title: GRAPH DATA MANAGEMENT AND MINING: A SURVEY OF ALGORITHMS AND APPLICATIONS

abstract: Graph mining and management has become a popular area of research in recent years because of its numerous applications in a wide variety of practical fields, including computational biology, software bug localization and computer networking. Different applications result in graphs of different sizes and com- plexities. Correspondingly, the applications have different requirements for the underlying mining algorithms. In this chapter, we will provide a survey of dif- ferent kinds of graph mining and management algorithms. We will also discuss a number of applications, which are dependent upon graph representations. We will discuss how the different graph mining algorithms can be adapted for different applications. Finally, we will discuss important avenues of future research in the area.


title: Cuff-free blood pressure estimation using pulse transit time and heart rate

abstract: It has been reported that the pulse transit time (PTT), the interval between the peak of the R-wave in electrocardiogram (ECG) and the fingertip photoplethysmogram (PPG), is related to arterial stiffness, and can be used to estimate the systolic blood pressure (SBP) and diastolic blood pressure (DBP). This phenomenon has been used as the basis to design portable systems for continuously cuff-less blood pressure measurement, benefiting numerous people with heart conditions. However, the PTT-based blood pressure estimation may not be sufficiently accurate because the regulation of blood pressure within the human body is a complex, multivariate physiological process. Considering the negative feedback mechanism in the blood pressure control, we introduce the heart rate (HR) and the blood pressure estimate in the previous step to obtain the current estimate. We validate this method using a clinical database. Our results show that the PTT, HR and previous estimate reduce the estimated error significantly when compared to the conventional PTT estimation approach (p<0.05).


title: Demystifying the dark side of the middle: a field study of middlebox failures in datacenters

abstract: Network appliances or middleboxes such as firewalls, intrusion detection and prevention systems (IDPS), load balancers, and VPNs form an integral part of datacenters and enterprise networks. Realizing their importance and shortcomings, the research community has proposed software implementations, policy-aware switching, consolidation appliances, moving middlebox processing to VMs, end hosts, and even offloading it to the cloud. While such efforts can use middlebox failure characteristics to improve their reliability, management, and cost-effectiveness, little has been reported on these failures in the field. In this paper, we make one of the first attempts to perform a large-scale empirical study of middlebox failures over two years in a service provider network comprising thousands of middleboxes across tens of datacenters. We find that middlebox failures are prevalent and they can significantly impact hosted services. Several of our findings differ in key aspects from commonly held views: (1) Most failures are grey dominated by connectivity errors and link flaps that exhibit intermittent connectivity, (2) Hardware faults and overload problems are present but they are not in majority, (3) Middleboxes experience a variety of misconfigurations such as incorrect rules, VLAN misallocation and mismatched keys, and (4) Middlebox failover is ineffective in about 33\% of the cases for load balancers and firewalls due to configuration bugs, faulty failovers and software version mismatch. Finally, we analyze current middlebox proposals based on our study and discuss directions for future research.


title: B2C web site quality and emotions during online shopping episodes: An empirical study

abstract: This paper explores the impact of the quality of a web site on the cognitive process leading to consumers' emotions---considered as direct antecedents to shopping behaviors and operationalized as mental states of readiness arising from the appraisal of events. A parsimonious theoretical model was defined and tested with data collected from 215 web-shopping episodes during which consumers were shopping for low-touch products. Analysis of the results showed that web site quality had a positive impact on the cognitive appraisal of situational state, which in turn influenced five of the six emotions of the proposed model: liking, joy, pride, dislike, and frustration. Results also showed that a substantial number of shoppers experienced intensively the emotions of liking and joy. Moreover, this paper highlights several implications that could help managers and webmasters improve the quality of their web sites


title: Design and Construction of Arc Shaped and Disc Shaped Pendulum for Vortex Bladeless Wind Generator

abstract: This study demonstrates the use of two types of pendulums on a vortex bladeless wind (VBW) turbine: arc-shaped and disc-shaped. Here, the kinetic energy of the wind as input to the VBW turbine gets converted into useful electrical energy for both types of pendulums. Permanent magnets are attached to the pendulums and act as a piston to the generator, while a coil is made as the stator with 2,200 turns each on the sides of the pendulum. Electricity is then produced as the pendulum moves from side to side because of the magnetic flux formed between the coils and the permanent magnets. The result shows a significant increase of voltage on the arc-shaped pendulum. Actual simulations of the prototype for controlled and uncontrolled wind speed reveal that an arc-shaped pendulum on a VBW turbine has a higher average voltage collected between the early morning and the late evening of a given day. For the concept's economic practicality, the prototype was constructed on a smaller scale; however, a larger scale setup of the prototype would harness more electrical energy.


title: Modulation recognition using hierarchical deep neural networks

abstract: We outline the core components of a modulation recognition system that uses hierarchical deep neural networks to identify data type, modulation class and modulation order. Our system utilizes a flexible front-end detector that performs energy detection, channelization and multi-band reconstruction on wideband data to provide raw narrowband signal snapshots. We automatically extract features from these snapshots using convolutional neural network layers, which produce decision class estimates. Initial experimentation on a small synthetic radio frequency dataset indicates the viability of deep neural networks applied to the communications domain. We plan to demonstrate this system at the Battle of the Mod Recs Workshop at IEEE DySpan 2017.


The Battery Energy Storage System (bess) Design Option for On-Campus Photovoltaic Charging Station (PV-CS)


title: Enhancing credit scoring model performance by a hybrid scoring matrix

abstract: Competition of the consumer credit market in Taiwan has become severe recently. Therefore, most financial institutions actively develop credit scoring models based on assessments of the credit approval of new customers and the credit risk management of existing customers. This study uses a genetic algorithm for feature selection and decision trees for customer segmentation. Moreover, it utilizes logistic regression to build the application and credit bureau scoring models where the two scoring models are combined for constructing the scoring matrix. The scoring matrix undergoes more accurate risk judgment and segmentation to further identify the parts required enhanced management or control within a personal loan portfolio. The analytical results demonstrate that the predictive ability of the scoring matrix outperforms both the application and credit bureau scoring models. Regarding the K-S value, the scoring matrix increases the prediction accuracy compared to the application and credit bureau scoring models by 18.40 and 5.70%, respectively. Regarding the AUC value, the scoring matrix increases the prediction accuracy compared to the application and credit bureau scoring models by 10.90 and 6.40%, respectively. Furthermore, this study applies the scoring matrix to the credit approval decisions for corresponding risk groups to strengthen bank’s risk management practices. ::: ::: ::: ::: Key words: Scoring matrix, application scoring model, credit bureau scoring model, genetic algorithm, logistic regression, decision trees.


title: CNN-based modulation classification in the complicated communication channel

abstract: Machine Learning are gradually applied in Automatic Modulation Classification (AMC) domain these years. However, in the past researches, the experiment conditions are mostly idealized or simplified. This paper proposes a modified structure of Convolutional Neural Network (CNN), which is proved to have strong capability of classification. The wavelet denoising technology is exploited to restrain the high frequency noise for input signal. As an analysis of the feasibility of this method for complicated channel, we compare the proposed method with the commonly used AMC methods through a large number of signal sequences, taking consider of various distractors. The result shows that the method proposed outperforms on most rugged conditions.


title: Ultra-wideband Dual-Polarized Scanning Meta-material / Meta-ferrite Arrays*

abstract: This paper presents the design and measured results for an ultra-wideband aperture with excellent gain-bandwidth-scanning performance across a 190–2300 MHz bandwidth. The aperture uses a meta-material frequency selective surface to achieve $+/-$ 60-degree scanning, and we present plans for incorporating a low loss MetaFerrite substrate in future versions. Finite simulations show that introducing ferrite substrates can reduce either the minimum frequency or the aperture thickness by a factor of 2 or more vs the current design.


title: How Chief Digital Officers Promote the Digital Transformation of their Companies

abstract: Chief Digital Officers (CDOs) are establishing themselves as new executives at the top management level of companies that go through a digital transformation. We present six case studies of CDOs and describe how they fulfill their positions. From these cases, we identify the main factors that drive the employment of CDOs, the three role types that CDOs primarily play and the skills and competencies they should have for each role type.


title: Breathing through a particular nostril can alter metabolism and autonomic activities.

abstract: There is increasing interest in the fact that breathing exclusively through one nostril may alter the autonomic functions. The present study aimed at checking whether such changes actually do occur, and whether breathing is consciously regulated. 48 male subjects, with ages ranging from 25 to 48 years were randomly assigned to different groups. Each group was asked to practice one out of three pranayamas (viz. right nostril breathing, left nostril breathing or alternate nostril breathing). These practices were carried out as 27 respiratory cycles, repeated 4 times a day for one month. Parameters were assessed at the beginning and end of the month, but not during the practice. The 'right nostril pranayama' group showed a significant increase, of 37% in baseline oxygen consumption. The 'alternate nostril' pranayama group showed an 18% increase, and the left nostril pranayama group also showed an increase, of 24%. This increase in metabolism could be due to increased sympathetic discharge to the adrenal medulla. The 'left nostril Pranayama' group showed an increase in volar galvanic skin resistance, interpreted as a reduction in sympathetic nervous system activity supplying the sweat glands. These results suggest that breathing selectively through either nostril could have a marked activating effect or a relaxing effect on the sympathetic nervous system. The therapeutic implications of being able to alter metabolism by changing the breathing pattern have been mentioned.


