title: Relating Scene Depth to Image Ratios

abstract: An alternative to the classic depth from stereo disparities is presented. In this new approach two scene points with different and finite depths are viewed by two identical cameras with parallel optic axes. Both the case of frontal views and of side views, where both cameras are rotated by the same angle is addressed. A simple construction for the vanishing point of the line connecting the two scene points is presented. Both for frontal views and side views it is shown that the relative scene depth of two points equals the reciprocal of the ratio of the image plane distances from the image points to the vanishing point of the line they define. For side views it is furthermore shown how the lens plane separation and ratios of image plane distances to vanishing points directly determine the absolute depths to the scene points. Neither camera focal length, image plane optic center, image coordinate scale nor coordinate disparities are used in the calculations of the absolute scene depths.


title: Consistent environmental modeling by use of occupancy grid maps, digital road maps, and multi-object tracking

abstract: Occupancy grid mapping, street topology estimation, and object tracking are basic modules of vehicular environmental perception systems. For the sake of expandability and adaptability, this contribution proposes a hierarchical modular environmental perception architecture (HMEP). It limits the interactivity between individual basic modules. In a combination module, the consistency between occupancy grid cells and object tracks is evaluated. This allows to distinguish between static and dynamic parts of the environment in a postprocessing step. Efficient algorithms for implementation of the combination module are provided and an evaluation based on simulation and real data is presented.


title: Functional neuroanatomy of intuitive physical inference

abstract: Abstract To engage with the world—to understand the scene in front of us, plan actions, and predict what will happen next—we must have an intuitive grasp of the world’s physical structure and dynamics. How do the objects in front of us rest on and support each other, how much force would be required to move them, and how will they behave when they fall, roll, or collide? Despite the centrality of physical inferences in daily life, little is known about the brain mechanisms recruited to interpret the physical structure of a scene and predict how physical events will unfold. Here, in a series of fMRI experiments, we identified a set of cortical regions that are selectively engaged when people watch and predict the unfolding of physical events—a “physics engine” in the brain. These brain regions are selective to physical inferences relative to nonphysical but otherwise highly similar scenes and tasks. However, these regions are not exclusively engaged in physical inferences per se or, indeed, even in scene understanding; they overlap with the domain-general “multiple demand” system, especially the parts of that system involved in action planning and tool use, pointing to a close relationship between the cognitive and neural mechanisms involved in parsing the physical content of a scene and preparing an appropriate action.


title: Analyzing .NET serialization components

abstract: In this paper we present a number of available serialization libraries for the .NET platform, both integrated and 3rd party solutions and we analyze and compare their performance. Through this analysis and comparison we aim to deduct how the process of serialization can be further optimized and which parts of the process act as a bottleneck. The comparison also gives insight into how the data exchange format itself affects the whole process.


Recognizing and Justifying Text Entailment through Distributional Navigation on Definition Graphs


title: The explanatory foundations of relationship marketing theory

abstract: Purpose – Drawing on resource‐advantage theory and a diverse literature base, this article seeks to further the development of the explanatory foundations of relationship marketing theory by proposing, and then providing, tentative answers to three “why?” questions in relationship marketing: why is relationship marketing so prominent now? Why do firms and consumers enter into relationships with other firms and consumers? Why are some efforts at relationship marketing more successful than others?Design/methodology/approach – Before addressing the three questions, the paper begins by discussing the different forms of relationship marketing.Findings – Although relationship marketing is a relatively young field of inquiry, relationship marketing theory is an extremely rich area of research. Relationship marketing can take many forms and, as a result, relationship marketing theory has the potential to increase one's understanding of many aspects of business strategy.Research limitations/implications – The answ...


title: Optical Communication in Space: Challenges and Mitigation Techniques

abstract: In recent years, free space optical (FSO) communication has gained significant importance owing to its unique features: large bandwidth, license free spectrum, high data rate, easy and quick deployability, less power, and low mass requirements. FSO communication uses optical carrier in the near infrared band to establish either terrestrial links within the Earth’s atmosphere or inter-satellite/deep space links or ground-to-satellite/satellite-to-ground links. It also finds its applications in remote sensing, radio astronomy, military, disaster recovery, last mile access, backhaul for wireless cellular networks, and many more. However, despite of great potential of FSO communication, its performance is limited by the adverse effects (viz., absorption, scattering, and turbulence) of the atmospheric channel. Out of these three effects, the atmospheric turbulence is a major challenge that may lead to serious degradation in the bit error rate performance of the system and make the communication link infeasible. This paper presents a comprehensive survey on various challenges faced by FSO communication system for ground-to-satellite/satellite-to-ground and inter-satellite links. It also provides details of various performance mitigation techniques in order to have high link availability and reliability. The first part of this paper will focus on various types of impairments that pose a serious challenge to the performance of optical communication system for ground-to-satellite/satellite-to-ground and inter-satellite links. The latter part of this paper will provide the reader with an exhaustive review of various techniques both at physical layer as well as at the other layers (link, network, or transport layer) to combat the adverse effects of the atmosphere. It also uniquely presents a recently developed technique using orbital angular momentum for utilizing the high capacity advantage of optical carrier in case of space-based and near-Earth optical communication links. This survey provides the reader with comprehensive details on the use of space-based optical backhaul links in order to provide high capacity and low cost backhaul solutions.


title: The role of the epidermal growth factor receptor in human gliomas: II. The control of glial process extension and the expression of glial fibrillary acidic protein.

abstract: Our earlier investigations of the biology of the epidermal growth factor receptor (EGFR) in human gliomas demonstrated that the level of EGFR expression did not directly predict the glioma growth response to EGF, suggesting that the function of the EGFR in glioblastomas might not be limited to mediating the growth effects of EGF. We conducted the current studies to investigate the function(s) of the EGFR not related to growth control in human gliomas. These investigations show that the EGFR mediates the stimulative effects of EGF on glial process extension and glial fibrillary acidic protein (GFAP) expression. In addition, the level of EGFR expression correlates inversely with glioma cell responsiveness to differentiation promoting agents (for example, nerve growth factor and transforming growth factor-beta) that act through transmembrane tyrosine kinase receptors. Thus, glioma lines with a high level of EGFR expression (for example, T-98G cells) responded to fewer differentiation promoting factors than lines with a low level of EGFR expression (such as U-373MG cells). Our results suggest that the EGFR in gliomas may participate in mediating the process extension and GFAP stimulative effects of both EGF and other differentiation promoting agents. These properties represent components of the differentiated state in glia because their expression is stimulated by dibutyryl cyclic adenosine monophosphate in normal astrocytes. The involvement of the EGFR in the expression of these glial specific properties suggests that the EGFR may play an important role in glial differentiation.


title: Evaluating opportunistic networks in disaster scenarios

abstract: Forwarding data in scenarios where devices have sporadic connectivity is a challenge. An example scenario is a disaster area, where forwarding information generated in the incident location, like victims' medical data, to a coordination point is critical for quick, accurate and coordinated intervention. New applications are being developed based on mobile devices and wireless opportunistic networks as a solution to destroyed or overused communication networks. But the performance of opportunistic routing methods applied to emergency scenarios is unknown today. In this paper, we compare and contrast the efficiency of the most significant opportunistic routing protocols through simulations in realistic disaster scenarios in order to show how the different characteristics of an emergency scenario impact in the behaviour of each one of them.


title: Emotion recognition using wireless signals

abstract: This paper demonstrates a new technology that can infer a person's emotions from RF signals reflected off his body. EQ-Radio transmits an RF signal and analyzes its reflections off a person's body to recognize his emotional state (happy, sad, etc.). The key enabler underlying EQ-Radio is a new algorithm for extracting the individual heartbeats from the wireless signal at an accuracy comparable to on-body ECG monitors. The resulting beats are then used to compute emotion-dependent features which feed a machine-learning emotion classifier. We describe the design and implementation of EQ-Radio, and demonstrate through a user study that its emotion recognition accuracy is on par with state-of-the-art emotion recognition systems that require a person to be hooked to an ECG monitor.


title: Placing search in context: the concept revisited

abstract: Keyword-based search engines are in widespread use today as a popular means for Web-based information retrieval. Although such systems seem deceptively simple, a considerable amount of skill is required in order to satisfy non-trivial information needs. This paper presents a new conceptual paradigm for performing search in context, that largely automates the search process, providing even non-professional users with highly relevant results. This paradigm is implemented in practice in the IntelliZap system, where search is initiated from a text query marked by the user in a document she views, and is guided by the text surrounding the marked query in that document ("the context"). The context-driven information retrieval process involves semantic keyword extraction and clustering to automatically generate new, augmented queries. The latter are submitted to a host of general and domain-specific search engines. Search results are then semantically reranked, using context. Experimental results testify that using context to guide search, effectively offers even inexperienced users an advanced search tool on the Web.


title: People, places, and play: player experience in a socio-spatial context

abstract: Digital games frequently give rise to engaging and meaningful social interactions, both over the internet and in the real and tangible world of the gamer. This is the focus of the present paper, which explores digital gaming as a situated experience, shaped by socio-spatial contingencies. In particular we discuss how co-players, audience, and their spatial organization shape play and player experience and review supporting evidence for this. We present a framework describing social processes underlying situated social play experience and how these are shaped by the player the game's socio-spatial and media context. The core of this framework describes various 'sociality characteristics', and discusses these both in terms of co-located and mediated social game settings.


