title: Understanding online behavioural advertising: User knowledge, privacy concerns and online coping behaviour in Europe

abstract: In response to new EU regulations, new Dutch cookie legislation requires prior informed consent.Dutch Internet users have insufficient knowledge when confronted with targeted advertising.Older and less-educated groups were most concerned about their privacy.Most users intended to protect their online privacy but not by reading privacy statements.Government should not only focus on enhancing OBA knowledge but also on reducing concerns. Online behavioural advertising (OBA) is a special form of targeted advertising. For OBA, it is necessary to collect data about online surfing behaviour, which is usually undertaken by installing 'cookies'. The use of cookies is heavily debated by policy makers in the US and Europe. Central to this discussion is whether users should provide informed consent prior to the installation of cookies. However, to provide informed consent, it is vital that website users understand the mechanisms of OBA and cookies. This study investigates whether the existing knowledge is sufficient, whether website users are concerned about their privacy, and how they cope with OBA, cookies and the requested informed consent. An online questionnaire (N=2022) was fielded briefly after new European cookie regulations were enacted. The results confirmed that the knowledge is still insufficient to obtain good understanding of this new advertising technique. In addition, the results showed that groups of Internet users did not differ in terms of knowledge, although they did differ in terms of privacy concerns. A comparison of privacy-concerned groups showed that a dual approach is needed in communicating about OBA, not only to inform but also to reduce worries, especially in older and less-educated groups.


title: A 76–84-GHz 16-Element Phased-Array Receiver With a Chip-Level Built-In Self-Test System

abstract: This paper presents a 16-element phased-array receiver for 76-84-GHz applications with built-in self-test (BIST) capabilities. The chip contains an in-phase/quadrature (I/Q) mixer suitable for automotive frequency-modulation continuous-wave radar applications, which is also used as part of the BIST system. The chip achieves 4-bit RF amplitude and phase control, an RF to IF gain of 30-35 dB at 77-84 GHz, I/Q balance of and at 76-84 GHz, and a system noise figure of 18 dB. The on-chip BIST covers the 76-84-GHz range and determines, without any calibration, the amplitude and phase of each channel, a normalized frequency response, and can measure the gain control using RF gain control. System-level considerations are discussed together with extensive results showing the effectiveness of the on-chip BIST as compared with standard S-parameter measurements.


title: Chemical Vapor Deposition Growth and Applications of Two-Dimensional Materials and Their Heterostructures

abstract: Two-dimensional (2D) materials have attracted increasing research interest because of the abundant choice of materials with diverse and tunable electronic, optical, and chemical properties. Moreover, 2D material based heterostructures combining several individual 2D materials provide unique platforms to create an almost infinite number of materials and show exotic physical phenomena as well as new properties and applications. To achieve these high expectations, methods for the scalable preparation of 2D materials and 2D heterostructures of high quality and low cost must be developed. Chemical vapor deposition (CVD) is a powerful method which may meet the above requirements, and has been extensively used to grow 2D materials and their heterostructures in recent years, despite several challenges remaining. In this review of the challenges in the CVD growth of 2D materials, we highlight recent advances in the controlled growth of single crystal 2D materials, with an emphasis on semiconducting transition meta...


title: 3D Euler spirals for 3D curve completion

abstract: Shape completion is an intriguing problem in geometry processing with applications in CAD and graphics. This paper defines a new type of 3D curves, which can be utilized for curve completion. It can be considered as the extension to three dimensions of the 2D Euler spiral. We prove several properties of these curves - properties that have been shown to be important for the appeal of curves. We illustrate their utility in two applications. The first is "fixing" curves detected by algorithms for edge detection on surfaces. The second is shape illustration in archaeology, where the user would like to draw curves that are missing due to the incompleteness of the input model.