title: What is data ethics?

abstract: This theme issue has the founding ambition of landscaping data ethics as a new branch of ethics that studies and evaluates moral problems related to data (including generation, recording, curation,...


title: Challenging the Long Tail Recommendation

abstract: The success of "infinite-inventory" retailers such as Amazon.com and Netflix has been largely attributed to a "long tail" phenomenon. Although the majority of their inventory is not in high demand, these niche products, unavailable at limited-inventory competitors, generate a significant fraction of total revenue in aggregate. In addition, tail product availability can boost head sales by offering consumers the convenience of "one-stop shopping" for both their mainstream and niche tastes. However, most of existing recommender systems, especially collaborative filter based methods, can not recommend tail products due to the data sparsity issue. It has been widely acknowledged that to recommend popular products is easier yet more trivial while to recommend long tail products adds more novelty yet it is also a more challenging task. ::: ::: In this paper, we propose a novel suite of graph-based algorithms for the long tail recommendation. We first represent user-item information with undirected edge-weighted graph and investigate the theoretical foundation of applying Hitting Time algorithm for long tail item recommendation. To improve recommendation diversity and accuracy, we extend Hitting Time and propose efficient Absorbing Time algorithm to help users find their favorite long tail items. Finally, we refine the Absorbing Time algorithm and propose two entropy-biased Absorbing Cost algorithms to distinguish the variation on different user-item rating pairs, which further enhances the effectiveness of long tail recommendation. Empirical experiments on two real life datasets show that our proposed algorithms are effective to recommend long tail items and outperform state-of-the-art recommendation techniques.


A Review of Information Systems Research on Online Social Networks


title: The relationships between otoacoustic emissions and relative lengths of fingers and toes in humans

abstract: Both otoacoustic emissions (OAEs) and the relative length of the index and ring fingers (the 2D:4D ratio) exhibit large sex differences, and both exhibit masculinization effects in female homosexuals and bisexuals. Because these sex differences exist in young children, the implication is that both types of measure are affected by prenatal androgen exposure, but it has been unknown to what degree these two types of measure are related. Accordingly, OAEs and the relative lengths of the fingers and the toes were measured in 59 heterosexual females, 55 heterosexual males, 29 homosexual females, and 33 homosexual males. The correlations between the two types of measure were unexpectedly quite low in both the heterosexual and nonheterosexual groups. For example, the correlation between number of spontaneous OAEs per ear and 2D:4D was less than 0.25, for both sexes and both sexual orientations. One interpretation of these results is that the prenatal hormonal mechanisms producing the sex differences in OAEs differ in quality, degree, or timing from those producing the sex differences in relative finger length. That is, OAEs and 2D:4D may be windows onto slightly different prenatal processes or times during prenatal development. Measures of mental-rotation ability also were obtained on these participants, and those correlations with relative finger length also were small.


title: Adjustable Autonomy: from theory to implementation

abstract: Recent exciting, ambitious applications in agent technology involve agents acting individually or in teams in support of critical activities of individual humans or entire human organizations. Applications range from intelligent homes [13], to "routine" organizational coordination[16], to electronic commerce[4] to long-term space missions[12, 6]. These new applications have brought forth an increasing interest in agents’ adjustable autonomy (AA), i.e., in agents’ dynamically adjusting their own level of autonomy based on the situation[8]. In fact, many of these applications will not be deployed, unless reliable AA reasoning is a central component. At the heart of AA is the question of whether and when agents should make autonomous decisions and when they should transfer decision-making control to other entities (e.g., human users). Unfortunately, previous work in adjustable autonomy has focused on individual agent-human interactions and tile techniques developed fail to scale-up to complex heterogeneous organizations. Indeed, as a first step, we focused on a smallscale, but real-world agent-human organization called Electric Elves, where an individual agent and human worked together within a larger multiagent context. Although’the application limits the interactions among entities, key weaknesses of previous approaches to adjustable autonomy are readily apparent. In particular, previous approaches to transferof-control are seen to be too rigid, employing one-shot transfersof-control that can result in unacceptable coordination failures. Furthermore, the previous approaches ignore potential costs (e.g., from delays) to an agent’s team due to such transfers of control. To remedy such problems, we propose a novel approach to AA, based on the notion of a transfer-of-control strategy. A transfer-of-control strategy consists of a conditional sequence of two types of actions: (i) actions to transfer decision-making control (e.g., from the agent to the user or vice versa) and (ii) actions to change an agent’s pre-specified coordination constraints with team members, aimed at minimizing miscoordination costs. The goal is for high quality individual decisions to be made with minimal disruption to the coordination of the team. We operationalize such strategies via Markov decision processes (MDPs) which select the optimal strategy given an uncertain environment and costs to individuals and teams. We have developed a general reward function and state representation for such an MDP, to facilitate application of the approach to different domains. We present results from a careful evaluation of this approach, including via its use in our real-world, deployed Electric Elves system.


title: Reversible Watermarking Algorithm Using Sorting and Prediction

abstract: This paper presents a reversible or lossless watermarking algorithm for images without using a location map in most cases. This algorithm employs prediction errors to embed data into an image. A sorting technique is used to record the prediction errors based on magnitude of its local variance. Using sorted prediction errors and, if needed, though rarely, a reduced size location map allows us to embed more data into the image with less distortion. The performance of the proposed reversible watermarking scheme is evaluated using different images and compared with four methods: those of Kamstra and Heijmans, Thodi and Rodriguez, and Lee et al. The results clearly indicate that the proposed scheme can embed more data with less distortion.


title: BYOD Security: A New Business Challenge

abstract: Bring Your Own Device (BYOD) is a rapidly growing trend in businesses concerned with information technology. BYOD presents a unique list of security concerns for businesses implementing BYOD policies. Recent publications indicate a definite awareness of risks involved in incorporating BYOD into business, however it is still an underrated issue compared to other IT security concerns. This paper focuses on two key BYOD security issues: security challenges and available frameworks. A taxonomy specifically classifying BYOD security challenges is introduced alongside comprehensive frameworks and solutions which are also analysed to gauge their limitations.


title: Methods of Electrodermal Recording

abstract: The second chapter of the book discusses the different methods used for electrodermal recording. As mentioned in the introduction to Chap. 1, the observation of electrodermal phenomena requires only relatively basic equipment. As a consequence, a variety of recording methods have been proposed.


title: A comprehensive survey on vehicular Ad Hoc network

abstract: Vehicular ad hoc networks (VANETs) are classified as an application of mobile ad hoc network (MANET) that has the potential in improving road safety and in providing travellers comfort. Recently VANETs have emerged to turn the attention of researchers in the field of wireless and mobile communications, they differ from MANET by their architecture, challenges, characteristics and applications. In this paper we present aspects related to this field to help researchers and developers to understand and distinguish the main features surrounding VANET in one solid document, without the need to go through other relevant papers and articles starting from VANET architecture and ending up with the most appropriate simulation tools to simulate VANET protocols and applications.


title: A method for classifying medical images using transfer learning: A pilot study on histopathology of breast cancer

abstract: The advance of deep learning has made huge changes in computer vision and produced various off-the-shelf trained models. Particularly, Convolutional Neural Network (CNN) has been widely used to build image classification model which allow researchers transfer the pre-trained learning model for other classifications. We propose a transfer learning method to detect breast cancer using histopathology images based on Google's Inception v3 model which were initially trained for the classification of non-medical images. The pilot study shows the feasibility of transfer learning in the detection of breast cancer with AUC of 0.93.


Toward Personalized Relational Learning


title: Flocking with Obstacle Avoidance: Cooperation with Limited Communication in Mobile Networks

abstract: In this paper, we provide a dynamic graph theoretic framework for flocking in presence of multiple obstacles. In particular, we give formal definitions of nets and flocks as spatially induced graphs and define flocking. We introduce the notion of framenets and describe a procedure for automatic construction of an energy function for groups of agents. The task of flocking is achieved via dissipation of this energy according to a protocol that only requires the use of local information. We show that all three rules of Reynolds are hidden in this single protocol. Three types of agents called /spl alpha/, /spl beta/, and /spl gamma/ agents are used to create flocking. Simulation results are provided that demonstrate flocking by 100 dynamic agents.