Generalized Adjustment Under Confounding and Selection Biases


title: EMDialog: Bringing Information Visualization into the Museum

abstract: Digital information displays are becoming more common in public spaces such as museums, galleries, and libraries. However, the public nature of these locations requires special considerations concerning the design of information visualization in terms of visual representations and interaction techniques. We discuss the potential for, and challenges of, information visualization in the museum context based on our practical experience with EMDialog, an interactive information presentation that was part of the Emily Carr exhibition at the Glenbow Museum in Calgary. EMDialog visualizes the diverse and multi-faceted discourse about this Canadian artist with the goal to both inform and provoke discussion. It provides a visual exploration environment that offers interplay between two integrated visualizations, one for information access along temporal, and the other along contextual dimensions. We describe the results of an observational study we conducted at the museum that revealed the different ways visitors approached and interacted with EMDialog, as well as how they perceived this form of information presentation in the museum context. Our results include the need to present information in a manner sufficiently attractive to draw attention and the importance of rewarding passive observation as well as both short- and longer term information exploration.


title: Flow-Based Identification of P2P Heavy-Hitters

abstract: One major new and often not welcome source of Internet traffic is P2P filesharing traffic. Banning P2P usage is not always possible or enforcible, especially in a university environment. A more restrained approach allows P2P usage, but limits the available bandwidth. This approach fails when users start to use non-default ports for the client software. We have developed the PeerTracker algorithm that allows detection of running P2P clients from NetFlow data in near real-time. The algorithm is especially suitable to identify clients that generate large amounts of traffic and can easily be used to find P2P heavy-hitters. A prototype system based on the PeerTracker algorithm is currently used by the network operations staff at the Swiss Federal Institute of Technology Zurich. We present measurements done on a medium sized Internet backbone and discuss accuracy issues, as well as possibilities and results from validation of the detection algorithm by direct polling in real-time.


title: Learning Perceptual Kernels for Visualization Design

abstract: Visualization design can benefit from careful consideration of perception, as different assignments of visual encoding variables such as color, shape and size affect how viewers interpret data. In this work, we introduce perceptual kernels: distance matrices derived from aggregate perceptual judgments. Perceptual kernels represent perceptual differences between and within visual variables in a reusable form that is directly applicable to visualization evaluation and automated design. We report results from crowd-sourced experiments to estimate kernels for color, shape, size and combinations thereof. We analyze kernels estimated using five different judgment types--including Likert ratings among pairs, ordinal triplet comparisons, and manual spatial arrangement--and compare them to existing perceptual models. We derive recommendations for collecting perceptual similarities, and then demonstrate how the resulting kernels can be applied to automate visualization design decisions.


title: Review and performance comparison of VANET protocols: AODV, DSR, OLSR, DYMO, DSDV & ZRP

abstract: In this paper a fast review and comparative study of the adhoc routing protocols used in VANET will be presented. The research filed in Vehicular adhoc network (VANET) is developing very fast. An extensive variety of utilizations has been served under various situation (Highway, urban, and Cities). Many protocols have been adopted to serve different topology and scenarios, these protocols faced various challenges. VANETs provide communication between vehicles moving on the roads. The routing protocols in VANET are affected by the vehicle high speed which leads to frequently link breaks between the communicated vehicles, so the adhoc routing protocols are adapted with the VANET characteristics to deliver the data between vehicles in short time. The main goal of VANET is to assemble a data system among vehicles that are moving on the roads, which enables the vehicles to communicate with each other for the safety manners. In this paper, an endeavor has been made to compare six surely understood protocols AODV, DSR, OLSR, DSDV, ZRP and DSDV. Used in VANET from the (packet deliver ratio, end to end delay, throughput, routing algorithm load, received packets, routing packets, dropped packets, ratio of packet loss, Average Jitter) point of view.


title: Data-augmentation for reducing dataset bias in person re-identification

abstract: In this paper we explore ways to address the issue of dataset bias in person re-identification by using data augmentation to increase the variability of the available datasets, and we introduce a novel data augmentation method for re-identification based on changing the image background. We show that use of data augmentation can improve the cross-dataset generalisation of convolutional network based re-identification systems, and that changing the image background yields further improvements.


title: Multi-beam antenna combining for 28 GHz cellular link improvement in urban environments

abstract: This article demonstrates the performance of multi-beam antenna combining for improving link quality in future millimeter-wave cellular systems. Using experimental data obtained from 28 GHz propagation measurements in New York City [8], we demonstrate how the combination of two, three and four beams, either noncoherently or coherently at the mobile receiver antenna, can improve the propagation link substantially. The results reveal that an average of 28.1 dB improvement in path loss can be achieved via combining the strongest four received signals coherently, when compared to the case of randomly received signals using a single beam at the receiver. This paper is the first to present the potential of multi-beam combining for improving link budget (e.g., extending range) in future mm-wave urban cellular systems.


optimizations for embedded systems and FFT


title: How Should Meta-Regression Analyses be . . .

abstract: SUMMARY Appropriate methods for meta-regression applied to a set of clinical trials, and the limitations and pitfalls in interpretation, are insuciently recognized. Here we summarize recent research focusing on these issues, and consider three published examples of meta-regression in the light of this work. One principal methodological issue is that meta-regression should be weighted to take account of both within-trial variances of treatment eects and the residual between-trial heterogeneity (that is, heterogeneity not explained by the covariates in the regression). This corresponds to random eects meta-regression. The associations derived from meta-regressions are observational, and have a weaker interpretation than the causal relationships derived from randomized comparisons. This applies particularly when averages of patient characteristics in each trial are used as covariates in the regression. Data dredging is the main pitfall in reaching reliable conclusions from meta-regression. It can only be avoided by prespecication of covariates that will be investigated as potential sources of heterogeneity. However, in practice this is not always easy to achieve. The examples considered in this paper show the tension between the scientic rationale for using meta-regression and the dicult interpretative problems to which such analyses are prone. Copyright ? 2002 John Wiley & Sons, Ltd.


title: Modeling and Control Design of the Interleaved Double Dual Boost Converter

abstract: The interleaved double dual boost (IDDB) is a non-isolated step-up dc-dc converter capable of high voltage gain and suitable to high-power applications. In this paper, the modeling and control design of this converter, valid for an arbitrary number of phases, is presented. The developed approach is then applied to a six-phase IDDB, and experimental results are obtained with a prototype operating with an input voltage of 60 V, an output voltage of 360 V, and with a nominal output power of 2.2 kW. The applications of this converter include electrical vehicles and renewable energy conversion.


title: A study about psychophysiological measures in user experience monitoring and evaluation

abstract: User experience (UX) has received increased attention in last years, in a reality of mobile applications and internet of things. In order to increase competitiveness, companies have been invested in understand user's emotions during the experience. However, this is still a challenge for HCI researchers. This article presents an investigation, as a part of a major research, based on 14 studies, about use of psychophysiological measures in HCI research. We found 1) that psychophysiological measures are still difficult to apply, once sensors are still invasive and difficult to understand and use; 2) each psychophysiological measure represents various emotions, and each emotion is measured by various psychophysiological measures; and 3) it is necessary to explore and discover more solutions to user's emotion measurement. This scenario shows an opportunity for UX evaluation methods, once a future work considers the use of psychophysiological measures.


title: Membrane Bioreactor (MBR) Technology for Wastewater Treatment and Reclamation: Membrane Fouling

abstract: The membrane bioreactor (MBR) has emerged as an efficient compact technology for municipal and industrial wastewater treatment. The major drawback impeding wider application of MBRs is membrane fouling, which significantly reduces membrane performance and lifespan, resulting in a significant increase in maintenance and operating costs. Finding sustainable membrane fouling mitigation strategies in MBRs has been one of the main concerns over the last two decades. This paper provides an overview of membrane fouling and studies conducted to identify mitigating strategies for fouling in MBRs. Classes of foulants, including biofoulants, organic foulants and inorganic foulants, as well as factors influencing membrane fouling are outlined. Recent research attempts on fouling control, including addition of coagulants and adsorbents, combination of aerobic granulation with MBRs, introduction of granular materials with air scouring in the MBR tank, and quorum quenching are presented. The addition of coagulants and adsorbents shows a significant membrane fouling reduction, but further research is needed to establish optimum dosages of the various coagulants/adsorbents. Similarly, the integration of aerobic granulation with MBRs, which targets biofoulants and organic foulants, shows outstanding filtration performance and a significant reduction in fouling rate, as well as excellent nutrients removal. However, further research is needed on the enhancement of long-term granule integrity. Quorum quenching also offers a strong potential for fouling control, but pilot-scale testing is required to explore the feasibility of full-scale application.


title: Automation of Analog IC Layout: Challenges and Solutions

abstract: Physical analog IC design has not been automated to the same degree as digital IC design. This shortfall is primarily rooted in the analog IC design problem itself, which is considerably more complex even for small problem sizes. Significant progress has been made in analog automation in several R&D target areas in recent years. Constraint engineering and generator-based module approaches are among the innovations that have emerged. Our paper will first present a brief review of the state of the art of analog layout automation. We will then introduce active and open research areas and present two visions -- a "continuous layout design flow" and a "bottom-up meets top-down design flow" -- which could significantly push analog design automation towards its goal of analog synthesis.


