation (pain on manipulation, lameness and range of motion), no statistically significant differences were detected between the groups and within the experimental period.


title: Fast Exploration with Simplified Models and Approximately Optimistic Planning in Model Based Reinforcement Learning

abstract: Humans learn to play video games significantly faster than the state-of-the-art reinforcement learning (RL) algorithms. People seem to build simple models that are easy to learn to support planning and strategic exploration. Inspired by this, we investigate two issues in leveraging model-based RL for sample efficiency. First we investigate how to perform strategic exploration when exact planning is not feasible and empirically show that optimistic Monte Carlo Tree Search outperforms posterior sampling methods. Second we show how to learn simple deterministic models to support fast learning using object representation. We illustrate the benefit of these ideas by introducing a novel algorithm, Strategic Object Oriented Reinforcement Learning (SOORL), that outperforms state-of-the-art algorithms in the game of Pitfall! in less than 50 episodes.


title: Understanding and Summarizing Answers in Community-Based Question Answering Services

abstract: Community-based question answering (cQA) services have accumulated millions of questions and their answers over time. In the process of accumulation, cQA services assume that questions always have unique best answers. However, with an in-depth analysis of questions and answers on cQA services, we find that the assumption cannot be true. According to the analysis, at least 78% of the cQA best answers are reusable when similar questions are asked again, but no more than 48% of them are indeed the unique best answers. We conduct the analysis by proposing taxonomies for cQA questions and answers. To better reuse the cQA content, we also propose applying automatic summarization techniques to summarize answers. Our results show that question-type oriented summarization techniques can improve cQA answer quality significantly.


title: SoundWave: using the doppler effect to sense gestures

abstract: Gesture is becoming an increasingly popular means of interacting with computers. However, it is still relatively costly to deploy robust gesture recognition sensors in existing mobile platforms. We present SoundWave, a technique that leverages the speaker and microphone already embedded in most commodity devices to sense in-air gestures around the device. To do this, we generate an inaudible tone, which gets frequency-shifted when it reflects off moving objects like the hand. We measure this shift with the microphone to infer various gestures. In this note, we describe the phenomena and detection algorithm, demonstrate a variety of gestures, and present an informal evaluation on the robustness of this approach across different devices and people.


title: A Novel Overmodulation and Field Weakening Strategy for Direct Torque Control of Induction Machines

abstract: This paper presents a new simple overmodulation strategy in direct torque control (DTC) of induction machines. The strategy increases the inverter voltage limit and therefore extends the constant torque region. The minor modification on the conventional flux weakening method is also introduced to improve the torque capability in the field weakening region and hence improving the operation over a wide speed range. These strategies are implemented with a DTC-constant switching frequency (CSF) based system. To perform the DTC-CSF in overmodulation mode, the flux error status is modified before it is being fed to the voltage vector selection table. With this proposed strategy, a single-mode overmodulation based on stator flux vector control with smooth PWM to six-step transition is developed. This strategy also proposes a dynamic overmodulation to obtain fast torque transient with six-step operation including the field weakening region. The proposed strategy has been verified through computer simulations and shown to improve the torque capability of DTC induction motor drives with a fast dynamic torque response.


title: Unsupervised meta-path selection for text similarity measure based on heterogeneous information networks

abstract: Heterogeneous information network (HIN) is a general representation of many different applications, such as social networks, scholar networks, and knowledge networks. A key development of HIN is called PathSim based on meta-path, which measures the pairwise similarity of two entities in the HIN of the same type. When using PathSim in practice, we usually need to handcraft some meta-paths which are paths over entity types instead of entities themselves. However, finding useful meta-paths is not trivial to human. In this paper, we present an unsupervised meta-path selection approach to automatically find useful meta-paths over HIN, and then develop a new similarity measure called KnowSim which is an ensemble of selected meta-paths. To solve the high computational cost of enumerating all possible meta-paths, we propose to use an approximate personalized PageRank algorithm to find useful subgraphs to allocate the meta-paths. We apply KnowSim to text clustering and classification problems to demonstrate that unsupervised meta-path selection can help improve the clustering and classification results. We use Freebase, a well-known world knowledge base, to conduct semantic parsing and construct HIN for documents. Our experiments on 20Newsgroups and RCV1 datasets show that KnowSim results in impressive high-quality document clustering and classification performance. We also demonstrate the approximate personalized PageRank algorithm can efficiently and effectively compute the meta-path based similarity.