Papular Epidermal Nevus with “Skyline” Basal Cell Layer Syndrome – Natural Course: Case Report and Literature Review


Chapter 2 Making Sense of Knowledge Integration Maps


title: Grammar-driven development of JSON processing applications

abstract: This paper describes how to use conventional parser generation tools for the development of JSON processing applications. According to the resulting grammar-driven development approach, JSON processing applications are architected as syntax-directed translators. Thus, the core part of these components can be described in terms of translation schemata and can be automatically generated by using suitable parser generators. It makes it possible to specify critical parts of the application (those interfacing with JSON documents) by using high-level, grammar-oriented descriptions, as well as to promote the separation of JSON processing concerns from other application-specific aspects. In consequence, the production and maintenance of JSON processing applications is facilitated (especially for applications involving JSON documents with intricate nested structures, as well as for applications in which JSON formats are exposed to frequent changes and evolutions in their surface structures). This paper illustrates the approach with JSON-P as the generic JSON processing framework, with ANTLR as the parser generation tool, and with a case study concerning the development of a player for simple man-machine dialogs shaped in terms of JSON documents.


title: Learning to aggregate vertical results into web search results

abstract: Aggregated search is the task of integrating results from potentially multiple specialized search services, or verticals, into the Web search results. The task requires predicting not only which verticals to present (the focus of most prior research), but also predicting where in the Web results to present them (i.e., above or below the Web results, or somewhere in between). Learning models to aggregate results from multiple verticals is associated with two major challenges. First, because verticals retrieve different types of results and address different search tasks, results from different verticals are associated with different types of predictive evidence (or features). Second, even when a feature is common across verticals, its predictiveness may be vertical-specific. Therefore, approaches to aggregating vertical results require handling an inconsistent feature representation across verticals, and, potentially, a vertical-specific relationship between features and relevance. We present 3 general approaches that address these challenges in different ways and compare their results across a set of 13 verticals and 1070 queries. We show that the best approaches are those that allow the learning algorithm to learn a vertical-specific relationship between features and relevance.


title: Loosely Coupled Transformer Structure and Interoperability Study for EV Wireless Charging Systems

abstract: As the wireless power transfer (WPT) technology has been proved to be a convenient and reliable charging method to plug-in hybrid electric vehicles and electric vehicles, the loosely coupled transformer structure and size are the primary and fundamental concern to design an efficient WPT system. In this paper, a double D (DD) coil and a unipolar coil are selected to conduct the study. We focus on the coil structure design to achieve the maximum coupling coefficient as well as efficiency with two situations: 1) with no misalignment, and 2) with a 75-mm door-to-door and 100-mm front-to-back misalignment at which the maximum operating capability can still be achieved. A coil size optimization process is proposed for both the DD coil and the unipolar coil configurations. The relationship between the size of the secondary (receiving) coil, which determines the weight of the pad on the vehicle, and achievable maximum efficiency is studied for both coil topologies. The interoperability between the two coil topologies is studied. The proposed transformer structures with aluminum shielding meet human exposure regulations of the International Commission on Non-Ionizing Radiation Protection guidelines as a foundation. Finally, experiments validated the analyses.


title: Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks

abstract: Object detection and segmentation represents the basis for many tasks in computer and machine vision. In biometric recognition systems the detection of the region-of-interest (ROI) is one of the most crucial steps in the overall processing pipeline, significantly impacting the performance of the entire recognition system. Existing approaches to ear detection, for example, are commonly susceptible to the presence of severe occlusions, ear accessories or variable illumination conditions and often deteriorate in their performance if applied on ear images captured in unconstrained settings. To address these shortcomings, we present in this paper a novel ear detection technique based on convolutional encoder-decoder networks (CEDs). For our technique, we formulate the problem of ear detection as a two-class segmentation problem and train a convolutional encoder-decoder network based on the SegNet architecture to distinguish between image-pixels belonging to either the ear or the non-ear class. The output of the network is then post-processed to further refine the segmentation result and return the final locations of the ears in the input image. Different from competing techniques from the literature, our approach does not simply return a bounding box around the detected ear, but provides detailed, pixel-wise information about the location of the ears in the image. Our experiments on a dataset gathered from the web (a.k.a. in the wild) show that the proposed technique ensures good detection results in the presence of various covariate factors and significantly outperforms the existing state-of-the-art.


title: Customer Loyalty to C2C Online Shopping Platforms: An Exploration of the Role of Customer Engagement

abstract: With the keen competition in C2C e-marketplaces, C2C online shopping platform providers are finding ways to keep and maintain customer relationship. In this study, a research model that examines the role of customer engagement in customer loyalty to C2C online shopping platforms is proposed and empirically tested with 385 customers who have used Taobao to perform online shopping. The results provide supports to our research hypotheses and illustrate the importance of customer engagement in building customer loyalty to C2C online shopping platforms. Findings of this study are expected to provide useful insights for emarketers to develop loyalty through customer engagement in online shopping platforms as well as for researchers to better understand the relationship between customer engagement and loyalty in consumer-based e-marketplace.


title: MarkIt: privacy markers for protecting visual secrets

abstract: The increasing popularity of wearable devices that continuously capture video, and the prevalence of third-party applications that utilize these feeds have resulted in a new threat to privacy. In many situations, sensitive objects/regions are maliciously (or accidentally) captured in a video frame by third-party applications. However, current solutions do not allow users to specify and enforce fine grained access control over video feeds. In this paper, we describe MarkIt, a computer vision based privacy marker framework, that allows users to specify and enforce fine grained access control over video feeds. We present two example privacy marker systems -- PrivateEye and WaveOff. We conclude with a discussion of the computer vision, privacy and systems challenges in building a comprehensive system for fine grained access control over video feeds.


title: A 80 kW isolated DC-DC converter for railway applications

abstract: This paper presents the design of a three phase dual active bridge (DAB) topology as high-power-density dc-dc converter for railway applications. The three-phase DAB is analyzed concerning the current intervals, the output power and soft-switching region, including the effect of zero-voltage switching (ZVS) capacitors. Furthermore, two methods are discussed, aiming to extend the soft-switching region, since no-load conditions in the entire operating range are required. A prototype design is made, in which optimal component values are calculated, to minimize losses. Finally, a prototype is built with the required specifications, resulting in an efficiency of 95:6% at 80kW of output power.


title: Growth and survival of larvae of Thaumetopoea pinivora inside and outside a local outbreak area

abstract: 1 Outbreaks of herbivorous insects tend to be spatially restricted, possibly because of demographic differences between inside and outside the outbreak area. In some cases, the margin of the outbreak area is distinct, allowing comparisons of adjacent areas that may identify factors leading to such differences in abundance. The northern pine processionary moth Thaumetopoea pinivora presently occurs at outbreak densities within a well-defined area of approximately 3000 ha on the island of Gotland, south Sweden. We investigated whether cohorts of young larvae (first and second instar) had higher growth rate and survival inside or outside the outbreak area. ::: ::: ::: ::: 2 Group-feeding appears to promote outbreaks in certain insect groups. Because T. pinivora larvae are highly social, we also compared larval performance between groups of different sizes inside and outside of the outbreak area: ‘small’ (33 eggs/group) and ‘normal’ (100 eggs/group). ::: ::: ::: ::: 3 Averaged over group size, whole colony mortality through the first two instars was two-fold higher in the non-outbreak area compared with the outbreak area. Mortality of individual larvae in the surviving colonies, however, did not differ between the two areas. There were only small differences in food quality (toughness, nitrogen content) between the areas, with no detectable effects on larval performance. ::: ::: ::: ::: 4 Larval relative growth rate did not differ between reduced and normal-sized groups, which is surprising given that growth rate is known to increase with group size in other group-feeding lepidopterans. ::: ::: ::: ::: 5 Reduced group size negatively affected larval survival, particularly in the outbreak area; by contrast, normal-sized groups survived equally well in the two areas. Wood ants (Formica spp.) were more common outside the outbreak area, and appeared to be the main cause of colony mortality at low larval density. A different result was observed with regard to per-capita mortality, which was higher in the outbreak area. We speculate that this could have been due to solitary predators being locally specialized on T. pinivora in the high-density area.


title: Just enough semantics: An information theoretic approach for IR-based software bug localization

abstract: Abstract Context Software systems are often shipped with defects. Whenever a bug is reported, developers use the information available in the associated report to locate source code fragments that need to be modified in order to fix the bug. However, as software systems evolve in size and complexity, bug localization can become a tedious and time-consuming process. To minimize the manual effort, contemporary bug localization tools utilize Information Retrieval (IR) methods for automated support. IR methods exploit the textual content of bug reports to automatically capture and rank relevant buggy source files. Objective In this paper, we propose a new paradigm of information-theoretic IR methods to support bug localization tasks in software systems. These methods, including Pointwise Mutual Information (PMI) and Normalized Google Distance (NGD), exploit the co-occurrence patterns of code terms in the software system to reveal hidden textual semantic dimensions that other methods often fail to capture. Our objective is establish accurate semantic similarity relations between source code and bug reports. Method Five benchmark datasets from different application domains are used to conduct our analysis. The proposed methods are compared against classical IR methods that are commonly used in bug localization research. Results The results show that information-theoretic IR methods significantly outperform classical IR methods, providing a semantically aware, yet, computationally efficient solution for bug localization in large and complex software systems. (A replication package is available at: http://seel.cse.lsu.edu/data/ist17.zip ). Conclusions Information-theoretic co-occurrence methods provide “just enough semantics” necessary to establish relations between bug reports and code artifacts, achieving a balance between simple lexical methods and computationally-expensive semantic IR methods that require substantial amounts of data to function properly.