title: World Cup 2014 in the Twitter World: A big data analysis of sentiments in U.S. sports fans' tweets

abstract: We used a big data approach to analyze U.S. soccer fans' sentiments in their tweets.Tweets were used to express joy and anticipation and to express emotions.U.S. fans' fear and anger were common and reflected U.S. team's goals or losses.Emotions in two non-U.S. games were unclear.This paper provides support for disposition theory of sport and sentiment analysis. The present project collected real-time tweets from U.S. soccer fans during five 2014 FIFA World Cup games (three games between the U.S. team and another opponent and two games between other teams) using Twitter search API. We used sentiment analysis to examine U.S. soccer fans' emotional responses in their tweets, particularly, the emotional changes after goals (either own or the opponent's). We found that during the matches that the U.S. team played, fear and anger were the most common negative emotions and in general, increased when the opponent team scored and decreased when the U.S. team scored. Anticipation and joy were also generally consistent with the goal results and the associated circumstances during the games. Furthermore, we found that during the matches between other teams, U.S. tweets showed more joy and anticipation than negative emotions (e.g., anger and fear) and that the patterns in response to goal or loss were unclear. This project revealed that sports fans use Twitter for emotional purposes and that the big data approach to analyze sports fans' sentiment showed results generally consistent with the predictions of the disposition theory when the fanship was clear and showed good predictive validity.


title: Intensification of growth factor receptor signalling by phorbol treatment of ligand-primed cells implies a dimer-stabilizing effect of protein kinase C-dependent juxtamembrane domain phosphorylation.

abstract: Abstract Protein kinase (PKC) phosphorylates the juxtamembrane domain of many growth factor receptors, but the physiologic effect of this modification on ligand signalling and desensitisation is unclear. Here we show that PKC-dependent transmodulation of EGFR and ErbB2 signalling is schedule-specific: prolonged pre-treatment of A431 cells with the PKC agonist phorbol dibutyrate potently inhibits subsequent ligand-induced EGFR signalling as expected, but EGF pre-treatment reverses the inhibitory effect of phorbol. The agonist activity of PKC on receptor signalling is even more apparent when cells are treated with phorbol in the presence of a tyrosine phosphatase inhibitor. Because these findings suggested a synergistic interaction between tyrosine- and PKC-dependent phosphorylation events, we sought to define the interactions of tyrosine-phosphorylated and PKC-modified ErbB2 subsets within EGF-inducible hetero-oligomers. Growth factor-dependent PKC transphosphorylation takes place exclusively within endocytosed tyrosine-phosphorylated receptor oligomers. Moreover, phorbol differentially affects two ErbB2 C-terminal autophosphorylation sites: whereas phosphorylation of Tyr1222 is reduced, phosphorylation of Tyr1139 is increased. These results suggest that PKC-dependent phosphorylation of the juxtamembrane domain may contribute positively to both internalisation and signalling of ligand-activated receptors, simultaneously accelerating termination of growth factor action. We propose that transient PKC-dependent signal amplification results from enhanced stability of liganded receptor oligomers due to phosphorylation-dependent juxtamembrane domain interactions, analogous to the protein-protein binding now known to be induced by serine-threonine phosphorylation of CREB and SMAD.


title: Business process reengineering: a survey of international experience

abstract: Despite the widespread adoption of business process re‐engineering (BPR), it has in many cases repeatedly failed to deliver its promised results. The lack of integrated implementation approach to exploiting BPR is seen as one of the important reasons amongst others, behind BPR failures. Yet, a relative void in the literature remains the scarcity of suitable models and frameworks that address the implementation issues surrounding BPR. This motivates the presented study to attempt to provide a “frame of reference” with which current practices can be re‐positioned. A survey was therefore designed to collect data from a sample of organizations in the USA and Europe. The survey assesses the level of importance placed on the essential elements of integrated BPR implementation. In doing so, the study was also able to identify the level of maturity of BPR concepts within organisations. Empirical findings are then discussed in the context of other studies.


title: Role of moisture on the lipid oxidation determined by D(2)O in a linoleic acid model system.

abstract: The possibility of moisture, as an active substance for lipid oxidation, was determined in linoleic acid model systems containing deuterium oxide (D2O) and deuterium free H2O, by measuring the headspace oxygen content and headspace volatiles. Mixtures of D2O and H2O, at the ratio of 3:0, 2:1, 1:2, and 0:3 (w/w), were added to linoleic acid and oxidised at 60°C. The existence of deuterium in headspace volatiles, was indirectly determined by comparing the amount of fragmented mass to charge ratio (m/z), which was 73.1/72.1 for d1-pentane/pentane, 57.0/56.0 for d1-2-propenal/2-propenal, 101.1/100.1 for d1-hexanal/hexanal, 113.1/112.1 for d1-t-2-heptenal-D/t-2-heptenal, and 153.2/152.2 for d1-2,4-decadieanl/2,4-decadieanl. As the oxidation time increased to 12h, the ratio of 73.1/72.1 (m/z) for pentane and 57.0/56.0 for 2-propenal increased significantly with a concentration dependent manner of deuterium oxide, which implies water was incorporated as a substrate during volatile formation. However, hexanal, t-2-heptenal, and 2,4-decadieanl did not follow the patterns of pentane and 2-propenal, implying that the formation mechanisms of these volatiles may not share the same pathways with pentane and 2-propenal. This study showed that moisture participates for the formation of volatiles and moisture may act as a substrate for lipid oxidation.


title: Cloudward bound: planning for beneficial migration of enterprise applications to the cloud

abstract: In this paper, we tackle challenges in migrating enterprise services into hybrid cloud-based deployments, where enterprise operations are partly hosted on-premise and partly in the cloud. Such hybrid architectures enable enterprises to benefit from cloud-based architectures, while honoring application performance requirements, and privacy restrictions on what services may be migrated to the cloud. We make several contributions. First, we highlight the complexity inherent in enterprise applications today in terms of their multi-tiered nature, large number of application components, and interdependencies. Second, we have developed a model to explore the benefits of a hybrid migration approach. Our model takes into account enterprise-specific constraints, cost savings, and increased transaction delays and wide-area communication costs that may result from the migration. Evaluations based on real enterprise applications and Azure-based cloud deployments show the benefits of a hybrid migration approach, and the importance of planning which components to migrate. Third, we shed insight on security policies associated with enterprise applications in data centers. We articulate the importance of ensuring assurable reconfiguration of security policies as enterprise applications are migrated to the cloud. We present algorithms to achieve this goal, and demonstrate their efficacy on realistic migration scenarios.


title: Instantaneous power control of a high speed permanent magnet synchronous generator based on a sliding mode observer and a phase locked loop

abstract: This paper proposes an instantaneous power control method for high speed permanent magnet synchronous generators (PMSG), to realize the decoupled control of active power and reactive power, through vector control based on a sliding mode observer (SMO), and a phase locked loop (PLL). Consequently, the high speed PMSG has a high internal power factor, to ensure efficient operation. Vector control and accurate estimation of the instantaneous power require an accurate estimate of the rotor position. The SMO is able to estimate the back electromotive force (EMF). The rotor position and speed can be obtained using a combination of the PLL technique and the phase compensation method. This method has the advantages of robust operation, and being resistant to noise when estimating the position of the rotor. Using instantaneous power theory, the relationship between the output active power, reactive power, and stator current of the PMSG is deduced, and the power constraint condition is analyzed for operation at the unit internal power factor. Finally, the accuracy of the rotor position detection, the instantaneous power detection, and the control methods are verified using simulations and experiments.


title: Large scale evolutionary optimization using cooperative coevolution q

abstract: Evolutionary algorithms (EAs) have been applied with success to many numerical and combinatorial optimization problems in recent years. However, they often lose their effectiveness and advantages when applied to large and complex problems, e.g., those with high dimensions. Although cooperative coevolution has been proposed as a promising framework for tackling high-dimensional optimization problems, only limited studies were reported by decomposing a high-dimensional problem into single variables (dimensions). Such methods of decomposition often failed to solve nonseparable problems, for which tight interactions exist among different decision variables. In this paper, we propose a new cooperative coevolution framework that is capable of optimizing large scale nonseparable problems. A random grouping scheme and adaptive weighting are introduced in problem decomposition and coevolution. Instead of conventional evolutionary algorithms, a novel differential evolution algorithm is adopted. Theoretical analysis is presented in this paper to show why and how the new framework can be effective for optimizing large nonseparable problems. Extensive computational studies are also carried out to evaluate the performance of newly proposed algorithm on a large number of benchmark functions with up to 1000 dimensions. The results show clearly that our framework and algorithm are effective as well as efficient for large scale evolutionary optimisation problems. We are unaware of any other evolutionary algorithms that can optimize 1000-dimension nonseparable problems as effectively and efficiently as we have done.


title: Instance-Aware Image and Sentence Matching with Selective Multimodal LSTM

abstract: Effective image and sentence matching depends on how to well measure their global visual-semantic similarity. Based on the observation that such a global similarity arises from a complex aggregation of multiple local similarities between pairwise instances of image (objects) and sentence (words), we propose a selective multimodal Long Short-Term Memory network (sm-LSTM) for instance-aware image and sentence matching. The sm-LSTM includes a multimodal context-modulated attention scheme at each timestep that can selectively attend to a pair of instances of image and sentence, by predicting pairwise instance-aware saliency maps for image and sentence. For selected pairwise instances, their representations are obtained based on the predicted saliency maps, and then compared to measure their local similarity. By similarly measuring multiple local similarities within a few timesteps, the sm-LSTM sequentially aggregates them with hidden states to obtain a final matching score as the desired global similarity. Extensive experiments show that our model can well match image and sentence with complex content, and achieve the state-of-the-art results on two public benchmark datasets.


