title: When the Tide is High: Estimating the Welfare Impact of Coastal Erosion Management

abstract: A choice experiment was undertaken at Buffalo beach, Whitianga, in order to investigate beach visitors’ preferences for various coastal erosion management options. Constructing rock seawalls is a common response to coastal erosion but seawalls can negatively affect visual amenity, biodiversity and recreational values. The choice experiment results from this study show that the average visitor would be willing to pay $20 per year to remove an existing rock wall at either end of Buffalo beach. Visitors place high value on useable sandy beaches and reserve areas behind the beach. A latent class analysis reveals there are distinct sub-groups with varying preferences for beach characteristics. This paper presents a model with separate classes for residents and visitors and the compensating variation estimates to calculate the overall welfare effect for three coastal management scenarios.


title: A General Framework for Manifold Alignment

abstract: Manifold alignment has been found to be useful in many fields of machine learning and data mining. In this paper we summarize our work in this area and introduce a general framework for manifold alignment. This framework generates a family of approaches to align manifolds by simultaneously matching the corresponding instances and preserving the local geometry of each given manifold. Some approaches like semi-supervised alignment and manifold projections can be obtained as special cases. Our framework can also solve multiple manifold alignment problems and be adapted to handle the situation when no correspondence information is available. The approaches are described and evaluated both theoretically and experimentally, providing results showing useful knowledge transfer from one domain to another. Novel applications of our methods including identification of topics shared by multiple document collections, and biological structure alignment are discussed in the paper.


title: Attention-based Ensemble for Deep Metric Learning

abstract: Deep metric learning aims to learn an embedding function, modeled as deep neural network. This embedding function usually puts semantically similar images close while dissimilar images far from each other in the learned embedding space. Recently, ensemble has been applied to deep metric learning to yield state-of-the-art results. As one important aspect of ensemble, the learners should be diverse in their feature embeddings. To this end, we propose an attention-based ensemble, which uses multiple attention masks, so that each learner can attend to different parts of the object. We also propose a divergence loss, which encourages diversity among the learners. The proposed method is applied to the standard benchmarks of deep metric learning and experimental results show that it outperforms the state-of-the-art methods by a significant margin on image retrieval tasks.


title: Breast shape (ptosis) as a marker of a woman's breast attractiveness and age: Evidence from Poland and Papua.

abstract: OBJECTIVES ::: A women's breast is a sex-specific and aesthetic bodily attribute. It is suggested that breast morphology signals maturity, health, and fecundity. The perception of a woman's attractiveness and age depends on various cues, such as breast size or areola pigmentation. Conducted in Poland and Papua, the current study investigated how breast attractiveness, and the further estimate of a woman's age based on her breast's appearance, is affected by the occurrence of breast ptosis (ie, sagginess, droopiness). ::: ::: ::: METHODS ::: In the Polish sample, 57 women and 50 men (N = 107) were presented with sketches of breasts manipulated to represent different stages of ptosis based on two different breast ptosis classifications. The participants were asked to rate the breast attractiveness and age of the woman whose breasts were depicted in each sketch. In Papua, 45 men aged 20 to 75 years took part in the study, which was conducted using only one of the classifications of breast ptosis. ::: ::: ::: RESULTS ::: Regardless of the classification used, the results showed that the assessed attractiveness of the breasts decreased as the estimated age increased with respect to the more ptotic breasts depicted in the sketches. The results for Papuan raters were the same as for the Polish sample. ::: ::: ::: CONCLUSIONS ::: Breast ptosis may be yet another physical trait that affects the perception and preferences of a potential sexual partner. The consistency in ratings between Polish and Papuan raters suggests that the tendency to assess ptotic breasts with aging and a loss of attractiveness is cross-culturally universal.


Pleistocene fauna and Holocene humans : a gazetteer of Paleontological and early archaeological sites on Cyprus


title: Testing for the consecutive ones property, interval graphs, and graph planarity using PQ-tree algorithms

abstract: A data structure called a PQ-tree is introduced. PQ-trees can be used to represent the permutations of a set U in which various subsets of U occur consecutively. Efficient algorithms are presented for manipulating PQ-trees. Algorithms using PQ-trees are then given which test for the consecutive ones property in matrices and for graph planarity. The consecutive ones test is extended to a test for interval graphs using a recently discovered fast recognition algorithm for chordal graphs. All of these algorithms require a number of steps linear in the size of their input.


title: Real-time anomaly detection and localization in crowded scenes

abstract: In this paper, we propose a method for real-time anomaly detection and localization in crowded scenes. Each video is defined as a set of non-overlapping cubic patches, and is described using two local and global descriptors. These descriptors capture the video properties from different aspects. By incorporating simple and cost-effective Gaussian classifiers, we can distinguish normal activities and anomalies in videos. The local and global features are based on structure similarity between adjacent patches and the features learned in an unsupervised way, using a sparse auto-encoder. Experimental results show that our algorithm is comparable to a state-of-the-art procedure on UCSD ped2 and UMN benchmarks, but even more time-efficient. The experiments confirm that our system can reliably detect and localize anomalies as soon as they happen in a video.


title: Hyperspectral Image-Based Night-Time Vehicle Light Detection Using Spectral Normalization and Distance Mapper for Intelligent Headlight Control

abstract: This paper proposes a vehicle light detection method using a hyperspectral camera instead of a Charge-Coupled Device (CCD) or Complementary metal-Oxide-Semiconductor (CMOS) camera for adaptive car headlamp control. To apply Intelligent Headlight Control (IHC), the vehicle headlights need to be detected. Headlights are comprised from a variety of lighting sources, such as Light Emitting Diodes (LEDs), High-intensity discharge (HID), and halogen lamps. In addition, rear lamps are made of LED and halogen lamp. This paper refers to the recent research in IHC. Some problems exist in the detection of headlights, such as erroneous detection of street lights or sign lights and the reflection plate of ego-car from CCD or CMOS images. To solve these problems, this study uses hyperspectral images because they have hundreds of bands and provide more information than a CCD or CMOS camera. Recent methods to detect headlights used the Spectral Angle Mapper (SAM), Spectral Correlation Mapper (SCM), and Euclidean Distance Mapper (EDM). The experimental results highlight the feasibility of the proposed method in three types of lights (LED, HID, and halogen).


title: MANAGEMENT OWNERSHIP AND STOCK PRICE INFORMATIVENESS IN INDONESIA STOCK MARKET

abstract: This paper investigates the impact of management ownership i.e. director’s and commissioner’s shares, on the amount of firm-specific information incorporated into stock prices, as measured by stock price synchronicity of Indonesian-listed firms over the 2013-2015 period. Studies have shown that at least there are two effects of management ownership in prices i.e. convergence-of-interests effect and entrenchment effect. Also, previous research shows that stock price efficiency depends on the cost of acquiring private information. We hypothesize that these characteristics will manifest itself primarily in the firm-specific component of returns. Our empirical test finds that director’s and commissioner’s ownership in a company nonlinearly increase the probability of capitalization of firm’s specific information to stock prices. Thereby, making firm’s stock prices less synchronous to the market and the industry movements. Overall, our findings support the contention that ownership structure plays a significant role in shaping the firm’s information environment. Keywords: management ownership, stock price synchronicity, Indonesia


title: Assortment Optimization Under General Choice

abstract: We consider the key operational problem of optimizing the mix of offered products to maximize revenues when product prices are exogenously set and product demand follows a general discrete choice model. The key challenge in making this decision is the computational difficulty of finding the best subset, which often requires exhaustive search. Existing approaches address the challenge by either deriving efficient algorithms for specific parametric structures or studying the performance of general-purpose heuristics. The former approach results in algorithms that lack portability to other structures; whereas the latter approach has resulted in algorithms with poor performance. We study a portable and easy-to-implement local search heuristic. We show that it efficiently finds the global optimum for the multinomial logit (MNL) model and derive performance guarantees for general choice structures. Empirically, it is better than prevailing heuristics when no efficient algorithms exist, and it is within 0.02% of optimality otherwise.


title: Optimization in dynamic environments: a survey on problems, methods and measures

abstract: This paper provides a survey of the research done on optimization in dynamic environments over the past decade. We show an analysis of the most commonly used problems, methods and measures together with the newer approaches and trends, as well as their interrelations and common ideas. The survey is supported by a public web repository, located at http://www.dynamic-optimization.orgwhere the collected bibliography is manually organized and tagged according to different categories.


title: Current-Mirror-Based Potentiostats for Three-Electrode Amperometric Electrochemical Sensors

abstract: We present a new circuit topology for potentiostats that interface with three-electrode amperometric electrochemical sensors. In this new topology, a current-copying circuit, e.g., a current mirror, is placed in the sensor current path to generate a mirrored image of the sensor current. The mirrored image is then measured and processed instead of the sensor current itself. The new potentiostat topology consumes very low power, occupies a very small die area, and has potentially very low noise. These characteristics make the new topology very suitable for portable or bioimplantable applications. In order to demonstrate the feasibility of the new topology, we present the results of a potentiostat circuit implemented in a 0.18-mum CMOS process. The circuit converts the sensor current to a frequency-modulated pulse waveform, for which the time difference between two consecutive pulses is inversely proportional to the sensor current. The potentiostat measures the sensor current from 1 nA to 1 muA with better than 0.1% of accuracy. It consumes only 70 muW of power from a 1.8-V supply voltage and occupies an area of 0.02 mm2.