title: Printed Slot Loaded Bow-Tie Antenna With Super Wideband Radiation Characteristics for Imaging Applications

abstract: A super wideband printed modified bow-tie antenna loaded with rounded-T shaped slots fed through a microstrip balun is proposed for microwave and millimeter-wave band imaging applications. The modified slot-loaded bow-tie pattern increases the electrical length of the bow-tie antenna reducing the lower band to 3.1 GHz. In addition, over the investigated frequency band up to 40 GHz, the proposed modified bow-tie pattern considerably flattens the input impedance response of the bow-tie resulting in a smooth impedance matching performance enhancing the reflection coefficient (S11) characteristics. The introduction of the modified ground plane printed underneath the bow-tie, on the other hand, yields to directional far-field radiation patterns with considerably enhanced gain performance. The S11 and E-plane/H-plane far-field radiation pattern measurements have been carried out and it is demonstrated that the fabricated bow-tie antenna operates across a measured frequency band of 3.1-40 GHz with an average broadband gain of 7.1 dBi.


title: The Wordometer -- Estimating the Number of Words Read Using Document Image Retrieval and Mobile Eye Tracking

abstract: We introduce the Wordometer, a novel method to estimate the number of words a user reads using a mobile eye tracker and document image retrieval. We present a reading detection algorithm which works with over 91 % accuracy over 10 test subjects using 10-fold cross validation. We implement two algorithms to estimate the read words using a line break detector. A simple version gives an average error rate of 13,5 % for 9 users over 10 documents. A more sophisticated word count algorithm based on support vector regression with an RBF kernel reaches an average error rate from only 8.2 % (6.5 % if one test subject with abnormal behavior is excluded). The achieved error rates are comparable to pedometers that count our steps in our daily life. Thus, we believe the Wordometer can be used as a step counter for the information we read to make our knowledge life healthier.


title: Design and Control for Three-Phase Grid-Connected Photovoltaic Inverter with LCL Filter

abstract: As the traditional resources have become rare, photovoltaic generation is developing quickly. The grid-connected issue is one of the most importance problem in this field. The voltage source inverter usually uses LC or LCL as the filter. LCL filter, which can reduce the required filtered inductance and save the cost, is adopted to connect the grid in this paper. However, reasonable design of LCL filter needs consideration in order to achieve optimal effect. Meanwhile, systems with LCL filter need more complex control strategies. This paper firstly introduces a whole scheme of LCL filter design, then analyzes a two-loop control method in detail which uses the capacitor current and grid current as the inner and outer control variable respectively. Detailed discussion about the role of the loops and the influence on the system caused by every parameter of the controller are given out. Finally experimental results verify the correctness of the analysis and availability of the control strategy.


title: Query-Dependent Aesthetic Model With Deep Learning for Photo Quality Assessment

abstract: The automatic assessment of photo quality from an aesthetic perspective is a very challenging problem. Most existing research has predominantly focused on the learning of a universal aesthetic model based on hand-crafted visual descriptors . However, this research paradigm can achieve only limited success because 1) such hand-crafted descriptors cannot well preserve abstract aesthetic properties , and 2) such a universal model cannot always capture the full diversity of visual content. To address these challenges, we propose in this paper a novel query-dependent aesthetic model with deep learning for photo quality assessment. In our method, deep aesthetic abstractions are discovered from massive images , whereas the aesthetic assessment model is learned in a query- dependent manner. Our work addresses the first problem by learning mid-level aesthetic feature abstractions via powerful deep convolutional neural networks to automatically capture the underlying aesthetic characteristics of the massive training images . Regarding the second problem, because photographers tend to employ different rules of photography for capturing different images , the aesthetic model should also be query- dependent . Specifically, given an image to be assessed, we first identify which aesthetic model should be applied for this particular image. Then, we build a unique aesthetic model of this type to assess its aesthetic quality. We conducted extensive experiments on two large-scale datasets and demonstrated that the proposed query-dependent model equipped with learned deep aesthetic abstractions significantly and consistently outperforms state-of-the-art hand-crafted feature -based and universal model-based methods.


