title: A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

abstract: Visual inspection of civil infrastructure is essential for condition assessment.We focus on concrete bridges, tunnels, underground pipes, and asphalt pavements.Accordingly, we review the latest computer vision based defect detection methods.Using computer vision most relevant types of defects can be automatically detected.Automatic defect properties retrieval and assessment has not been achieved yet. To ensure the safety and the serviceability of civil infrastructure it is essential to visually inspect and assess its physical and functional condition. This review paper presents the current state of practice of assessing the visual condition of vertical and horizontal civil infrastructure; in particular of reinforced concrete bridges, precast concrete tunnels, underground concrete pipes, and asphalt pavements. Since the rate of creation and deployment of computer vision methods for civil engineering applications has been exponentially increasing, the main part of the paper presents a comprehensive synthesis of the state of the art in computer vision based defect detection and condition assessment related to concrete and asphalt civil infrastructure. Finally, the current achievements and limitations of existing methods as well as open research challenges are outlined to assist both the civil engineering and the computer science research community in setting an agenda for future research.


title: Abstract Strategic development and SWOT analysis at the University of Warwick

abstract: Abstract SWOT analysis is an established method for assisting the formulation of strategy. An application to strategy formulation and its incorporation into the strategic development process at the University of Warwick is described. The application links SWOT analysis to resource-based planning, illustrates it as an iterative rather than a linear process and embeds it within the overall planning process. Lessons are drawn both for the University and for the strategy formulation process itself.


title: An efficient stream cipher using Genetic Algorithm

abstract: Cryptography is essential to protect and secure data using a key. Different types of cryptographic techniques are found for data security. Genetic Algorithm is essentially used for obtaining optimal solution. Also, it can be efficiently used for random number generation which are very important in cryptography. This paper discusses the application of genetic algorithms for stream ciphers. Key generation is the most important factor in stream ciphers. In this paper Genetic Algorithm is used in the key generation process where key selection depends upon the fitness function. Here genetic algorithm is repeated for key selection. In each iteration, the key having highest fitness value is selected which further be compared with the threshold value. Selected key was unique and non-repeating. Therefore encryption with selected key are highly encrypted because of more randomness of key. This paper shows that the generated keys using GA are unique and more secure for encryption of data.


title: You can’t always get what you want: Educational attainment, agency, and choice

abstract: Using educational attainment to indicate socioeconomic status, the authors examined models of agency and effects of choice among European American adults of different educational backgrounds in 3 studies. Whereas college-educated (BA) participants and their preferred cultural products (i.e., rock music lyrics) emphasized expressing uniqueness, controlling environments, and influencing others, less educated (HS) participants and their preferred cultural products (i.e., country music lyrics) emphasized maintaining integrity, adjusting selves, and resisting influence. Reflecting these models of agency, HS and BA participants differently responded to choice in dissonance and reactance paradigms: BA participants liked chosen objects more than unchosen objects, but choice did not affect HS participants' preferences. Results suggest that HS and BA models of agency qualitatively differ, despite overlap between HS and BA worlds.


title: The evolution of social ties online: A longitudinal study in a massively multiplayer online game

abstract: How do social ties in online worlds evolve over time? This research examined the dynamic processes of relationship formation, maintenance, and demise in a massively multiplayer online game. Drawing from evolutionary and ecological theories of social networks, this study focuses on the impact of three sets of evolutionary factors in the context of social relationships in the online game EverQuest II (EQII): the aging and maturation processes, social architecture of the game, and homophily and proximity. A longitudinal analysis of tie persistence and decay demonstrated the transient nature of social relationships in EQII, but ties became considerably more durable over time. Also, character level similarity, shared guild membership, and geographic proximity were powerful mechanisms in preserving social relationships.


title: Design and implementation of a cross-layer IoT protocol

abstract: Abstract Cross-layer communication protocols can significantly improve several aspects of wireless networks, particularly energy consumption and bandwidth. However, they break with the traditional layered architecture that has been so successful for over 30 years. A fully fledged cross-layer stack requires a sophisticated software design to be maintainable and reusable. The Trustful Space-Time Protocol (TSTP) is a cross-layer protocol designed to deliver authenticated, encrypted, timed, and georeferenced messages containing data compliant with the International System of Units (SI) in a resource-efficient way. By integrating shared data from multiple networking services into a single communication infrastructure, TSTP is able to eliminate replication of information across services, achieving small overhead regarding control messages. The complexity of TSTP's features, its broad range – from the application to the Medium Access Control, – and its experimental nature bring diverse requirements beyond those usually considered in most software designs. In this work, we show how we avoided a monolithic implementation of the cross-layer approach with a component-based design, exploring template metaprogramming techniques to adapt and combine basic building blocks. An event-driven architecture that makes use of zero-copy buffers and metadata is used to handle crosscutting concerns. We validate the proposed design with an implementation for IEEE 802.15.4 networks and a set of OMNet++ simulations for relevant scenarios, showing that we achieve a light TSTP implementation that is successfully able to save energy and bandwidth while keeping a delivery ratio close to 100%.


title: Microbubbling and microencapsulation by co-axial electrohydrodynamic atomization

abstract: Microbubbles coated with polymers or surfactants have been used in medical imaging ::: for several years as ultrasound contrast agent particles and are now being investigated ::: by researchers as drug and gene delivery vehicles and blood substitutes. Current ::: methods available for the preparation of microbubbles are insufficient as they result in ::: microbubbles with a wide size distribution and as such filtration is necessary before ::: their use. With a view to fill the above demand, a detailed investigation has been carried ::: out in this research to learn the viability of co-axial electrohydrodynamic atomization ::: (CEHDA) technique to prepare microbubbles. The research also focuses on the effects ::: of the process parameters such as flow rates, applied voltage and material parameters ::: such as electrical conductivity, surface tension and viscosity with the objective of ::: preparing polymer or surfactant coated stabilized microbubbles with diameters < 8 μm ::: and with a narrow size distribution. A model glycerol-air system was used so that the ::: CEHDA technique was modified to generate suspensions of microbubbles to a diameter ::: < 8 μm with a narrow size distribution and then to characterise the CEHDA ::: microbubbling process in terms of size and stability with varying process parameters ::: and material parameters. Construction of a parametric plot between the air flow rate and ::: the liquid flow rate was extremely useful in identifying the flow rate regime of air and ::: liquid or suspension or solution for the continuous microbubbling of the system used. ::: With further investigations into the CEHDA microbubbling technique, it was possible ::: to develop strategies, first, to prepare suspensions of stabilized phospholipids-coated ::: microbubbles with a mean diameter of ~ 5 μm and a polydispersivity index of 9%, and ::: second, polymeric microspheres with a mean diameter of 400 nm and a polydispersivity ::: index of 8% using a biocompatible polymer.


title: Density-based clustering and radial basis function modeling to generate credit card fraud scores

abstract: Historical information on credit card transactions can be used to generate a fraud score which can then be used to reduce credit card fraud. The report describes a fraud-nonfraud classification methodology using a radial basis function network (RBFN) with a density based clustering approach. The input data is transformed into the cardinal component space and clustering as well as RBFN modeling is done using a few cardinal components. The methodology has been tested on a fraud detection problem and the preliminary results obtained are satisfactory.


title: Programming sensor networks with state-centric services

abstract: This paper presents the uDSSP (“micro DSSP”) programming model which simplifies the development of distributed sensor network applications that make use of complex in-network processing. Using uDSSP, an application is composed of state-centric services. These services interact by accessing the state of other services or by subscribing to changes of that state. uDSSP supports heterogeneous networks that consist of PCs, resource-rich sensor nodes, and resource-limited nodes with just a few kilobytes of RAM. The evaluation uses a non-trivial application to compare it to Abstract Regions and Tenet.


title: THE ROLE OF SOCIAL ENTERPRISES IN THE DEVELOPMENT OF THE TOURISM INDUSTRY

abstract: Social enterprises, as social businesses established to provide real employment opportunities for local residents from prioritized target groups, have become important development actors in the emerging economies over the last two decades. Furthermore, non-governmental organizations (as part of the social enterprise sector) are increasingly involved in “sustainable” activities, becoming the main service providers in countries where the government is unable to fulfill its traditional role. However, there has been given little attention to how social enterprises have an impact on the development of some industries such as tourism. Therefore, the aim of this article is to present the role and importance of social responsibility and social economy in the development of tourism. After a literature review on the social dimensions of business, social responsibility, social economy and their relationship with tourism, the paper presents a research based on an analysis of statistical data regarding the evolution of the number of social enterprises and tourists registered in the eight development regions in Romania. It was found that the regions with the most developed sector of social enterprises register the highest growth rate in the number of tourist arrivals. The analysis can be successfully used in a more comprehensive future research, involving the business environment.


title: Formulaic Language in Native and Second Language Speakers: Psycholinguistics, Corpus Linguistics, and TESOL

abstract: Natural language makes considerable use of recurrent formulaic patterns of words. This article triangulates the construct of formula from corpus linguistic, psycholinguistic, and educational perspectives. It describes the corpus linguistic extraction of pedagogically useful formulaic sequences for academic speech and writing. It determines English as a second language (ESL) and English for academic purposes (EAP) instructors' evaluations of their pedagogical importance. It summarizes three experiments which show that different aspects of formulaicity affect the accuracy and fluency of processing of these formulas in native speakers and in advanced L2 learners of English. The language processing tasks were selected to sample an ecologically valid range of language processing skills: spoken and written, production and comprehension. Processing in all experiments was affected by various corpus-derived metrics: length, frequency, and mutual information (MI), but to different degrees in the different populations. For native speakers, it is predominantly the MI of the formula which determines processability; for nonnative learners of the language, it is predominantly the frequency of the formula. The implications of these findings are discussed for (a) the psycholinguistic validity of corpus-derived formulas, (b) a model of their acquisition, (c) ESL and EAP instruction and the prioritization of which formulas to teach.