title: Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret

abstract: Lifelong reinforcement learning provides a promising framework for developing versatile agents that can accumulate knowledge over a lifetime of experience and rapidly learn new tasks by building upon prior knowledge. However, current lifelong learning methods exhibit non-vanishing regret as the amount of experience increases, and include limitations that can lead to suboptimal or unsafe control policies. To address these issues, we develop a lifelong policy gradient learner that operates in an adversarial setting to learn multiple tasks online while enforcing safety constraints on the learned policies. We demonstrate, for the first time, sublinear regret for lifelong policy search, and validate our algorithm on several benchmark dynamical systems and an application to quadrotor control.


title: Radar Foliage Penetration Measurements at Millimeter Wavelengths

abstract: Abstract : A series of radar measurements on the penetration of foliage has been made at 9.4, 16.2, 35, and 95 GHz. Measurements were made for both the one-way and two-way cases at 9.4 and 16.4 GHz at similar foliage areas for comparison. Most of the measurements were made at depression angles below 3 degrees, although a set of one-way measurements was made at 9.4 and 16.2 GHz for a depression angle of 29 degrees. Attenuation properties, polarization ratios, and noncoherent spectral correlation properties were investigated as a function of frequency, polarization, depth of foliage and wind speed. Limited comparisons are made with data previously available at lower frequencies.


title: Automatic Design of Decision-Tree Algorithms with Evolutionary Algorithms

abstract: This study reports the empirical analysis of a hyper-heuristic evolutionary algorithm that is capable of automatically designing top-down decision-tree induction algorithms. Top-down decision-tree algorithms are of great importance, considering their ability to provide an intuitive and accurate knowledge representation for classification problems. The automatic design of these algorithms seems timely, given the large literature accumulated over more than 40 years of research in the manual design of decision-tree induction algorithms. The proposed hyper-heuristic evolutionary algorithm, HEAD-DT, is extensively tested using 20 public UCI datasets and 10 microarray gene expression datasets. The algorithms automatically designed by HEAD-DT are compared with traditional decision-tree induction algorithms, such as C4.5 and CART. Experimental results show that HEAD-DT is capable of generating algorithms which are significantly more accurate than C4.5 and CART.


title: A cost effective estimation of depth from stereo image pairs using shallow siamese convolutional networks

abstract: We propose a cost effective depth estimation method using stereo camera and convolutional neural networks for SLAM algorithm. Convolutional neural networks outperform the traditional computer vision approaches in estimating depth of stereo image pairs. However, the performance gain of neural networks approach causes substantial increase in computation cost, which consequently decreases the operation time of mobile robots like domestic robots. To alleviate the high computation problem, this paper proposes quantized shallow Siamese convolutional neural networks which compute the similarity between patches of rectified stereo image pairs to estimate depth. Quantization of weights and reduction of layers in the neural networks can degrade the performance. To mitigate the performance degradation, this paper initially maximizes networks performance with three different methods of batch-normalization, optimal negative matching similarity training, and retraining with a global loss function. Then, the final retrained network is nonuniformly quantized. This non-uniform quantization provides efficient computation with the minimum performance loss. The final quantized shallow Siamese networks achieve 3.29% error rate for KITTI 2012.


title: Ontologies for Cultural Heritage

abstract: In the cultural heritage domain information systems are increasingly deployed, digital representations of physical objects are produced in immense numbers and there is a strong political pressure on memory institutions to make their holdings accessible to the public in digital form. The sector splits into a set of disciplines with highly specialized fields. Due to the resulting diversity, one can hardly speak about a “domain” in the sense of “domain ontologies” [33]. On the other side, study and research of the past is highly interdisciplinary. Characteristically, archaeology employs a series of “auxiliary” disciplines, such as archaeometry, archaeomedicine, archaeobotany, archaeometallurgy, archaeoastronomy, etc., but also historical sources and social theories.


title: Crowdsourcing BIM-guided collection of construction material library from site photologs

abstract: With advances in technologies that enabled massive visual data collection and BIM, the AEC industry now has an unprecedented amount of visual data (e.g., images and videos) and BIMs. One of the past efforts to leverage these data includes the Construction Material Library (CML) that was created for inferring construction progress by automatically detecting construction materials. CML has a limited number of construction material classes because it is merely impossible for an individual or a group of researchers to collect all possible variations of construction materials. This paper proposes a web-based platform that streamlines the data collection process for creating annotated material patches guided by BIM overlays. Construction site images with BIM overlays are automatically generated after image-based 3D reconstruction. These images are deployed on a web-based platform for annotations. The proposed crowdsourcing method using this platform has potential to scale up data collection for expanding the existing CML. A case study was conducted to validate the feasibility of the proposed method and to improve the web interface before deployment to a public cloud environment.


title: A Utility-Scale Flywheel Energy Storage System with a Shaftless, Hubless, High-Strength Steel Rotor

abstract: Energy storage is crucial for both smart grids and renewable energy sources such as wind or solar, which are intermittent in nature. Compared to electrochemical batteries, flywheel energy storage systems (ESSs) offer many unique benefits such as low environmental impact, high power quality, and larger life cycles. This paper presents a novel utility-scale flywheel ESS that features a shaftless, hubless flywheel. The unique shaftless design gives it the potential of doubled energy density and a compact form factor. Its energy and power capacities are 100 kWh and 100 kW, respectively. The flywheel is made of high-strength steel, which makes it much easier to manufacture, assemble, and recycle. Steels also cost much less than composite materials. Design and analysis of the shaftless flywheel are presented first. In addition, the system incorporates a new combination active magnetic bearing. Its working principle and levitation control for the flywheel are discussed. The design of an integrated coreless permanent-magnet (PM) motor/generator for the flywheel is given as well. Initial test results show that the magnetic bearing provides stable levitation for the 5443-kg flywheel with small current consumption.


title: A theoretical and empirical analysis of Expected Sarsa

abstract: This paper presents a theoretical and empirical analysis of Expected Sarsa, a variation on Sarsa, the classic on-policy temporal-difference method for model-free reinforcement learning. Expected Sarsa exploits knowledge about stochasticity in the behavior policy to perform updates with lower variance. Doing so allows for higher learning rates and thus faster learning. In deterministic environments, Expected Sarsas updates have zero variance, enabling a learning rate of 1. We prove that Expected Sarsa converges under the same conditions as Sarsa and formulate specific hypotheses about when Expected Sarsa will outperform Sarsa and Q-learning. Experiments in multiple domains confirm these hypotheses and demonstrate that Expected Sarsa has significant advantages over these more commonly used methods.


title: Photonic effects caused by multiple discontinuities in optical waveguides

abstract: The scattering properties of discontinuities between dielectric waveguides are obtained by using a new generalised scattering matrix concept, together with the generalised telegraphist equations formulism and the modal matching technique. There is no restriction either on the cross-section shape of the dielectrics or on the refractive index profile functions of the waveguides. Modal propagation characteristics, radiation losses and complex scattering coefficients between proper modes can be obtained for monomode and multimode dielectric waveguides connected in cascade. Results of the complex scattering coefficients were evaluated. Photonic bandgaps, photonic windows and optical switching properties were obtained for periodic structures at optical frequencies.


1 Social-driven Internet of Connected Objects


title: A chip-scale packaging technology for 60-GHz wireless chipsets

abstract: In this paper, we present a cost-effective chip-scale packaging solution for a 60-GHz industrial-scientific-medical band receiver (Rx) and transmitter (Tx) chipset capable of gigabit-per-second wireless communications. Envisioned applications of the packaged chipset include 1-3-Gb/s directional links using amplitude shift-keying or phase shift-keying modulation and 500-Mb/s-1-Gb/s omni-directional links using orthogonal frequency-division multiplexing modulation. This paper demonstrates the first fully package-integrated 60-GHz chipset including receive and transmit antennas in a cost-effective plastic package. A direct-chip-attach (DCA) and surface mountable land-grid-array (LGA) package technology is presented. The size of the DCA package is 7times11 mm2 and the LGA package size is 6times13 mm2. Optionally, the Tx and Rx chip can be packaged together with Tx and Rx antennas in a combined 13times13 mm2 LGA transceiver package


title: Aquatic debris monitoring using smartphone-based robotic sensors

abstract: Monitoring aquatic debris is of great interest to the ecosystems, marine life, human health, and water transport. This paper presents the design and implementation of SOAR - a vision-based surveillance robot system that integrates an off-the-shelf Android smartphone and a gliding robotic fish for debris monitoring. SOAR features real-time debris detection and coverage-based rotation scheduling algorithms. The image processing algorithms for debris detection are specifically designed to address the unique challenges in aquatic environments. The rotation scheduling algorithm provides effective coverage of sporadic debris arrivals despite camera's limited angular view. Moreover, SOAR is able to dynamically offload computation-intensive processing tasks to the cloud for battery power conservation. We have implemented a SOAR prototype and conducted extensive experimental evaluation. The results show that SOAR can accurately detect debris in the presence of various environment and system dynamics, and the rotation scheduling algorithm enables SOAR to capture debris arrivals with reduced energy consumption.