title: Architecture support for guest-transparent VM protection from untrusted hypervisor and physical attacks

abstract: The privacy and integrity of tenant's data highly rely on the infrastructure of multi-tenant cloud being secure. However, with both hardware and software being controlled by potentially curious or even malicious cloud operators, it is no surprise to see frequent reports of data leakages or abuses in cloud. Unfortunately, most prior solutions require intrusive changes to the cloud platform and none can protect a VM against adversaries controlling the physical machine. This paper analyzes the challenges of transparent VM protection against sophisticated adversaries controlling the whole software and hardware stack. Based on the analysis, this paper proposes HyperCoffer, a hardware-software framework that guards the privacy and integrity of tenant's VMs. HyperCoffer only trusts the processor chip and makes no security assumption on external memory and devices. Hyper-Coffer extends existing processor virtualization with memory encryption and integrity checking to secure data communication with off-chip memory. Unlike prior hardware-based approaches, HyperCoffer retains transparency with existing virtual machines (i.e., operating systems) and requires very few changes to the (untrusted) hypervisor. HyperCoffer introduces a mechanism called VM-Shim that runs in-between a guest VM and the hypervisor. Each VM-Shim instance for a VM runs in a separate protected context and only declassifies necessary information designated by the VM to the hypervisor and external environments (e.g., through NICs). We have implemented a prototype of HyperCoffer in a QEMU-based full-system emulator and the VM-Shim mechanism in a real machine. Performance measurement using trace-based simulation and on a real hardware platform shows that the performance overhead is small (ranging from 0.6% to 13.9% on simulated platform and 0.3% to 6.8% on real hardware for the VM-Shim mechanism).


title: Role of uncertainty in sensorimotor control

abstract: Neural signals are corrupted by noise and this places limits on information processing. We review the processes involved in goal-directed movements and how neural noise and uncertainty determine aspects of our behaviour. First, noise in sensory signals limits perception. We show that, when localizing our hand, the central nervous system (CNS) integrates visual and proprioceptive information, each with different noise properties, in a way that minimizes the uncertainty in the overall estimate. Second, noise in motor commands leads to inaccurate movements. We review an optimal-control framework, known as 'task optimization in the presence of signal-dependent noise', which assumes that movements are planned so as to minimize the deleterious consequences of noise and thereby minimize inaccuracy. Third, during movement, sensory and motor signals have to be integrated to allow estimation of the body's state. Models are presented that show how these signals are optimally combined. Finally, we review how the CNS deals with noise at the neural and network levels. In all of these processes, the CNS carries out the tasks in such a way that the detrimental effects of noise are minimized. This shows that it is important to consider effects at the neural level in order to understand performance at the behavioural level.


title: A multicast protocol in ad hoc networks inter-vehicle geocast

abstract: The intelligent transportation systems ITS cause a significant passion since the appearance of new inter-vehicles communication mechanisms based on mobile networks. The wireless ad hoc networks, completely distributed and not depending on infrastructures, allow the fast and cheap development of such mechanisms. This article concerns multicast in wireless ad-hoc networks applied to ITS. We propose a new protocol called IVG inter-vehicles geocast which consists in informing all the vehicles of a highway about any danger such as an accident or any other obstacle. In this case, risk areas are determined according to the driving direction and the positioning of the vehicles. These vehicles define a restricted broadcast group, so-called, multicast group. Multicast group, contrary to the classical methods, which use node identities, is defined temporally and dynamically by the location, speed and driving direction of vehicles. Some simulations thanks to Glomosim tool, for which we have defined a model for the road transport, and studies of the complexity are given. The results show that IVG is an efficient broadcast method for secure highway transportation and reduces the number of useful messages.


UvA-DARE ( Digital Academic Repository ) New approaches to the implementation of cardiovascular disease prevention


title: Fast range image-based segmentation of sparse 3D laser scans for online operation

abstract: Object segmentation from 3D range data is an important topic in mobile robotics. A robot navigating in a dynamic environment needs to be aware of objects that might change or move. A segmentation of the laser scans into individual objects is typically the first processing step before a further analysis is performed. In this paper, we present a fast method that segments 3D range data into different objects, runs online, and has small computational demands. Our approach avoids the explicit computation of the 3D point cloud and performs all computations directly on a 2D range image, which enables a fast segmentation for each scan. A further relevant aspect of our method is that we can segment objects even if the 3D data is sparse. This is important for scanners such as the new Velodyne Puck. We implemented our approach in C++ and ROS and thoroughly tested it using different 3D scanners. Our method can operate at over 100 Hz for the 64-beam Velodyne scanner on a single core of a mobile CPU while producing high quality segmentation results. In addition to this, we make the source code for the approach available.


title: FakeBook: Detecting Fake Profiles in On-Line Social Networks

abstract: On-line Social Networks (OSNs) are increasingly influencing the way people communicate with each other and share personal, professional and political information. Like the cyberspace in Internet, the OSNs are attracting the interest of the malicious entities that are trying to exploit the vulnerabilities and weaknesses of the OSNs. Increasing reports of the security and privacy threats in the OSNs is attracting security researchers trying to detect and mitigate threats to individual users. With many OSNs having tens or hundreds of million users collectively generating billions of personal data content that can be exploited, detecting and preventing attacks on individual user privacy is a major challenge. Most of the current research has focused on protecting the privacy of an existing online profile in a given OSN. Instead, we note that there is a risk of not having a profile in the last fancy social network! The risk is due to the fact that an adversary may create a fake profile to impersonate a real person on the OSN. The fake profile could be exploited to build online relationship with the friends of victim of identity theft, with the final target of stealing personal information of the victim, via interacting online with the friends of the victim. In this paper, we report on the investigation we did on a possible approach to mitigate this problem. In doing so, we also note that we are the first ones to analyze social network graphs from a dynamic point of view within the context of privacy threats.


[CA125 in patients with pleural effusion--in comparison with ascites patients].


title: SystemML: Declarative machine learning on MapReduce

abstract: MapReduce is emerging as a generic parallel programming paradigm for large clusters of machines. This trend combined with the growing need to run machine learning (ML) algorithms on massive datasets has led to an increased interest in implementing ML algorithms on MapReduce. However, the cost of implementing a large class of ML algorithms as low-level MapReduce jobs on varying data and machine cluster sizes can be prohibitive. In this paper, we propose SystemML in which ML algorithms are expressed in a higher-level language and are compiled and executed in a MapReduce environment. This higher-level language exposes several constructs including linear algebra primitives that constitute key building blocks for a broad class of supervised and unsupervised ML algorithms. The algorithms expressed in SystemML are compiled and optimized into a set of MapReduce jobs that can run on a cluster of machines. We describe and empirically evaluate a number of optimization strategies for efficiently executing these algorithms on Hadoop, an open-source MapReduce implementation. We report an extensive performance evaluation on three ML algorithms on varying data and cluster sizes.


title: Spreading dynamics in complex networks

abstract: Searching for influential spreaders in complex networks is an issue of great significance for applications across various domains, ranging from the epidemic control, innovation diffusion, viral marketing, social movement to idea propagation. In this paper, we first display some of the most important theoretical models that describe spreading processes, and then discuss the problem of locating both the individual and multiple influential spreaders respectively. Recent approaches in these two topics are presented. For the identification of privileged single spreaders, we summarize several widely used centralities, such as degree, betweenness centrality, PageRank, k-shell, etc. We investigate the empirical diffusion data in a large scale online social community -- LiveJournal. With this extensive dataset, we find that various measures can convey very distinct information of nodes. Of all the users in LiveJournal social network, only a small fraction of them involve in spreading. For the spreading processes in LiveJournal, while degree can locate nodes participating in information diffusion with higher probability, k-shell is more effective in finding nodes with large influence. Our results should provide useful information for designing efficient spreading strategies in reality.


title: Performance Comparison of Three Types of Autoencoder Neural Networks

abstract: This paper presents a comparison performance on three types of autoencoders, namely, the traditional autoencoder with Restricted Boltzmann Machine (RBM), the stacked autoencoder without RBM and the stacked autoencoder with RBM. The performances are compared based on the reconstruction error for face images and using the same values for the parameters such as the number of neurons in the hidden layers, the training method, and the learning rate. The results show that the RBM stacked autoencoder gives better performance in terms of the reconstruction error compared to the other two architectures.


title: A convolution approach to the circle Hough transform for arbitrary radius

abstract: The Hough transform is a well-established family of algorithms for locating and describing geometric figures in an image. However, the computational complexity of the algorithm used to calculate the transform is high when used to target complex objects. As a result, the use of the Hough transform to find objects more complex than lines is uncommon in real-time applications. We describe a convolution method for calculating the Hough transform for finding circles of arbitrary radius. The algorithm operates by performing a three-dimensional convolution of the input image with an appropriate Hough kernel. The use of the fast Fourier transform to calculate the convolution results in a Hough transform algorithm with reduced computational complexity and thus increased speed. Edge detection and other convolution-based image processing operations can be incorporated as part of the transform, which removes the need to perform them with a separate pre-processing or post-processing step. As the Discrete Fourier Transform implements circular convolution rather than linear convolution, consideration must be given to padding the input image before forming the Hough transform.


title: Terrain analysis in real-time strategy games: an integrated approach to choke point detection and region decomposition

abstract: Autonomous agents in real-time strategy (RTS) games lack an integrated framework for reasoning about choke points and regions of open space in their environment. This paper presents an algorithm which partitions the environment into a set of polygonal regions and computes optimal choke points between adjacent regions. This representation can be used as a component for AI agents to reason about terrain, plan multiple routes of attack, and make other tactical decisions. The algorithm is tested on a set of popular maps commonly used in international Starcraft competitions and evaluated against answers made by human participants. The algorithm identified 97% of the choke points that the participants found and also identified a number of bottlenecks that human participants did not recognize as choke points.