title: Modeling and Analyzing of Magnetic Field of Segmented Halbach Array Permanent Magnet Machine Considering Gap Between Segments

abstract: The Halbach array permanent magnet machine has several advantages. Most notably, it has more sinusoidal air-gap flux density distribution, higher air-gap flux density amplitude, and self-shielding magnetization property. In engineering, the segmented Halbach array is usually used considering the complexity of fabrication, and certain gap exists between segments inevitably. To analyze the effect of the gap between segments on air-gap flux density, an analytical model of the Halbach array permanent magnet machine considering the gap between segments is established in this paper. The finite element model is adopted to validate the established analytical model. Furthermore, effects of the parameters of the segmented Halbach array, including gap between segments, segment number per pole, and pole pair number on the fundamental amplitude, and waveform distortion factor of the radial component of air-gap flux density are analyzed using the established analytical model.


title: Emotional reactions in moral decision-making are influenced by empathy and alexithymia

abstract: ABSTRACTThe role of emotional processes in driving moral choices remains debated. In particular, diminished emotional processing and reduced empathy have been associated with unusual high rates of utilitarian responses in moral judgments while, to date, the effects of diminished emotional processing and empathy on moral decision-making have been only partially considered. In this study, we investigated the influence of empathy and alexithymia on behavior and emotional responses while participants performed a moral decision task. Self-report (valence and arousal ratings) and physiological (skin conductance and heart rate) measures were collected during the task. Results showed that empathy and alexithymia shaped emotional reactions to moral decisions but did not bias moral choices. The more empathic the participants, the more dilemmas were perceived as unpleasant and arousing, and the greater the increase in skin conductance. Conversely, alexithymia was characterized by a reduced physiological activation d...


title: The heating of the solar corona by the resonant absorption of alfven waves

abstract: Abstract In this paper, upward propagating Magnetoacoustic waves in an ideal atmosphere are considered. It is shown that the magnetic field creates a nonabsorbing reflecting layer. An equation for the resonance is derived, which shows that resonance may occur for many values of the magnetic field and of the frequency if the wavelength is matched to the strength of the magnetic field. At the resonance frequencies the values of the magnetic and kinetic energies will increase to very large values, and this may account for the heating process. Consequently, for large β-plasma the mechanism for coronal heating will be acoustic.


title: An empirical investigation of online consumer purchasing behavior

abstract: This article is focused on examining the factors and relationships that influence the browsing and buying behavior of individuals when they shop online. Specifically, we are interested in individual buyers using business-to-consumer sites. We are also interested in examining shopping preferences based on various demographic categories that might exhibit distinct purchasing attitudes and behaviors for certain categories of products and services. We examine these behaviors in the context of both products and services. After a period of decline in recent months, online shopping is on the rise again. By some estimates, total U.S. spending on online sales increased to $5.7 billion in December 2001 from $3.2 billion in June of 2001 [3, 5]. By these same estimates, the number of households shopping online increased to 18.7 million in December


title: A Review of the Minimum Maximum Criterion for Optimal Bit Allocation among Dependent Quantizers

abstract: In this paper, we review a general framework for the optimal bit allocation among dependent quantizers based on the minimum maximum (MINMAX) distortion criterion. The pros and cons of this optimization criterion are discussed and compared to the well-known Lagrange multiplier method for the minimum average (MINAVE) distortion criterion. We argue that, in many applications, the MINMAX criterion is more appropriate than the more popular MINAVE criterion. We discuss the algorithms for solving the optimal bit allocation problem among dependent quantizers for both criteria and highlight the similarities and differences. We point out that any problem which can be solved with the MINAVE criterion can also be solved with the MINMAX criterion, since both approaches are based on the same assumptions. We discuss uniqueness of the MINMAX solution and the way both criteria can be applied simultaneously within the same optimization framework. Furthermore, we show how the discussed MINMAX approach can be directly extended to result in the lexicographically optimal solution. Finally, we apply the discussed MINMAX solution methods to still image compression, intermode frame compression of H.263, and shape coding applications.


title: Towards a legislation driven framework for access control and privacy protection in public cloud

abstract: Cloud computing is an emerging IT paradigm proving cost reduction and flexibility benefits. However security and privacy are serious issues challenging its adoption and sustainability in both social and commercial areas. Public clouds, in particular, present a controversial which is brought up by the need to exchange critical and protected data (even sensitive) between heterogeneous domains that are governed by multiple legislation. Access control is one of the essential and traditional security arms of data protection. However, in the context of open and dynamic environments such as clouds, access control becomes more complicated. This is because the security policies, models and related mechanisms have to be defined across various security domains and enforced in an integrated manner as required. Thus, improving the current access control paradigms is crucial in order to ensure privacy compliance in open and heterogeneous environments. In this paper, we propose a framework that is driven by legislation and which aims to assure an access control that preserves privacy while dealing with personal data hosted in public clouds. In addition, the proposed framework deals with the problem of interoperability between heterogeneous policies governing the processing of personal data on a cloud environment. In this regards, the need for access control delegation is also presented and tackled.


title: FLAME: A Probabilistic Model Combining Aspect Based Opinion Mining and Collaborative Filtering

abstract: Aspect-based opinion mining from online reviews has attracted a lot of attention recently. Given a set of reviews, the main task of aspect-based opinion mining is to extract major aspects of the items and to infer the latent aspect ratings from each review. However, users may have different preferences which might lead to different opinions on the same aspect of an item. Even if fine-grained aspect rating analysis is provided for each review, it is still difficult for a user to judge whether a specific aspect of an item meets his own expectation. In this paper, we study the problem of estimating personalized sentiment polarities on different aspects of the items. We propose a unified probabilistic model called Factorized Latent Aspect ModEl (FLAME), which combines the advantages of collaborative filtering and aspect based opinion mining. FLAME learns users' personalized preferences on different aspects from their past reviews, and predicts users' aspect ratings on new items by collective intelligence. Experiments on two online review datasets show that FLAME outperforms state-of-the-art methods on the tasks of aspect identification and aspect rating prediction.


title: The Elusive Nature of Executive Functions: A Review of our Current Understanding

abstract: Executive functions include abilities of goal formation, planning, carrying out goal-directed plans, and effective performance. This article aims at reviewing some of the current knowledge surrounding executive functioning and presenting the contrasting views regarding this concept. The neural substrates of the executive system are examined as well as the evolution of executive functioning, from development to decline. There is clear evidence of the vulnerability of executive functions to the effects of age over lifespan. The first executive function to emerge in children is the ability to inhibit overlearned behavior and the last to appear is verbal fluency. Inhibition of irrelevant information seems to decline earlier than set shifting and verbal fluency during senescence. The sequential progression and decline of these functions has been paralleled with the anatomical changes of the frontal lobe and its connections with other brain areas. Generalization of the results presented here are limited due to methodological differences across studies. Analysis of these differences is presented and suggestions for future research are offered.


title: A Survey of Fog Computing: Concepts, Applications and Issues

abstract: Despite the increasing usage of cloud computing, there are still issues unsolved due to inherent problems of cloud computing such as unreliable latency, lack of mobility support and location-awareness. Fog computing can address those problems by providing elastic resources and services to end users at the edge of network, while cloud computing are more about providing resources distributed in the core network. This survey discusses the definition of fog computing and similar concepts, introduces representative application scenarios, and identifies various aspects of issues we may encounter when designing and implementing fog computing systems. It also highlights some opportunities and challenges, as direction of potential future work, in related techniques that need to be considered in the context of fog computing.


title: Real-time high-fidelity facial performance capture

abstract: We present the first real-time high-fidelity facial capture method. The core idea is to enhance a global real-time face tracker, which provides a low-resolution face mesh, with local regressors that add in medium-scale details, such as expression wrinkles. Our main observation is that although wrinkles appear in different scales and at different locations on the face, they are locally very self-similar and their visual appearance is a direct consequence of their local shape. We therefore train local regressors from high-resolution capture data in order to predict the local geometry from local appearance at runtime. We propose an automatic way to detect and align the local patches required to train the regressors and run them efficiently in real-time. Our formulation is particularly designed to enhance the low-resolution global tracker with exactly the missing expression frequencies, avoiding superimposing spatial frequencies in the result. Our system is generic and can be applied to any real-time tracker that uses a global prior, e.g. blend-shapes. Once trained, our online capture approach can be applied to any new user without additional training, resulting in high-fidelity facial performance reconstruction with person-specific wrinkle details from a monocular video camera in real-time.