title: Attend, Adapt and Transfer: Attentive Deep Architecture for Adaptive Transfer from Multiple Source Tasks

abstract: Transferring knowledge from prior source tasks in solving a new target task can be useful in several learning applications. The application of transfer poses two serious challenges which have not been adequately addressed. First, the agent should be able to avoid negative transfer, which happens when the transfer hampers or slows down the learning instead of helping it. Second, the agent should be able to selectively transfer, which is the ability to select and transfer from different and multiple source tasks for different parts of the state space of the target task. We propose A2T (Attend, Adapt and Transfer), an attentive deep architecture which adapts and transfers from these source tasks. Our model is generic enough to effect transfer of either policies or value functions. Empirical evaluations on different learning algorithms show that A2T is an effective architecture for transfer by being able to avoid negative transfer while transferring selectively from multiple source tasks in the same domain.


title: Exploring emotional and imitational android-based interactions in autistic spectrum disorders

abstract: 1 Interdepartmental Research Center “E. Piaggio”, University of Pisa, Italy 2 Scientific Institute Stella Maris (IRCCS), Pisa, Italy Individuals with Autistic Spectrum Disorders (ASDs) have impairments in processing of social and emotional information. To widen emotive responsiveness, the employment of robotic systems to engage proactive interactive responses in children with ASDs has been recently suggested. Understanding and teaching the processing of socio-emotional abilities is the inspiring principle of this novel approach and could be of tremendous clinical significance. Encouraging studies with robotic dolls, mobile robots and humanoids acting as social mediators have provided important insights and demonstrate the necessity of long term studies. In this study we report on a series of experiments on four subjects affected by ASDs as they interact with a biomimetic android. We assessed both their spontaneous behavior and reactions to therapist presses in correlation with the time course of the physiological and behavioral data, as well as the focusing of attention towards the android’s eye movements and the spontaneous ability to imitate gesture and facial expressions. Overall, subjects demonstrated a decrease in dysfunction in the areas of social communication, implying a marked improvement in these areas after interacting with the android. EXPLORING EMOTIONAL AND IMITATIONAL ANDROID-BASED INTERACTIONS IN AUTISTIC SPECTRUM DISORDERS


title: Unconscious effects of language-specific terminology on preattentive color perception

abstract: It is now established that native language affects one's perception of the world. However, it is unknown whether this effect is merely driven by conscious, language-based evaluation of the environment or whether it reflects fundamental differences in perceptual processing between individuals speaking different languages. Using brain potentials, we demonstrate that the existence in Greek of 2 color terms—ghalazio and ble—distinguishing light and dark blue leads to greater and faster perceptual discrimination of these colors in native speakers of Greek than in native speakers of English. The visual mismatch negativity, an index of automatic and preattentive change detection, was similar for blue and green deviant stimuli during a color oddball detection task in English participants, but it was significantly larger for blue than green deviant stimuli in native speakers of Greek. These findings establish an implicit effect of language-specific terminology on human color perception.


title: Rogue access point detection methods: A review

abstract: Rogues are unwanted whether they are access points or clients as they steal critical data and bandwidth. To detect a rogue access point different approaches are used. These approaches are briefly classified as Client-side approach, Server-side and Hybrid approach. Every approach has its own advantages and disadvantages. Clients have limited resources and do not possess much control over network when compared with servers. Amongst all approaches Hybrid approach is efficient because it minimizes the inabilities of client side approach and adds server control for detection of Rogue Access Points (AP). This paper aims at surveying different methods of rogue Access Point detection.


Infarction of the Colon


title: Curiosity-Driven Exploration by Self-Supervised Prediction

abstract: In many real-world scenarios, rewards extrinsic to the agent are extremely sparse, or absent altogether. In such cases, curiosity can serve as an intrinsic reward signal to enable the agent to explore its environment and learn skills that might be useful later in its life. We formulate curiosity as the error in an agent's ability to predict the consequence of its own actions in a visual feature space learned by a self-supervised inverse dynamics model. Our formulation scales to high-dimensional continuous state spaces like images, bypasses the difficulties of directly predicting pixels, and, critically, ignores the aspects of the environment that cannot affect the agent. The proposed approach is evaluated in two environments: VizDoom and Super Mario Bros. Three broad settings are investigated: 1) sparse extrinsic reward, where curiosity allows for far fewer interactions with the environment to reach the goal; 2) exploration with no extrinsic reward, where curiosity pushes the agent to explore more efficiently; and 3) generalization to unseen scenarios (e.g. new levels of the same game) where the knowledge gained from earlier experience helps the agent explore new places much faster than starting from scratch. Demo video and code available at this https URL


title: A Machine Learning Approach for Semantic Structuring of Scientific Charts in Scholarly Documents

abstract: Large scholarly repositories are designed to provide scientists and researchers with a wealth of information that is retrieved from data present in a variety of formats. A typical scholarly document contains information in a combined layout of texts and graphic images. Common types of graphics found in these documents are scientific charts that are used to represent data values in a visual format. Experimental results are rarely described without the aid of one form of a chart or another, whether it is 2D plot, bar chart, pie chart, etc. Metadata of these graphics is usually the only content that is made available for search by user queries. By processing the image content and extracting the data represented in the graphics, search engines will be able to handle more specific queries related to the data itself. In this paper we describe a machine learning based system that extracts and recognizes the various data fields present in a bar chart for semantic labeling. Our approach comprises of a graphics and text separation and extraction phase, followed by a component role classification for both text and graphic components that are in turn used for semantic analysis and representation of the chart. The proposed system is tested on a set of over 200 bar charts extracted from over 1,000 scientific articles in PDF format.


title: LBP Texture Analysis Based on the Local Adaptive Niblack Algorithm

abstract: Local Binary Pattern (LBP) proposed by Ojala T is a typical texture analysis algorithm. It is widely used in the texture classification domain and etc. In this paper, we consider the local pixels’ effects among them efficiently when the LBP algorithm is used. In our proposed LBP algorithm, the central pixel in the local region was added into the procedure to calculate the local threshold using the Niblack algorithm. The histogram equalization is always used to actualize the illumination invariability in LBP algorithm. But the histogram equalization can not avoid the variety of local grayscale caused by the illumination. In order to avoid this weakness, a kind of illumination factor is brought into the Niblack algorithm. The values of the illumination factor can be got adaptively with the changes of illumination. By using the improved Niblack algorithm, the local threshold can be calculated adaptively. The proposed LBP algorithm in this paper can get more texture information and it has illumination adaptability.


title: A clustering fuzzy approach for image segmentation

abstract: Abstract Segmentation is a fundamental step in image description or classification. In recent years, several computational models have been used to implement segmentation methods but without establishing a single analytic solution. However, the intrinsic properties of neural networks make them an interesting approach, despite some measure of inefficiency. This paper presents a clustering approach for image segmentation based on a modified fuzzy approach for image segmentation (ART) model. The goal of the proposed approach is to find a simple model able to instance a prototype for each cluster avoiding complex post-processing phases. Results and comparisons with other similar models presented in the literature (like self-organizing maps and original fuzzy ART) are also discussed. Qualitative and quantitative evaluations confirm the validity of the approach proposed.


title: What is LTE actually used for? An answer through multi-operator, crowd-sourced measurement

abstract: LTE networks are commonplace nowadays; however, comparatively little is known about where (and why) they are deployed, and the demand they serve. We shed some light on these issues through large-scale, crowd-sourced measurement. Our data, collected by users of the WeFi app, spans multiple operators and multiple cities, allowing us to observe a wide variety of deployment patterns. Surprisingly, we find that LTE is frequently used to improve the {\em coverage} of network rather than the capacity thereof, and that no evidence shows that video traffic be a primary driver for its deployment. Our insights suggest that such factors as pre-existing networks and commercial policies have a deeper impact on deployment decisions than purely technical considerations.


title: A distributed ADMM approach for mobile data offloading in software defined network

abstract: Mobile data offloading has been introduced to alleviate the congestion of cellular networks and to improve the quality of service for mobile end users. This paper presents a distributed mechanism for mobile data offloading in software defined network (SDN) at the network edge. In SDN, the data traffic of base stations (BSs) can be dynamically offloaded to access points (APs), which is enabled by the SDN controller. The SDN controller formulates a revenue maximization problem to optimize the data offloading decision, and solves the problem in a fully distributed fashion. The proposed mechanism is based on the proximal Jacobian multi-block alternating direction method of multipliers (ADMM). BSs and APs perform the offloading decision update concurrently, and are coordinated by the SDN controller through dual variables to reach a consensus on the offloading demand and supply. Numerical simulations validate the effectiveness of the proposed algorithm.


title: Prognostics and health management of electronics

abstract: There has been a growing interest in monitoring the ongoing "health" of products and systems in order to predict failures and provide warning to avoid catastrophic failure. Here, health is defined as the extent of degradation or deviation from an expected normal condition. While the application of health monitoring, also referred to as prognostics, is well established for assessment of mechanical systems, this is not the case for electronic systems. However, electronic systems are integral to the functionality of most systems today, and their reliability is often critical for system reliability. This paper presents the state-of-practice and the current state-of-research in the area of electronics prognostics and health management. Four current approaches include built-in-test (BIT), use of fuses and canary devices, monitoring and reasoning of failure precursors, and modeling accumulated damage based on measured life-cycle loads. Examples are provided for these different approaches, and the implementation challenges are discussed.