Chapter 2 Accelerating Lloyd ’ s Algorithm for k-Means Clustering


title: Other minds in the brain: a functional imaging study of “theory of mind” in story comprehension

abstract: The ability of normal children and adults to attribute independent mental states to self and others in order to explain and predict behaviour ("theory of mind") has been a focus of much recent research. Autism is a biologically based disorder which appears to be characterised by a specific impairment in this "mentalising" process. The present paper reports a functional neuroimaging study with positron emission tomography in which we studied brain activity in normal volunteers while they performed story comprehension tasks necessitating the attribution of mental states. The resultant brain activity was compared with that measured in two control tasks: "physical" stories which did not require this mental attribution, and passages of unlinked sentences. Both story conditions, when compared to the unlinked sentences, showed significantly increased regional cerebral blood flow in the following regions: the temporal poles bilaterally, the left superior temporal gyrus and the posterior cingulate cortex. Comparison of the "theory of mind" stories with "physical" stories revealed a specific pattern of activation associated with mental state attribution: it was only this task which produced activation in the medial frontal gyrus on the left (Brodmann's area 8). This comparison also showed significant activation in the posterior cingulate cortex. These surprisingly clear-cut findings are discussed in relation to previous studies of brain activation during story comprehension. The localisation of brain regions involved in normal attribution of mental states and contextual problem solving is feasible and may have implications for the neural basis of autism.


Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks


title: Learning typographic style: from discrimination to synthesis

abstract: Typography is a ubiquitous art form that affects our understanding, perception and trust in what we read. Thousands of different font-faces have been created with enormous variations in the characters. In this paper, we learn the style of a font by analyzing a small subset of only four letters. From these four letters, we learn two tasks. The first is a discrimination task: given the four letters and a new candidate letter, does the new letter belong to the same font? Second, given the four basis letters, can we generate all of the other letters with the same characteristics as those in the basis set? We use deep neural networks to address both tasks, quantitatively and qualitatively measure the results in a variety of novel manners, and present a thorough investigation of the weaknesses and strengths of the approach. All of the experiments are conducted with publicly available font sets.


title: Mutual Coupling Reduction for UWB MIMO Antennas With a Wideband Neutralization Line

abstract: A wideband neutralization line is proposed to reduce the mutual coupling of a compact ultrawideband (UWB) multiple-input-multiple-output (MIMO) antenna. With the introduced decoupling method, the designed UWB MIMO antenna covers the band of 3.1-5 GHz with an isolation of higher than 22 dB. The proposed wideband neutralization line is not necessarily placed in the clearance area between two MIMO elements and can be put above the copper ground. A small clearance (antenna area) of 35 ×16 mm ::: 2 ::: is achieved. The designed UWB MIMO antenna is fabricated. Sparameters, radiation patterns, total efficiency, and realized gain of the prototype are measured and compared to the simulations.


title: The NIDS cluster: Scalable, stateful network intrusion detection on commodity hardware

abstract: In this work we present a NIDS cluster as a scalable solution for realizing high-performance, stateful network intrusion detection on commodity hardware. The design addresses three challenges: (i) distributing traffic evenly across an extensible set of analysis nodes in a fashion that minimizes the communication required for coordination, (ii) adapting the NIDS's operation to support coordinating its low-level analysis rather than just aggregating alerts; and (iii) validating that the cluster produces sound results. Prototypes of our NIDS cluster now operate at the Lawrence Berkeley National Laboratory and the University of California at Berkeley. In both environments the clusters greatly enhance the power of the network security monitoring.


title: Text Reuse Detection using a Composition of Text Similarity Measures

abstract: Detecting text reuse is a fundamental requirement for a variety of tasks and applications, ranging from journalistic text reuse to plagiarism detection. Text reuse is traditionally detected by computing similarity between a source text and a possibly reused text. However, existing text similarity measures exhibit a major limitation: They compute similarity only on features which can be derived from the content of the given texts, thereby inherently implying that any other text characteristics are negligible. In this paper, we overcome this traditional limitation and compute similarity along three characteristic dimensions inherent to texts: content, structure, and style. We explore and discuss possible combinations of measures along these dimensions, and our results demonstrate that the composition consistently outperforms previous approaches on three standard evaluation datasets, and that text reuse detection greatly benefits from incorporating a diverse feature set that reflects a wide variety of text characteristics.