Research issues on K-means Algorithm: An Experimental Trial Using Matlab


title: Informal (small) groups and nonverbal behaviouralanalysis for emergence leadership characteristics

abstract: An increasing number of incidents during crowded events over the last ::: 30 years made people more aware of public safety. Visitors of events ::: tend to come in groups of minimally 2 persons and interact with each ::: other. CCTV systems are used to monitor crowds and could be an ::: adequate tool for safety o�cers to manage public safety. For an more ::: e�ective intervention, it is valuable to know beforehand who the leader ::: of a group is. In an observational setting, 32 groups with 4 people each ::: were created. First a series of team building games were used to create ::: the group feeling. Second a task that required interaction with each ::: other. These sessions were used to �nd leadership within the group. ::: All activity is recorded on video. A combination of introspective domi- ::: nance scales, dominance & leadership rankings and observed behaviour ::: are used in the analysis. The expected results show that initiative tak- ::: ing is a predictor for emerging leadership. The observed team member ::: dominance is signi�cantly correlated with initiative taking walking ac- ::: tion. Initiative taking showed no correlations with the introspective ::: dominance questionnaires. In order to predict leadership, introspective ::: dominance scales, from well tested measures are not su�cient. The ::: Team Member Dominance Scale gives an indication for leadership as a ::: measure. ::: Keywords: Dominance, Leadership, Small Group, Informal Group, ::: Initiative Taking


title: Real-Time Tracking of Multiple People Using Continuous Detection

abstract: Recent investigations have shown the advantages of keeping multiple hypotheses during visual tracking. In this paper we explore an alternative method that keeps just a single hypothesis per tracked object for computational e ciency, but displays robust performance and recovery from error by employing continuous detection during tracking. The method is implemented in the domain of people-tracking, using a novel combination of stereo information for continuous detection and intensity image correlation for tracking. Real-time stereo provides extended information for 3D detection and tracking, even in the presence of crowded scenes, obscuring objects, and large scale changes. We are able to reliably detect and track people in natural environments, on an implemented system that runs at more than 10 Hz on standard PC


title: Fast Gpu-Based Interpolation for SAR Backprojection

abstract: We introduce and discuss a parallel SAR backprojection algorithm using a Non-Uniform FFT (NUFFT) routine implemented on a GPU in CUDA language. The details of a convenient GPU implementation of the NUFFT-based SAR backprojection algorithm, amenable to further generalizations to a multi-GPU architecture, are also given. The performance of the approach is analyzed in terms of accuracy and computational speed by comparisons to a \standard", parallel version of the backprojection algorithm exploiting FFT+interpolation instead of the NUFFT. Difierent interpolators have been considered for the latter processing scheme. The NUFFT-based backprojection has proven signiflcantly more accurate than all the compared approach, with a computing time of the same order. An analysis of the computational burden of all the difierent steps involved in both the considered approaches (i.e., standard and NUFFT backprojections) has been also reported. Experimental results against the Air Force Research Laboratory (AFRL) airborne data delivered under the \challenge problem for SAR-based Ground Moving Target Identiflcation (GMTI) in urban environments" and collected over circular ∞ight paths are also shown.


title: Genetic Improvement of Software: A Comprehensive Survey

abstract: Genetic improvement (GI) uses automated search to find improved versions of existing software. We present a comprehensive survey of this nascent field of research with a focus on the core papers in the area published between 1995 and 2015. We identified core publications including empirical studies, 96% of which use evolutionary algorithms (genetic programming in particular). Although we can trace the foundations of GI back to the origins of computer science itself, our analysis reveals a significant upsurge in activity since 2012. GI has resulted in dramatic performance improvements for a diverse set of properties such as execution time, energy and memory consumption, as well as results for fixing and extending existing system functionality. Moreover, we present examples of research work that lies on the boundary between GI and other areas, such as program transformation, approximate computing, and software repair, with the intention of encouraging further exchange of ideas between researchers in these fields.


title: Pulse width modulation scheme for the Modular Multilevel Converter

abstract: The Modular Multilevel Converter (M2C) is an emerging and highly attractive multilevel converter topology for medium and high voltage applications. In this paper, a new pulse width modulation (PWM) scheme will be derived. For this pulse width modulation scheme, characteristic simulated waveforms are described and analyzed.


title: Dataset fingerprints: Exploring image collections through data mining

abstract: As the amount of visual data increases, so does the need for summarization tools that can be used to explore large image collections and to quickly get familiar with their content. In this paper, we propose dataset fingerprints, a new and powerful method based on data mining that extracts meaningful patterns from a set of images. The discovered patterns are compositions of discriminative midlevel features that co-occur in several images. Compared to earlier work, ours stands out because i) it's fully unsupervised, ii) discovered patterns cover large parts of the images, often corresponding to full objects or meaningful parts thereof, and iii) different patterns are connected based on co-occurrence, allowing a user to “browse” the images from one pattern to the next and to group patterns in a semantically meaningful manner.


Hidden in Plain Sight: Storing and Managing Secrets on a Public Ledger.


title: Computability and λ-definability

abstract: Several definitions have been given to express an exact meaning corresponding to the intuitive idea of ‘effective calculability’ as applied for instance to functions of positive integers. The purpose of the present paper is to show that the computable functions introduced by the author are identical with the λ-definable functions of Church and the general recursive functions due to Herbrand and Godel and developed by Kleene. It is shown that every λ-definable function is computable and that every computable function is general recursive. There is a modified form of λ-definability, known as λ- K -definability, and it turns out to be natural to put the proof that every λ-definable function is computable in the form of a proof that every λ- K -definable function is computable; that every λ-definable function is λ- K -definable is trivial. If these results are taken in conjunction with an already available proof that every general recursive function is λ-definable we shall have the required equivalence of computability with λ-definability and incidentally a new proof of the equivalence of λ-definability and λ- K -definability. A definition of what is meant by a computable function cannot be given satisfactorily in a short space. I therefore refer the reader to Computable pp. 230–235 and p. 254. The proof that computability implies recursiveness requires no more knowledge of computable functions than the ideas underlying the definition: the technical details are recalled in §5.


title: Fully convolutional networks for multi-modality isointense infant brain image segmentation

abstract: The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development. In the isointense phase (approximately 6–8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, resulting in extremely low tissue contrast and thus making the tissue segmentation very challenging. The existing methods for tissue segmentation in this isointense phase usually employ patch-based sparse labeling on single T1, T2 or fractional anisotropy (FA) modality or their simply-stacked combinations without fully exploring the multi-modality information. To address the challenge, in this paper, we propose to use fully convolutional networks (FCNs) for the segmentation of isointense phase brain MR images. Instead of simply stacking the three modalities, we train one network for each modality image, and then fuse their high-layer features together for final segmentation. Specifically, we conduct a convolution-pooling stream for multimodality information from T1, T2, and FA images separately, and then combine them in high-layer for finally generating the segmentation maps as the outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense phase brain images. Results showed that our proposed model significantly outperformed previous methods in terms of accuracy. In addition, our results also indicated a better way of integrating multi-modality images, which leads to performance improvement.


title: Attack and defence in cellular decision-making: lessons from machine learning

abstract: Machine learning algorithms can be fooled by small well-designed adversarial perturbations. This is reminiscent of cellular decision-making where ligands (called antagonists) prevent correct signalling, like in early immune recognition. We draw a formal analogy between neural networks used in machine learning and models of cellular decision-making (adaptive proofreading). We apply attacks from machine learning to simple decision-making models, and show explicitly the correspondence to antagonism by weakly bound ligands. Such antagonism is absent in more nonlinear models, which inspired us to implement a biomimetic defence in neural networks filtering out adversarial perturbations. We then apply a gradient-descent approach from machine learning to different cellular decision-making models, and we reveal the existence of two regimes characterized by the presence or absence of a critical point for the gradient. This critical point causes the strongest antagonists to lie close to the decision boundary. This is validated in the loss landscapes of robust neural networks and cellular decision-making models, and observed experimentally for immune cells. For both regimes, we explain how associated defence mechanisms shape the geometry of the loss landscape, and why different adversarial attacks are effective in different regimes. Our work connects evolved cellular decision-making to machine learning, and motivates the design of a general theory of adversarial perturbations, both for in vivo and in silico systems.


title: Nowcasting earthquake damages with Twitter

abstract: The Modified Mercalli intensity scale (Mercalli scale for short) is a qualitative measure used to express the perceived intensity of an earthquake in terms of damages. Accurate intensity reports are vital to estimate the type of emergency response required for a particular earthquake. In addition, Mercalli scale reports are needed to estimate the possible consequences of strong earthquakes in the future, based on the effects of previous events. Emergency offices and seismological agencies worldwide are in charge of producing Mercalli scale reports for each affected location after an earthquake. However, this task relies heavily on human observers in the affected locations, who are not always available or accurate. Consequently, Mercalli scale reports may take up to hours or even days to be published after an earthquake. We address this problem by proposing a method for early prediction of spatial Mercalli scale reports based on people’s reactions to earthquakes in social networks. By tracking users’ comments about real-time earthquakes, we create a collection of Mercalli scale point estimates at municipality (i.e., state subdivisions) level granularity. We introduce the concept of reinforced Mercalli support, which combines Mercalli scale point estimates with locally supported data (named ‘local support’). We use this concept to provide Mercalli scale estimates for real-world events by providing smooth point estimates using a spatial smoother that incorporates the distribution of municipalities in each affected region. Our method is the first method based on social media that can provide spatial reports of damages in the Mercalli intensity scale. Experimental results show that our method is accurate and provides early spatial Mercalli reports 30 minutes after an earthquake. Furthermore, we show that our method performs well for earthquake spatial detection and maximum intensity prediction tasks. Our findings indicate that social media is a valuable source of spatial information for quickly estimating earthquake damages.