License Plate Segmentation Using Connected Component Analysis


title: Emotion encoding in Human-Drone Interaction

abstract: Drones are becoming more popular and may soon be ubiquitous. As they enter our everyday environments, it becomes critical to ensure their usability through natural Human-Drone Interaction (HDI). Previous work in Human-Robot Interaction (HRI) shows that adding an emotional component is part of the key to success in robots' acceptability. We believe the adoption of personal drones would also benefit from adding an emotional component. This work defines a range of personality traits and emotional attributes that can be encoded in drones through their flight paths. We present a user study (N=20) and show how well three defined emotional states can be recognized. We draw conclusions on interaction techniques with drones and feedback strategies that use the drone's flight path and speed.


title: Understanding individual contribution and collaboration in student software teams

abstract: Software development is an inherently team-based activity, and many software-engineering courses are structured around team projects, in order to provide students with an authentic learning experience. The collaborative-development tools through which student developers define, share and manage their tasks generate a detailed record in the process. Albeit not designed for this purpose, this record can provide the instructor with insights into the students' work, the team's progress over time, and the individual team-member's contributions. In this paper, we describe an analysis and visualization toolkit that enables instructors to interactively explore the trace of the team's collaborative work, to better understand the team dynamics, and the tasks of the individual team developers. We also discuss our grounded-theory analysis of one team's work, based on their email exchanges, questionnaires and interviews. Our analyses suggest that the inferences supported by our toolkit are congruent with the developers' feedback, while there are some discrepancies with the reflections of the team as a whole.


title: Coherence Models for Dialogue

abstract: Coherence across multiple turns is a major challenge for state-of-the-art dialogue models. Arguably the most successful approach to automatically learning text coherence is the entity grid, which relies on modelling patterns of distribution of entities across multiple sentences of a text. Originally applied to the evaluation of automatic summaries and the news genre, among its many extensions, this model has also been successfully used to assess dialogue coherence. Nevertheless, both the original grid and its extensions do not model intents, a crucial aspect that has been studied widely in the literature in connection to dialogue structure. We propose to augment the original grid document representation for dialogue with the intentional structure of the conversation. Our models outperform the original grid representation on both text discrimination and insertion, the two main standard tasks for coherence assessment across three different dialogue datasets, confirming that intents play a key role in modelling dialogue coherence.


title: Molecular Structure Extraction From Documents Using Deep Learning

abstract: Chemical structure extraction from documents remains a hard problem because of both false positive identification of structures during segmentation and errors in the predicted structures. Current approaches rely on handcrafted rules and subroutines that perform reasonably well generally but still routinely encounter situations where recognition rates are not yet satisfactory and systematic improvement is challenging. Complications impacting the performance of current approaches include the diversity in visual styles used by various software to render structures, the frequent use of ad hoc annotations, and other challenges related to image quality, including resolution and noise. We present end-to-end deep learning solutions for both segmenting molecular structures from documents and predicting chemical structures from the segmented images. This deep-learning-based approach does not require any handcrafted features, is learned directly from data, and is robust against variations in image quality and style....


title: Prediction-Based Energy Saving Mechanism in 3GPP NB-IoT Networks

abstract: The current expansion of the Internet of things (IoT) demands improved communication platforms that support a wide area with low energy consumption. The 3rd Generation Partnership Project introduced narrowband IoT (NB-IoT) as IoT communication solutions. NB-IoT devices should be available for over 10 years without requiring a battery replacement. Thus, a low energy consumption is essential for the successful deployment of this technology. Given that a high amount of energy is consumed for radio transmission by the power amplifier, reducing the uplink transmission time is key to ensure a long lifespan of an IoT device. In this paper, we propose a prediction-based energy saving mechanism (PBESM) that is focused on enhanced uplink transmission. The mechanism consists of two parts: first, the network architecture that predicts the uplink packet occurrence through a deep packet inspection; second, an algorithm that predicts the processing delay and pre-assigns radio resources to enhance the scheduling request procedure. In this way, our mechanism reduces the number of random accesses and the energy consumed by radio transmission. Simulation results showed that the energy consumption using the proposed PBESM is reduced by up to 34% in comparison with that in the conventional NB-IoT method.


title: Corona protection in high voltage machines

abstract: In electrical machines from 5 kV upwards there are electrical discharges (corona manifestations) that destroy the insulating materials, which leads to the destruction of the machine. The application of so called corona protection materials helps to avoid this destructive process. The application of the corona protection materials in electric insulating technique is split up into four groups stating: 1st: outer corona protection, in the slot 2nd: inner corona protection, inside of the insulation 3rd: electrical field control on the coil head 4th: slot linings with conductive sheets The application of these materials means a remarkable increment of service life for medium voltage machines (3…8 kV) and renders actually possible the construction of higher voltage machines.


title: Modeling Graphs Using a Mixture of Kronecker Models

abstract: Generative models for graphs are increasingly becoming a popular tool for researchers to generate realistic approximations of graphs. While in the past, focus was on generating graphs which follow general laws, such as the power law for degree distribution, current models have the ability to learn from observed graphs and generate synthetic approximations. The primary emphasis of existing models has been to closely match different properties of a single observed graph. Such models, though stochastic, tend to generate samples which do not have significant variance in terms of the various graph properties. We argue that in many cases real graphs are sampled drawn from a graph population (e.g., networks sampled at various time points, social networks for individual schools, healthcare networks for different geographic regions, etc.). Such populations typically exhibit significant variance. However, existing models are not designed to model this variance, which could lead to issues such as overfitting. We propose a graph generative model that focuses on matching the properties of real graphs and the natural variance expected for the corresponding population. The proposed model adopts a mixture-model strategy to expand the expressiveness of Kronecker product based graph models (KPGM), while building upon the two strengths of KPGM, viz., ability to model several key properties of graphs and to scale to massive graph sizes using its elegant fractal growth based formulation. The proposed model, called x-Kronecker Product Graph Model, or xKPGM, allows scalable learning from observed graphs and generates samples that match the mean and variance of several salient graph properties. We experimentally demonstrate the capability of the proposed model to capture the inherent variability in real world graphs on a variety of publicly available graph data sets.


title: A visual editor for language-independent scripting for BPMN modeling

abstract: This paper tackles a scripting problem associated with script tasks in BPMN modeling. Usually a BPMN process modeling tool provides a plain text editor without any assisting features for writing a script for a BPMN script task. This is not very convenient even for skilled programmers. There is also a limitation on exporting the script written in one language to different process modeling and execution runtimes that support other scripting languages. To provide easy-to-use and platform-independent scripting, we present a visual block programming editor for defining a visual script for a BPMN script task. The internal representation of the visual script is an XML-based business process script, or XBPScript, from which executable scripts for different target runtimes can be generated. This paper discusses, in particular, how the editor supports a complex function block that can be used in building a script. It also discusses, as an example, how to integrate the visual editor and XBPScript with open source business process management software. An evaluation of the visual editor productivity in comparison with plain text editing is presented.


title: An iterative Kalman smoother for robust 3D localization on mobile and wearable devices

abstract: In this paper, we introduce an Iterative Kalman Smoother (IKS) for tracking the 3D motion of a mobile device in real-time using visual and inertial measurements. In contrast to existing Extended Kalman Filter (EKF)-based approaches, smoothing can better approximate the underlying nonlinear system and measurement models by re-linearizing them. Additionally, by iteratively optimizing over all measurements available, the IKS increases the convergence rate of critical parameters (e.g., IMU-camera clock drift) and improves the positioning accuracy during challenging conditions (e.g., scarcity of visual features). Furthermore, and in contrast to existing inverse filters, the proposed IKS's numerical stability allows for efficient 32-bit implementations on resource-constrained devices, such as cell phones and wearables. We validate the IKS for performing vision-aided inertial navigation on Google Glass, a wearable device with limited sensing and processing, and demonstrate positioning accuracy comparable to that achieved on cell phones. To the best of our knowledge, this work presents the first proof-of-concept real-time 3D indoor localization system on a commercial-grade wearable computer.