title: Shaping High-Power IGBT Switching Transitions by Active Voltage Control for Reduced EMI Generation

abstract: High-performance power switching devices [insulated-gate bipolar transistors (IGBTs)] realize high-performance power converters. Unfortunately, with a high switching speed of the IGBT-freewheel diode chopper cell, the circuit becomes intrinsic sources of high-level electromagnetic interference (EMI). Therefore, costly EMI filters or shielding is normally demanded on the load and supply sides. An S-shaped voltage transient with a high-order time derivative eliminates the discontinuity in the switching transient and can suppress high-frequency spectrum of EMI emissions. More importantly, it provides an improved tradeoff between EMI generation and switching time, therefore switching losses. This promising tradeoff can be explored further for better high-frequency EMI suppression by using the infinitely differentiable characteristics of Gaussian S-shaped transients. However, current gate drive schemes cannot achieve the S shaping. In this paper, active voltage control is applied and improved successfully to define IGBT switching dynamics with a smoothed Gaussian waveform.


title: Mind wandering and attention during focused meditation : A fi ne-grained temporal analysis of fl uctuating cognitive states

abstract: article i nfo Studies have suggested that the default mode network is active during mind wandering, which is often experienced intermittently during sustained attention tasks. Conversely, an anticorrelated task-positive network is thought to subserve various forms of attentional processing. Understanding how these two systems work together is central for understanding many forms of optimal and sub-optimal task performance. Here we present a basic model of naturalistic cognitive fluctuations between mind wandering and attentional states derived from the practice of focused attention meditation. This model proposes four intervals in a cognitive cycle: mind wandering, awareness of mind wandering, shifting of attention, and sustained attention. People who train in this style of meditation cultivate their abilities to monitor cognitive processes related to attention and distraction, making them well suited to report on these mental events. Fourteen meditation practitioners performed breath-focused meditation while undergoing fMRI scanning. When participants realized their mind had wandered, they pressed a button and returned their focus to the breath. The four intervals above were then constructed around these button presses. We hypothesized that periods of mind wandering would be associated with default mode activity, whereas cognitive processes engaged during awareness of mind wandering, shifting of attention and sustained attention would engage attentional subnetworks. Analyses revealed activity in brain regions associated with the default mode during mind wandering, and in salience network regions during awareness of mind wandering. Elements of the executive network were active during shifting and sustained attention. Furthermore, activations during these cognitive phases were modulated by lifetime meditation experience. These findings support and extend theories about cognitive correlates of distributed brain networks.


title: Language Independent n-Gram-Based Text Categorization with Weighting Factors: A Case Study

abstract: We introduce a new language independent text categorization technique based on n-grams profile representation of restricted size of both document and a category, an n-gram weighting factors scheme, and a simple algorithm for comparing profiles. The technique does not require any morphological analysis of texts, any preprocessing steps, or any prior information about document content or language. We apply it to the text categorization problem in two widely spoken yet paradigmatically quite different languages – English and Arabic, thus demonstrating language-independence. We used their publicly available document collections – 20-Newsgroups and Mesleh-10, respectively. Experimental results presented in terms of macro- and micro-averaged F1 measures imply that the new technique outperforms other n-gram based and bag-of-words machine learning techniques when applied to English and Arabic text categorization.


title: From humor recognition to irony detection: The figurative language of social media

abstract: The research described in this paper is focused on analyzing two playful domains of language: humor and irony, in order to identify key values components for their automatic processing. In particular, we are focused on describing a model for recognizing these phenomena in social media, such as ''tweets''. Our experiments are centered on five data sets retrieved from Twitter taking advantage of user-generated tags, such as ''#humor'' and ''#irony''. The model, which is based on textual features, is assessed on two dimensions: representativeness and relevance. The results, apart from providing some valuable insights into the creative and figurative usages of language, are positive regarding humor, and encouraging regarding irony.


title: Minimization of vias in PCB implementations of planar coils with litz-wire structure

abstract: PCB implementation of planar windings replicates the structure of conventional litz wires in order to reduce the ac losses. This structure consists of a set of fine trace electrically connected in parallel which are transposed by means of changes of both trajectory and layer. An appropriate transposition strategy is essential to reduce the winding ac losses, however PCBs with high number of vias could result expensive and not reliable. Taking advantage of the field profile of planar windings, in this work is studied the minimization of the number of vias without significant reduction of performance. The work is carried out by means of a combination of finite-element analysis (FEA) simulations and experimental verification on ultra-thin (0.3 mm) PCB prototypes of planar inductors oriented to a domestic induction heating application.


title: Spontaneous bladder rupture following enterocystoplasty can be treated conservatively.

abstract: Spontaneous bladder rupture following enterocystoplasty has been reported recently. The etiology remains unclear but appears to be multifactorial. Common factors are a high outlet resistance with total urinary continence and the presence of an augmented, dysfunctional native bladder, which may result in the development of high intravesical pressures or increased wall tension. We present a case of spontaneous bladder rupture 4 weeks following enterocystoplasty in which a segment of detubularized ileum was used to augment a contracted bladder. Our patient was treated by continuous bladder drainage for 21 days, antibiotics and appropriate supportive care.


title: Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks

abstract: Learning a similarity function between pairs of objects is at the core of learning to rank approaches. In information retrieval tasks we typically deal with query-document pairs, in question answering -- question-answer pairs. However, before learning can take place, such pairs needs to be mapped from the original space of symbolic words into some feature space encoding various aspects of their relatedness, e.g. lexical, syntactic and semantic. Feature engineering is often a laborious task and may require external knowledge sources that are not always available or difficult to obtain. Recently, deep learning approaches have gained a lot of attention from the research community and industry for their ability to automatically learn optimal feature representation for a given task, while claiming state-of-the-art performance in many tasks in computer vision, speech recognition and natural language processing. In this paper, we present a convolutional neural network architecture for reranking pairs of short texts, where we learn the optimal representation of text pairs and a similarity function to relate them in a supervised way from the available training data. Our network takes only words in the input, thus requiring minimal preprocessing. In particular, we consider the task of reranking short text pairs where elements of the pair are sentences. We test our deep learning system on two popular retrieval tasks from TREC: Question Answering and Microblog Retrieval. Our model demonstrates strong performance on the first task beating previous state-of-the-art systems by about 3\% absolute points in both MAP and MRR and shows comparable results on tweet reranking, while enjoying the benefits of no manual feature engineering and no additional syntactic parsers.


title: Sentiment Analysis, Visualization and Classification of Summarized News Articles: A Novel Approach

abstract: Due to advancement in technology, enormous amount of data is generated every day. One of the main challenges of large amount of data is user overloaded with huge volume of data. Hence effective methods are highly required to help user to comprehend large amount of data. This research work proposes effective methods to extract and represent the data. The summarization is applicable to obtain a brief overview of the text and sentiment analysis can obtain emotions expressed in the text computationally. The combined text summarization and sentiment analysis is proposed on BBC news articles. A pronoun replacement based text summarization method is developed and VADER sentiment analyzer is used to determine sentiment information. The 3-D visualization schemes have been provided to represent the sentiment information. The sentiment analysis and classification are performed on original BBC news articles as well as on summarized articles using classifiers, such as Logistic Regression, Random Forest and Adaboost. On original news articles highest classification rate of 84.93%, using summarization of ratio 25%, 50% and 75% highest classification rates of 78.73%, 83.06% and 83.23%, respectively are observed.


title: Clarifying business models: Origins, present, and future of the concept

abstract: This paper aims to clarify the concept of business models, its usages, and its roles in the Information Systems domain. A review of the literature shows a broad diversity of understandings, usages, and places in the firm. The paper identifies the terminology or ontology used to describe a business model, and compares this terminology with previous work. Then the general usages, roles and potential of the concept are outlined. Finally, the connection between the business model concept and Information Systems is described in the form of eight propositions to be analyzed in future work.


title: Novel Approaches for Privacy Preserving Data Mining in K-Anonymity Model

abstract: In privacy preserving data mining, anonymization based approaches have been used to preserve the privacy of an individual. Existing literature addresses various anonymization based approaches for preserving the sensitive private information of an individual. The k-anonymity model is one of the widely used anonymization based approach. However, the anonymization based approaches suffer from the issue of information loss. To minimize the information loss various state-of-the-art anonymization based clustering approaches viz. Greedy k-member algorithm and Systematic clustering algorithm have been proposed. Among them, the Systematic clustering algorithm gives lesser information loss. In addition, these approaches make use of all attributes during the creation of an anonymized database. Therefore, the risk of disclosure of sensitive private data is higher via publication of all the attributes. In this paper, we propose two approaches for minimizing the disclosure risk and preserving the privacy by using systematic clustering algorithm. First approach creates an unequal combination of quasi-identifier and sensitive attribute. Second approach creates an equal combination of quasi-identifier and sensitive attribute. We also evaluate our approach empirically focusing on the information loss and execution time as vital metrics. We illustrate the effectiveness of the proposed approaches by comparing them with the existing clustering algorithms.