title: Architecture Compliance Checking of Semantically Rich Modular Architectures: A Comparative Study of Tool Support

abstract: Architecture Compliance Checking (ACC) is an approach to verify the conformance of implemented program code to high-level models of architectural design. ACC is used to prevent architectural erosion during the development and evolution of a software system. Static ACC, based on static software analysis techniques, focuses on the modular architecture and especially on rules constraining the modular elements. A semantically rich modular architecture (SRMA) is expressive and may contain modules with different semantics, like layers and subsystems, constrained by rules of different types. To check the conformance to an SRMA, ACC-tools should support the module and rule types used by the architect. This paper presents requirements regarding SRMA support and an inventory of common module and rule types, on which basis eight commercial and non-commercial tools were tested. The test results show large differences between the tools, but all could improve their support of SRMA, what might contribute to the adoption of ACC in practice.


title: Large-scale assessment of mobile notifications

abstract: Notifications are a core feature of mobile phones. They inform users about a variety of events. Users may take immediate action or ignore them depending on the importance of a notification as well as their current context. The nature of notifications is manifold, applications use them both sparsely and frequently. In this paper we present the first large-scale analysis of mobile notifications with a focus on users' subjective perceptions. We derive a holistic picture of notifications on mobile phones by collecting close to 200 million notifications from more than 40,000 users. Using a data-driven approach, we break down what users like and dislike about notifications. Our results reveal differences in importance of notifications and how users value notifications from messaging apps as well as notifications that include information about people and events. Based on these results we derive a number of findings about the nature of notifications and guidelines to effectively use them.


title: Automatic Recognition of Exudative Maculopathy using Fuzzy CMeans Clustering and Neural Networks

abstract: Retinal exudates are typically manifested as spatially random yellow/white patches of varying sizes and shapes. They are a characteristic feature of retinal diseases such as diabetic maculopathy. An automatic method for the detection of exudate regions is introduced comprising image colour normalisation, enhancing the contrast between the objects and background, segmenting the colour retinal image into homogenous regions using Fuzzy C-Means clustering, and classifying the regions into exudates and non exudates patches using a neural network. Experimental results indicate that we are able to achieve 92% sensitivity and 82% specificity.


title: ePluribus : Ethnicity on Social Networks

abstract: We propose an approach to determine the ethnic breakdown of a population based solely on people's names and data provided by the U.S. Census Bureau. We demonstrate that our approach is able to predict the ethnicities of individuals as well as the ethnicity of an entire population better than natural alternatives. We apply our technique to the population of U.S. Facebook users and uncover the demographic characteristics of ethnicities and how they relate. We also discover that while Facebook has always been diverse, diversity has increased over time leading to a population that today looks very similar to the overall U.S. population. We also find that different ethnic groups relate to one another in an assortative manner, and that these groups have different profiles across demographics, beliefs, and usage of site features.


title: Design of voltage level shifter for multi-supply voltage design

abstract: This paper describes architecture of voltage level shifter used in multi-supply design applications and an application of voltage level shifter. Voltage level shifter is a device which converts one voltage level to another. Voltage level shifters are used to interface various circuit blocks operating at different supply voltages. At the boundaries of different voltage islands on the system-on-chip (SoC) voltage level shifter is used. The proposed voltage level shifter converts low input voltage level into high level voltage output with increased speed and much less power consumption. The architecture is designed with multi-threshold voltage CMOS technique. The multi-threshold voltage CMOS technique is used in the design of voltage level shifter in order to reduce delay and power. Low-threshold voltage devices are used to reduce delay and power dissipation is reduced with the use of high threshold voltage devices. The design has been implemented in a 90-nm BSIM4 level-54 CMOS technology. Simulation is carried in tanner v15.0 software at schematic level. Simulation results show that the design converts minimum of 0.1 V input signal into 1 V output signal. The level shifter has propagation delay of 10.99 ns and power dissipation of 1.52nW. The operating condition for the proposed design: VDDL = 0.2 V, VDDH = 1 V.


title: The Financial Life Well-Lived: Psychological Benefits of Financial Planning

abstract: As the financial planning industry undergoes a series of reforms aimed at increased professionalism and improved quality of advice, financial planner training in Australia and elsewhere has begun to acknowledge the importance of interdisciplinary knowledge bases in informing both curriculum design and professoinal practice (e.g. FPA2009). This paper underscores the importance of the process of financial planning by providing a conceptual analysis of the six step financial planning process using key mechanisms derived from theory and research in cognate disciplines such as psychology and well-being. The paper identifies how these mechanisms may operate to impact client well-being in the financial planning context. The conceptual mapping of th emechanisms to process elements of financial planning is a unique contribution to the financial planning literature and offers a further framework in the armamentarium of researchers interested in pursuing questions around the value of financial planning. The conceptual framework derived from the analysis also adds to the growing body of literature aimed at developing an integrated model of financial planning.


title: Design and preparation of bio-based dielectric elastomer with polar and plasticized side chains

abstract: A new dielectric elastomer with large actuated strain driven by low electric field was synthesized from di-n-butyl itaconate and isoprene through free radical redox emulsion polymerization. The effect of the copolymerized proportion of poly(di-n-butyl itaconate-co-isoprene) (PDBII) and the dosage of crosslinking agent on the elastic modulus, dielectric properties, and actuated strain of the elastomer were investigated, and a potential dielectric elastomer candidate containing 70 wt% di-n-butyl itaconate was obtained. The permittivity of the PDBII crosslinked by 3.0 phr of dicumyl peroxide was 5.68 at 103 Hz, which was higher than that of commercial acrylic and silicone dielectric elastomers. Without any prestrain, an actuated strain of 20% was obtained at an electric field of 30 kV mm−1. In order to further increase the actuated strain, barium titanate (BaTiO3), a high-dielectric-constant ceramic powder, was utilized to fill the PDBII to form a BaTiO3/PDBII composite. The dielectric constant of the composite increased with increasing content of BaTiO3, and the elastic modulus of the composite was lower than that of the unfilled PDBII, leading to a larger dielectric actuated strain of the composite.


title: Empowering convolutional networks for malware classification and analysis

abstract: Performing large-scale malware classification is increasingly becoming a critical step in malware analytics as the number and variety of malware samples is rapidly growing. Statistical machine learning constitutes an appealing method to cope with this increase as it can use mathematical tools to extract information out of large-scale datasets and produce interpretable models. This has motivated a surge of scientific work in developing machine learning methods for detection and classification of malicious executables. However, an optimal method for extracting the most informative features for different malware families, with the final goal of malware classification, is yet to be found. Fortunately, neural networks have evolved to the state that they can surpass the limitations of other methods in terms of hierarchical feature extraction. Consequently, neural networks can now offer superior classification accuracy in many domains such as computer vision and natural language processing. In this paper, we transfer the performance improvements achieved in the area of neural networks to model the execution sequences of disassembled malicious binaries. We implement a neural network that consists of convolutional and feedforward neural constructs. This architecture embodies a hierarchical feature extraction approach that combines convolution of n-grams of instructions with plain vectorization of features derived from the headers of the Portable Executable (PE) files. Our evaluation results demonstrate that our approach outperforms baseline methods, such as simple Feedforward Neural Networks and Support Vector Machines, as we achieve 93% on precision and recall, even in case of obfuscations in the data.


title: A reduced-dimension fMRI shared response model

abstract: Multi-subject fMRI data is critical for evaluating the generality and validity of findings across subjects, and its effective utilization helps improve analysis sensitivity. We develop a shared response model for aggregating multi-subject fMRI data that accounts for different functional topographies among anatomically aligned datasets. Our model demonstrates improved sensitivity in identifying a shared response for a variety of datasets and anatomical brain regions of interest. Furthermore, by removing the identified shared response, it allows improved detection of group differences. The ability to identify what is shared and what is not shared opens the model to a wide range of multi-subject fMRI studies.


title: Analysis and Design of Class DE Amplifier With Nonlinear Shunt Capacitances

abstract: In this paper, equations for waveforms and design relationships are derived for class DE amplifiers with nonlinear shunt capacitances. Using the analytical waveform equations, it is shown that the nonlinearity of the shunt capacitances affects the waveforms only during the dead-time intervals. Additionally, the waveforms with nonlinear and linear shunt capacitances are not very different if class E zero-voltage switching/zero-derivative switching conditions are satisfied. The operation of a class DE amplifier has two dead-time intervals in one period. The switch-voltage waveforms for both dead-time intervals exhibit symmetry. Therefore, the nonlinearity does not have any effect on the series-resonant-circuit elements. As a result, analytical design equations indicate that the series-resonant-circuit elements are independent of the nonlinearity of the shunt capacitances when the supply voltage and the output power are given as design specifications, which is an important result of this paper. The validity of our analysis is confirmed by PSpice simulations and the circuit experiment.


title: Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo

abstract: Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) algorithms have become increasingly popular for Bayesian inference in large-scale applications. Even though these methods have proved useful in several scenarios, their performance is often limited by their bias. In this study, we propose a novel sampling algorithm that aims to reduce the bias of SG-MCMC while keeping the variance at a reasonable level. Our approach is based on a numerical sequence acceleration method, namely the Richardson-Romberg extrapolation, which simply boils down to running almost the same SG-MCMC algorithm twice in parallel with different step sizes. We illustrate our framework on the popular Stochastic Gradient Langevin Dynamics (SGLD) algorithm and propose a novel SG-MCMC algorithm referred to as Stochastic Gradient Richardson-Romberg Langevin Dynamics (SGRRLD). We provide formal theoretical analysis and show that SGRRLD is asymptotically consistent, satisfies a central limit theorem, and its non-asymptotic bias and the mean squared-error can be bounded. Our results show that SGRRLD attains higher rates of convergence than SGLD in both finite-time and asymptotically, and it achieves the theoretical accuracy of the methods that are based on higher-order integrators. We support our findings using both synthetic and real data experiments.