title: Small codes and large image databases for recognition

abstract: The Internet contains billions of images, freely available online. Methods for efficiently searching this incredibly rich resource are vital for a large number of applications. These include object recognition, computer graphics, personal photo collections, online image search tools. In this paper, our goal is to develop efficient image search and scene matching techniques that are not only fast, but also require very little memory, enabling their use on standard hardware or even on handheld devices. Our approach uses recently developed machine learning techniques to convert the Gist descriptor (a real valued vector that describes orientation energies at different scales and orientations within an image) to a compact binary code, with a few hundred bits per image. Using our scheme, it is possible to perform real-time searches with millions from the Internet using a single large PC and obtain recognition results comparable to the full descriptor. Using our codes on high quality labeled images from the LabelMe database gives surprisingly powerful recognition results using simple nearest neighbor techniques.


title: Dilated FCN for Multi-Agent 2D/3D Medical Image Registration

abstract: 2D/3D image registration to align a 3D volume and 2D X-ray images is a challenging problem due to its ill-posed nature and various artifacts presented in 2D X-ray images. In this paper, we propose a multi-agent system with an auto attention mechanism for robust and efficient 2D/3D image registration. Specifically, an individual agent is trained with dilated Fully Convolutional Network (FCN) to perform registration in a Markov Decision Process (MDP) by observing a local region, and the final action is then taken based on the proposals from multiple agents and weighted by their corresponding confidence levels. The contributions of this paper are threefold. First, we formulate 2D/3D registration as a MDP with observations, actions, and rewards properly defined with respect to X-ray imaging systems. Second, to handle various artifacts in 2D X-ray images, multiple local agents are employed efficiently via FCN-based structures, and an auto attention mechanism is proposed to favor the proposals from regions with more reliable visual cues. Third, a dilated FCN-based training mechanism is proposed to significantly reduce the Degree of Freedom in the simulation of registration environment, and drastically improve training efficiency by an order of magnitude compared to standard CNN-based training method. We demonstrate that the proposed method achieves high robustness on both spine cone beam Computed Tomography data with a low signal-to-noise ratio and data from minimally invasive spine surgery where severe image artifacts and occlusions are presented due to metal screws and guide wires, outperforming other state-of-the-art methods (single agent-based and optimization-based) by a large margin.


title: Periodic leaky-wave antenna with transverse slots based on substrate integrated waveguide

abstract: In this paper a new periodic leaky-wave antenna based on substrate integrated waveguide (SIW) is proposed and investigated. The proposed antenna uses multiple transverse slots in the upper plane of the SIW for radiation into the free space. This antenna operates in the first negative Floquet mode. Therefore, it scans the backward quadrant by a single beam over the frequency band of 14.6 to 20.2 GHz. The proposed antenna is simulated and the results are investigated.


title: ENEG and ENEPIG surface finish for long term solderability

abstract: The long-term reliability of flip chip and boardlevel solder joint is significantly affected by the properties of the surface finish. Various surface finishes such as Immersion Sn, Immersion Ag, Organic solderability preservatives (OSP), Electrolytic Ni/Au (ENEG), and electroless Ni/immersion Au (ENIG) surface finish have been widely used on based Cu pads and can be compliant with lead free SAC solder alloys. Recently Electroless Ni/Electroless Pd/Immersion Au (ENEPIG) is being offered as an alternative surface finish with high solder joint quality and wire bondability. It is claimed to be more cost effective as an Au layer of lower thickness can be used. To evaluate the performance of solder joints upon ENEG and ENEPIG surface finishes, extended reflow tests at 245°C were conducted. In the case of ENEPIG surface finish, micro-porosities are found to be present on the Au plating surface. PdO oxide forms as a result of Pd exposure, which causes deterioration of solderability as compared with ENEG plating. Following extended reflow, it is found that columnar Cu-Ni-Sn IMCs with small amount of Pd and P-rich Ni layer have formed at the interface of ENEPIG /solder system. In contrast, layer-type Cu-Ni-Sn IMCs formed at the interface of ENEG/solder system in the absence of Pd. While Ni and Ni(P) layers act as barrier to diffusion, in some pad of the ENEPIG samples, the Ni(P) layer is found to be less than 1um after 120 minutes reflow, which is ineffective as a diffusion barrier. By comparison, ENEG surface finish remains effective with 3.5um of Ni barrier layer remaining on Cu after 120minutes reflow. This serves as a good barrier to prevent the diffusion of Cu atoms from the base Cu underneath. Therefore, it has been demonstrated that solder joint on ENEG surface finish is more reliable and suitable to be used for long-term reliability of electronic products.


title: Determinants of Corporate Borrowing: A Behavioral Perspective

abstract: This article integrates an earnings-based capital structure model into a simple real options framework to analyze the effects of managerial optimism and overconfidence on the interaction between financing and investment decisions. Several empirical implications follow from solving the model. Notably, my analysis reveals that managerial traits can ameliorate bondholder-shareholder conflicts, such as the debt overhang problem. While debt delays investment inefficiently, mildly biased managers can overcome this problem, even though they tend to issue more debt. Similar properties and results are discussed for other real options, such as the asset stripping or risk-shifting problems.


title: Iatrogenic swollen penis

abstract: We report what we think is the first case of localised angioedema of the glans penis as a result of contact allergy to prilocaine in EMLAP cream. We also propose a new term, contact angioedema for this condition.


title: Two-stage ELM for phishing Web pages detection using hybrid features

abstract: Increasing high volume phishing attacks are being encountered every day due to attackers' high financial returns. Recently, there has been significant interest in applying machine learning for phishing Web pages detection. Different from literatures, this paper introduces predicted labels of textual contents to be part of the features and proposes a novel framework for phishing Web pages detection using hybrid features consisting of URL-based, Web-based, rule-based and textual content-based features. We achieve this framework by developing an efficient two-stage extreme learning machine (ELM). The first stage is to construct classification models on textual contents of Web pages using ELM. In particular, we take Optical Character Recognition (OCR) as an assistant tool to extract textual contents from image format Web pages in this stage. In the second stage, a classification model on hybrid features is developed by using a linear combination model-based ensemble ELMs (LC-ELMs), with the weights calculated by the generalized inverse. Experimental results indicate the proposed framework is promising for detecting phishing Web pages.


title: A Novel Planar Endfire Circularly Polarized Antenna With Wide Axial-Ratio Beamwidth and Wide Impedance Bandwidth

abstract: A novel wideband planar endfire circularly polarized (CP) antenna with wide 3 dB axial ratio (AR) beamwidth is presented. The operation principle of the proposed CP antenna is described at first through the combination of a planar magnetic dipole and a V-shape open loop. Then, it is demonstrated how its AR beamwidth can be precisely controlled with resorting to the shape and thickness of the open loop element. Finally, the theoretical design approach is numerically verified, and the results are then experimentally validated. It is observed that a 3 dB AR beamwidth is achieved in an extremely wide angular region up to 250° within the principal elevation plane over a frequency range of 2.41–2.51 GHz. The proposed antenna has exhibited a wide impedance bandwidth of 22.23% in fraction, a 3 dB AR bandwidth of no less than 8.00% in fraction, and a simple planar profile in geometry. In addition, its endfire beam is in parallel with its plane.


title: Racial Disparity in Natural Language Processing: A Case Study of Social Media African-American English

abstract: We highlight an important frontier in algorithmic fairness: disparity in the quality of natural language processing algorithms when applied to language from authors of different social groups. For example, current systems sometimes analyze the language of females and minorities more poorly than they do of whites and males. We conduct an empirical analysis of racial disparity in language identification for tweets written in African-American English, and discuss implications of disparity in NLP.


title: Verifying haskell programs using constructive type theory

abstract: Proof assistants based on dependent type theory are closely related to functional programming languages, and so it is tempting to use them to prove the correctness of functional programs. In this paper, we show how Agda, such a proof assistant, can be used to prove theorems about Haskell programs. Haskell programs are translated into an Agda model of their semantics, by translating via GHC's Core language into a monadic form specially adapted to represent Haskell's polymorphism in Agda's predicative type system. The translation can support reasoning about either total values only, or total and partial values, by instantiating the monad appropriately. We claim that, although these Agda models are generated by a relatively complex translation process, proofs about them are simple and natural, and we offer a number of examples to support this claim.


title: Waveform and Numerology to Support 5G Services and Requirements

abstract: The standardization of the next generation 5G radio access technology has just started in 3GPP with the ambition of making it commercially available by 2020. There are a number of features that are unique for 5G radio access compared to the previous generations such as a wide range of carrier frequencies and deployment options, diverse use cases with very different user requirements, small-size base stations, self-backhaul, massive MIMO, and large channel bandwidths. In this article, we propose a flexible physical layer for the NR to meet the 5G requirements. A symmetric physical layer design with OFDM is proposed for all link types, including uplink, downlink, device-to-device, and backhaul. A scalable OFDM waveform is proposed to handle the wide range of carrier frequencies and deployments.


title: Information-centric networking for the industrial IoT

abstract: The wireless Internet of Things interconnects numerous constrained devices such as sensors and actuators not only with each other, but also with cloud services. We demonstrate a low power and lossy Information-Centric Network interworking with a cloud in an industrial application. Our approach includes a lightweight publish-subscribe system for NDN and an ICN-to-MQTT gateway which translates between NDN names and MQTT topics. This demo is based on RIOT and CCN-lite.