Segmentation and alignment of parallel text for statistical machine translation


title: Electronic Evidenced and Computer Forensics by L. VoloninoELECTRONIC EVIDENCE AND COMPUTER FORENSICS

abstract: Information and communication systems are now breeding grounds for electronic-evidence (eevidence) in audits, investigations, or litigation. Increasingly organizations are being ordered by law or lawsuit to preserve, retrieve, and hand-over relevant electronic records (e-records) because "the courts are uniformly recognizing the discoverability of electronic communication and documents" [Nimsger and Lange, 2002]. This trend is an outgrowth of aggressive tactics by regulators to ensure corporate accountability and deter fraud. In cases ranging from Securities and Exchange Commission probes of corporate malfeasance and insider trading to employment lawsuits, e-records are subpoenaed. Investigations conducted by the National Association of Security Dealers, Department of Justice, and Department of Homeland Security routinely require companies, their business partners, or third parties to preserve and disclose e-records, including internal e-mail and instant messages (IM). A highprofile example is the probe into alleged White House leaks of a covert CIA agent's identity in which White House employees received e-mail stating: ''You must preserve all materials that might in any way be related to the department's investigation.'' E-mail, telephone logs, and other electronic documents were mentioned specifically. Any communication or file storage device is subject to computer forensic searches to identify, examine, and preserve potential e-evidence—the electronic equivalent of a "smoking gun." Preserving e-records and then restoring them so that they can be searched can seriously disrupt IS and over-burden Information Systems staff. What's more, a preservation order might specify not only the type of e-records (data files or email), but also stipulate that processes that over-write data be suspended, or that backup tapes be retained for unspecified duration. These stipulations are very disruptive to IS operations. That disruption depends largely on whether the company had an e-record management (ERM) system to systemically review, retain, and destroy e-records received or created in the course of business. This article presents an overview of e-evidence and computer forensics and their implications for Information Systems. It aims to encourage research into ERM and fully-indexed, searchable email archives by providing compelling reasons for how these approaches mitigate e-evidence risks and cost. These research issues are important for several reasons. Rarely are IS 458 Communications of the Association for Information Systems (Volume 12, 2003) 457-468 Electronic Evidence and Computer Forensics by L. Volonino departments prepared for the challenges that evidentiary rules impose on active and archival data operations. Retaining unessential e-records increases costs and risks. Companies may need to justify their e-record retention and destruction policies as proof of compliance with their accounting, regulatory, or legal obligations. Courts impose severe sanctions on employers who claim they are unable to comply with e-record requests because of Information Systems design flaws or sloppy e-records management if it obstructs an investigation.


title: TUT: a statistical model for detecting trends, topics and user interests in social media

abstract: The rapid development of online social media sites is accompanied by the generation of tremendous web contents. Web users are shifting from data consumers to data producers. As a result, topic detection and tracking without taking users' interests into account is not enough. This paper presents a statistical model that can detect interpretable trends and topics from document streams, where each trend (short for trending story) corresponds to a series of continuing events or a storyline. A topic is represented by a cluster of words frequently co-occurred. A trend can contain multiple topics and a topic can be shared by different trends. In addition, by leveraging a Recurrent Chinese Restaurant Process (RCRP), the number of trends in our model can be determined automatically without human intervention, so that our model can better generalize to unseen data. Furthermore, our proposed model incorporates user interest to fully simulate the generation process of web contents, which offers the opportunity for personalized recommendation in online social media. Experiments on three different datasets indicated that our proposed model can capture meaningful topics and trends, monitor rise and fall of detected trends, outperform baseline approach in terms of perplexity on held-out dataset, and improve the result of user participation prediction by leveraging users' interests to different trends.


title: EEG signal processing for eye tracking

abstract: Head-mounted Video-Oculography (VOG) eye tracking is visually intrusive due to a camera in the peripheral view. Electrooculography (EOG) eye tracking is socially intrusive because of face-mounted electrodes. In this work we explore Electroencephalography (EEG) eye tracking from less intrusive wireless cap scalp-based electrodes. Classification algorithms to detect eye movement and the focus of foveal attention are proposed and evaluated on data from a matched dataset of VOG and 16-channel EEG. The algorithms utilise EOG artefacts and the brain’s steady state visually evoked potential (SSVEP) response while viewing flickering stimulus. We demonstrate improved performance by extracting features from source signals estimated by Independent Component Analysis (ICA) rather than the traditional band-pass preprocessed EEG channels. The work envisages eye tracking technologies that utilise non-facially intrusive EEG brain sensing via wireless dry contact scalp based electrodes.