Mobile Cloud Computing Service Models : A User-Centric Approach


title: Network structure of cerebral cortex shapes functional connectivity on multiple time scales

abstract: Neuronal dynamics unfolding within the cerebral cortex exhibit complex spatial and temporal patterns even in the absence of external input. Here we use a computational approach in an attempt to relate these features of spontaneous cortical dynamics to the underlying anatomical connectivity. Simulating nonlinear neuronal dynamics on a network that captures the large-scale interregional connections of macaque neocortex, and applying information theoretic measures to identify functional networks, we find structure–function relations at multiple temporal scales. Functional networks recovered from long windows of neural activity (minutes) largely overlap with the underlying structural network. As a result, hubs in these long-run functional networks correspond to structural hubs. In contrast, significant fluctuations in functional topology are observed across the sequence of networks recovered from consecutive shorter (seconds) time windows. The functional centrality of individual nodes varies across time as interregional couplings shift. Furthermore, the transient couplings between brain regions are coordinated in a manner that reveals the existence of two anticorrelated clusters. These clusters are linked by prefrontal and parietal regions that are hub nodes in the underlying structural network. At an even faster time scale (hundreds of milliseconds) we detect individual episodes of interregional phase-locking and find that slow variations in the statistics of these transient episodes, contingent on the underlying anatomical structure, produce the transfer entropy functional connectivity and simulated blood oxygenation level-dependent correlation patterns observed on slower time scales.


LIPN at SemEval-2017 Task 10: Filtering Candidate Keyphrases from Scientific Publications with Part-of-Speech Tag Sequences to Train a Sequence Labeling Model


title: To Scroll or Not to Scroll: Scrolling, Working Memory Capacity, and Comprehending Complex Texts:

abstract: Objective: The purpose of these experiments was to examine the effects of user characteristics on learning from scrolling interfaces. Background: Although scrolling Web pages are now common, few studies have explored the effects of scrolling on understanding the content that is being conveyed. Method: This set of studies investigated whether presenting text in two particular formats has an effect on comprehension for readers who differ in working memory capacity. Results: Results from both studies indicated that a scrolling format reduced understanding of complex topics from Web pages, especially for readers who were lower in working memory capacity. Conclusion: These findings show that the way text is presented can interact with learner abilities to affect learning outcomes. Application: These results have implications for both educational technology and human interfaces that present information using displays that can vary in size and construction.


title: Automated synaptic connectivity inference for volume electron microscopy

abstract: Teravoxel volume electron microscopy data sets from neural tissue can now be acquired in weeks, but data analysis requires years of manual labor. We developed the SyConn framework, which uses deep convolutional neural networks and random forest classifiers to infer a richly annotated synaptic connectivity matrix from manual neurite skeleton reconstructions by automatically identifying mitochondria, synapses and their types, axons, dendrites, spines, myelin, somata and cell types. We tested our approach on serial block-face electron microscopy data sets from zebrafish, mouse and zebra finch, and computed the synaptic wiring of songbird basal ganglia. We found that, for example, basal-ganglia cell types with high firing rates in vivo had higher densities of mitochondria and vesicles and that synapse sizes and quantities scaled systematically, depending on the innervated postsynaptic cell types.


title: It's Not You, It's Me:: Identity, Self-Verification, and Amazon Reviews

abstract: Online retailers often incorporate crowdsourced product reviews to make customers feel more informed and comfortable with online purchases, and thus increase profits. The evaluation of these reviews is also crowdsourced, ostensibly to identify "helpful" reviews. The resulting helpfulness ratings are frequently used as measures for discerning what makes reviews helpful, and are used to determine which reviews are given priority viewing on the site. However, there is no empirical evidence that helpfulness voting reflects customers' attempts to evaluate product reviews objectively. This study examines review helpfulness voting from the position of the subjective customer rather than the objective anatomy of the review. We develop and empirically test a model, informed by self-verification theory, which explains relationships between online reviewers' overall opinions of products under consideration (star ratings), product type, and perceived helpfulness of online product reviews. Results suggest that customers' unconscious attempts to confirm what they already know and believe about themselves, referred to as self-verification, influences helpfulness voting. This work contributes to theoretical understanding of the role of reviews from the users' perspective and how, through suggesting new ways to identify helpful reviews, human behaviors can inform design of recommender systems.


title: An Integrated Optimization + Learning Approach to Optimal Dynamic Pricing for the Retailer with Multi-type Customers in Smart Grids

abstract: Abstract In this paper, we consider a realistic and meaningful scenario in the context of smart grids where an electricity retailer serves three different types of customers, i.e., customers with an optimal home energy management system embedded in their smart meters (C-HEMS), customers with only smart meters (C-SM), and customers without smart meters (C-NONE). The main objective of this paper is to support the retailer to make optimal day-ahead dynamic pricing decisions in such a mixed customer pool. To this end, we propose a two-level decision-making framework where the retailer acting as upper-level agent firstly announces its electricity prices of next 24 h and customers acting as lower-level agents subsequently schedule their energy usages accordingly. For the lower level problem, we model the price responsiveness of different customers according to their unique characteristics. For the upper level problem, we optimize the dynamic prices for the retailer to maximize its profit subject to realistic market constraints. The above two-level model is tackled by genetic algorithms (GA) based distributed optimization methods while its feasibility and effectiveness are confirmed via simulation results.


title: An alternative view of the mental lexicon

abstract: An essential aspect of knowing language is knowing the words of that language. This knowledge is usually thought to reside in the mental lexicon, a kind of dictionary that contains information regarding a word's meaning, pronunciation, syntactic characteristics, and so on. In this article, a very different view is presented. In this view, words are understood as stimuli that operate directly on mental states. The phonological, syntactic and semantic properties of a word are revealed by the effects it has on those states.


title: Rapid MIMO-OFDM software defined radio system prototyping

abstract: Combining MIMO with OFDM, it is possible to significantly reduce receiver complexity as OFDM greatly simplifies equalization at the receiver. MIMO-OFDM is currently being considered for a number of developing wireless standards; consequently, the study of MIMO-OFDM in realistic environments is of great importance. This paper describes an approach for prototyping a MIMO-OFDM systems using a flexible software defined radio (SDR) system architecture in conjunction with commercially available hardware. An emphasis on software permits a focus on algorithm and system design issues rather than implementation and hardware configuration. The penalty of this flexibility, however, is that the ease of use comes at the expense of overall throughput. To illustrate the benefits of the proposed architecture, applications to MIMO-OFDM system prototyping and preliminary MIMO channel measurements are presented. A detailed description of the hardware is provided along with downloadable software to reproduce the system.


BIOLOGICAL CONTROL OF THE JAPANESE BEETLE


THREE S Assessment for Learning in Preservice Teacher Education Performance-Based Assessments


title: A tool for checking ANSI-C programs

abstract: We present a tool for the formal verification of ANSI-C programs using Bounded Model Checking (BMC). The emphasis is on usability: the tool supports almost all ANSI-C language features, including pointer constructs, dynamic memory allocation, recursion, and the float and double data types. From the perspective of the user, the verification is highly automated: the only input required is the BMC bound. The tool is integrated into a graphical user interface. This is essential for presenting long counterexample traces: the tool allows stepping through the trace in the same way a debugger allows stepping through a program.


title: Three-phase three-level boost for UPS applications using FPGA

abstract: This paper presents a three-phase three-level boost for Uninterruptible Power Supply (UPS) applications using FPGA. Its main features are: high power factor, reduced conduction losses, weight and volume, simple control strategy based on One-cycle Control (OCC), and connection between input and output enabling the use of inverter and bypass. A theoretical analysis, simulation results and preliminaries experimental results from a 9kW development stage lab model are presented.


title: Performance Prediction for Apache Spark Platform

abstract: Apache Spark is an open source distributed data processing platform that uses distributed memory abstraction to process large volume of data efficiently. However, performance of a particular job on Apache Spark platform can vary significantly depending on the input data type and size, design and implementation of the algorithm, and computing capability, making it extremely difficult to predict the performance metric of a job such as execution time, memory footprint, and I/O cost. To address this challenge, in this paper, we present a simulation driven prediction model that can predict job performance with high accuracy for Apache Spark platform. Specifically, as Apache spark jobs are often consist of multiple sequential stages, the presented prediction model simulates the execution of the actual job by using only a fraction of the input data, and collect execution traces (e.g., I/O overhead, memory consumption, execution time) to predict job performance for each execution stage individually. We evaluated our prediction framework using four real-life applications on a 13 node cluster, and experimental results show that the model can achieve high prediction accuracy.


title: Cuckoo Hashing with Pages

abstract: A downside of cuckoo hashing is that it requires lookups to multiple locations, making it a less compelling alternative when lookups are expensive. One such setting is when memory is arranged in large pages, and the major cost is the number of page accesses. We propose the study of cuckoo hashing with pages, advocating approaches where each key has several possible locations, or cells, on a single page, and additional choices on a second backup page. We show experimentally that with k cell choices on one page and a single backup cell choice, one can achieve nearly the same loads as when each key has k + 1 random cells to choose from, with most lookups requiring just one page access, even when keys are placed online using a simple algorithm. While our results are currently experimental, they suggest several interesting new open theoretical questions for cuckoo hashing with pages.