Context Aware Middleware Architectures: Survey and Challenges


title: On the need for different security methods on mobile phones

abstract: Mobile phones are rapidly becoming small-size general purpose computers, so-called smartphones. However, applications and data stored on mobile phones are less protected from unauthorized access than on most desktop and mobile computers. This paper presents a survey on users' security needs, awareness and concerns in the context of mobile phones. It also evaluates acceptance and perceived protection of existing and novel authentication methods. The responses from 465 participants reveal that users are interested in increased security and data protection. The current protection by using PIN (Personal Identification Number) is perceived as neither adequate nor convenient in all cases. The sensitivity of data stored on the devices varies depending on the data type and the context of use, asking for the need for another level of protection. According to these findings, a two-level security model for mobile phones is proposed. The model provides differential data and service protection by utilizing existing capabilities of a mobile phone for authenticating users.


title: Are Cars Just 3D Boxes? Jointly Estimating the 3D Shape of Multiple Objects

abstract: Current systems for scene understanding typically represent objects as 2D or 3D bounding boxes. While these representations have proven robust in a variety of applications, they provide only coarse approximations to the true 2D and 3D extent of objects. As a result, object-object interactions, such as occlusions or ground-plane contact, can be represented only superficially. In this paper, we approach the problem of scene understanding from the perspective of 3D shape modeling, and design a 3D scene representation that reasons jointly about the 3D shape of multiple objects. This representation allows to express 3D geometry and occlusion on the fine detail level of individual vertices of 3D wireframe models, and makes it possible to treat dependencies between objects, such as occlusion reasoning, in a deterministic way. In our experiments, we demonstrate the benefit of jointly estimating the 3D shape of multiple objects in a scene over working with coarse boxes, on the recently proposed KITTI dataset of realistic street scenes.


title: Extending SAT Solvers to Cryptographic Problems

abstract: Cryptography ensures the confidentiality and authenticity of information but often relies on unproven assumptions. SAT solvers are a powerful tool to test the hardness of certain problems and have successfully been used to test hardness assumptions. This paper extends a SAT solver to efficiently work on cryptographic problems. The paper further illustrates how SAT solvers process cryptographic functions using automatically generated visualizations, introduces techniques for simplifying the solving process by modifying cipher representations, and demonstrates the feasibility of the approach by solving three stream ciphers. ::: ::: To optimize a SAT solver for cryptographic problems, we extended the solver's input language to support the XOR operation that is common in cryptography. To better understand the inner workings of the adapted solver and to identify bottlenecks, we visualize its execution. Finally, to improve the solving time significantly, we remove these bottlenecks by altering the function representation and by pre-parsing the resulting system of equations. ::: ::: The main contribution of this paper is a new approach to solving cryptographic problems by adapting both the problem description and the solver synchronously instead of tweaking just one of them. Using these techniques, we were able to solve a well-researched stream cipher 26 times faster than was previously possible.


title: Machine learning-based defense against process-aware attacks on Industrial Control Systems

abstract: The modernization of Industrial Control Systems (ICS), primarily targeting increased efficiency and controllability through integration of Information Technologies (IT), introduced the unwanted side effect of extending the ICS cyber-security threat landscape. ICS are facing new security challenges and are exposed to the same vulnerabilities that plague IT, as demonstrated by the increasing number of incidents targeting ICS. Due to the criticality and unique nature of these systems, it is important to devise novel defense mechanisms that incorporate knowledge of the underlying physical model, and can detect attacks in early phases. To this end, we study a benchmark chemical process, and enumerate the various categories of attack vectors and their practical applicability on hardware controllers in a Hardware-In-The-Loop testbed. Leveraging the observed implications of the categorized attacks on the process, as well as the profile of typical disturbances, we follow a data-driven approach to detect anomalies that are early indicators of malicious activity.


title: Selectively attending to auditory objects

abstract: The ability to maintain a conversation with one person while at a noisy cocktail party has often been used to illustrate a general characteristic of auditory selective attention, namely that perceivers' attention is usually directed to a particular set of sounds and not to others. Part of the cocktail party problem involves parsing co-occurring speech sounds and simultaneously integrating these various speech tokens into meaningful units ("auditory scene analysis"). Here, we review auditory perception and selective attention studies in an attempt to determine the role of perceptual organization in selective attention. Results from several behavioral and electrophysiological studies indicate that the ability to focus attention selectively on a particular sound source depends on a preliminary analysis that partitions the auditory input into distinct perceptual objects. Most findings can be accounted for by an object-based hypothesis in which auditory attention is allocated to perceptual objects derived from the auditory scene according to perceptual grouping principles.


title: Detecting Events in Online Social Networks: Definitions, Trends and Challenges

abstract: Event detection is a research area that attracted attention during the last years due to the widespread availability of social media data. The problem of event detection has been examined in multiple social media sources like Twitter, Flickr, YouTube and Facebook. The task comprises many challenges including the processing of large volumes of data and high levels of noise. In this article, we present a wide range of event detection algorithms, architectures and evaluation methodologies. In addition, we extensively discuss on available datasets, potential applications and open research issues. The main objective is to provide a compact representation of the recent developments in the field and aid the reader in understanding the main challenges tackled so far as well as identifying interesting future research directions.


title: How digital identity on blockchain can contribute in a smart city environment

abstract: Nowadays, in this digital world, one of the biggest concerns for business and many other public entities, is to know precisely the “identity” of the users that are behind of their systems. Since the data can define a person, there have been many tries to develop technology to determine accurately who the users are and certify their basics attributes like name, address, credit record, as well as other personal characteristics like health status, hobbies and others. That is why Digital Identity has taken a significantly important role in this area and becoming as a crucial security measure in this interconnected environment. This research is a systematic mapping review with the goal of collecting all relevant existing research of Digital Identity on Blockchain technology implemented in a smart city environment. The objective of this paper is to understand the current research topics, challenges and future directions of these areas from the technical point of view. It is expected that this paper can stimulate interest in theory and practice to further discussions and research in these areas.


title: On Passion for Life Activities

abstract: Abstract The purpose of this chapter is to review research that focuses on a new conceptualization on passion for activities, the Dualistic Model of Passion (DMP) [Vallerand, R. J., Blanchard, C. M., Mageau, G. A., Koestner, R., Ratelle, C. F., Leonard, M., et al. (2003). Les passions de l'âme: On obsessive and harmonious passion. Journal of Personality and Social Psychology, 85, 756–767.]. Passion is defined as a strong inclination toward a self-defining activity that people love, find important, and in which they invest time and energy. This model further posits the existence of two types of passion (harmonious and obsessive) each associated with different determinants, outcomes, and psychological processes. Harmonious passion originates from an autonomous internalization of the activity in identity, leading people to choose to engage in the activity that they love. It is expected to mainly lead to adaptive outcomes. Conversely, obsessive passion is derived from a controlled internalization and is experienced as an uncontrollable desire to engage in the activity that one loves. Obsessive passion is hypothesized to typically predict less adaptive outcomes. Results of several studies conducted with participants of all ages engaged in a diverse range of activities provide support for the model. These findings reveal that passion matters not only with respect to intrapersonal outcomes (e.g., cognition, affect, psychological well-being, physical health, and performance), but also for interpersonal, intergroup, and societal consequences. The determinants of passion as well as the importance of taking into account the nature of the situation to better predict the consequences of the two types of passion are also addressed. Overall, the research reviewed clearly supports the DMP and attests to the significant role of passion in people's lives.


title: A framework for the governance of information security

abstract: This paper highlights the importance of protecting an organization's vital business information assets by investigating several fundamental considerations that should be taken into account in this regard. Based on this, it is illustrated that information security should be a priority of executive management, including the Board and CEO and should therefore commence as a corporate governance responsibility. This paper, therefore, motivates that there is a need to integrate information security into corporate governance through the development of an information security governance (ISG) framework. This paper further proposes such a framework to aid an organization in its ISG efforts.


title: Complex Empirical Mode Decomposition

abstract: A method for the empirical mode decomposition (EMD) of complex-valued data is proposed. This is achieved based on the filter bank interpretation of the EMD mapping and by making use of the relationship between the positive and negative frequency component of the Fourier spectrum. The so-generated intrinsic mode functions (IMFs) are complex-valued, which facilitates the extension of the standard EMD to the complex domain. The analysis is supported by simulations on both synthetic and real-world complex-valued signals


title: The Persistence and Transience of Memory

abstract: The predominant focus in the neurobiological study of memory has been on remembering (persistence). However, recent studies have considered the neurobiology of forgetting (transience). Here we draw parallels between neurobiological and computational mechanisms underlying transience. We propose that it is the interaction between persistence and transience that allows for intelligent decision-making in dynamic, noisy environments. Specifically, we argue that transience (1) enhances flexibility, by reducing the influence of outdated information on memory-guided decision-making, and (2) prevents overfitting to specific past events, thereby promoting generalization. According to this view, the goal of memory is not the transmission of information through time, per se. Rather, the goal of memory is to optimize decision-making. As such, transience is as important as persistence in mnemonic systems.


title: The role of exercise in evaluating and managing pulmonary disorders in children and adolescents

abstract: Exercise testing, as a diagnostic and therapeutic modality in managing pulmonary diseases in children, has not been fully utilized. Monitoring the unique response to the stress of exercise that children possess can aid in the diagnosis of pulmonary pathophysiology. This review addresses the exercise response in pulmonary diseases and discusses the role of exercise in the continuing management of these disorders. Pediatric pulmonary disorders including asthma, cystic fibrosis, chest-wall deformities, interstitial lung disease, vocal cord dysfunction, and lung transplantation are highlighted.