title: Self-Reflection and Articulated Consumer Preferences

abstract: Accurate measurement of consumer preferences reduces development costs and leads to successful products. Some product-development teams use quantitative methods such as conjoint analysis or structured methods such as Casemap. Other product-development teams rely on unstructured methods such as direct conversations with consumers, focus groups, or qualitative interviews. All methods assume that measured consumer preferences endure and are relevant for consumers' marketplace decisions. This article suggests that if consumers are not first given tasks to encourage preference self-reflection, unstructured methods may not measure accurate and enduring preferences. This paper provides evidence that consumers learn their preferences as they make realistic decisions. Sufficiently challenging decision tasks encourage preference self-reflection which, in turn, leads to more accurate and enduring measures. Evidence suggests further that if consumers are asked to articulate preferences before self-reflection, then that articulation interferes with consumers' abilities to articulate preferences even after they have a chance to self-reflect. The evidence that self-reflection enhances accuracy is based on experiments in the automotive and mobile phone markets. Consumers completed three rotated incentive-aligned preference measurement methods (revealed-preference measures [as in conjoint analysis], a structured method [Casemap], and an unstructured preference-articulation method). The stimuli were designed to be managerially relevant and realistic (53 aspects in automobiles, 22 aspects for mobile phones) so that consumers' decisions approximated in vivo decisions. One to three weeks later, consumers were asked which automobiles (or mobile phones) they would consider. Qualitative comments and response times are consistent with the implications of the measures of predictive ability.


title: A parallel particle swarm optimization algorithm with communication strategies

abstract: Particle swarm optimization (PSO) is an alternative population-based evolutionary computation technique. It has been shown to be capable of optimizing hard mathematical problems in continuous or binary space. We present here a parallel version of the particle swarm optimization (PPSO) algorithm together with three communication strategies which can be used according to the independence of the data. The first strategy is designed for solution parameters that are independent or are only loosely correlated, such as the Rosenbrock and Rastrigrin functions. The second communication strategy can be applied to parameters that are more strongly correlated such as the Griewank function. In cases where the properties of the parameters are unknown, a third hybrid communication strategy can be used. Experimental results demonstrate the usefulness of the proposed PPSO algorithm.


title: Extraction of Microservices from Monolithic Software Architectures

abstract: Driven by developments such as mobile computing, cloud computing infrastructure, DevOps and elastic computing, the microservice architectural style has emerged as a new alternative to the monolithic style for designing large software systems. Monolithic legacy applications in industry undergo a migration to microservice-oriented architectures. A key challenge in this context is the extraction of microservices from existing monolithic code bases. While informal migration patterns and techniques exist, there is a lack of formal models and automated support tools in that area. This paper tackles that challenge by presenting a formal microservice extraction model to allow algorithmic recommendation of microservice candidates in a refactoring and migration scenario. The formal model is implemented in a web-based prototype. A performance evaluation demonstrates that the presented approach provides adequate performance. The recommendation quality is evaluated quantitatively by custom microservice-specific metrics. The results show that the produced microservice candidates lower the average development team size down to half of the original size or lower. Furthermore, the size of recommended microservice conforms with microservice sizing reported by empirical surveys and the domain-specific redundancy among different microservices is kept at a low rate.


title: An ultra-compact 25.78-Gbit/s × 4-ch active optical cable with a high heat-dissipation structure

abstract: A 25.78-Gbit/s × 4-ch active optical cable (AOC) with a ultra-compact form factor is proposed. The size of the proposed AOC is 5.2 cm3, which is 45-% smaller than standard form factor of a μQSFP. By utilizing a high heat-dissipation structure, the proposed AOC demonstrated 25.78-Gbit/s error-free optical transmission under all-channels (4-ch) operation at case temperature (T c ) of 70 °C.


title: Understanding replication in databases and distributed systems

abstract: Replication is an area of interest to both distributed systems and databases. The solutions developed from these two perspectives are conceptually similar but differ in many aspects: model, assumptions, mechanisms, guarantees provided, and implementation. In this paper, we provide an abstract and "neutral" framework to compare replication techniques from both communities. The framework has been designed to emphasize the role played by different mechanisms and to facilitate comparisons. The paper describes the replication techniques used in both communities, compares them, and points out ways in which they can be integrated to arrive to better, more robust replication protocols.


title: OctoPocus: a dynamic guide for learning gesture-based command sets

abstract: We describe OctoPocus, an example of a dynamic guide that combines on-screen feedforward and feedback to help users learn, execute and remember gesture sets. OctoPocus can be applied to a wide range of single-stroke gestures and recognition algorithms and helps users progress smoothly from novice to expert performance. We provide an analysis of the design space and describe the results of two experi-ments that show that OctoPocus is significantly faster and improves learning of arbitrary gestures, compared to con-ventional Help menus. It can also be adapted to a mark-based gesture set, significantly improving input time compared to a two-level, four-item Hierarchical Marking menu.


title: Recovering non-rigid 3D shape from image streams

abstract: The paper addresses the problem of recovering 3D non-rigid shape models from image sequences. For example, given a video recording of a talking person, we would like to estimate a 3D model of the lips and the full face and its internal modes of variation. Many solutions that recover 3D shape from 2D image sequences have been proposed; these so-called structure-from-motion techniques usually assume that the 3D object is rigid. For example, C. Tomasi and T. Kanades' (1992) factorization technique is based on a rigid shape matrix, which produces a tracking matrix of rank 3 under orthographic projection. We propose a novel technique based on a non-rigid model, where the 3D shape in each frame is a linear combination of a set of basis shapes. Under this model, the tracking matrix is of higher rank, and can be factored in a three-step process to yield pose, configuration and shape. To the best of our knowledge, this is the first model free approach that can recover from single-view video sequences nonrigid shape models. We demonstrate this new algorithm on several video sequences. We were able to recover 3D non-rigid human face and animal models with high accuracy.


title: A Hybrid I/O Virtualization Framework for RDMA-capable Network Interfaces

abstract: DMA-capable interconnects, providing ultra-low latency and high bandwidth, are increasingly being used in the context of distributed storage and data processing systems. However, the deployment of such systems in virtualized data centers is currently inhibited by the lack of a flexible and high-performance virtualization solution for RDMA network interfaces. In this work, we present a hybrid virtualization architecture which builds upon the concept of separation of paths for control and data operations available in RDMA. With hybrid virtualization, RDMA control operations are virtualized using hypervisor involvement, while data operations are set up to bypass the hypervisor completely. We describe HyV (Hybrid Virtualization), a virtualization framework for RDMA devices implementing such a hybrid architecture. In the paper, we provide a detailed evaluation of HyV for different RDMA technologies and operations. We further demonstrate the advantages of HyV in the context of a real distributed system by running RAMCloud on a set of HyV-enabled virtual machines deployed across a 6-node RDMA cluster. All of the performance results we obtained illustrate that hybrid virtualization enables bare-metal RDMA performance inside virtual machines while retaining the flexibility typically associated with paravirtualization.


title: OBA2: An Onion approach to Binary code Authorship Attribution

abstract: A critical aspect of malware forensics is authorship analysis. The successful outcome of such analysis is usually determined by the reverse engineer’s skills and by the volume and complexity of the code under analysis. To assist reverse engineers in such a tedious and error-prone task, it is desirable to develop reliable and automated tools for supporting the practice of malware authorship attribution. In a recent work, machine learning was used to rank and select syntax-based features such as n-grams and flow graphs. The experimental results showed that the top ranked features were unique for each author, which was regarded as an evidence that those features capture the author’s programming styles. In this paper, however, we show that the uniqueness of features does not necessarily correspond to authorship. Specifically, our analysis demonstrates that many “unique” features selected using this method are clearly unrelated to the authors’ programming styles, for example, unique IDs or random but unique function names generated by the compiler; furthermore, the overall accuracy is generally unsatisfactory. Motivated by this discovery, we propose a layered Onion Approach for Binary Authorship Attribution called OBA2. The novelty of our approach lies in the three complementary layers: preprocessing, syntax-based attribution, and semantic-based attribution. Experiments show that our method produces results that not only are more accurate but have a meaningful connection to the authors’ styles. a 2014 The Author. Published by Elsevier Ltd on behalf of DFRWS. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).


title: Survey of data-selection methods in statistical machine translation

abstract: Statistical machine translation has seen significant improvements in quality over the past several years. The single biggest factor in this improvement has been the accumulation of ever larger stores of data. We now find ourselves, however, the victims of our own success, in that it has become increasingly difficult to train on such large sets of data, due to limitations in memory, processing power, and ultimately, speed (i.e. data-to-models takes an inordinate amount of time). Moreover, the training data has a wide quality spectrum. A variety of methods for data cleaning and data selection have been developed to address these issues. Each of these methods employs a search or filtering algorithm to select a subset of the data, given a defined set of feature functions. In this paper we provide a comparative overview of research in this area based on application scenario, feature functions and search method.


title: A reproducing kernel Hilbert space framework for pairwise time series distances

abstract: A good distance measure for time series needs to properly incorporate the temporal structure, and should be applicable to sequences with unequal lengths. In this paper, we propose a distance measure as a principled solution to the two requirements. Unlike the conventional feature vector representation, our approach represents each time series with a summarizing smooth curve in a reproducing kernel Hilbert space (RKHS), and therefore translate the distance between time series into distances between curves. Moreover we propose to learn the kernel of this RKHS from a population of time series with discrete observations using Gaussian process-based non-parametric mixed-effect models. Experiments on two vastly different real-world problems show that the proposed distance measure leads to improved classification accuracy over the conventional distance measures.