Speech enhancement with weighted denoising auto-encoder.


title: A new peptide-silica bio-inspired stationary phase with an improved approach for hydrophilic interaction liquid chromatography

abstract: A new hydrophilic tripeptide-based organosilica hybrid novel stationary phase has been developed for hydrophilic interaction chromatography (HILIC), enabling the efficient separation of bioactive polar molecules.


title: Conceptualizing Business Models for Competitive Advantage Research by Integrating the Resource and Market-Based Views

abstract: Two opposing theories have been proposed to explain competitive advantage of firms. First, the market-based view (MBV) is focused on product or market positions and competition while second, the resource-based view (RBV) aims at explaining success by inwardly looking at unique resources and capabilities of a firm. Research has been struggling to distinguish impacts of these theories for illuminating performance. Business models are seen as an important concept to systemize the business and value creation logic of firms by defining different core components. Thus, this paper tries to assess associations between these components and MBV or RBV perspectives by applying content analysis. Two of the business model components were found to have strong links with the MBV while three of them showed indications of their roots lying in the resource-based perspective. These results are discussed and theorized in a final step by suggesting frameworks of the corresponding perspectives for further explaining competitive advantage.


Privads: Privacy Preserving Targeted Advertising


title: Perspectives of accounting students and teachers on the changing role of management accountants in organisations

abstract: Owing to a highly competitive global market, economic crises and changes in manufacturing and information technology, organisations are compelled to improve their financial planning and control functions. These and other changes have contributed to the evolving role of the management accounting function in organisations. Whereas cost accounting’s main focus was on fi nding the cost of manufacturing products, the objective of management accounting is to use financial and non-financial information to assist management with decision making. Although management accountants have long played multiple roles in organisations, there is a definite shift towards a more strategic approach, opening up new vistas of opportunity. However, this widening scope of the changing practice of management accounting demonstrates a lack of a recognisable and coherent occupational identity. The objective of this study was to determine whether prospective accounting students and accounting teachers are aware of the changing role and status of management accountants. This study presents the results of a survey among first-year accounting students as well as interviews with accounting teachers to assess their perceptions of the current role of management accountants in organisations. This study exposes a lack of knowledge of the status of management accountants and their strategic role in organisations. Recommendations are made for tertiary institutions to alleviate this information gap and for further research on this issue. Perspectives on the changing role of management accountants in organisations Key words : management accounting, management accountant, chartered management accountant, strategic management accountant


title: Correction of entropion and ectropion.

abstract: The ocular surface is dependent on the physiological and anatomical integrity of the eyelids and associated adnexal structures. Entropion and ectropion are commonly encountered in clinical practice, and pose a risk to the success of both ocular surface and intraocular surgery. This chapter covers the causes of such lid malpositions, which can be congenital or acquired (e.g. involutional, cicatricial or paralytic) and the clinical examination of the eyelids. Principles underlying corrective surgery are demonstrated and practical aspects of the some more commonly performed and effective operations are described.


title: RTOS based Home Automation System using Android

abstract: With the openness, flexibility and features that Android offers, it has been widely adopted in applications beyond just SmartPhones. This paper presents the design and implementation of a low cost yet compact and secure Android smart phone based home automation system. This design is based on the popular open sourced Arduino prototyping board where the sensors and electrical appliances are connected to the input/output ports of the board. In order to enhance the system responsiveness and to make it more dynamic, we’ve integrated a popular and open source RTOS, the scmRTOS, which has a very small footprint on the microcontroller. The controlling application which has been developed for Android devices can also be easily developed on other popular SmartPhone operating systems like Apple’s iOS, Microsoft’s WP7/8 and BlackBerry OS. Pattern based password protection is implemented to allow only authorized users to control the appliances. Another add-on included is the integration of Google’s voice recognition feature that recognizes users’ voice commands to control appliances.


title: Using the Humanoid Robot KASPAR to Autonomously Play Triadic Games and Facilitate Collaborative Play Among Children With Autism

abstract: This paper presents a novel design, implementation, and first evaluation of a triadic, collaborative game involving the humanoid robot, kinesics and synchronization in personal assistant robotics (KASPAR), playing games with pairs of children with autism. Children with autism have impaired social communication and social interaction skills which make it difficult for them to participate in many different forms of social and collaborative play. Our proof-of-concept 10-week, long term study demonstrates how a humanoid robot can be used to foster and support collaborative play among children with autism. In this work, KASPAR operates fully autonomously, and uses information on the state of the game and behavior of the children to engage, motivate, encourage, and advise pairs of children playing an imitation game. Results are presented from a first evaluation study which examined whether having pairs of children with autism play an imitative, collaborative game with a humanoid robot affected the way these children would play the same game without the robot. Our initial evaluation involved six children with autism who each participated in 23 controlled play sessions both with and without the robot, using a specially designed imitation-based collaborative game. In total 78 play sessions were run. Detailed observational analyses of the children's behaviors indicated that different pairs of children with autism showed improved social behaviors in playing with each other after they played as pairs with the robot KASPAR compared to before they did so. These results are encouraging and provide a proof-of-concept of using an autonomously operating robot to encourage collaborative skills among children with autism.


title: Online Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret

abstract: Online learning algorithms are designed to learn even when their input is generated by an adversary. The widely-accepted formal definition of an online algorithm's ability to learn is the game-theoretic notion of regret. We argue that the standard definition of regret becomes inadequate if the adversary is allowed to adapt to the online algorithm's actions. We define the alternative notion of policy regret, which attempts to provide a more meaningful way to measure an online algorithm's performance against adaptive adversaries. Focusing on the online bandit setting, we show that no bandit algorithm can guarantee a sublinear policy regret against an adaptive adversary with unbounded memory. On the other hand, if the adversary's memory is bounded, we present a general technique that converts any bandit algorithm with a sublinear regret bound into an algorithm with a sublinear policy regret bound. We extend this result to other variants of regret, such as switching regret, internal regret, and swap regret.


Deep Bayesian Inversion Computational uncertainty quantification for large scale inverse problems


title: An Organic Architecture for Traffic Light Controllers

abstract: Efficient control of traffic networks is a complex but important task. A successful network management vitally depends on the abilities of the traffic light controllers to adapt to changing traffic situations. In this paper a control architecture for traffic nodes is presented that is inspired by the principles of Organic Computing. It allows a node to quickly adapt to changing traffic situations and enables it to autonomously learn new control strategies if necessary.


title: Deep Interactive Region Segmentation and Captioning

abstract: With recent innovations in dense image captioning, it is now possible to describe every object of the scene with a caption while objects are determined by bounding boxes. However, interpretation of such an output is not trivial due to the existence of many overlapping bounding boxes. Furthermore, in current captioning frameworks, the user is not able to involve personal preferences to exclude out of interest areas. In this paper, we propose a novel hybrid deep learning architecture for interactive region segmentation and captioning where the user is able to specify an arbitrary region of the image that should be processed. To this end, a dedicated Fully Convolutional Network (FCN) named Lyncean FCN (LFCN) is trained using our special training data to isolate the User Intention Region (UIR) as the output of an efficient segmentation. In parallel, a dense image captioning model is utilized to provide a wide variety of captions for that region. Then, the UIR will be explained with the caption of the best match bounding box. To the best of our knowledge, this is the first work that provides such a comprehensive output. Our experiments show the superiority of the proposed approach over state-of-the-art interactive segmentation methods on several well-known datasets. In addition, replacement of the bounding boxes with the result of the interactive segmentation leads to a better understanding of the dense image captioning output as well as accuracy enhancement for the object detection in terms of Intersection over Union (IoU).


title: Authalic Parameterization of General Surfaces Using Lie Advection

abstract: Parameterization of complex surfaces constitutes a major means of visualizing highly convoluted geometric structures as well as other properties associated with the surface. It also enables users with the ability to navigate, orient, and focus on regions of interest within a global view and overcome the occlusions to inner concavities. In this paper, we propose a novel area-preserving surface parameterization method which is rigorous in theory, moderate in computation, yet easily extendable to surfaces of non-disc and closed-boundary topologies. Starting from the distortion induced by an initial parameterization, an area restoring diffeomorphic flow is constructed as a Lie advection of differential 2-forms along the manifold, which yields equality of the area elements between the domain and the original surface at its final state. Existence and uniqueness of result are assured through an analytical derivation. Based upon a triangulated surface representation, we also present an efficient algorithm in line with discrete differential modeling. As an exemplar application, the utilization of this method for the effective visualization of brain cortical imaging modalities is presented. Compared with conformal methods, our method can reveal more subtle surface patterns in a quantitative manner. It, therefore, provides a competitive alternative to the existing parameterization techniques for better surface-based analysis in various scenarios.