title: Occupancy Mapping and Surface Reconstruction Using Local Gaussian Processes With Kinect Sensors

abstract: Although RGB-D sensors have been successfully applied to visual SLAM and surface reconstruction, most of the applications aim at visualization. In this paper, we propose a noble method of building continuous occupancy maps and reconstructing surfaces in a single framework for both navigation and visualization. Particularly, we apply a Bayesian nonparametric approach, Gaussian process classification, to occupancy mapping. However, it suffers from high-computational complexity of O(n3)+O(n2m), where n and m are the numbers of training and test data, respectively, limiting its use for large-scale mapping with huge training data, which is common with high-resolution RGB-D sensors. Therefore, we partition both training and test data with a coarse-to-fine clustering method and apply Gaussian processes to each local clusters. In addition, we consider Gaussian processes as implicit functions, and thus extract iso-surfaces from the scalar fields, continuous occupancy maps, using marching cubes. By doing that, we are able to build two types of map representations within a single framework of Gaussian processes. Experimental results with 2-D simulated data show that the accuracy of our approximated method is comparable to previous work, while the computational time is dramatically reduced. We also demonstrate our method with 3-D real data to show its feasibility in large-scale environments.


title: Humanities and social sciences The Route to Crisis: Cities, Trade, and Epidemics of the Roman Empire

abstract: The goal of this paper is to provide a universal model that outlines the origins of disease contraction and transmission in the ancient Mediterranean world. While numerous scholars have written on the subject of epidemics and their effects on the Roman Empire, very limited studies focus on the general causes of epidemics throughout the Roman era. I propose that Roman culture, urbanism, and the interdependence between cities and provinces led to the entry and transmission of communicable diseases, contributing to conditions that ultimately caused the fall of the empire. I first discuss and describe the causal factors of epidemics. I start by presenting Roman ideologies as this will aid in the understanding of city structure. After a description of a typical city layout, I present the conditions which encouraged the spread of disease within cities. Cities and provinces were not in isolation, but rather part of a greater economic network. I, therefore, also describe the nature of trade routes and how trade impacted the global spread of pathogens. I conclude by applying the proposed theories to two plagues—the Antonine Plague and Justinianic Plague—from different, yet comparable, time periods. I also gauge the effects of disease on Roman economy, demography, and society by using these case studies.


Human factors in programming and software development


title: The Use of Attack and Protection Trees to Analyze Security for an Online Banking System

abstract: Online banking has become increasingly important to the profitability of financial institutions as well as adding convenience for their customers. As the number of customers using online banking increases, online banking systems are becoming more desirable targets for criminals to attack. To maintain their customers' trust and confidence in the security of their online bank accounts, financial institutions must identify how attackers compromise accounts and develop methods to protect them. Attack trees and protection trees are a cost effective way to do this. Attack trees highlight the weaknesses in a system and protection trees provide a methodical means of mitigating these weaknesses. In this paper, a notional online banking system is analyzed and protection solutions are proposed for varying budgets


title: NAViGaTOR: Network Analysis, Visualization and Graphing Toronto

abstract: Summary: NAViGaTOR is a powerful graphing application for the 2D and 3D visualization of biological networks. NAViGaTOR includes a rich suite of visual mark-up tools for manual and automated annotation, fast and scalable layout algorithms and OpenGL hardware acceleration to facilitate the visualization of large graphs. Publication-quality images can be rendered through SVG graphics export. NAViGaTOR supports community-developed data formats (PSI-XML, BioPax and GML), is platform-independent and is extensible through a plug-in architecture. Availability: NAViGaTOR is freely available to the research community from http://ophid.utoronto.ca/navigator/. Installers and documentation are provided for 32- and 64-bit Windows, Mac, Linux and Unix.


title: Principles of Layered Attestation

abstract: Systems designed with measurement and attestation in mind are often layered, with the lower layers measuring the layers above them. Attestations of such systems, which we call layered attestations, must bundle together the results of a diverse set of application-specific measurements of various parts of the system. Some methods of layered attestation are more trustworthy than others, so it is important for system designers to understand the trust consequences of different system configurations. This paper presents a formal framework for reasoning about layered attestations, and provides generic reusable principles for achieving trustworthy results.


title: Breaking the Hierarchy: Distributed Control&Economic Optimality in Microgrids

abstract: Modeled after the hierarchical control architecture of power transmission systems, a layering of primary, secondary, and tertiary control has become the standard operation paradigm for islanded microgrids. Despite this superficial similarity, the control objectives in microgrids across these three layers are varied and ambitious, and they must be achieved while allowing for robust plug-and-play operation and maximal flexibility, without hierarchical decision making and time-scale separations. In this work, we explore control strategies for these three layers and illuminate some possibly-unexpected connections and dependencies among them. Building from a first-principle analysis of decentralized primary droop control, we study centralized, decentralized, and distributed architectures for secondary frequency regulation. We find that averaging-based distributed controllers using communication among the generation units offer the best combination of flexibility and performance. We further leverage these results to study constrained AC economic dispatch in a tertiary control layer. Surprisingly, we show that the minimizers of the economic dispatch problem are in one-to-one correspondence with the set of steady-states reachable by droop control. In other words, the adoption of droop control is necessary and sufficient to achieve economic optimization. This equivalence results in simple guidelines to select the droop coefficients, which include the known criteria for power sharing. We illustrate the performance and robustness of our designs through simulations.


title: Shadow Suppression using RGB and HSV Color Space in Moving Object Detection

abstract: Video-surveillance and traffic analysis systems can be heavily improved using vision-based techniques to extract, manage and track objects in the scene. However, problems arise due to shadows. In particular, moving shadows can affect the correct localization, measurements and detection of moving objects. This work aims to present a technique for shadow detection and suppression used in a system for moving visual object detection and tracking. The major novelty of the shadow detection technique is the analysis carried out in the HSV color space to improve the accuracy in detecting shadows. This paper exploits comparison of shadow suppression using RGB and HSV color space in moving object detection and results in this paper are more encouraging using HSV colour space over RGB colour space.


title: Energy Saving in Electric Heater of Carbon Fiber Stabilization Oven

abstract: Carbon fiber is an advanced material with high tensile strength and modulus, ideally suited for light weight applications. Carbon fiber properties are directly dependent on all aspects of production, especially the process step of thermal stabilization. Stabilization is considered to be one of the most critical process steps. Moreover, the stabilization process is the most energy consuming, time consuming and costly step. As oxidation is an exothermic process, constant airflow to uniformly remove heat from all tows across the towband is indispensable. Our approach is to develop an intelligent computational system that can construct an optimal Computational Fluid Dynamics (CFD) solution. In this study, an electrical heater has been designed by CFD modeling and intelligently controlled. The model results show that the uniform airflow and minimum turbulence kinetic energy can be achieved by combining intelligent system technology with CFD analysis strategy.


title: Adversarial Audio Synthesis

abstract: Audio signals are sampled at high temporal resolutions, and learning to synthesize audio requires capturing structure across a range of timescales. Generative adversarial networks (GANs) have seen wide success at generating images that are both locally and globally coherent, but they have seen little application to audio generation. In this paper we introduce WaveGAN, a first attempt at applying GANs to unsupervised synthesis of raw-waveform audio. WaveGAN is capable of synthesizing one second slices of audio waveforms with global coherence, suitable for sound effect generation. Our experiments demonstrate that, without labels, WaveGAN learns to produce intelligible words when trained on a small-vocabulary speech dataset, and can also synthesize audio from other domains such as drums, bird vocalizations, and piano. We compare WaveGAN to a method which applies GANs designed for image generation on image-like audio feature representations, finding both approaches to be promising.


title: Defining Domain Language of Graphical User Interfaces

abstract: Domain-specific languages are computer (programming, modeling, specification) languages devoted to solving problems in a specific domain. The least examined DSL development phases are analysis and design. Various formal methodologies exist, however domain analysis is still done informally most of the time. There are also methodologies of deriving DSLs from existing ontologies but the presumption is to have an ontology for the specific domain. We propose a solution of a user interface driven domain analysis and we ::: focus on how it can be incorporated into the DSL design phase. We will present the preliminary results of the DEAL prototype, which can be used to transform GUIs to DSL grammars incorporating concepts from a domain and thus to help in the preliminary phases of the DSL design.


title: An improved beta-galactosidase reporter gene.

abstract: The coding sequence for the E. coli beta-galactosidase gene was codon-optimised for expression in mammalian cells. When expressed in mammalian cells the codon-optimised gene results in the expression of beta-galactosidase at levels 15-fold higher than those resulting from an analogous construct containing the native E. coli gene sequence. RNA analysis suggests the enhancement of beta-galactosidase expression is due both to enhanced transcript stability and increased translational efficiency. When used in a lentiviral construct the codon-optimised gene results in an approximately five-fold increase in apparent titre, as determined by 5-bromo-4-chloro-3-indolyl-beta-D-galactopyranoside staining, in comparison to an analogous construct containing the native E. coli gene. Southern blot analysis shows this is due to an increased efficiency of detection of transduced cells. In addition, codon-optimisation results in the elimination of several cryptic splice acceptor sites that are present in the native E. coli gene sequence. In a lentiviral vector containing a 5' splice donor the use of the codon-optimised gene in place of the native E. coli beta-galactosidase gene resulted in increased amounts of un-spliced, full-length genomic RNA. Therefore, as a marker/reporter gene in mammalian cells the codon-optimised beta-galactosidase gene has a number of advantages over the native E. coli gene sequence. A variant of the codon-optimised beta-galactosidase gene sequence that includes an effective nuclear localisation signal was also made.