title: Automatic musical pattern feature extraction using convolutional neural network

abstract: Music genre classification has been a challenging yet promising task in the field of music information retrieval (MIR). Due to the highly elu- sive characteristics of audio musical data, retrieving informative and reliable features from audio signals is crucial to the performance of any music genre classi- fication system. Previous work on audio music genre classification systems mainly concentrated on using timbral features, which limits the performance. To address this problem, we propose a novel approach to extract musical pattern features in audio music using convolutional neural network (CNN), a model widely adopted in image information retrieval tasks. Our experiments show that CNN has strong capacity to capture informative features from the variations of musical patterns with minimal prior knowledge pro- vided. is adopted in image information retrieval tasks. Migrat- ing technologies from another research field brings new opportunities to break through the current bottleneck of music genre classification. The proposed musical pat- tern feature extractor has advantages in several aspects. It requires minimal prior knowledge to build up. Once obtained, the process of feature extraction is highly ef- ficient. These two advantages guarantee the scalability of our feature extractors. Moreover, our musical pattern features are complementary to other main-stream feature sets used in other classification systems. Our experiments show that musical data have very similar characteristics to image data so that the variation of musical patterns can be captured using CNN. We also show that the mu- sical pattern features are informative for genre classifica- tion tasks.


title: Single aperture monopulse horn antenna

abstract: This paper describes the design and performance of a conical Potter horn antenna which can generate sum or difference far field patterns suitable for monopulse tracking applications. The antenna consists of a horn antenna fed with a metal post loaded polarizer which is energised by suitably phased 50-/spl Omega/ matched coaxial probes. The prototype demonstrator designed here operates in the frequency range 8-8.6GHz with sum and difference patterns obtained by using a very simple phasing circuit. At 8.5GHz, the gain of the sum pattern is 18.6dB and the null in the difference pattern is 26dB at boresight.


Gender Dimensions of Product Design Expert paper prepared by:


title: Real-Time Simulation of Mining and Earthmoving Operations: A Level Set-Based Model for Tool-Induced Terrain Deformations

abstract: In this work we present a novel level set-based model for the real-time simulation of soil deformations. A level set defined by a signed distance function and sampled in a regular 3D grid represents and tracks the soil volume under deformation. Moving away from classical 2.5D heightfield representations of soil to a full 3D volume representation allows for improved tracking of cutting tool operations and the simulation of near-vertical or vertical soil faces. The proposed level set representation furthermore provides a versatile mathematical platform for modeling additional effects such as soil slip, and does not suffer from the sampling limitations of commonly used heightfields. Cutting forces applied to the tool are simulated via a formulation based on the Fundamental Equation of Earthmoving, modified to support inclined soil surfaces and transient states of tool motion. Discretisation of this 2D cutting force model with respect to the tool surface allows capturing the effects of irregular 3D terrain shapes on blades and buckets. The surcharge created during cutting operations is tracked in the form of particles and included in the soil failure force computation. We make use of an adaptive, hybrid level set-based and particle-based soil deformation scheme, which allows the soil deformation to be simulated in real-time.


title: Modeling of 3-D Surface Roughness Effects With Application to $\mu$-Coaxial Lines

abstract: Effects of 3-D surface roughness on the propagation constant of transverse electromagnetic transmission lines are calculated using finite-element method software by solving for the fields inside conductors. The modeling is validated by comparison with available literature results for the special case of 2-D surface roughness and by simulations using the finite-integration technique. Results for cubical, semiellipsoidal, and pyramidal indentations, as well as rectangular, semicircular, and triangular grooves in conductor surfaces are presented. A developed surface roughness model is applied to rectangular mu-coaxial lines. It is shown that roughness contributes up to 9.2% to their overall loss for frequencies below 40 GHz


title: Opinion Dynamics with Varying Susceptibility to Persuasion

abstract: A long line of work in social psychology has studied variations in people's susceptibility to persuasion -- the extent to which they are willing to modify their opinions on a topic. This body of literature suggests an interesting perspective on theoretical models of opinion formation on social networks: in addition to considering interventions that directly modify people's intrinsic opinions, it is also natural to consider those that modify people's susceptibility to persuasion. Here, we adopt a popular model for social opinion dynamics, and formalize the opinion maximization and minimization problems where interventions happen at the level of susceptibility. We show that modeling interventions at the level of susceptibility leads to an interesting family of new questions in network opinion dynamics. We find that the questions are quite different depending on whether there is an overall budget constraining the number of agents we can target or not. We give a polynomial-time algorithm for finding the optimal target-set to optimize the sum of opinions when there are no budget constraints on the size of the target-set. We show that this problem is NP-hard when there is a budget, and that the objective function is neither submodular nor supermodular. Finally, we propose a heuristic for the budgeted opinion optimization problem and show its efficacy at finding target-sets that optimize the sum of opinions on real world networks, including a Twitter network with real opinion estimates.