title: A new approach to intelligent fault diagnosis of rotating machinery

abstract: This paper presents a new approach to intelligent fault diagnosis based on statistics analysis, an improved distance evaluation technique and adaptive neuro-fuzzy inference system (ANFIS). The approach consists of three stages. First, different features, including time-domain statistical characteristics, frequency-domain statistical characteristics and empirical mode decomposition (EMD) energy entropies, are extracted to acquire more fault characteristic information. Second, an improved distance evaluation technique is proposed, and with it, the most superior features are selected from the original feature set. Finally, the most superior features are fed into ANFIS to identify different abnormal cases. The proposed approach is applied to fault diagnosis of rolling element bearings, and testing results show that the proposed approach can reliably recognise different fault categories and severities. Moreover, the effectiveness of the proposed feature selection method is also demonstrated by the testing results.


title: A Spread Spectrum Clock Generator for DisplayPort 1.2 with a Hershey-Kiss Modulation Profile

abstract: This paper describes a spread spectrum clock generator (SSCG) circuit for DisplayPort 1.2 standard. A Hershey-Kiss modulation profile is generated by dual sigma-delta modulators. The structure generates various modulation slopes to shape a non-linear modulation profile. The proposed SSCC for DisplayPort 1.2 generates clock signals with 5000 ppm down spreading with a Hershey-Kiss modulation profile at three different clock frequencies, 540㎒, 270㎒ AND 162㎒. The measured peak power reduction is about 15.6 ㏈ at 540 ㎒ with the chip fabrciated using a 0.13 ㎛ CMOS technology.


The IWSLT 2016 Evaluation Campaign


title: Direct yaw moment control for four wheel independent steering and drive vehicles based on centripetal force detection

abstract: In this paper, a deterministic yaw moment controller for four wheel independent steering and drive vehicles is proposed to enhance driving stability and controllability. Different to conventional methods that track a desired yaw rate, the proposed controller stabilizes a vehicle by additionally tracking the heading angle of a vehicle which is more efficient and robust. The heading angle of a vehicle is obtained by a novel method which is based on centripetal force detection. It eliminates the prerequisite knowledge of the characteristics between wheels and road surface which are time varying and difficult to be measured in real time. The proposed system only requires low cost sensing equipment such as wheel speed sensor and accelerometer that makes the system practical to be utilized. The proposed heading angle detection method can be generally applied to any kind of vehicle. The deterministic yaw moment controller is also applicable to any type of four wheel independent drive vehicles.


title: Meta-knowledge in systems design: panacea...or undelivered promise

abstract: In this study we present a review of the emerging field of meta-knowledge components as practised over the past decade among a variety of practitioners. We use the artificially defined term “meta-knowledge” to encompass all those different but overlapping notions used by the artificial intelligence and software engineering communities to represent reusable modelling frameworks: ontologies, problem-solving methods, patterns and experience factories and bases, to name but a few. We then elaborate on how meta-knowledge is deployed in the context of system's design to improve its reliability by consistency-checking, enhance its reuse potential and manage its knowledge-sharing. We speculate on its usefulness and explore technologies for supporting deployment of meta-knowledge. We argue that, despite the different approaches being followed in systems design by divergent communities, meta-knowledge is present in all cases, in a tacit or explicit form, and its utilisation depends on pragmatic aspects which we try to identify and critically review on criteria of effectiveness.


title: Channel adaptation of plda for text-independent speaker verification

abstract: Probabilistic linear discriminant analysis (PLDA) has shown to be effective for modeling channel variability in the i-vector space for text-independent speaker verification. Speaker verification is a binary hypothesis testing. Given a test segment, the verification score could be computed as the log-likelihood ratio between a speaker-adapted PLDA and the universal PLDA model. This work proposes to infer the channel factor specific to each test segment and to include the channel estimate in the PLDA models, which essentially shifts the scoring function to better match that of the test channel. We also explore the influence of covariance adaptation in both speaker and channel adaptations. Experimental results on NIST SRE'08 and SRE'10 dataset confirm that the proposed channel adaptation can be effective when the covariance is kept un-adapted, while the covariance adaptation is necessary in the speaker adaptation.


title: Toward assimilation of observation-derived mixing heights to improve atmospheric tracer transport models

abstract: Common transport models use the mixing height (MH) to determine turbulent coefficients and to obtain tracer concentrations in the planetary boundary layer (PBL). We conducted a pseudo data experiment to elucidate the impact of assimilating MHs to improve CO2 transport within the Stochastic Time-Inverted Lagrangian Transport model (STILT). Transport of CO2 was simulated for August 2006 with a receptor located at Bialystok, Poland. STILT was driven by meteorology obtained from the Weather Research and Forecasting (WRF) model, using the Yonsei University (YSU) and Mellor-Yamada-Janjic (MYJ) PBL parameterizations, which differ substantially in the produced MHs. To quantify model-data mismatch in CO2 to errors in vertical mixing, we defined the WRF-YSU simulation as known truth. Pseudo MH observations were sampled from WRF-YSU at locations of real radiosonde stations. These point observations were interpolated in space-time to the entire WRF domain using kriging with an external drift, which combines observed and modeled MHs to create a "best guess" MH field. We prescribed MHs in STILT driven by WRF-MYJ winds with the best guess to study the impact on CO 2 concentrations. Differences in CO2 between the STILT simulations were on the order of 0-1 and 1-10 ppm on average (i.e., bias), with standard deviations of 1-3 and 4-14 ppm (random error) during day (12 UTC) and nighttime (0 UTC), respectively. These were reduced when using STILT with the best guess (50%-80% of the bias, 10%-20% of the random error). Simulated CO2 concentrations and MHs were also compared to measurements made at the Bialystok tall tower. © 2012. American Geophysical Union. All Rights Reserved. (Less)


title: The ExodUX Framework: Supporting Comprehensive User Experience Design

abstract: Designing positive User Experience (UX) with products enables the creation of unique selling propositions. Being strongly driven by cognitive processes rather than mere product properties, UX is difficult to realize successfully. While User Experience Design (UXD) is a rather young discipline within product development other disciplines outside engineering design traditionally focus on creating experiences. We aim at transferring knowledge from those disciplines to support the design of fascinating UX with technical products. Our work is based on the ExodUX model (from Experience Oriented Disciplines to User eXperience) consisting of 41 experience from disciplines outside engineering design (e.g. sports). Our overarching goal is to support UXD based on the ExodUX approach - extending the model to a broader framework. As part of this, we documented concrete design information for all 41 experience triggers in a consistent and applicable way. Furthermore, we present a supplementing process how to implement gained knowledge of single triggers in a product development project. Finally, we provide situation specific application methodologies for the whole framework.


title: A three-way merge for XML documents

abstract: Three-way merging is a technique that may be employed for reintegrating changes to a document in cases where multiple independently modified copies have been made. While tools for three-way merge of ASCII text files exist in the form of the ubiquitous diff and patch tools these are of limited applicability to XML documents. We present a method for three-way merging of XML which is targeted at merging XML formats that model human-authored documents as ordered trees (e.g. rich text formats structured text drawings etc.). To this end we investigate a number of use cases on XML merging (collaborative editing propagating changes across document variants) from which we derive a set of high-level merge rules. Our merge is based on these rules. We propose that our merge is easy to both understand and implement yet sufficiently expressive to handle several important cases of merging on document structure that are beyond the capabilities of traditional text-based tools. In order to justify these claims we applied our merging method to the merging tasks contained in the use cases. The overall performance of the merge was found to be satisfactory. The key contributions of this work are: a set of merge rules derived from use cases on XML merging a compact and versatile XML merge in accordance with these rules and a classification of conflicts in the context of that merge.


title: A 90-nm CMOS Power Amplifier for 802.16e (WiMAX) Applications

abstract: In this paper, we demonstrate a single-stage 90-nm CMOS power amplifier (PA) for 2.3-2.7-GHz WiMAX (802.16e) band applications. An integrated balun is used to match the output to 50-? load. The PA gain and saturated power ( P SAT) are +18 and +32 dBm, respectively, working from a 3.3-V supply, with a peak power-added efficiency of 48%. A digital-predistortion technique is used to enhance the PA linearity. The measured error vector magnitude for a 64 quadrature amplitude modulation orthogonal frequency-division multiplexing signal is improved from -24 to -30 dB at +25-dBm output power. Compliance with the 802.16e standard 10-MHz WiMAX mask and Federal Communications Commission regulations is demonstrated at + 25 dBm of output power with power efficiency of ~ 25% and with a measured second harmonic level of - 31 dBm/MHz. Using a crest factor reduction technique, mask compliance is achieved at + 27 dBm with tradeoff on EVM = -20 dB, sufficient for quadrature phase-shift keying.