title: Persian handwritten digit recognition by random forest and convolutional neural networks

abstract: Persian handwritten digit recognition has attracted some interests in the research community by introduction of large Hoda dataset. In this paper, the well-known random forest (RF) and convolutional neural network (CNN) algorithms are investigated for Persian handwritten digit recognition on the Hoda dataset. Using the Hoda dataset as a standard testbed, we have performed some experiments with different preprocessing steps, feature types, and baselines. It is then shown that RFs and CNNs perform competitively with the state-of-the-art methods on this dataset, while CNNs being the fastest if appropriate hardware is available.


title: Morphological Image Processing Approach Using K-Means Clustering for Detection of Tumor in Brain

abstract: The brain is the anterior part of the central nervous system. Along with the spinal cord, it forms the central nervous system (CNS). Brain tumor is an abnormal growth caused by cells reproducing themselves in an uncontrolled manner. Magnetic resonance imaging (MRI) is the commonly used technique for diagnosis of brain tumors. In MRI, the amount of data obtained is very large to manually analyze and interpret. Segmentation is an important process in medical image analysis and it is a challenging problem due to noise present in the input images. Clustering is an efficient method for biomedical image segmentation. In this study, we propose to use K-means clustering algorithm under Morphological Image Processing (MIP). The input to this algorithm is an MR image of the human brain. The position of tumor objects is detected from an MR image by using a clustering algorithm. This enhances the tumor boundaries more precisely and the performance is evaluated based on execution time and accuracy of the algorithms. It produces the reliable results that are less sensitive to error.


title: Large displacement optical flow

abstract: The literature currently provides two ways to establish point correspondences between images with moving objects. On one side, there are energy minimization methods that yield very accurate, dense flow fields, but fail as displacements get too large. On the other side, there is descriptor matching that allows for large displacements, but correspondences are very sparse, have limited accuracy, and due to missing regularity constraints there are many outliers. In this paper we propose a method that can combine the advantages of both matching strategies. A region hierarchy is established for both images. Descriptor matching on these regions provides a sparse set of hypotheses for correspondences. These are integrated into a variational approach and guide the local optimization to large displacement solutions. The variational optimization selects among the hypotheses and provides dense and subpixel accurate estimates, making use of geometric constraints and all available image information.


title: Stable aqueous dispersions of graphitic nanoplatelets via the reduction of exfoliated graphite oxide in the presence of poly(sodium 4-styrenesulfonate)

abstract: For the first time, stable aqueous dispersions of polymer-coated graphitic nanoplatelets can be prepared via an exfoliation/in-situ reduction of graphite oxide in the presence of poly(sodium 4-styrenesulfonate).


title: Understanding persistence in the use of Online Fitness Communities: Comparing novice and experienced users

abstract: Mobile and wearable technologies facilitate physiological data collection for health and wellness purposes. Users typically access these data via Online Fitness Community (OFC) platforms (e.g., Fitbit, Strava, RunKeeper). These platforms present users with functionalities centered on self-monitoring, social networking and enjoyment. In order to fully benefit from these functionalities, users need to make a habit out of integrating OFC use into their everyday workout routines. However, research suggests that users often fail to use OFCs over a longer period of time. This study sheds light on the factors that explain persisted OFC use. To that end, the study compares novice and experienced users in terms of their OFC use motives and how these motives contribute to the habitual integration of OFCs into everyday workout routines. Based on the survey responses of 394 OFC users, a multi-sample structural equation model indicates that self-regulatory and social motives directly predict habitual OFC use, and that enjoyment and self-regulatory motives indirectly predict habitual OFC use, by driving the perceived usefulness of OFCs. Moderation analysis revealed that, for novice users, self-regulatory motives are the prime drivers of habitual OFC use, while social motives and enjoyment are more important for experienced users.


title: Generic Inference in Latent Gaussian Process Models

abstract: We develop an automated variational method for inference in models with Gaussian process (GP) priors and general likelihoods. The method supports multiple outputs and multiple latent functions and does not require detailed knowledge of the conditional likelihood, only needing its evaluation as a black-box function. Using a mixture of Gaussians as the variational distribution, we show that the evidence lower bound and its gradients can be estimated efficiently using empirical expectations over univariate Gaussian distributions. Furthermore, the method is scalable to large datasets which is achieved by using an augmented prior via the inducing-variable approach underpinning most sparse GP approximations, along with parallel computation and stochastic optimization. We evaluate our method with experiments on small datasets, medium-scale datasets and a large dataset, showing its competitiveness under different likelihood models and sparsity levels. Moreover, we analyze learning in our model under batch and stochastic settings, and study the effect of optimizing the inducing inputs. Finally, in the large-scale experiment, we investigate the problem of predicting airline delays and show that our method is on par with the state-of-the-art hard-coded approach for scalable GP regression.


title: The neurobiology of Meditation and its clinical effectiveness in psychiatric disorders

abstract: This paper reviews the evidence for changes of Meditation on body and brain physiology and for clinical effectiveness in disorders of psychiatry. The aim of Meditation is to reduce or eliminate irrelevant thought processes through training of internalised attention, thought to lead to physical and mental relaxation, stress reduction, psycho-emotional stability and enhanced concentration. Physiological evidence shows a reduction with Meditation of stress-related autonomic and endocrine measures, while neuroimaging studies demonstrate the functional up-regulation of brain regions of affect regulation and attention control. Clinical studies show some evidence for the effectiveness of Meditation in disorders of affect, anxiety and attention. The combined evidence from neurobiological and clinical studies seems promising. However, a more thorough understanding of the neurobiological mechanisms of action and clinical effectiveness of the different Meditative practices is needed before Meditative practices can be leveraged in the prevention and intervention of mental illness.


title: Online Recognition of Handwritten Korean and English Characters

abstract: Abstract —In this study, an improved HMM based recognition model is proposed for online English and Korean handwritten characters. The pattern elements of the handwriting model are sub character strokes and ligatures. To deal with the problem of handwriting style variations, a modified Hierarchical Clustering approach is introduced to partition different writing styles into several classes. For each of the English letters and each primitive grapheme in Korean characters, one HMM that models the temporal and spatial variability of the handwriting is constructed based on each class. Then the HMMs of Korean graphemes are concatenated to form the Korean character models. The recognition of handwritten characters is implemented by a modified level building algorithm, which incorporates the Korean character combination rules within the efficient network search procedure. Due to the limitation of the HMM based method, a post-processing procedure that takes the global and structural features into account is proposed. Experiments showed that the proposed recognition system achieved a high writer independent recognition rate on unconstrained samples of both English and Korean characters. The comparison with other schemes of HMM-based recognition was also performed to evaluate the system


title: Efficiently correlating complex events over live and archived data streams

abstract: Correlating complex events over live and archived data streams, which we call Pattern Correlation Queries (PCQs), provides many benefits for domains which need real time forecasting of events or identification of causal dependencies, while handling data at high rates and in massive amounts, like in financial or medical settings. Existing work has focused either on complex event processing over a single type of stream source (i.e., either live or archived), or on simple stream correlation queries (e.g., live events trigerring a database lookup). In this paper, we specifically focus on recency-based PCQs and provide clear, useful, and optimizable semantics for them. PCQs raise a number of challenges in optimizing data management and query processing, which we address in the setting of the DejaVu complex event processing system. More specifically, we propose three complementary optimizations including recent input buffering, query result caching, and join source ordering. Furthermore, we capture the relevant query processing tradeoffs in a cost model. An extensive performance study on synthetic and real-life data sets not only validates this cost model, but also shows that our optimizations are very effective, achieving more than two orders magnitude throughput improvement and much better scalability compared to a conventional approach.


title: Practical solutions to the problem of diagonal dominance in kernel document clustering

abstract: In supervised kernel methods, it has been observed that the performance of the SVM classifier is poor in cases where the diagonal entries of the Gram matrix are large relative to the off-diagonal entries. This problem, referred to as diagonal dominance, often occurs when certain kernel functions are applied to sparse high-dimensional data, such as text corpora. In this paper we investigate the implications of diagonal dominance for unsupervised kernel methods, specifically in the task of document clustering. We propose a selection of strategies for addressing this issue, and evaluate their effectiveness in producing more accurate and stable clusterings.


title: RNAex: an RNA secondary structure prediction server enhanced by high-throughput structure-probing data

abstract: Several high-throughput technologies have been developed to probe RNA base pairs and loops at the transcriptome level in multiple species. However, to obtain the final RNA secondary structure, extensive effort and considerable expertise is required to statistically process the probing data and combine them with free energy models. Therefore, we developed an RNA secondary structure prediction server that is enhanced by experimental data (RNAex). RNAex is a web interface that enables non-specialists to easily access cutting-edge structure-probing data and predict RNA secondary structures enhanced by in vivo and in vitro data. RNAex annotates the RNA editing, RNA modification and SNP sites on the predicted structures. It provides four structure-folding methods, restrained MaxExpect, SeqFold, RNAstructure (Fold) and RNAfold that can be selected by the user. The performance of these four folding methods has been verified by previous publications on known structures. We re-mapped the raw sequencing data of the probing experiments to the whole genome for each species. RNAex thus enables users to predict secondary structures for both known and novel RNA transcripts in human, mouse, yeast and Arabidopsis. The RNAex web server is available at http://RNAex.ncrnalab.org/.