title: Ethnic and Nationalist Violence

abstract: Work on ethnic and nationalist violence has emerged from two largely nonintersecting literatures: studies of ethnic conflict and studies of political violence. Only recently have the former begun to attend to the dynamics of violence and the latter to the dynamics of ethnicization. Since the emergent literature on ethnic violence is not structured by clearly defined theoretical oppositions, we organize our review by broad similarities of methodological approach: (a) Inductive work at various levels of aggregation seeks to identify the patterns, mechanisms, and recurrent processes implicated in ethnic violence. (b) Theory-driven work employs models of rational action drawn from international relations theory, game theory, and general rational action theory. (c) Culturalist work highlights the discursive, symbolic, and ritualistic aspects of ethnic violence. We conclude with a plea for the disaggregated analysis of the heterogeneous phenomena we too casually lump together as iethnic violence.i


title: Lessons Learned With Girls, Games, and Design

abstract: Girls have long been dismissed and trivialized by the game industry. The Girls' Game Movement of the 1990s aimed to create games specifically for girls, but ultimately struggled to reach consensus on whether to make games catering to the feminine content that girls expressed interest in, or whether to challenge gender stereotypes and guide the ways that girls engage with games. Other research-based programs and interventions to engage girls in game design have faced similar difficulties, attempting to find balance between respecting girls' values and empowering them as designers. This paper offers a review of these programs, highlighting similarities in findings about what girls value in games and design, and synthesizing shared challenges and struggles. Analyzing past programs can be invaluable to contemporary educators, scholars, and designers looking to engage girls with game design and technology.


title: Joint Doppler and DOA estimation using 2D MUSIC in presence of phase residual

abstract: This paper investigates the joint Doppler and DOA (Direction-of-Arrival) estimation with a wideband phased array in presence of phase residual due to range-Doppler coupling appearing in pulse-Doppler radars. 2D MUSIC algorithm is applied and a compensation approach is developed to eliminate the influence of phase residual. Simulation data validate the improvement of joint Doppler and DOA estimation performance using the proposed method.


title: Multipath QUIC: Design and Evaluation

abstract: Quick UDP Internet Connection (QUIC) is a recent protocol initiated by Google that combines the functions of HTTP/2, TLS, and TCP directly over UDP, with the goal to reduce the latency of client-server communication. It can replace the traditional HTTP/TLS/TCP stack and the IETF has chartered a working group to standardize it. QUIC encrypts all data and most protocol headers to prevent interferences from middleboxes. Motivated by the success of Multipath TCP (MPTCP), we design Multipath QUIC (MPQUIC), a QUIC extension that enables a QUIC connection to use different paths such as WiFi and LTE on smartphones, or IPv4 and IPv6 on dual-stack hosts. We implement MPQUIC as an extension of the quic-go implementation. We evaluate the benefits of QUIC and MPQUIC by comparing them with TCP and MPTCP in a variety of settings. MPQUIC maintains MPTCP's benefits (aggregation benefit, network handover). Without packet losses, while performance of single-path TCP and single-path QUIC are similar, MPQUIC can outperform MPTCP. In lossy scenarios, (MP)QUIC is more suited than (MP)TCP.


title: Towards Principles of Large-Scale Agile Development: A Summary of the workshop at XP2014 and a revised research agenda1

abstract: Large projects are increasingly adopting agile development practices, and this raises new challenges for research. The workshop on principles of large-scale agile development focused on central topics in large-scale: the role of architecture, inter-team coordination, portfolio management and scaling agile practices. We propose eight principles for large-scale agile development, and present a revised research agenda.


Integrating Topic Modeling with Word Embeddings by Mixtures of vMFs


title: Spatio-Temporal Coding for Wireless Communication

abstract: Multipath signal propagation has long been viewed as an impairment to reliable communication in wireless channels. This paper shows that the presence of multipath greatly improves achievable data rate if the appropriate communication structure is employed. A compact model is developed for the multiple-input multiple-output (MIMO) dispersive spatially selective wireless communication channel. The multivariate information capacity is analyzed. For high signal-to-noise ratio (SNR) conditions, the MIMO channel can exhibit a capacity slope in bits per decibel of power increase that is proportional to the minimum of the number multipath components, the number of input antennas, or the number of output antennas. This desirable result is contrasted with the lower capacity slope of the well-studied case with multiple antennas at only one side of the radio link. A spatio-temporal vector-coding (STVC) communication structure is suggested as a means for achieving MIMO channel capacity. The complexity of STVC motivates a more practical reduced-complexity discrete matrix multitone (DMMT) space-frequency coding approach. Both of these structures are shown to be asymptotically optimum. An adaptive-lattice trellis-coding technique is suggested as a method for coding across the space and frequency dimensions that exist in the DMMT channel. Experimental examples that support the theoretical results are presented.