title: Improving Variational Inference with Inverse Autoregressive Flow

abstract: We propose a simple and scalable method for improving the flexibility of variational inference through a transformation with autoregressive networks. Autoregressive networks, such as RNNs and MADE, are very powerful models; however, ancestral sampling in such networks is a sequential operation, therefore unappealing for direct use as approximate posteriors in variational inference on parallel hardware such as GPUs. We find that by inverting autoregressive networks we can obtain equally powerful data transformations that can often be computed in parallel. We show that such data transformations, inverse autoregressive flows (IAF), can be used to transform a simple distribution over the latent variables into a much more flexible distribution, while still allowing us to compute the resulting variables' probability density function. The method is simple to implement, can be made arbitrarily flexible, and (in contrast with previous work) is naturally applicable to latent variables that are organized in multidimensional tensors, such as 2D grids or time series. The method is applied to a novel deep architecture of variational auto-encoders. In experiments we demonstrate that autoregressive flow leads to significant performance gains when applied to variational autoencoders for natural images.


title: Keyphrase Generation with Correlation Constraints

abstract: In this paper, we study automatic keyphrase generation. Although conventional approaches to this task show promising results, they neglect correlation among keyphrases, resulting in duplication and coverage issues. To solve these problems, we propose a new sequence-to-sequence architecture for keyphrase generation named CorrRNN, which captures correlation among multiple keyphrases in two ways. First, we employ a coverage vector to indicate whether the word in the source document has been summarized by previous phrases to improve the coverage for keyphrases. Second, preceding phrases are taken into account to eliminate duplicate phrases and improve result coherence. Experiment results show that our model significantly outperforms the state-of-the-art method on benchmark datasets in terms of both accuracy and diversity.


title: Learning Hierarchical Semantic Image Manipulation through Structured Representations

abstract: Understanding, reasoning, and manipulating semantic concepts of images have been a fundamental research problem for decades. Previous work mainly focused on direct manipulation on natural image manifold through color strokes, key-points, textures, and holes-to-fill. In this work, we present a novel hierarchical framework for semantic image manipulation. Key to our hierarchical framework is that we employ a structured semantic layout as our intermediate representation for manipulation. Initialized with coarse-level bounding boxes, our structure generator first creates pixel-wise semantic layout capturing the object shape, object-object interactions, and object-scene relations. Then our image generator fills in the pixel-level textures guided by the semantic layout. Such framework allows a user to manipulate images at object-level by adding, removing, and moving one bounding box at a time. Experimental evaluations demonstrate the advantages of the hierarchical manipulation framework over existing image generation and context hole-filing models, both qualitatively and quantitatively. Benefits of the hierarchical framework are further demonstrated in applications such as semantic object manipulation, interactive image editing, and data-driven image manipulation.


title: Associations of exercise-induced hormone profiles and gains in strength and hypertrophy in a large cohort after weight training

abstract: The purpose of this study was to investigate associations between acute exercise-induced hormone responses and adaptations to high intensity resistance training in a large cohort (n = 56) of young men. Acute post-exercise serum growth hormone (GH), free testosterone (fT), insulin-like growth factor (IGF-1) and cortisol responses were determined following an acute intense leg resistance exercise routine at the midpoint of a 12-week resistance exercise training study. Acute hormonal responses were correlated with gains in lean body mass (LBM), muscle fibre cross-sectional area (CSA) and leg press strength. There were no significant correlations between the exercise-induced elevations (area under the curve—AUC) of GH, fT and IGF-1 and gains in LBM or leg press strength. Significant correlations were found for cortisol, usually assumed to be a hormone indicative of catabolic drive, AUC with change in LBM (r = 0.29, P < 0.05) and type II fibre CSA (r = 0.35, P < 0.01) as well as GH AUC and gain in fibre area (type I: r = 0.36, P = 0.006; type II: r = 0.28, P = 0.04, but not lean mass). No correlations with strength were observed. We report that the acute exercise-induced systemic hormonal responses of cortisol and GH are weakly correlated with resistance training-induced changes in fibre CSA and LBM (cortisol only), but not with changes in strength.


title: Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration

abstract: Objective: The advent of Electronic Medical Records (EMR) with large electronic imaging databases along with advances in deep neural networks with machine learning has provided a unique opportunity to achieve milestones in automated image analysis. Optical coherence tomography (OCT) is the most commonly obtained imaging modality in ophthalmology and represents a dense and rich dataset when combined with labels derived from the EMR. We sought to determine if deep learning could be utilized to distinguish normal OCT images from images from patients with Age-related Macular Degeneration (AMD). Methods: Automated extraction of an OCT imaging database was performed and linked to clinical endpoints from the EMR. OCT macula scans were obtained by Heidelberg Spectralis, and each OCT scan was linked to EMR clinical endpoints extracted from EPIC. The central 11 images were selected from each OCT scan of two cohorts of patients: normal and AMD. Cross-validation was performed using a random subset of patients. Area under receiver operator curves (auROC) were constructed at an independent image level, macular OCT level, and patient level. Results: Of an extraction of 2.6 million OCT images linked to clinical datapoints from the EMR, 52,690 normal and 48,312 AMD macular OCT images were selected. A deep neural network was trained to categorize images as either normal or AMD. At the image level, we achieved an auROC of 92.78% with an accuracy of 87.63%. At the macula level, we achieved an auROC of 93.83% with an accuracy of 88.98%. At a patient level, we achieved an auROC of 97.45% with an accuracy of 93.45%. Peak sensitivity and specificity with optimal cutoffs were 92.64% and 93.69% respectively. Conclusions: Deep learning techniques are effective for classifying OCT images. These findings have important implications in utilizing OCT in automated screening and computer aided diagnosis tools.


title: RF power amplifiers for wireless communications

abstract: A wide variety of semiconductor devices are used in wireless power amplifiers. The RF performance and other attributes of cellphone RF power amplifiers using Si and GaAs based technologies are reviewed and compared.


title: Targeted advertising using behavioural data and social data mining

abstract: The explosive growth of social networks has led to prolific availability in customer tastes and preferences. This data can be exploited to serve the customers better and offer them the advertisements they would be delighted to see. To provide relevant advertisements to consumers, one has to consider the location of the consumer as well. The consumers will be highly contented if the offers shown to them are easily accessible in nearby areas. In this paper, we propose a model combining the idea of social and spatial data to provide targeted advertisements. Social data is acquired through user's Facebook profile and location of the user is found with the help of Beacons.


title: HybridNet: A Hybrid Neural Architecture to Speed-up Autoregressive Models

abstract: This paper introduces HybridNet, a hybrid neural network to speed-up autoregressive models for raw audio waveform generation. As an example, we propose a hybrid model that combines an autoregressive network named WaveNet and a conventional LSTM model to address speech synthesis. Instead of generating one sample per time-step, the proposed HybridNet generates multiple samples per time-step by exploiting the long-term memory utilization property of LSTMs. In the evaluation, when applied to text-to-speech, HybridNet yields state-of-art performance. HybridNet achieves a 3.83 subjective 5-scale mean opinion score on US English, largely outperforming the same size WaveNet in terms of naturalness and provide 2x speed up at inference.


title: Calibration of robot tool centre point using camera-based system

abstract: Robot Tool Centre Point (TCP) calibration problem is of great importance for a number of industrial applications, and it is well known both in theory and in practice. Although various techniques have been proposed for solving this problem, they mostly require tool jogging or long processing time, both of which affect process performance by extending cycle time. This paper presents an innovative way of TCP calibration using a set of two cameras. The robot tool is placed in an area where images in two orthogonal planes are acquired using cameras. Using robust pattern recognition, even deformed tool can be identified on images, and information about its current position and orientation forwarded to control unit for calibration. Compared to other techniques, test results show significant reduction in procedure complexity and calibration time. These improvements enable more frequent TCP checking and recalibration during production, thus improving the product quality.


title: The Profitability of Deep Discounts ∗

abstract: We examine the profitability and implications of online discount vouchers, a relatively new marketing tool that offers consumers large discounts when they prepay for participating firms' goods and services. Within a model of repeat experience good purchase, we examine two mechanisms by which a discount voucher service can benefit affiliated firms: price discrimination and advertising. For vouchers to provide successful price discrimination, the valuations of consumers who have access to vouchers must generally be lower than those of consumers who do not have access to vouchers. Offering vouchers tends to be more profitable for firms which are patient or relatively unknown, and for firms with low marginal costs. Extensions to our model accommodate the possibilities of multiple voucher purchases and firm price re-optimization. Despite the potential benefits of online discount vouchers to certain firms in certain circumstances, our analysis reveals the narrow conditions in which vouchers are likely to increase firm profits.


title: A Method of Preventing Unauthorized Data Transmission in Controller Area Network

abstract: There is a strong demand for the security of Controller Area Network (CAN), a major in-vehicle network. A number of methods to detect unauthorized data transmission, such as anomaly detection and misuse detection, have already been proposed. However, all of them have no capability of preventing unauthorized data transmission itself. In this paper, we propose a novel method that realizes the prevention as well as detection. Our method can be effectively implemented with minimal changes in the current architecture of Electronic Control Unit. The method works even in a CAN with multiple buses interconnected by gateways.