title: Twitter Sentiment Analysis for Large-Scale Data: An Unsupervised Approach

abstract: Millions of tweets are generated each day on multifarious issues. Topical diversity in content demands domain-independent solutions for analysing twitter sentiments. Scalability is another issue when dealing with huge amount of tweets. This paper presents an unsupervised method for analysing tweet sentiments. Polarity of tweets is evaluated by using three sentiment lexicons—SenticNet, SentiWordNet and SentislangNet. SentislangNet is a sentiment lexicon built from SenticNet and SentiWordNet for slangs and acronyms. Experimental results show fairly good \(F\)-score. The method is implemented and tested in parallel python framework and is shown to scale well with large volume of data on multiple cores.


title: VERTEXICA: Your Relational Friend for Graph Analytics!

abstract: In this paper, we present Vertexica, a graph analytics tools on top of a relational database, which is user friendly and yet highly efficient. Instead of constraining programmers to SQL, Vertexica offers a popular vertex-centric query interface, which is more natural for analysts to express many graph queries. The programmers simply provide their vertex-compute functions and Vertexica takes care of efficiently executing them in the standard SQL engine. The advantage of using Vertexica is its ability to leverage the relational features and enable much more sophisticated graph analysis. These include expressing graph algorithms which are difficult in vertex-centric but straightforward in SQL and the ability to compose end-to-end data processing pipelines, including pre- and post- processing of graphs as well as combining multiple algorithms for deeper insights. Vertexica has a graphical user interface and we outline several demonstration scenarios including, interactive graph analysis, complex graph analysis, and continuous and time series analysis.


title: SUN1 Regulates HIV-1 Nuclear Import in a Manner Dependent on the Interaction between the Viral Capsid and Cellular Cyclophilin A.

abstract: Human immunodeficiency virus type 1 (HIV-1) can infect nondividing cells via passing through the nuclear pore complex. The nuclear membrane-imbedded protein SUN2 was recently reported to be involved in the nuclear import of HIV-1. Whether SUN1, which shares many functional similarities with SUN2, is involved in this process remained to be explored. Here we report that overexpression of SUN1 specifically inhibited infection by HIV-1 but not that by simian immunodeficiency virus (SIV) or murine leukemia virus (MLV). Overexpression of SUN1 did not affect reverse transcription but led to reduced accumulation of the 2-long-terminal-repeat (2-LTR) circular DNA and integrated viral DNA, suggesting a block in the process of nuclear import. HIV-1 CA was mapped as a determinant for viral sensitivity to SUN1. Treatment of SUN1-expressing cells with cyclosporine (CsA) significantly reduced the sensitivity of the virus to SUN1, and an HIV-1 mutant containing CA-G89A, which does not interact with cyclophilin A (CypA), was resistant to SUN1 overexpression. Downregulation of endogenous SUN1 inhibited the nuclear entry of the wild-type virus but not that of the G89A mutant. These results indicate that SUN1 participates in the HIV-1 nuclear entry process in a manner dependent on the interaction of CA with CypA.IMPORTANCE HIV-1 infects both dividing and nondividing cells. The viral preintegration complex (PIC) can enter the nucleus through the nuclear pore complex. It has been well known that the viral protein CA plays an important role in determining the pathways by which the PIC enters the nucleus. In addition, the interaction between CA and the cellular protein CypA has been reported to be important in the selection of nuclear entry pathways, though the underlying mechanisms are not very clear. Here we show that both SUN1 overexpression and downregulation inhibited HIV-1 nuclear entry. CA played an important role in determining the sensitivity of the virus to SUN1: the regulatory activity of SUN1 toward HIV-1 relied on the interaction between CA and CypA. These results help to explain how SUN1 is involved in the HIV-1 nuclear entry process.