title: An unsupervised fuzzy clustering method for twitter sentiment analysis

abstract: Cluster based techniques on sentiment analysis is a novel approach for analyzing sentiments expressed in social media sites. It is a main task of exploratory data mining, and a common technique used in machine learning. In contrast to supervised learning technique, the cluster based techniques produce essentially accurate experimental results without manual processing, linguistic knowledge or training time. This paper presents a novel fuzzy clustering model to analyze twitter feeds regarding the sentiments of a particular brand using the real dataset collected over a period of one year. Then a comparative analysis is made with the existing partitioning clustering techniques namely K Means and Expectation Maximization algorithms based on metrics namely accuracy, precision, recall and execution time. According to the experimental analysis, the proposed approach is tested to be practicable in performing high quality twitter sentiment analysis results.


title: Multi-behaviors Finite State Machine

abstract: A classic Finite State Machine (FSM) is a model of behavior composed of a finite number of states, transitions between those states, and actions. However, there are some cases where two or more behaviors are contained in one FSM, which is referred as MULTI-BEHAVIORS FINITE STATE MACHINE (MFSM) in this paper. Among these behaviors, some of their states differ from each other while others don't. How to distinct those differences as well as to keep and take advantage of their similarities would be a key to the management of this special type of Finite State Machine. This paper presents you some of the core characteristics of a MFSM at first, and then illustrates the practical implementation. In addition, some engineering experiences gained from real practices are provided.


title: PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach

abstract: The problem of evaluating the performance of soccer players is attracting the interest of many companies and the scientific community, thanks to the availability of massive data capturing all the events generated during a match (e.g., tackles, passes, shots, etc.). Unfortunately, there is no consolidated and widely accepted metric for measuring performance quality in all of its facets. In this paper, we design and implement PlayeRank, a data-driven framework that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players. We build our framework by deploying a massive dataset of soccer-logs and consisting of millions of match events pertaining to four seasons of 18 prominent soccer competitions. By comparing PlayeRank to known algorithms for performance evaluation in soccer, and by exploiting a dataset of players' evaluations made by professional soccer scouts, we show that PlayeRank significantly outperforms the competitors. We also explore the ratings produced by {\sf PlayeRank} and discover interesting patterns about the nature of excellent performances and what distinguishes the top players from the others. At the end, we explore some applications of PlayeRank -- i.e. searching players and player versatility --- showing its flexibility and efficiency, which makes it worth to be used in the design of a scalable platform for soccer analytics.


title: Deep and Confident Prediction for Time Series at Uber

abstract: Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing. At Uber, probabilistic time series forecasting is used for robust prediction of number of trips during special events, driver incentive allocation, as well as real-time anomaly detection across millions of metrics. Classical time series models are often used in conjunction with a probabilistic formulation for uncertainty estimation. However, such models are hard to tune, scale, and add exogenous variables to. Motivated by the recent resurgence of Long Short Term Memory networks, we propose a novel end-to-end Bayesian deep model that provides time series prediction along with uncertainty estimation. We provide detailed experiments of the proposed solution on completed trips data, and successfully apply it to large-scale time series anomaly detection at Uber.


title: Competition in two-sided markets

abstract: There are many examples of markets involving two groups of agents who need to interact via 'platforms', and where one group's benefit from joining a platform depends on the number of agents from the other group who join the same platform. This paper presents theoretical models for three variants of such markets: a monopoly platform; a model of competing platforms where each agent must choose to join a single platform; and a model of 'competing bottlenecks', where one group wishes to join all platforms. The main determinants of equilibrium prices are (i) the relative sizes of the cross-group externalities, (ii) whether fees are levied on a lump-sum or per-transaction basis, and (iii) whether a group joins just one platform or joins all platforms.


title: Anisotropic Laplace-Beltrami Operators for Shape Analysis

abstract: This paper introduces an anisotropic Laplace-Beltrami operator for shape analysis. While keeping useful properties of the standard Laplace-Beltrami operator, it introduces variability in the directions of principal curvature, giving rise to a more intuitive and semantically meaningful diffusion process. Although the benefits of anisotropic diffusion have already been noted in the area of mesh processing (e.g. surface regularization), focusing on the Laplacian itself, rather than on the diffusion process it induces, opens the possibility to effectively replace the omnipresent Laplace-Beltrami operator in many shape analysis methods. After providing a mathematical formulation and analysis of this new operator, we derive a practical implementation on discrete meshes. Further, we demonstrate the effectiveness of our new operator when employed in conjunction with different methods for shape segmentation and matching.


title: Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning

abstract: In this paper we study how to learn stochastic, multimodal transition dynamics in reinforcement learning (RL) tasks. We focus on evaluating transition function estimation, while we defer planning over this model to future work. Stochasticity is a fundamental property of many task environments. However, discriminative function approximators have difficulty estimating multimodal stochasticity. In contrast, deep generative models do capture complex high-dimensional outcome distributions. First we discuss why, amongst such models, conditional variational inference (VI) is theoretically most appealing for model-based RL. Subsequently, we compare different VI models on their ability to learn complex stochasticity on simulated functions, as well as on a typical RL gridworld with multimodal dynamics. Results show VI successfully predicts multimodal outcomes, but also robustly ignores these for deterministic parts of the transition dynamics. In summary, we show a robust method to learn multimodal transitions using function approximation, which is a key preliminary for model-based RL in stochastic domains.


title: An Analysis of Augmented Reality Systems

abstract: Augmented Reality Systems (ARSs) are a logical evolution of virtual reality systems. This new type of system enriches reality by mixing real elements with electronic information. This paper identifies the common tasks performed by ARSs, the underlying software and hardware components that support them and the most common architectures for ARSs. Subsequently, the paper reviews some ARSs, identifying their main hardware and software components. Thus, the goal of this paper is to serve as a guide for those entities that are planning to deploy an augmented reality system.


title: Two-Segments Compact Dielectric Resonator Antenna for UWB Application

abstract: A new compact two-segments dielectric resonator antenna (TSDR) for ultrawideband (UWB) application is presented and studied. The design consists of a thin monopole printed antenna loaded with two dielectric resonators with different dielectric constant. By applying a combination of U-shaped feedline and modified TSDR, proper radiation characteristics are achieved. The proposed antenna provides an ultrawide impedance bandwidth, high radiation efficiency, and compact antenna with an overall size of 18 × 36 × 11 mm . From the measurement results, it is found that the realized dielectric resonator antenna with good radiation characteristics provides an ultrawide bandwidth of about 110%, covering a range from 3.14 to 10.9 GHz, which covers UWB application.


title: Semantic Object Maps for robotic housework - representation, acquisition and use

abstract: In this article we investigate the representation and acquisition of Semantic Objects Maps (SOMs) that can serve as information resources for autonomous service robots performing everyday manipulation tasks in kitchen environments. These maps provide the robot with information about its operation environment that enable it to perform fetch and place tasks more efficiently and reliably. To this end, the semantic object maps can answer queries such as the following ones: “What do parts of the kitchen look like?”, “How can a container be opened and closed?”, “Where do objects of daily use belong?”, “What is inside of cupboards/drawers?”, etc. The semantic object maps presented in this article, which we call SOM+, extend the first generation of SOMs presented by Rusu et al. [1] in that the representation of SOM+ is designed more thoroughly and that SOM+ also include knowledge about the appearance and articulation of furniture objects. Also, the acquisition methods for SOM+ substantially advance those developed in [1] in that SOM+ are acquired autonomously and with low-cost (Kinect) instead of very accurate (laser-based) 3D sensors. In addition, perception methods are more general and are demonstrated to work in different kitchen environments.


title: Dynamic state estimation in distributed aircraft electric control systems via adaptive submodularity

abstract: We consider the problem of estimating the discrete state of an aircraft electric system under a distributed control architecture through active sensing. The main idea is to use a set of controllable switches to reconfigure the system in order to gather more information about the unknown state. By adaptively making a sequence of reconfiguration decisions with uncertain outcome, then correlating measurements and prior information to make the next decision, we aim to reduce the uncertainty. A greedy strategy is developed that maximizes the one-step expected uncertainty reduction. By exploiting recent results on adaptive submodularity, we give theoretical guarantees on the worst-case performance of the greedy strategy. We apply the proposed method in a fault detection scenario where the discrete state captures possible faults in various circuit components. In addition, simple abstraction rules are proposed to alleviate state space explosion and to scale up the strategy. Finally, the efficiency of the proposed method is demonstrated empirically on different circuits.


title: Scalable Semantic Parsing with Partial Ontologies

abstract: We consider the problem of building scalable semantic parsers for Freebase, and present a new approach for learning to do partial analyses that ground as much of the input text as possible without requiring that all content words be mapped to Freebase concepts. We study this problem on two newly introduced large-scale noun phrase datasets, and present a new semantic parsing model and semi-supervised learning approach for reasoning with partial ontological support. Experiments demonstrate strong performance on two tasks: referring expression resolution and entity attribute extraction. In both cases, the partial analyses allow us to improve precision over strong baselines, while parsing many phrases that would be ignored by existing techniques.