title: Expert video-surveillance system for real-time detection of suspicious behaviors in shopping malls

abstract: Tracking-by-detection based on segmentation, Kalman predictions and LSAP association.Occlusion management: SVM kernel metric for GCH+LBP+HOG image features.Overall performance near to 85% while tracking under occlusions in CAVIAR dataset.Human behavior analysis (exits, loitering, etc.) in naturalistic scenes in shops.Real-time multi-camera performance with a processing capacity near to 50fps/camera. Expert video-surveillance systems are a powerful tool applied in varied scenarios with the aim of automatizing the detection of different risk situations and helping human security officers to take appropriate decisions in order to enhance the protection of assets. In this paper, we propose a complete expert system focused on the real-time detection of potentially suspicious behaviors in shopping malls. Our video-surveillance methodology contributes several innovative proposals that compose a robust application which is able to efficiently track the trajectories of people and to discover questionable actions in a shop context. As a first step, our system applies an image segmentation to locate the foreground objects in scene. In this case, the most effective background subtraction algorithms of the state of the art are compared to find the most suitable for our expert video-surveillance application. After the segmentation stage, the detected blobs may represent full or partial people bodies, thus, we have implemented a novel blob fusion technique to group the partial blobs into the final human targets. Then, we contribute an innovative tracking algorithm which is not only based on people trajectories as the most part of state-of-the-art methods, but also on people appearance in occlusion situations. This tracking is carried out employing a new two-step method: (1) the detections-to-tracks association is solved by using Kalman filtering combined with an own-designed cost optimization for the Linear Sum Assignment Problem (LSAP); and (2) the occlusion management is based on SVM kernels to compute distances between appearance features such as GCH, LBP and HOG. The application of these three features for recognizing human appearance provides a great performance compared to other description techniques, because color, texture and gradient information are effectively combined to obtain a robust visual description of people. Finally, the resultant trajectories of people obtained in the tracking stage are processed by our expert video-surveillance system for analyzing human behaviors and identifying potential shopping mall alarm situations, as are shop entry or exit of people, suspicious behaviors such as loitering and unattended cash desk situations. With the aim of evaluating the performance of some of the main contributions of our proposal, we use the publicly available CAVIAR dataset for testing the proposed tracking method with a success near to 85% in occlusion situations. According to this performance, we corroborate in the presented results that the precision and efficiency of our tracking method is comparable and slightly superior to the most recent state-of-the-art works. Furthermore, the alarms given off by our application are evaluated on a naturalistic private dataset, where it is evidenced that our expert video-surveillance system can effectively detect suspicious behaviors with a low computational cost in a shopping mall context.


title: Active Decision Boundary Annotation with Deep Generative Models

abstract: This paper is on active learning where the goal is to reduce the data annotation burden by interacting with a (human) oracle during training. Standard active learning methods ask the oracle to annotate data samples. Instead, we take a profoundly different approach: we ask for annotations of the decision boundary. We achieve this using a deep generative model to create novel instances along a 1d line. A point on the decision boundary is revealed where the instances change class. Experimentally we show on three data sets that our method can be plugged into other active learning schemes, that human oracles can effectively annotate points on the decision boundary, that our method is robust to annotation noise, and that decision boundary annotations improve over annotating data samples.


title: Scanning Spaceborne Synthetic Aperture Radar with Integrated Radiometer

abstract: Spaceborne synthetic aperture radar systems are severely constrained to a narrow swath by ambiguity limitations. Here a vertically scanned-beam synthetic aperture system (SCANSAR) is proposed as a solution to this problem. The potential length of synthetic aperture must be shared between beam positions, so the along-track resolution is poorer; a direct tradeoff exists between resolution and swath width. The length of the real aperture is independently traded against the number of scanning positions. Design curves and equations are presented for spaceborne SCANSARs for altitudes between 400 and 1400 km and inner angles of incidence between 20° and 40°. When the real antenna is approximately square, it may also be used for a microwave radiometer. The combined radiometer and synthetic-aperture (RADISAR) should be useful for those applications where the poorer resolution of the radiometer is useful for some purposes, but the finer resolution of the radar is needed for others.


Oligomeric Dithienopyrrole-Thienopyrrolodione ( DTP-TPD ) Donor-Acceptor Copolymer for Organic Photovoltaics Preprint


Job Satisfaction in Agile Development Teams: Agile Development as Work Redesign


SDN-Based Intrusion Detection System Literature review


title: Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)

abstract: Electroencephalogram (EEG) signals are frequently used in brain–computer interfaces (BCIs), but they are easily contaminated by artifacts and noise, so preprocessing must be done before they are fed into a machine learning algorithm for classification or regression. Spatial filters have been widely used to increase the signal-to-noise ratio of EEG for BCI classification problems, but their applications in BCI regression problems have been very limited. This paper proposes two common spatial pattern (CSP) filters for EEG-based regression problems in BCI, which are extended from the CSP filter for classification, by using fuzzy sets. Experimental results on EEG-based response speed estimation from a large-scale study, which collected 143 sessions of sustained-attention psychomotor vigilance task data from 17 subjects during a 5-month period, demonstrate that the two proposed spatial filters can significantly increase the EEG signal quality. When used in LASSO and $k$ -nearest neighbors regression for user response speed estimation, the spatial filters can reduce the root-mean-square estimation error by $10.02-19.77\%$ , and at the same time increase the correlation to the true response speed by $19.39-86.47\%$ .


title: Performance evaluation of Sentinel-1 data in SAR ship detection

abstract: This study addresses the performances of ship detection with data acquired by the newly launched Sentinel-1 SAR sensor. An automatic validation approach based on coastal AIS data is employed for measuring the detection efficiency. Results are compared with ship detection capabilities conducted on Radarsat-2 and CosmoSkymed datasets. The influence of different key parameters, such as SAR imaging characteristics (polarization, incidence angle) or meteorological conditions, is addressed. Such an analysis is useful for operational services to determine data specifications that assure optimum vessel detection for maritime surveillance applications.


title: Preoperative Planning for Primary Total Hip Arthroplasty

abstract: Preoperative planning is of paramount importance in obtaining reproducible results in modern hip arthroplasty. Planning helps the surgeon visualize the operation after careful review of the clinical and radiographic findings. A standardized radiograph with a known magnification should be used for templating. The cup template should be placed relative to the ilioischial line, the teardrop, and the superolateral acetabular margin, so that the removal of the supportive subchondral bone is minimal and the center of rotation of the hip is restored. When acetabular abnormalities are encountered, additional measures are necessary to optimize cup coverage and minimize the risk of malposition. Templating the femoral side for cemented and cementless implants should aim to optimize limb length and femoral offset, thereby improving the biomechanics of the hip joint. Meticulous preoperative planning allows the surgeon to perform the procedure expediently and precisely, anticipate potential intraoperative complications, and achieve reproducible results.


title: A machine-learning classifier implemented in a standard 6T SRAM array

abstract: This paper presents a machine-learning classifier where the computation is performed within a standard 6T SRAM array. This eliminates explicit memory operations, which otherwise pose energy/performance bottlenecks, especially for emerging algorithms (e.g., from machine learning) that result in high ratio of memory accesses. We present an algorithm and prototype IC (in 130nm CMOS), where a 128×128 SRAM array performs storage of classifier models and complete classifier computations. We demonstrate a real application, namely digit recognition from MNIST-database images. The accuracy is equal to a conventional (ideal) digital/SRAM system, yet with 113× lower energy. The approach achieves accuracy >95% with a full feature set (i.e., 28×28=784 image pixels), and 90% when reduced to 82 features (as demonstrated on the IC due to area limitations). The energy per 10-way digit classification is 633pJ at a speed of 50MHz.


title: The Design Fictions of Philanthropic IT: Stuck Between an Imperfect Present and an Impossible Future

abstract: In this paper, we examine the stories about philanthropic IT that circulate via product websites, marketing materials, and third-party news articles. Through a series of product-centered case studies, we surface these texts' implicit and explicit visions about the (near) future of philanthropy. We detail their prescriptions about how, why, and in service of what ends nonprofit organizations could, should, and ought to leverage IT. We also examine their underlying assumptions about philanthropy: how social good is accomplished, how philanthropic organizations are - and might be more - effective, to whom organizations and beneficiaries should be accountable, and the terms of that accountability. Analyzing these visions as design fictions, we argue that they help cultivate unrealistic anticipatory relationships to the present and entail concomitantly unrealistic imperatives for the philanthropic sector. We conclude by arguing for the crucial role of HCI scholars in disrupting these impossible futures, and by highlighting areas needing further, re-imagined, research.


A Multi-Scale Method for the Re-Assembly of Fragmented Objects


title: Semantic Segmentation of Colon Glands with Deep Convolutional Neural Networks and Total Variation Segmentation

abstract: Segmentation of histopathology sections is an ubiquitous requirement in digital pathology and due to the large variability of biological tissue, machine learning techniques have shown superior performance over standard image processing methods. As part of the GlaS@MICCAI2015 colon gland segmentation challenge, we present a learning-based algorithm to segment glands in tissue of benign and malignant colorectal cancer. Images are preprocessed according to the Hematoxylin-Eosin staining protocol and two deep convolutional neural networks (CNN) are trained as pixel classifiers. The CNN predictions are then regularized using a figure-ground segmentation based on weighted total variation to produce the final segmentation result. On two test sets, our approach achieves a tissue classification accuracy of 98% and 94%, making use of the inherent capability of our system to distinguish between benign and malignant tissue.