title: A privacy-preserving approach to policy-based content dissemination

abstract: We propose a novel scheme for selective distribution of content, encoded as documents, that preserves the privacy of the users to whom the documents are delivered and is based on an efficient and novel group key management scheme. Our document broadcasting approach is based on access control policies specifying which users can access which documents, or subdocuments. Based on such policies, a broadcast document is segmented into multiple subdocuments, each encrypted with a different key. In line with modern attribute-based access control, policies are specified against identity attributes of users. However our broadcasting approach is privacy-preserving in that users are granted access to a specific document, or subdocument, according to the policies without the need of providing in clear information about their identity attributes to the document publisher. Under our approach, not only does the document publisher not learn the values of the identity attributes of users, but it also does not learn which policy conditions are verified by which users, thus inferences about the values of identity attributes are prevented. Moreover, our key management scheme on which the proposed broadcasting approach is based is efficient in that it does not require to send the decryption keys to the users along with the encrypted document. Users are able to reconstruct the keys to decrypt the authorized portions of a document based on subscription information they have received from the document publisher. The scheme also efficiently handles new subscription of users and revocation of subscriptions.


Review of Natural Pastures and Their Management Problems on the North Coast of Tanzania


Kinematic Geometry of Mechanisms (K. H. Hunt)


title: Evaluating the accuracy of wearable heart rate monitors

abstract: Heart rate monitors have become a popular tool, which have been built in many different wearable devices, such as Smart Watches and Chest Straps. These wearable monitoring of heart rate (HR) during exercises have given the user some understanding of the heart rate variations. Even though this is not 100% accurate as electrocardiogram (ECG), its being very popular tool for consumers. The technology uses in smart watches is photoplethysmography (PPG) signal and Chest Straps use ECG signal. The main aim of this paper is to compare two wearable devices to monitor heart rate. 50 healthy volunteers participated in this study by completing three different exercises sitting, walking and jogging. The experiment results illustrates that it's harder to get a unique percentage for the variation between the readings of these two devices, however in normal condition both the devices read almost the similar value, but during the exercises it varies and can be with a maximum of 10% variation in the readings. Overall it appears that chest strap utilizing ECG signal appears to be a more accurate than the Apple Watch which utilizes PPG for monitoring heart rates while exercising.


title: A Survey of Cyber-Physical Advances and Challenges of Wind Energy Conversion Systems: Prospects for Internet of Energy

abstract: Wind energy has the biggest market share of renewable energy around the world. High growth and development rates of wind energy lead to a massive increase in complexity and scale of wind energy conversion systems (WECSs). Therefore, it is required to upgrade methods and strategies for design and implementation of WECS. Considering WECS as cyber-physical systems (CPSs) will enable wind energy for the Internet of Energy (IoE). IoE is a cloud network where power sources with embedded and distributed intelligence are interfaced to smart grid and mass of consumption devices like smart buildings, appliances, and electric vehicles. Research trends of CPS for energy applications are mainly focusing on smart grids and energy systems for demand-side management and smart buildings with less attention given to generation systems. This paper introduces potentials of cyber-physical (CP) integration of next-generation WECS. In addition, this paper surveys the advances and state-of-the-art technologies that enable WECS for IoE. Challenges and new requirements of future WECS as CPS like abstractions, networking, control, safety, security, sustainability, and social components are discussed.


MODELING DELAYS IN STATE TRANSITION OF A BISTABLE GENETIC SWITCH UNDER THE INFLUENCE OF EXTRINSIC NOISE


Brain Tumor Detection Using Clustering Method


title: What Does Viewing a Pro-Anorexia Website Do? An Experimental Examination of Website Exposure and Moderating Effects

abstract: Objective: ::: ::: This study experimentally examined the effects of viewing a pro-anorexia website. ::: ::: ::: ::: Method: ::: ::: Following construction of a prototypic pro-anorexia website, 235 female undergraduates were randomly assigned to view either the pro-anorexia website or one of two comparison websites related to female fashion (using average-sized models) or home decor. Post-website affect, cognitions, and behavioral expectations were examined along with moderator effects. ::: ::: ::: ::: Results: ::: ::: Study participants exposed to the pro-anorexia website had greater negative affect, lower social self-esteem, and lower appearance self-efficacy post-website than those who viewed a comparison website. Additionally, they perceived themselves as heavier, reported a greater likelihood of exercising and thinking about their weight in the near future, and engaged in more image comparison. Minimal support was found for moderator effects. ::: ::: ::: ::: Conclusion: ::: ::: Results provide an empirical basis for concern (expressed by clinicians, researchers, the media) that pro-anorexia websites are a troubling new form of thin-ideal exposure that warrants further examination. © 2007 by Wiley Periodicals, Inc.