title: HDRM: A Resolution Complete Dynamic Roadmap for Real-Time Motion Planning in Complex Scenes

abstract: In this letter, we first theoretically prove the conditions and boundaries of resolution completeness for deterministic roadmap methods with a discretized workspace. A novel variant of such methods, the hierarchical dynamic roadmap (HDRM), is then proposed for solving complex planning problems. A unique hierarchical structure to efficiently encode the configuration-to-workspace occupation information is introduced and allows the robot to check the collision state of tens of millions of samples on-the-fly—the number of which was previously strictly limited by available memory. The hierarchical structure also significantly reduces the time for path searching, hence, the robot is able to find feasible motion plans in real-time in extremely constrained environments. A rigorous benchmarking shows that HDRM is robust and computationally fast compared with classical dynamic roadmap methods and other state-of-the-art planning algorithms. Experiments on the seven degree-of-freedom KUKA LWR robotic arm integrated with live perception further validate the effectiveness of HDRM in complex environments.


title: Scalar quantization for large scale image search

abstract: Bag-of-Words (BoW) model based on SIFT has been widely used in large scale image retrieval applications. Feature quantization plays a crucial role in BoW model, which generates visual words from the high dimensional SIFT features, so as to adapt to the inverted file structure for indexing. Traditional feature quantization approaches suffer several problems: 1) high computational cost---visual words generation (codebook construction) is time consuming especially with large amount of features; 2) limited reliability---different collections of images may produce totally different codebooks and quantization error is hard to be controlled; 3) update inefficiency--once the codebook is constructed, it is not easy to be updated. In this paper, a novel feature quantization algorithm, scalar quantization, is proposed. With scalar quantization, a SIFT feature is quantized to a descriptive and discriminative bit-vector, of which the first tens of bits are taken out as code word. Our quantizer is independent of collections of images. In addition, the result of scalar quantization naturally lends itself to adapt to the classic inverted file structure for image indexing. Moreover, the quantization error can be flexibly reduced and controlled by efficiently enumerating nearest neighbors of code words. The performance of scalar quantization has been evaluated in partial-duplicate Web image search on a database of one million images. Experiments reveal that the proposed scalar quantization achieves a relatively 42% improvement in mean average precision over the baseline (hierarchical visual vocabulary tree approach), and also outperforms the state-of-the-art Hamming Embedding approach and soft assignment method.


A Review on Techniques for Computer Aided Diagnosis of Soft Markers for Detection of Down Syndrome in Ultrasound Fetal Images


title: Online Semi-Supervised Learning with Deep Hybrid Boltzmann Machines and Denoising Autoencoders

abstract: Two novel deep hybrid architectures, the Deep Hybrid Boltzmann Machine and the Deep Hybrid Denoising Auto-encoder, are proposed for handling semi-supervised learning problems. The models combine experts that model relevant distributions at different levels of abstraction to improve overall predictive performance on discriminative tasks. Theoretical motivations and algorithms for joint learning for each are presented. We apply the new models to the domain of data-streams in work towards life-long learning. The proposed architectures show improved performance compared to a pseudo-labeled, drop-out rectifier network.


title: Modeling and Analysis of Model Reference Adaptive Control by Using MIT and Modified MIT Rule for Speed Control of DC Motor

abstract: Normal feedback controllers may not perform well, because of the variations in process or Plant due to nonlinear actuators, changes in environmental conditions. The design of a controller for speed control of DC Motor with Model Reference Adaptive Control scheme using the MIT rule for adaptive mechanism is presented in this paper. The controller gives reasonable results, but to the changes in the amplitude of reference signal it is very sensitive. It is shown from the simulation work carried out in this paper that adaptive system becomes oscillatory if the value of adaptation gain or the amplitude of reference signal is sufficiently large. This paper also deals with the use of MIT rule along with the normalized algorithm to handle the variations in the reference signal, and this adaptation law is referred as modified MIT rule. The Modeling of MRAC is shown by means of simulation on MATLAB.


title: Recurrent Continuous Translation Models

abstract: We introduce a class of probabilistic continuous translation models called Recurrent Continuous Translation Models that are purely based on continuous representations for words, phrases and sentences and do not rely on alignments or phrasal translation units. The models have a generation and a conditioning aspect. The generation of the translation is modelled with a target Recurrent Language Model, whereas the conditioning on the source sentence is modelled with a Convolutional Sentence Model. Through various experiments, we show first that our models obtain a perplexity with respect to gold translations that is > 43% lower than that of stateof-the-art alignment-based translation models. Secondly, we show that they are remarkably sensitive to the word order, syntax, and meaning of the source sentence despite lacking alignments. Finally we show that they match a state-of-the-art system when rescoring n-best lists of translations.


title: Evaluating the Financial Impact of Branding Using Trademarks: A Framework and Empirical Evidence

abstract: Abstract Firms spend considerable efforts to build brand awareness and associations among consumers. Yet there is a limited understanding of the financial returns of such investments. In this article, the authors present a framework that uses trademarks as measures of firms' branding efforts. They classify trademarks into two categories—brand-identification trademarks and brand-association trademarks—and propose that they are indicators of firm efforts to build brand awareness and associations among consumers, respectively. The authors then evaluate the chain of effects linking such assets with metrics of firms' financial value. A longitudinal analysis of data collected from secondary sources reveals that the stock (i.e., total number) of brand-association trademarks available to firms in time period t increases their cash flow, Tobin's q, return on assets, and stock returns and reduces their cash-flow variability in period t + 1. Furthermore, the authors observe that the stock of brand-identification tra...


title: Service Provider DevOps

abstract: Although there is consensus that software defined networking and network functions virtualization overhaul service provisioning and deployment, the community still lacks a definite answer on how carrier-grade operations praxis needs to evolve. This article presents what lies beyond the first evolutionary steps in network management, identifies the challenges in service verification, observability, and troubleshooting, and explains how to address them using our Service Provider DevOps (SP-DevOps) framework. We compendiously cover the entire process from design goals to tool realization and employ an elastic version of an industry-standard use case to show how on-the-fly verification, software-defined monitoring, and automated troubleshooting of services reduce the cost of fault management actions. We assess SP-DevOps with respect to key attributes of software-defined telecommunication infrastructures both qualitatively and quantitatively, and demonstrate that SP-DevOps paves the way toward carrier-grade operations and management in the network virtualization era.


title: Predictors of Registered Nurses' Lifting Behavior

abstract: 1. Nurses continue to experience back pain and injury as a result of occupational stressors, with most attributed to lifting clients. 2. Research found that only 2% of nurses lifted clients as prescribed by current nursing textbooks. 3. Nurses' lifting behavior was related to the age of the nurse and the type of client movement. 4. Ongoing education regarding prescribed lifting behavior in clinical settings and continued research regarding strategies to increase the prevalence of safe behavior in the workplace are recommended.


title: A single stage integrated bidirectional AC/DC and DC/DC converter for plug-in hybrid electric vehicles

abstract: The add-on battery pack in plug-in hybrid electric vehicles can be charged from an AC outlet, feed power back to the grid, provide power for electric traction, and capture regenerative energy when braking. Conventionally, three-stage bidirectional converter interfaces are used to fulfil these functions. In this paper, a single stage integrated converter is proposed based on direct AC/DC conversion theory. The proposed converter eliminates the full bridge rectifier, reduces the number of semiconductor switches and high current inductors, and improves the conversion efficiency.


Data Mining in Healthcare : Current Applications and Issues By


title: EPBC: Efficient Public Blockchain Client for lightweight users

abstract: Public blockchains provide a decentralized method for storing transaction data and have many applications in different sectors. In order for a user to track transactions, a simple method is that every user keeps a local copy of the entire public ledger. Since the size of a ledger keeps growing, this method becomes increasingly less practical, especially for lightweight users such as IoT devices and smartphones. In order to deal with this problem, there have been some proposals. However, existing solutions either achieve a limited storage reduction (e.g., simple payment verification), or rely on some strong security assumption (e.g., the use of trusted server). We propose EPBC, a novel and efficient transaction verification scheme for public ledgers, which only requires lightweight users to store a small amount of data that is independent of the size of the blockchain. We analyze EPBC's performance and security, and discuss its integration with existing public ledger systems. Experimental results confirm that EPBC is practical for lightweight users.


title: Improving first order differential power attacks through digital signal processing

abstract: Side-channel attacks pose a critical threat to the deployment of secure embedded systems. Differential-power analysis is a technique relying on measuring the power consumption of device while it computes a cryptographic primitive, and extracting the secret information from it exploiting the knowledge of the operations involving the key. There is no open literature describing how to properly employ Digital Signal Processing (DSP) techniques in order to improve the effectiveness of the attacks. This paper presents a pre-processing technique based on DSP, reducing the number of traces needed to perform an attack by an order of magnitude with respect to the results obtained with raw datasets, and puts it into practical use attacking a commercial 32-bit software implementation of AES running on a Cortex-M3 CPU. The main contribution of this paper is proposing a leakage model for software implemented cryptographic primitives and an effective framework to extract it.


title: Critical factors for successful ERP implementation: Exploratory findings from four case studies

abstract: As more and more organizations move from functional to process-based IT infrastructure, ERP systems are becoming one of today's most widespread IT solutions. However, not all firms have been successful in their ERP implementations. Using a case study methodology grounded in business process change theory, this research tries to understand the factors that lead to the success or failure of ERP projects. The results from our comparative case study of 4 firms that implemented an ERP system suggest that a cautious, evolutionary, bureaucratic implementation process backed with careful change management, network relationships, and cultural readiness have a positive impact on several ERP implementations. Understanding such effects will enable managers to be more proactive and better prepared for ERP implementation. Managerial implications of the findings and future research directions are discussed.