title: Drone Networks: Communications, Coordination, and Sensing

abstract: Abstract Small drones are being utilized in monitoring, transport, safety and disaster management, and other domains. Envisioning that drones form autonomous networks incorporated into the air traffic, we describe a high-level architecture for the design of a collaborative aerial system consisting of drones with on-board sensors and embedded processing, coordination, and networking capabilities. We implement a multi-drone system consisting of quadcopters and demonstrate its potential in disaster assistance, search and rescue, and aerial monitoring. Furthermore, we illustrate design challenges and present potential solutions based on the lessons learned so far.


title: The Robot that Won the DARPA Grand Challenge

abstract: This article describes the robot Stanley, which won the 2005 DARPA Grand Challenge. Stanley was developed for high-speed desert driving without human intervention. The robot’s software system relied predominately on state-of-the-art AI technologies, such as machine learning and probabilistic reasoning. This article describes the major components of this architecture, and discusses the results of the Grand Challenge race.


title: Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty

abstract: We propose new methods to speed up convergence of the Alternating Direction Method of Multipliers (ADMM), a common optimization tool in the context of large scale and distributed learning. The proposed method accelerates the speed of convergence by automatically deciding the constraint penalty needed for parameter consensus in each iteration. In addition, we also propose an extension of the method that adaptively determines the maximum number of iterations to update the penalty. We show that this approach effectively leads to an adaptive, dynamic network topology underlying the distributed optimization. The utility of the new penalty update schemes is demonstrated on both synthetic and real data, including a computer vision application of distributed structure from motion.


title: Investigation of driver circuits for GaN HEMTs in leaded packages

abstract: GaN devices have superior performance over Si-based devices, but even a very small parasitic inductance makes GaN HEMT hard to drive. Many advanced leadless packages are proposed for lower parasitic inductance to fully realize advantages of GaN devices. However, leaded packages are still dominant in industrial applications because of their simplicity for PCB assembly and capability for a wide variety of heat-sinking techniques. In this paper, the performance of a basic half bridge power circuit based on GaN HEMTs with TO package (TO-220) is analyzed, and GaN device driver design is also discussed. Simulation and experimental results are provided for validation.


Determining Image Similarity from Pattern Matching of Abstract Syntax Trees of Tree Picture Grammars


title: Optimal paths for a car that goes both forwards and backwards

abstract: The path taken by a car with a given minimum turning radius has a lower bound on its radius of curvature at each point, but the path has cusps if the car shifts into or out of reverse gear. What is the shortest such path a car can travel between two points if its starting and ending directions are specified? One need consider only paths with at most 2 cusps or reversals


title: Disaggregated End-Use Energy Sensing for the Smart Grid

abstract: This article surveys existing and emerging disaggregation techniques for energy-consumption data and highlights signal features that might be used to sense disaggregated data in an easily installed and cost-effective manner.


title: Frontal Sinus Cholesterol Granuloma: Case report

abstract: A case report of a massive cholesterol granuloma (CG) of the frontal sinus in a 15-year-old male subject treated endoscopically is reported. CGs are slowly expanding, cystic lesions that are rarely observed in the frontal sinus. Frontal sinus CGs characteristically present with proptosis, diplopia, and a unilateral painless expanding mass above the orbit. Patients frequently report a history of chronic nasal obstruction or head trauma. Although the pathogenesis is unclear, it is likely multifactorial in etiology. Surgical resection via endoscopic sinus surgery has been gaining popularity because of the minimally invasive approach and lower rates of recurrence.


title: How are they different? A quantitative domain comparison of information visualization and data visualization (2000–2014)

abstract: Information visualization and data visualization are often viewed as similar, but distinct domains, and they have drawn an increasingly broad range of interest from diverse sectors of academia and industry. This study systematically analyzes and compares the intellectual landscapes of the two domains between 2000 and 2014. The present study is based on bibliographic records retrieved from the Web of Science. Using a topic search and a citation expansion, we collected two sets of data in each domain. Then, we identified emerging trends and recent developments in information visualization and data visualization, captivated in intellectual landscapes, landmark articles, bursting keywords, and citation trends of the domains. We found out that both domains have computer engineering and applications as their shared grounds. Our study reveals that information visualization and data visualization have scrutinized algorithmic concepts underlying the domains in their early years. Successive literature citing the datasets focuses on applying information and data visualization techniques to biomedical research. Recent thematic trends in the fields reflect that they are also diverging from each other. In data visualization, emerging topics and new developments cover dimensionality reduction and applications of visual techniques to genomics. Information visualization research is scrutinizing cognitive and theoretical aspects. In conclusion, information visualization and data visualization have co-evolved. At the same time, both fields are distinctively developing with their own scientific interests.