title: Risk Aversion and Engagement in the Sharing Economy

abstract: Abstract: The sharing economy is a new online community that has important implications for offline behavior. This study evaluates whether engagement in the sharing economy is associated with an actor’s aversion to risk. Us ing a web-based survey and a field experiment, we apply an adaptation of Holt and Laury’s (2002) risk lottery game to a representative sample of sharing economy participants. We find that frequency of activity in the sharing economy predicts risk aversion, but only in interaction with satisfaction. While greater satisfaction with sharing economy websites is associated with a decrease in risk aversion, greater frequency of usage is associated with greater risk aversion. This analysis shows the limitations of a static perspective on how risk attitudes relate to participation in the sharing economy. Keywords: risk; online; sharing economy 1. Introduction In the 1960s, the hippy movement promoted the resurg ence of barter systems and the end of personal property. This movement largely did not survive the decade, but contributed to a similar phenomenon that has recently taken shape [1]. In place of communes, members of the sharing economy meet and exchange goods or services via websites and email listservs. Rather than pilgrimaging to Berkeley, sharing economy members with common interests connect from around the globe through servers based in Palo Alto. As much as 40% of the US population has participated in the sharing economy [2,3]. The


title: Effects of Organizational Learning and Knowledge Transfer on Investment Decisions Under Uncertainty

abstract: Because uncertainties around innovative technologies resolve over time, investments in such technologies are often made in stages so that organizations can use the knowledge gained from earlier stages to decide the next step. Previous studies usually assume that once some uncertainty is resolved, it becomes common knowledge within the investing organization. We develop a game-theoretical model to study how different parties within an organization gain and transfer knowledge about new technologies while investing in these technologies, and how the learning process may affect the investment decisions. We show that managers with incentives misaligned with the organization may transfer their knowledge untruthfully and distort the learning process of decision makers. Such behavior may lead to inefficient investment decisions. We also study the effect of uncertainty on the misreporting problem and the investment decisions. Mechanisms to mitigate or prevent untruthful knowledge transfer are also proposed. In particular, powerful incentive schemes may alleviate, but not prevent, the misreporting problem; punishing managers who are caught misreporting may deter the misreporting behavior, but in practice such mechanisms are difficult to implement.


title: A 16-element phased-array receiver IC for 60-GHz communications in SiGe BiCMOS

abstract: A 0.12-µm SiGe phased-array Rx IC for beam-steered wireless communication in the 60-GHz band is described. It has 16 RF phase-shifting front-ends with 11° digital phase resolution and hybrid passive-active RF signal combining. It achieves 7.4–7.9 dB NF (not including 12-dB array gain) over the 4 IEEE channels. The IC has a double-conversion superheterodyne Rx core with a maximum of 72 dB of power gain in 1-dB steps, and the on-chip synthesizer achieves ≪ −90 dBc/Hz Rx phase noise at 1MHz offset. The IC draws 1.8 W at 2.7 V with a die area of 38 mm2. It has been packaged with 16 antennas in a 288-pin organic BGA and phased-array beamsteering has been demonstrated, along with 5+ Gb/s wireless links using 16-QAM OFDM.


title: Miniaturized ultra-broadband SIR filter using LTCC technology

abstract: A novel broadband quarter-wavelength SIR BPF using LTCC technology is proposed. In this filter, three different coupling schemes are adopted. Edge-coupled structure is used to realize week coupling coefficient while great coefficient is obtained by applying direct magnetic coupling between two resonators or overlapping resonators in different LTCC layers. Based on the proposed strong coupling topology, the filter bandwidth is extended while the size is compacted. Besides, spur lines are adopted to improve the stopband rejection.


Transformation Knowledge in Pattern Analysis with Kernel Methods Distance and Integration Kernels


Implicit Subjective and Sentimental Usages in Multi-sense Word Embeddings


title: SparseFool: A Few Pixels Make a Big Difference

abstract: Deep Neural Networks have achieved extraordinary results on image classification tasks, but have been shown to be vulnerable to attacks with carefully crafted perturbations of the input data. Although most attacks usually change values of many image's pixels, it has been shown that deep networks are also vulnerable to sparse alterations of the input. However, no computationally efficient method has been proposed to compute sparse perturbations. In this paper, we exploit the low mean curvature of the decision boundary, and propose SparseFool, a geometry inspired sparse attack that controls the sparsity of the perturbations. Extensive evaluations show that our approach computes sparse perturbations very fast, and scales efficiently to high dimensional data. We further analyze the transferability and the visual effects of the perturbations, and show the existence of shared semantic information across the images and the networks. Finally, we show that adversarial training can only slightly improve the robustness against sparse additive perturbations computed with SparseFool.


title: MapReduce in MPI for Large-scale graph algorithms

abstract: We describe a parallel library written with message-passing (MPI) calls that allows algorithms to be expressed in the MapReduce paradigm. This means the calling program does not need to include explicit parallel code, but instead provides ''map'' and ''reduce'' functions that operate independently on elements of a data set distributed across processors. The library performs needed data movement between processors. We describe how typical MapReduce functionality can be implemented in an MPI context, and also in an out-of-core manner for data sets that do not fit within the aggregate memory of a parallel machine. Our motivation for creating this library was to enable graph algorithms to be written as MapReduce operations, allowing processing of terabyte-scale data sets on traditional MPI-based clusters. We outline MapReduce versions of several such algorithms: vertex ranking via PageRank, triangle finding, connected component identification, Luby's algorithm for maximally independent sets, and single-source shortest-path calculation. To test the algorithms on arbitrarily large artificial graphs we generate randomized R-MAT matrices in parallel; a MapReduce version of this operation is also described. Performance and scalability results for the various algorithms are presented for varying size graphs on a distributed-memory cluster. For some cases, we compare the results with non-MapReduce algorithms, different machines, and different MapReduce software, namely Hadoop. Our open-source library is written in C++, is callable from C++, C, Fortran, or scripting languages such as Python, and can run on any parallel platform that supports MPI.


title: Identifying the Academic Rising Stars

abstract: Predicting the fast-rising young researchers (Academic Rising Stars) in the future provides useful guidance to the research community, e.g., offering competitive candidates to university for young faculty hiring as they are expected to have success academic careers. In this work, given a set of young researchers who have published the first first-author paper recently, we solve the problem of how to effectively predict the top k% researchers who achieve the highest citation increment in \Delta t years. We explore a series of factors that can drive an author to be fast-rising and design a novel impact increment ranking learning (IIRL) algorithm that leverages those factors to predict the academic rising stars. Experimental results on the large ArnetMiner dataset with over 1.7 million authors demonstrate the effectiveness of IIRL. Specifically, it outperforms all given benchmark methods, with over 8% average improvement. Further analysis demonstrates that the prediction models for different research topics follow the similar pattern. We also find that temporal features are the best indicators for rising stars prediction, while venue features are less relevant.


Extraction with Semi-supervised Neural Tagging


title: Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks

abstract: Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a = ( u , v ) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages.


Can Models Learned from a Dataset Reflect Acquisition of Procedural Knowledge? An Experiment with Automatic Measurement of Online Review Quality


title: Cartman: The Low-Cost Cartesian Manipulator that Won the Amazon Robotics Challenge

abstract: The Amazon Robotics Challenge enlisted sixteen teams to each design a pick-and-place robot for autonomous warehousing, addressing development in robotic vision and manipulation. This paper presents the design of our custom-built. cost-effective robot system Cartman, which won first place in the competition finals by stowing 14 (out of 16) and picking all 9 items in 27 minutes, scoring a total of 272 points. We highlight our experience-centred design methodology and key aspects of our system that contributed to our competitiveness. We believe these aspects are crucial to building robust and effective robotic systems.


title: Automating regression testing for real-time software in a distributed environment

abstract: Many real time systems evolve over time due to new requirements and technology improvements. Each revision requires regression resting to ensure that existing functionality is not affected by such changes. Testing these systems often require specialized hardware and software, and both are expensive. While the overall regression testing process is similar across different organizations, the strategies and tools used by them vary according to their product needs. Hence a good framework for regression testing should provide the flexibility to configure it depending on the particular organization's needs while at the same time maximizing utilization. Manual processes are typically slow and error prone and result in under-utilization of valuable test resources. The paper proposes an automated distributed regression testing framework that provides flexibility to the user to configure it to their needs while at the same time optimizing resource usage.


title: A Test of a Four-Category Model

abstract: A new 4-group model of attachment styles in adulthood is proposed. Four prototypic attachment patterns are defined using combinations of a person's self-image (positive or negative) and image of others (positive or negative). In Study 1, an interview was developed to yield continuous and categorical ratings of the 4 attachment styles. Intercorrelations of the attachment ratings were consistent with the proposed model. Attachment ratings were validated by self-report measures of self-concept and interpersonal functioning. Each style was associated with a distinct profile of interpersonal problems, according to both self- and friend-reports. In Study 2, attachment styles within the family of origin and with peers were assessed independently. Results of Study 1 were replicated. The proposed model was shown to be applicable to representations of family relations; Ss' attachment styles with peers were correlated with family attachment ratings.


title: Parallel Interdigitated Distributed Networks within the Individual Estimated by Intrinsic Functional Connectivity

abstract: Summary Certain organizational features of brain networks present in the individual are lost when central tendencies are examined in the group. Here we investigated the detailed network organization of four individuals each scanned 24 times using MRI. We discovered that the distributed network known as the default network is comprised of two separate networks possessing adjacent regions in eight or more cortical zones. A distinction between the networks is that one is coupled to the hippocampal formation while the other is not. Further exploration revealed that these two networks were juxtaposed with additional networks that themselves fractionate group-defined networks. The collective networks display a repeating spatial progression in multiple cortical zones, suggesting that they are embedded within a broad macroscale gradient. Regions contributing to the newly defined networks are spatially variable across individuals and adjacent to distinct networks, raising issues for network estimation in group-averaged data and applied endeavors, including targeted neuromodulation.