How and Why Do Students Use Learning Strategies? A Mixed Methods Study on Learning Strategies and Desirable Difficulties With Effective Strategy Users


title: Structuring Cooperative Behavior Planning Implementations for Automated Driving

abstract: Cooperative behavior planning for automated vehicles is getting more and more attention in the research community. This paper introduces two dimensions to structure cooperative driving tasks. The authors suggest to distinguish driving tasks by the used communication channels and by the hierarchical level of cooperative skills and abilities. In this manner, this paper presents the cooperative behavior skills of "Jack", our automated vehicle driving from Stanford to Las Vegas in January 2015.


title: FEATURE EXTRACTION USING SURF ALGORITHM FOR OBJECT RECOGNITION

abstract: Video surveillance is active research topic in computer vision research area for humans & vehicles, so it is used over a great extent. Multiple images generated using a fixed camera contains various objects, which are taken under different variations, illumination changes after that the object’s identity and orientation are provided to the user. This scheme is used to represent individual images as well as various objects classes in a single, scale and rotation invariant model.The objective is to improve object recognition accuracy for surveillance purposes & to detect multiple objects with sufficient level of scale invariance.Multiple objects detection& recognition is important in the analysis of video data and higher level security system. This method can efficiently detect the objects from query images as well as videos by extracting frames one by one. When given a query image at runtime, by generating the set of query features and it will find best match it to other sets within the database. Using SURF algorithm find the database object with the best feature matching, then object is present in the query image. Keywords— Image recognition, Query image, Local feature, Surveillance system, SURF algorithm.


title: Parallel operation of one-cycle controlled grid connected three-phase inverters

abstract: Parallel operation of three-phase grid connected inverters extends the power range to a much higher level and allows modular design. However, current sharing and circulating current among the modules are the potential problems. This paper studies these phenomena within two paralleled inverters and proposes a new parallel operation method based on one-cycle control (OCC) with combined vector operation and bipolar operation. With some minor addition to the original OCC circuit and a simple add-on communication path among the individual modules, the current is properly shared among all the inverters and the circulating current is limited to an acceptable range, while the OCC advantages, such as constant switching frequency, no multipliers, and simple circuitry, are preserved. Two OCC inverters of 1.5 kW each were built and tied together using the proposed parallel operation method. Experiments have shown the simplicity and the effectiveness of this method.


title: Hands-on math: a page-based multi-touch and pen desktop for technical work and problem solving

abstract: Students, scientists and engineers have to choose between the flexible, free-form input of pencil and paper and the computational power of Computer Algebra Systems (CAS) when solving mathematical problems. Hands-On Math is a multi-touch and pen-based system which attempts to unify these approaches by providing virtual paper that is enhanced to recognize mathematical notations as a means of providing in situ access to CAS functionality. Pages can be created and organized on a large pannable desktop, and mathematical expressions can be computed, graphed and manipulated using a set of uni- and bi-manual interactions which facilitate rapid exploration by eliminating tedious and error prone transcription tasks. Analysis of a qualitative pilot evaluation indicates the potential of our approach and highlights usability issues with the novel techniques used.


title: Stream Data Mining: Platforms, Algorithms, Performance Evaluators and Research Trends

abstract: Streaming data are potentially infinite sequence of incoming data at very high speed and may evolve over the time. This causes several challenges in mining large scale high speed data streams in real time. Hence, this field has gained a lot of attention of researchers in previous years. This paper discusses various challenges associated with mining such data streams. Several available stream data mining algorithms of classification and clustering are specified along with their key features and significance. Also, the significant performance evaluation measures relevant in streaming data classification and clustering are explained and their comparative significance is discussed. The paper illustrates various streaming data computation platforms that are developed and discusses each of them chronologically along with their major capabilities. This paper clearly specifies the potential research directions open in high speed large scale data stream mining from algorithmic, evolving nature and performance evaluation measurement point of view. Finally, Massive Online Analysis (MOA) framework is used as a use case to show the result of key streaming data classification and clustering algorithms on the sample benchmark dataset and their performances are critically compared and analyzed based on the performance evaluation parameters specific to streaming data mining.


title: Modeling Split-Ring Resonator (SRR) and Complementary Split-Ring Resonator (CSRR) Loaded Transmission Lines Exhibiting Cross-Polarization Effects

abstract: The purpose of this letter is to understand and model the electromagnetic properties of transmission lines loaded with split-ring resonators (SRRs) and complementary split-ring resonators (CSRRs) arbitrarily oriented. It is shown that if the slits of these resonators are aligned in a nonorthogonal direction to the line axis, cross-polarization effects arise. Namely, the particles (SRRs or CSRRs) are excited through both magnetic and electric coupling. It is pointed out that the previously reported lumped element equivalent circuit models of SRR- and CSRR-loaded lines (where cross polarization is not considered) are valid as long as the slits are orthogonally oriented to the line axis, and new models that include cross polarization are presented and discussed. The validity and accuracy of the models is demonstrated through parameter extraction and comparison to full-wave electromagnetic simulations and measurements.


title: Core values: doctor or everyman?

abstract: Doctors have recently been grappling with their changing role in society. To this end the first summit meeting of the profession since 1961 was held in November 1994 and a consultation document, Core Values for the Medical Profession in the 21st Century, was produced. This was intended to stimulate debate about the future of medicine but seems to have provoked little comment. Why? ::: ::: The problem stems from the question that the conference set itself: “What are the core values for the medical profession in the 21st century?” This has encouraged the production of a list of good activities and attributes which might be associated with the practice of medicine but are not inherent to it or defining of it. A better question might have been “What is the good doctor good at?” An example of the vagueness attached to the document's broad central question is the list of basic and ancient virtues, which are given as “commitment, caring, compassion, integrity, competence, spirit of inquiry, confidentiality, responsibility, and advocacy.” These read like a list of good things which, we might hope, would be associated with any upright citizen, not just a member of the medical profession. ::: ::: Why does the profession so readily adopt the moral high ground, and why, indeed, is it encouraged …


title: Metric localization using Google Street View

abstract: Accurate metrical localization is one of the central challenges in mobile robotics. Many existing methods aim at localizing after building a map with the robot. In this paper, we present a novel approach that instead uses geo-tagged panoramas from the Google Street View as a source of global positioning. We model the problem of localization as a non-linear least squares estimation in two phases. The first estimates the 3D position of tracked feature points from short monocular camera sequences. The second computes the rigid body transformation between the Street View panoramas and the estimated points. The only input of this approach is a stream of monocular camera images and odometry estimates. We quantified the accuracy of the method by running the approach on a robotic platform in a parking lot by using visual fiducials as ground truth. Additionally, we applied the approach in the context of personal localization in a real urban scenario by using data from a Google Tango tablet.


Persistent Point Feature Histograms for 3D Point Clouds


title: ATLAS: A scalable and high-performance scheduling algorithm for multiple memory controllers

abstract: Modern chip multiprocessor (CMP) systems employ multiple memory controllers to control access to main memory. The scheduling algorithm employed by these memory controllers has a significant effect on system throughput, so choosing an efficient scheduling algorithm is important. The scheduling algorithm also needs to be scalable — as the number of cores increases, the number of memory controllers shared by the cores should also increase to provide sufficient bandwidth to feed the cores. Unfortunately, previous memory scheduling algorithms are inefficient with respect to system throughput and/or are designed for a single memory controller and do not scale well to multiple memory controllers, requiring significant finegrained coordination among controllers. This paper proposes ATLAS (Adaptive per-Thread Least-Attained-Service memory scheduling), a fundamentally new memory scheduling technique that improves system throughput without requiring significant coordination among memory controllers. The key idea is to periodically order threads based on the service they have attained from the memory controllers so far, and prioritize those threads that have attained the least service over others in each period. The idea of favoring threads with least-attained-service is borrowed from the queueing theory literature, where, in the context of a single-server queue it is known that least-attained-service optimally schedules jobs, assuming a Pareto (or any decreasing hazard rate) workload distribution. After verifying that our workloads have this characteristic, we show that our implementation of least-attained-service thread prioritization reduces the time the cores spend stalling and significantly improves system throughput. Furthermore, since the periods over which we accumulate the attained service are long, the controllers coordinate very infrequently to form the ordering of threads, thereby making ATLAS scalable to many controllers. We evaluate ATLAS on a wide variety of multiprogrammed SPEC 2006 workloads and systems with 4–32 cores and 1–16 memory controllers, and compare its performance to five previously proposed scheduling algorithms. Averaged over 32 workloads on a 24-core system with 4 controllers, ATLAS improves instruction throughput by 10.8%, and system throughput by 8.4%, compared to PAR-BS, the best previous CMP memory scheduling algorithm. ATLAS's performance benefit increases as the number of cores increases.