title: An intelligent short term stock trading fuzzy system for assisting investors in portfolio management

abstract: The proposed short-term fuzzy system uses a set of appropriate technical indicators.The returns of the proposed system are higher than those of the B&H strategy.The proposed system avoids big losses during bear markets.During bull markets the system produces lower returns than the B&H strategy.Transaction costs significantly affect the performance of the proposed system. Financial markets are complex systems influenced by many interrelated economic, political and psychological factors and characterised by inherent nonlinearities. Recently, there have been many efforts towards stock market prediction, applying various fuzzy logic techniques and using technical analysis methods.This paper presents a short term trading fuzzy system using a novel trading strategy and an "amalgam" between altered commonly used technical indicators and rarely used ones, in order to assist investors in their portfolio management. The sample consists of daily data from the general index of the Athens Stock Exchange over a period of more than 15 years (15/11/1996 to 5/6/2012), which was also divided into distinctive groups of bull and bear market periods.The results suggest that, with or without taking into consideration transaction costs, the return of the proposed fuzzy model is superior to the returns of the buy and hold strategy. ?he proposed system can be characterised as conservative, since it produces smaller losses during bear market periods and smaller gains during bull market periods compared with the buy and hold strategy.


title: Classifier based graph construction for video segmentation

abstract: Video segmentation has become an important and active research area with a large diversity of proposed approaches. Graph-based methods, enabling top-performance on recent benchmarks, consist of three essential components: 1. powerful features account for object appearance and motion similarities; 2. spatio-temporal neighborhoods of pixels or superpixels (the graph edges) are modeled using a combination of those features; 3. video segmentation is formulated as a graph partitioning problem. While a wide variety of features have been explored and various graph partition algorithms have been proposed, there is surprisingly little research on how to construct a graph to obtain the best video segmentation performance. This is the focus of our paper. We propose to combine features by means of a classifier, use calibrated classifier outputs as edge weights and define the graph topology by edge selection. By learning the graph (without changes to the graph partitioning method), we improve the results of the best performing video segmentation algorithm by 6% on the challenging VSB100 benchmark, while reducing its runtime by 55%, as the learnt graph is much sparser.


title: Design of Step-Down Broadband and Low-Loss Ruthroff-Type Baluns Using IPD Technology

abstract: Ruthroff-type transmission line transformers (TLTs) and baluns prevail over their broadband and low-loss performance. A typical Ruthroff-type balun with a 1:4 step-up impedance transformation ratio was successfully presented using integrated passive devices (IPDs) process. Moreover, two proposed baluns with step-down impedance transformation ratios of 1:1 and 9:4 were developed by modifying the combination of one Ruthroff-type TLT and one Ruthroff-type balun. The two proposed impedance step-down baluns whose balanced impedance is lower than unbalanced impedance make Ruthroff-type balun more flexible for applications; meanwhile, fabricating the baluns using IPD process can help to enhance the low-loss performance. The measured results show that the proposed 1:1 balun exhibits an insertion loss of 0.46 dB with 1-dB fractional bandwidth of 138.9%, and the proposed 9:4 balun exhibits an insertion loss of 0.75 dB with 1-dB fractional bandwidth of 72.4%. The chip areas of the proposed 1:1 and 9:4 baluns, including the pads, are 0.6 and 0.64 ${\rm mm}^{2}$ , respectively. The two proposed baluns are the first on-chip step-down Ruthroff-type baluns and having an option of center tap, which is highly contributive to wideband and high-efficiency power amplifier design.


title: Background and skin colour independent hand region extraction and static gesture recognition

abstract: Hand extraction and gesture recognition has always been a challenging problem in its general form. In this paper, we consider a fixed set of standard gestures and a reasonably structured environment and develop three effective procedures for extracting hand from the image, two of which are for plain non-complex static background and one for complex static background making it independent of the skin and background colours. The second part is of recognizing the gesture and making it scale and rotation invariant. For hand extraction, the three basic concepts used are 1. Gaussian distribution, 2. K-Mean classification and 3. Simple background subtraction and consecutive frame subtraction to find the palm region in the complete image. In gesture recognition, we extracted some features like centre of hand region, no. of fingers and the distance between the fingers. Using these features, the gestures are classified into seven standard hand gestures.


title: Chapter 1 DATA MINING FOR FINANCIAL APPLICATIONS

abstract: In this paper we consider a new strategy for supporting timing decisions in stock markets. The approach uses the logic data miner Lsquare, based on logic optimisation techniques. We adopt a novel concept of good session, based on the best return expected within a given time horizon. Such definition links indirectly the buying decision with the selling decision and make it possible to exploit particular features of stock time series. The method is translated into an investment strategy and then it is compared with the standard buy & hold strategy.


title: Video deblurring for hand-held cameras using patch-based synthesis

abstract: Videos captured by hand-held Cameras often contain significant camera shake, causing many frames to be blurry. Restoring shaky videos not only requires smoothing the camera motion and stabilizing the content, but also demands removing blur from video frames. However, video blur is hard to remove using existing single or multiple image deblurring techniques, as the blur kernel is both spatially and temporally varying. This paper presents a video deblurring method that can effectively restore sharp frames from blurry ones caused by camera shake. Our method is built upon the observation that due to the nature of camera shake, not all video frames are equally blurry. The same object may appear sharp on some frames while blurry on others. Our method detects sharp regions in the video, and uses them to restore blurry regions of the same content in nearby frames. Our method also ensures that the deblurred frames are both spatially and temporally coherent using patch-based synthesis. Experimental results show that our method can effectively remove complex video blur under the presence of moving objects and other outliers, which cannot be achieved using previous deconvolution-based approaches.


title: Empathy and social functioning in late adulthood

abstract: Objectives: Both cognitive and affective empathy are regarded as essential prerequisites for successful social functioning, and recent studies have suggested that cognitive, but not affective, empathy may be adversely affected as a consequence of normal adult aging. This decline in cognitive empathy is of concern, as older adults are particularly susceptible to the negative physical and mental health consequences of loneliness and social isolation. Method: The present study compared younger (N = 80) and older (N = 49) adults on measures of cognitive empathy, affective empathy, and social functioning. Results: Whilst older adults’ self-reported and performance-based cognitive empathy was significantly reduced relative to younger adults, there were no age-related differences in affective empathy. Older adults also reported involvement in significantly fewer social activities than younger adults, and cognitive empathy functioned as a partial mediator of this relationship. Conclusion: These findings are consistent with theoretical models that regard cognitive empathy as an essential prerequisite for good interpersonal functioning. However, the cross-sectional nature of the study leaves open the question of causality for future studies.


title: Implementing sparse matrix-vector multiplication on throughput-oriented processors

abstract: Sparse matrix-vector multiplication (SpMV) is of singular importance in sparse linear algebra. In contrast to the uniform regularity of dense linear algebra, sparse operations encounter a broad spectrum of matrices ranging from the regular to the highly irregular. Harnessing the tremendous potential of throughput-oriented processors for sparse operations requires that we expose substantial fine-grained parallelism and impose sufficient regularity on execution paths and memory access patterns. We explore SpMV methods that are well-suited to throughput-oriented architectures like the GPU and which exploit several common sparsity classes. The techniques we propose are efficient, successfully utilizing large percentages of peak bandwidth. Furthermore, they deliver excellent total throughput, averaging 16 GFLOP/s and 10 GFLOP/s in double precision for structured grid and unstructured mesh matrices, respectively, on a GeForce GTX 285. This is roughly 2.8 times the throughput previously achieved on Cell BE and more than 10 times that of a quad-core Intel Clovertown system.


title: Low-cost indoor health monitoring system

abstract: The advent of smart infrastructure or Internet of Things (IoT) has enabled scenarios in which objects with unique identifiers can communicate and transfer data over a network without human to human/computer interactions. Incorporating hardware in such networks is so cheap that it has opened the possibility of connecting just about anything from simple nodes to complex, remotely-monitored sensor networks. In the paper, we describe a low-cost scalable and potentially ubiquitous system for indoor remote health monitoring using low energy bluetooth beacons and a smartwatch. Our system was implemented in a rehabilitation facility in Los Angeles and the overall assessments revealed promising results.


title: Wireless Body Area Network for Heart Attack Detection [Education Corner]

abstract: This article describes a body area network (BAN) for measuring an electrocardiogram (ECG) signal and transmitting it to a smartphone via Bluetooth for data analysis. The BAN uses a specially designed planar inverted F-antenna (PIFA) with a small form factor, realizable with low-fabricationcost techniques. Furthermore, due to the human body's electrical properties, the antenna was designed to enable surface-wave propagation around the body. The system utilizes the user's own smartphone for data processing, and the built-in communications can be used to raise an alarm if a heart attack is detected. This is managed by an application for Android smartphones that has been developed for this system. The good functionality of the system was confirmed in three real-life user case scenarios.


title: VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

abstract: Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality. To interface a highly sparse LiDAR point cloud with a region proposal network (RPN), most existing efforts have focused on hand-crafted feature representations, for example, a bird's eye view projection. In this work, we remove the need of manual feature engineering for 3D point clouds and propose VoxelNet, a generic 3D detection network that unifies feature extraction and bounding box prediction into a single stage, end-to-end trainable deep network. Specifically, VoxelNet divides a point cloud into equally spaced 3D voxels and transforms a group of points within each voxel into a unified feature representation through the newly introduced voxel feature encoding (VFE) layer. In this way, the point cloud is encoded as a descriptive volumetric representation, which is then connected to a RPN to generate detections. Experiments on the KITTI car detection benchmark show that VoxelNet outperforms the state-of-the-art LiDAR based 3D detection methods by a large margin. Furthermore, our network learns an effective discriminative representation of objects with various geometries, leading to encouraging results in 3D detection of pedestrians and cyclists, based on only LiDAR.


Research on Simulation of the Hybrid Electric Vehicle Based on Software ADVISOR