title: Seismic demands on steel braced frame buildings with buckling-restrained braces

abstract: Some results are highlighted in this paper from a research effort being undertaken to identify ground motion and structural characteristics that control the earthquake response of concentrically braced steel frames and to identify improved design procedures and code provisions. The focus of this paper is on the seismic response of three and six story concentrically braced frames utilizing buckling-restrained braces. A brief discussion is provided regarding the mechanical properties of such braces and the benefits of their use. Results of detailed nonlinear dynamic analyses are then examined for specific cases as well as statistically for several suites of ground motions in order to characterize the effect on key response parameters of various structural configurations and proportions.


title: Estimation of Mental Workload during Motorcycle Operation

abstract: Abstract In this paper, we propose a method of estimating the mental workload (MWL) of a motorcycle rider through the application of machine learning methods. The eye movement parameters of the motorcycle rider are measured under two run objectives, the one leads to a high MWL and the other one leads to a low MWL. The parameters are saccade duration, eye fixation (short stop), tracking frequency, saccade amplitude, and most frequency eye movement velocity. They are taken as explanatory variables for a discriminant function. By applying machine learning methods, we find that we can determine rider's MWL under preset running conditions with a high accuracy (≥80%).


title: Jointly Representing Images and Text: Dependency Graphs, Word Senses, and Multimodal Embeddings

abstract: The amount of image data available on the web is growing rapidly: on Facebook alone, 350 million new images are uploaded every day. Making sense of this data requires new ways of efficiently indexing, annotating, and querying such enormous collections. Research in computer vision has tackled this problem by developing algorithms for localizing and labeling objects in images. Object classification algorithms have been recently scaled up to work on thousands of object classes based on the ImageNet database. The next frontier in analyzing images is to go beyond classifying objects: to truly understand a visual scene, we need to identify how the objects in that scene relate to each other, which actions and events they are involved in, and ultimately recognize the intentions of the actors depicted in the scene. The key to achieving this goal is to develop methods for parsing images into structured representations. A number of approaches have recently been proposed in the literature, including Visual Dependency Representations, Scene Graphs, and Scene Description Graphs. All of these models represent an image as a structured collection of objects, attributes, and relations between them. In this presentation, we will focus on Visual Dependency Representations (VDRs), the only approach to image structure that is explicitly multimodal. VDRs start from the observation that images typically do not exist in isolation, but co-occur with textual data such as comments, captions, or tags; well-established techniques exist for extracting structure from such textual data. The VDR model exploits this observation by positing an image structure that links objects through geometric relations. Text accompanying the image can be parsed into a syntactic dependency graph, and the two representations are aligned, yielding a multimodal graph (see Figure 1). Well-established synchronous parsing techniques from machine translation can be applied to this task, and resulting VDRs are useful for image description and retrieval. Parsing images into multimodal graph structures is an important step towards image understanding. However, for full understanding, representing the semantics of the image is also crucial. For example, the images in Figure 2 can all be described using the verb "play" (and presumably are assigned similar VDRs). However, a different meaning (verb sense) of "play" is evoked by each image. This has led to the new task of visual verb sense disambiguation: given an image and a verb, assign the correct sense of the verb, i.e., the one that corresponds to the action depicted in the image. We propose an unsupervised algorithm based on Lesk which performs visual sense disambiguation using textual, visual, and multimodal embeddings. In this presentation, we will discuss how the two tasks of VDR parsing and visual verb disambiguation can be combined to yield more complete syntactico-semantic image representations, which can then underpin applications such as image retrieval, image description, and visual question answering.