title: Haptic Graphs for Blind Computer Users

abstract: In this paper we discuss the design of computer-based haptic graphs for blind and visually impaired people with the support of our preliminary experimental results. Since visual impairment makes data visualisation techniques inappropriate for blind people, we are developing a system that can make graphs accessible through haptic and audio media. The disparity between human haptic perception and the sensation simulated by force feedback devices is discussed. Our strategies to tackle technical difficulties posed by the limitations of force feedback devices are explained. Based on the results of experiments conducted on both blind and sighted people, we suggested two techniques: engraving and the use of texture to model curved lines on haptic graphs. Integration of surface property and auditory cues in our system are proposed to assist blind users in exploring haptic graphs.


title: AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training

abstract: Highly distributed training of Deep Neural Networks (DNNs) on future compute platforms (offering 100 of TeraOps/s of computational capacity) is expected to be severely communication constrained. To overcome this limitation, new gradient compression techniques are needed that are computationally friendly, applicable to a wide variety of layers seen in Deep Neural Networks and adaptable to variations in network architectures as well as their hyper-parameters. In this paper we introduce a novel technique - the Adaptive Residual Gradient Compression (AdaComp) scheme. AdaComp is based on localized selection of gradient residues and automatically tunes the compression rate depending on local activity. We show excellent results on a wide spectrum of state of the art Deep Learning models in multiple domains (vision, speech, language), datasets (MNIST, CIFAR10, ImageNet, BN50, Shakespeare), optimizers (SGD with momentum, Adam) and network parameters (number of learners, minibatch-size etc.). Exploiting both sparsity and quantization, we demonstrate end-to-end compression rates of ~200X for fully-connected and recurrent layers, and ~40X for convolutional layers, without any noticeable degradation in model accuracies.


title: Exploring Fast Artistic Style Merging on Heterogenous Architecture

abstract: We propose a novel approach to realize fast style merging, which provides unique understanding for art pieces from eastern and western world onto a cache-memory hier- archy in a CPU-mGPU architecture. We successfully generate style merged images with a stream of source images and produce 34% faster than the original framework, which converge iterations in a limited number(e.g., 300).


title: Deep neural networks on graph signals for brain imaging analysis

abstract: Brain imaging data such as EEG or MEG are high-dimensional spatiotemporal data often degraded by complex, non-Gaussian noise. For reliable analysis of brain imaging data, it is important to extract discriminative, low-dimensional intrinsic representation of the recorded data. This work proposes a new method to learn the low-dimensional representations from the noise-degraded measurements. In particular, our work proposes a new deep neural network design that integrates graph information such as brain connectivity with fully-connected layers. Our work leverages efficient graph filter design using Chebyshev polynomial and recent work on convolutional nets on graph-structured data. Our approach exploits graph structure as the prior side information, localized graph filter for feature extraction and neural networks for high capacity learning. Experiments on real MEG datasets show that our approach can extract more discriminative representations, leading to improved accuracy in a supervised classification task.


title: Cascaded temporal spatial features for video action recognition

abstract: Extracting spatial-temporal descriptors is a challenging task for video-based human action recognition. We decouple the 3D volume of video frames directly into a cascaded temporal spatial domain via a new convolutional architecture. The motivation behind this design is to achieve deep nonlinear feature representations with reduced network parameters. First, a 1D temporal network with shared parameters is first constructed to map the video sequences along the time axis into feature maps in temporal domain. These feature maps are then organized into channels like those of RGB image (named as Motion Image here for abbreviation), which is desired to preserve both temporal and spatial information. Second, the Motion Image is regarded as the input of the latter cascaded 2D spatial network. With the combination of the 1D temporal network and the 2D spatial network together, the size of whole network parameters is largely reduced. Benefiting from the Motion Image, our network is an end-to-end system for the task of action recognition, which can be trained with the classical algorithm of back propagation. Quantities of comparative experiments on two benchmark datasets demonstrate the effectiveness of our new architecture.


title: A Quick Startup Technique for High- $Q$ Oscillators Using Precisely Timed Energy Injection

abstract: High-quality ( $Q$ ) oscillators are notorious for being extremely slow during startup. Their long startup time increases the average power consumption in duty-cycled systems. This paper presents a novel precisely timed energy injection technique to speed up the startup behavior of high- $Q$ oscillators. The proposed solution is also insensitive to the frequency variations of the injection signal over a wide enough range that makes it possible to employ an integrated oscillator to provide the injection signal. A theoretical analysis is carried out to calculate the optimal injection duration. As a proof-of-concept, the proposed technique is incorporated in the design of crystal oscillators and is realized in a TSMC 65-nm CMOS technology. To verify the robustness of our technique across resonator parameters and frequency variations, six crystal resonators from different manufacturers with different packagings and $Q$ factors were tested. The implemented IC includes multiple crystal oscillators at 1.84, 10, and 50 MHz frequencies, with measured startup times of 58, 10, and 2 $\mu \text{s}$ , while consuming 6.7, 45.5, and 195 $\mu \text{W}$ at steady state, respectively. To the authors’ best knowledge, this is the fastest, reported startup time in the literature, with >15 $\times$ improvement over prior art, while requiring the smallest startup energy (~12 nJ).