CSA: Streaming Engine for Internet of Things


title: Thermal properties of graphene and multilayer graphene: Applications in thermal interface materials

abstract: We review the thermal properties of graphene and multilayer graphene, and discuss graphene’s applications in thermal management of advanced electronics and optoelectronics. A special attention is paid to the use of the liquid-phase-exfoliated graphene and multilayer graphene as the fillers in the thermal interface materials. It has been demonstrated that addition of an optimized mixture of graphene and multilayer graphene to the composites with different matrix materials produces the record-high enhancement of the effective thermal conductivity at the small filler loading fraction (fr 10 vol%). The thermal conductivity enhancement due to the presence of graphene in the composites has been observed for a range of matrix materials used by industry. The hybrid composites where graphene is utilized together with metallic micro- and nanoparticles allow one to tune both the thermal and electrical conductivity of these materials. Theoretical considerations indicate that the graphenebased thermal interface materials can outperform those with carbon nanotubes, metal nanoparticles and other fillers owing to graphene’s geometry, mechanical flexibility and lower Kapitza resistance at the graphene–base material interface.


title: A Deep Learning Approach for Question Answering Over Knowledge Base

abstract: With the increase of the scale of the knowledge base, it’s important to answer question over knowledge base. In this paper, we will introduce a method to extract answers from Chinese knowledge base for Chinese questions. Our method uses a classifier to judge whether the relation in the triple is what the question asked, question-relation pairs are used to train the classifier. It’s difficult to identify the right relation, so we find out the focus of the question and leverage the resource of lexical paraphrase in the preprocessing of the question. And the use of lexical paraphrase also can alleviate the out of vocabulary (OOV) problem. In order to let the right answer at the top of candidate answers, we present a ranking method to rank these candidate answers. The result of the final evaluation shows that our method achieves a good result.


title: Food Volume Estimation in a Mobile Phone Based Dietary Assessment System

abstract: There is now convincing evidence that poor diet, in combination with physical inactivity are key determinants of an individual's risk of developing chronic diseases, such as obesity, cancer, cardiovascular disease or diabetes. Assessing what people eat is fundamental to establishing the link between diet and disease. Food records are considered the best approach for assessing energy intake. However, this method requires literate and highly motivated subjects and adolescents and young adults are the least likely to undertake food records. The ready access of the majority of the population to mobile phones has opened up new opportunities for dietary assessment. In such systems, the camera in the mobile phone is used for capturing images of food consumed and these images are then processed to automatically estimate the nutritional content of the food. A vital step in this process is the estimation of the volume of the food in the image. In this paper we propose a food volume estimation approach which requires only a pair of stereo images to be captured. Our experimental results show that the proposed approach can provide an accurate estimate of the volume of typical food items in a passive manner without the need for manual fitting of 3D models to the food items.


Impact of Customer Relationship Management on Customer Loyalty , Customer Retention and Customer Profitability for Hotelier Sector


title: Euro Banknote Recognition System for Blind People

abstract: This paper presents the development of a portable system with the aim of allowing blind people to detect and recognize Euro banknotes. The developed device is based on a Raspberry Pi electronic instrument and a Raspberry Pi camera, Pi NoIR (No Infrared filter) dotted with additional infrared light, which is embedded into a pair of sunglasses that permit blind and visually impaired people to independently handle Euro banknotes, especially when receiving their cash back when shopping. The banknote detection is based on the modified Viola and Jones algorithms, while the banknote value recognition relies on the Speed Up Robust Features (SURF) technique. The accuracies of banknote detection and banknote value recognition are 84% and 97.5%, respectively.


title: Automated Tracking of Quantitative Assessments of Tumor Burden in Clinical Trials

abstract: There are two key challenges hindering effective use of quantitative assessment of imaging in cancer response assessment: 1) Radiologists usually describe the cancer lesions in imaging studies subjectively and sometimes ambiguously, and 2) it is difficult to repurpose imaging data, because lesion measurements are not recorded in a format that permits machine interpretation and interoperability. We have developed a freely available software platform on the basis of open standards, the electronic Physician Annotation Device (ePAD), to tackle these challenges in two ways. First, ePAD facilitates the radiologist in carrying out cancer lesion measurements as part of routine clinical trial image interpretation workflow. Second, ePAD records all image measurements and annotations in a data format that permits repurposing image data for analyses of alternative imaging biomarkers of treatment response. To determine the impact of ePAD on radiologist efficiency in quantitative assessment of imaging studies, a radiologist evaluated computed tomography (CT) imaging studies from 20 subjects having one baseline and three consecutive follow-up imaging studies with and without ePAD. The radiologist made measurements of target lesions in each imaging study using Response Evaluation Criteria in Solid Tumors 1.1 criteria, initially with the aid of ePAD, and then after a 30-day washout period, the exams were reread without ePAD. The mean total time required to review the images and summarize measurements of target lesions was 15% (P < .039) shorter using ePAD than without using this tool. In addition, it was possible to rapidly reanalyze the images to explore lesion cross-sectional area as an alternative imaging biomarker to linear measure. We conclude that ePAD appears promising to potentially improve reader efficiency for quantitative assessment of CT examinations, and it may enable discovery of future novel image-based biomarkers of cancer treatment response.


Multimedia information and learning


title: WeChat Text Messages Service Flow Traffic Classification Using Machine Learning Technique

abstract: In this era of information technology, Network Traffic Classification is a very important and hot topic from the perspective of network security and management due to substantial use of dynamic applications. Numerous research models have been proposed in Network Traffic Classification to classify different types of applications and achieve significant accuracy results. However, no work has been done to classify WeChat messages flow traffic. WeChat is a free instant messaging application. Hence, it is very important to classify WeChat text messages traffic. In this paper, we classify WeChat messages flows traffic using two different data sets, which are first captured using Wireshark tool from two different locations network environments, Harbin Institute of Technology Lab and Jinyuan Hotel and then 50 features are extracted from captured traffic. After that four machine learning algorithms SVM, C4.5, Bayes Net and Naïve Byes are applied to classify the WeChat text messages traffic. Experimental results show that all classifiers give very high accuracy results using two different data sets. Using Jinyuan data set SVM and C4.5 decision tree algorithm give 100% accuracy result as compared to Bayes Net and Naïve Bayes algorithm and using Harbin Institute of Technology Lab data set all classifiers give 99.7% high accuracy results.


title: An evaluation of navigational ability comparing Redirected Free Exploration with Distractors to Walking-in-Place and joystick locomotio interfaces

abstract: We report on a user study evaluating Redirected Free Exploration with Distractors (RFED), a large-scale, real-walking, locomotion interface, by comparing it to Walking-in-Place (WIP) and Joystick (JS), two common locomotion interfaces. The between-subjects study compared navigation ability in RFED, WIP, and JS interfaces in VEs that are more than two times the dimensions of the tracked space. The interfaces were evaluated based on navigation and wayfinding metrics and results suggest that participants using RFED were significantly better at navigating and wayfinding through virtual mazes than participants using walking-in-place and joystick interfaces. Participants traveled shorter distances, made fewer wrong turns, pointed to hidden targets more accurately and more quickly, and were able to place and label targets on maps more accurately. Moreover, RFED participants were able to more accurately estimate VE size.


title: A Survey on Sentiment Analysis and Opinion Mining Techniques

abstract: Sentiment Analysis (SA), an application of Natural Language processing (NLP), has been witnessed a blooming interest over the past decade. It is also known as opinion mining, mood extraction and emotion analysis. The basic in opinion mining is classifying the polarity of text in terms of positive (good), negative (bad) or neutral (surprise). Mood Extraction automates the decision making performed by human. It is the important aspect for capturing public opinion about product preferences, marketing campaigns, political movements, social events and company strategies. In addition to sentiment analysis for English and other European languages, this task is applied on various Indian languages like Bengali, Hindi, Telugu and Malayalam. This paper describes the survey on main approaches for performing sentiment extraction.


title: Parallel and Dynamic Searchable Symmetric Encryption

abstract: Searchable symmetric encryption (SSE) enables a client to outsource a collection of encrypted documents in the cloud and retain the ability to perform keyword searches without revealing information about the contents of the documents and queries. Although efficient SSE constructions are known, previous solutions are highly sequential. This is mainly due to the fact that, currently, the only method for achieving sub-linear time search is the inverted index approach (Curtmola, Garay, Kamara and Ostrovsky, CCS ’06) which requires the search algorithm to access a sequence of memory locations, each of which is unpredictable and stored at the previous location in the sequence. Motivated by advances in multi-core architectures, we present a new method for constructing sub-linear SSE schemes. Our approach is highly parallelizable and dynamic. With roughly a logarithmic number of cores in place, searches for a keyword w in our scheme execute in o(r) parallel time, where r is the number of documents containing keyword w (with more cores, this bound can go down to O(logn), i.e., independent of the result size r). Such time complexity outperforms the optimal Θ(r) sequential search time—a similar bound holds for the updates. Our scheme also achieves the following important properties: (a) it enjoys a strong notion of security, namely security against adaptive chosen-keyword attacks; (b) compared to existing sub-linear dynamic SSE schemes (e.g., Kamara, Papamanthou, Roeder, CCS ’12), updates in our scheme do not leak any information, apart from information that can be inferred from previous search tokens; (c) it can be implemented efficiently in external memory (with logarithmic I/O overhead). Our technique is simple and uses a red-black tree data structure; its security is proven in the random oracle model.


A self-adaptive distance correction localisation algorithm based on RSSI-LQI for WSNs


title: Joint Face Detection and Facial Expression Recognition with MTCNN

abstract: The Multi-task Cascaded Convolutional Networks (MTCNN) has recently demonstrated impressive results on jointly face detection and alignment. By using the hard sample ming and training a model on FER2013 datasets, we exploit the inherent correlation between face detection and facial express-ion recognition, and report the results of facial expression recognition based on MTCNN.