title: Real Time Face Detection and Facial Expression Recognition: Development and Applications to Human Computer Interaction.

abstract: Computer animated agents and robots bring a social dimension to human computer interaction and force us to think in new ways about how computers could be used in daily life. Face to face communication is a real-time process operating at a a time scale in the order of 40 milliseconds. The level of uncertainty at this time scale is considerable, making it necessary for humans and machines to rely on sensory rich perceptual primitives rather than slow symbolic inference processes. In this paper we present progress on one such perceptual primitive. The system automatically detects frontal faces in the video stream and codes them with respect to 7 dimensions in real time: neutral, anger, disgust, fear, joy, sadness, surprise. The face finder employs a cascade of feature detectors trained with boosting techniques [15, 2]. The expression recognizer receives image patches located by the face detector. A Gabor representation of the patch is formed and then processed by a bank of SVM classifiers. A novel combination of Adaboost and SVM's enhances performance. The system was tested on the Cohn-Kanade dataset of posed facial expressions [6]. The generalization performance to new subjects for a 7- way forced choice correct. Most interestingly the outputs of the classifier change smoothly as a function of time, providing a potentially valuable representation to code facial expression dynamics in a fully automatic and unobtrusive manner. The system has been deployed on a wide variety of platforms including Sony's Aibo pet robot, ATR's RoboVie, and CU animator, and is currently being evaluated for applications including automatic reading tutors, assessment of human-robot interaction.


title: Regulation of the Neural Circuitry of Emotion by Compassion Meditation: Effects of Meditative Expertise

abstract: Recent brain imaging studies using functional magnetic resonance imaging (fMRI) have implicated insula and anterior cingulate cortices in the empathic response to another's pain. However, virtually nothing is known about the impact of the voluntary generation of compassion on this network. To investigate these questions we assessed brain activity using fMRI while novice and expert meditation practitioners generated a loving-kindness-compassion meditation state. To probe affective reactivity, we presented emotional and neutral sounds during the meditation and comparison periods. Our main hypothesis was that the concern for others cultivated during this form of meditation enhances affective processing, in particular in response to sounds of distress, and that this response to emotional sounds is modulated by the degree of meditation training. The presentation of the emotional sounds was associated with increased pupil diameter and activation of limbic regions (insula and cingulate cortices) during meditation (versus rest). During meditation, activation in insula was greater during presentation of negative sounds than positive or neutral sounds in expert than it was in novice meditators. The strength of activation in insula was also associated with self-reported intensity of the meditation for both groups. These results support the role of the limbic circuitry in emotion sharing. The comparison between meditation vs. rest states between experts and novices also showed increased activation in amygdala, right temporo-parietal junction (TPJ), and right posterior superior temporal sulcus (pSTS) in response to all sounds, suggesting, greater detection of the emotional sounds, and enhanced mentation in response to emotional human vocalizations for experts than novices during meditation. Together these data indicate that the mental expertise to cultivate positive emotion alters the activation of circuitries previously linked to empathy and theory of mind in response to emotional stimuli.


title: Optimal Cycle Program of Traffic Lights With Particle Swarm Optimization

abstract: Optimal staging of traffic lights, and in particular optimal light cycle programs, is a crucial task in present day cities with potential benefits in terms of energy consumption, traffic flow management, pedestrian safety, and environmental issues. Nevertheless, very few publications in the current literature tackle this problem by means of automatic intelligent systems, and, when they do, they focus on limited areas with elementary traffic light schedules. In this paper, we propose an optimization approach in which a particle swarm optimizer (PSO) is able to find successful traffic light cycle programs. The solutions obtained are simulated with simulator of urban mobility, a well-known microscopic traffic simulator. For this study, we have tested two large and heterogeneous metropolitan areas with hundreds of traffic lights located in the cities of Bahia Blanca in Argentina (American style) and Malaga in Spain (European style). Our algorithm is shown to obtain efficient traffic light cycle programs for both kinds of cities. In comparison with expertly predefined cycle programs (close to real ones), our PSO achieved quantitative improvements for the two main objectives: 1) the number of vehicles that reach their destination and 2) the overall journey time.


title: The Theory of Dyadic Morality: Reinventing Moral Judgment by Redefining Harm

abstract: The nature of harm—and therefore moral judgment—may be misunderstood. Rather than an objective matter of reason, we argue that harm should be redefined as an intuitively perceived continuum. This redefinition provides a new understanding of moral content and mechanism—the constructionist Theory of Dyadic Morality (TDM). TDM suggests that acts are condemned proportional to three elements: norm violations, negative affect, and—importantly—perceived harm. This harm is dyadic, involving an intentional agent causing damage to a vulnerable patient (A→P). TDM predicts causal links both from harm to immorality (dyadic comparison) and from immorality to harm (dyadic completion). Together, these two processes make the “dyadic loop,” explaining moral acquisition and polarization. TDM argues against intuitive harmless wrongs and modular “foundations,” but embraces moral pluralism through varieties of values and the flexibility of perceived harm. Dyadic morality impacts understandings of moral character, moral emotion...


title: Predicting Body Movement and Recognizing Actions: An Integrated Framework for Mutual Benefits

abstract: Human action recognition and body movement prediction are important tasks. They are different and have traditionally been addressed separately. These tasks, however, provide mutual benefits to each other, and existing methods fail to capture these benefits. In this paper, we propose a method for jointly recognizing the action and predicting the movement of a person. Our method is based on two Long-Short Term Memory (LSTM) recurrent neural networks, but extend them to provide and receive benefits of each other. In particular, we design two LSTM architectures. One LSTM can generate a sequence of body movement conditioned on the past movement and the predicted class of the action, and the other LSTM can recognize the human action based on the predicted sequence of body movement. Experiments on Montalbano and MSR Action 3D datasets show that movement prediction provides benefits to early recognition of human action, which in turn improves the quality of the predicted movement.


title: Intrusion Detection and Prevention System: Issues and Challenges

abstract: In In spite of the tremendous growth of technologies in computer networking and information technology, still we lack in preventing our resources from theft/attacks. This problem is very big as far as industry/ companies are concerned. As maximum of the organizations are facing an increasing number of threats every day in the form of viruses and attack etc. Since many different mechanisms were opted by organizations in the form of intrusion detection and prevention system to protect its organizations for these kinds of attacks, still there are many security breaches in every organization. In order to understand the security risks and IDPS, we will first survey about the common security breaches and then after discuss what are different opportunities and challenges in this particular field. In this paper we made a survey on the overall progress of intrusion detection systems. We survey the existing types, techniques and architectures of Intrusion Detection Systems in the literature. Finally we outline the present research challenges and issue.


title: Automated detection of optic disc and blood vessel in retinal image using morphological, edge detection and feature extraction technique

abstract: Reliable, fast and efficient optic disc localization and blood-vessel detection are the primary tasks in computer analyses of retinal image. Most of the existing algorithms suffer due to inconsistent image contrast, varying individual condition, noises and computational complexity. This paper presents an algorithm to automatically detect landmark features of retinal image, such as optic disc and blood vessel. First, optic disc and blood vessel pixels are detected from blue plane of the image. Then, using OD location the vessel pixels are connected. The detection scheme utilizes basic operations like edge detection, binary thresholding and morphological operation. This method was evaluated on standard retinal image databases, such as STARE and DRIVE. Experimental results demonstrate that the high accuracy achieved by the proposed method is comparable to that reported by the most accurate methods in literature, alongside a substantial reduction of execution time. Thus the method may provide a reliable solution in automatic mass screening and diagnosis of the retinal diseases.


title: Process and temperature compensation in a 7-MHz CMOS clock oscillator

abstract: This paper reports on the design and characterization of a process, temperature and supply compensation technique for a 7-MHz clock oscillator in a 0.25-/spl mu/m, two-poly five-metal (2P5M) CMOS process. Measurements made across a temperature range of -40/spl deg/C to 125/spl deg/C and 94 samples collected over four fabrication runs indicate a worst case combined variation of /spl plusmn/2.6% (with process, temperature and supply). No trimming was performed on any of these samples. The oscillation frequencies of 95% of the samples were found to fall within /spl plusmn/0.5% of the mean frequency and the standard deviation was 9.3 kHz. The variation of frequency with power supply was /spl plusmn/0.31% for a supply voltage range of 2.4-2.75 V. The clock generator is based on a three-stage differential ring oscillator. The variation of the frequency of the oscillator with temperature and process has been discussed and an adaptive biasing scheme incorporating a unique combination of a process corner sensing scheme and a temperature compensating network is developed. The biasing circuit changes the control voltage of the differential ring oscillator to maintain a constant frequency. A comparator included at the output stage ensures rail-to-rail swing. The oscillator is intended to serve as a start-up clock for micro-controller applications.