title: Budget-Optimal Task Allocation for Reliable Crowdsourcing Systems

abstract: Crowdsourcing systems, in which numerous tasks are electronically distributed to numerous “information pieceworkers,” have emerged as an effective paradigm for human-powered solving of large-scale problems in domains such as image classification, data entry, optical character recognition, recommendation, and proofreading. Because these low-paid workers can be unreliable, nearly all such systems must devise schemes to increase confidence in their answers, typically by assigning each task multiple times and combining the answers in an appropriate manner, e.g., majority voting. ::: ::: In this paper, we consider a general model of such crowdsourcing tasks and pose the problem of minimizing the total price i.e., number of task assignments that must be paid to achieve a target overall reliability. We give a new algorithm for deciding which tasks to assign to which workers and for inferring correct answers from the workers' answers. We show that our algorithm, inspired by belief propagation and low-rank matrix approximation, significantly outperforms majority voting and, in fact, is optimal through comparison to an oracle that knows the reliability of every worker. Further, we compare our approach with a more general class of algorithms that can dynamically assign tasks. By adaptively deciding which questions to ask to the next set of arriving workers, one might hope to reduce uncertainty more efficiently. We show that, perhaps surprisingly, the minimum price necessary to achieve a target reliability scales in the same manner under both adaptive and nonadaptive scenarios. Hence, our nonadaptive approach is order optimal under both scenarios. This strongly relies on the fact that workers are fleeting and cannot be exploited. Therefore, architecturally, our results suggest that building a reliable worker-reputation system is essential to fully harnessing the potential of adaptive designs.


title: Image Retrieval using Harris Corners and Histogram of Oriented Gradients

abstract: based image retrieval is the technique to retrieve similar images from a database that are visually similar to a given query image. It is an active and emerging research field in computer vision. In our proposed system, the Interest points based Histogram of Oriented Gradients (HOG) feature descriptor is used to retrieve the relevant images from the database. The dimensionality of the HOG feature vector is reduced by Principle Component analysis (PCA). To improve the retrieval accuracy of the system the Colour Moments along with HOG feature descriptor are used in this system. The Interest points are detected using the Harris-corner detector in order to extract the image features. The KD-tree is used for matching and indexing the features of the query image with the database images.


title: Matlab/Simulink Simulation of Solar Energy Storage System

abstract: This paper investigates the energy storage technologies that can potentially enhance the use of solar energy. Water electrolysis systems are seen as the principal means of producing a large amount of hydrogen in the future. Starting from the analysis of the models of the system components, a complete simulation model was realized in the Matlab-Simulink environment. Results of the numerical simulations are provided. The operation of electrolysis and photovoltaic array combination is verified at various insulation levels. It is pointed out that solar cell arrays and electrolysers are producing the expected results with solar energy inputs that are continuously varying. Keywords—Electrolyzer, Simulink, solar energy, storage system.


title: Cloud Computing: Security Issues and Research Challenges

abstract: Cloud computing is an architecture for providing computing service via the internet on demand and pay per use access to a pool of shared resources namely networks, storage, servers, services and applications, without physically acquiring them. So it saves managing cost and time for organizations. Many industries, such as banking, healthcare and education are moving towards the cloud due to the efficiency of services provided by the pay-per-use pattern based on the resources such as processing power used, transactions carried out, bandwidth consumed, data transferred, or storage space occupied etc. Cloud computing is a completely internet dependent technology where client data is stored and maintain in the data center of a cloud provider like Google, Amazon, Salesforce.som and Microsoft etc. Limited control over the data may incur various security issues and threats which include data leakage, insecure interface, sharing of resources, data availability and inside attacks. There are various research challenges also there for adopting cloud computing such as well managed service level agreement (SLA), privacy, interoperability and reliability. This research paper outlines what cloud computing is, the various cloud models and the main security risks and issues that are currently present within the cloud computing industry. This research paper also analyzes the key research and challenges that presents in cloud computing and offers best practices to service providers as well as enterprises hoping to leverage cloud service to improve their bottom line in this severe economic climate.


title: Applicability evaluation of Okumura, Ericsson 9999 and winner propagation models for coverage planning in 3.5 GHZ WiMAX systems

abstract: Within areas lacking appropriate or having no wire infrastructure at all, fixed WiMAX is recognized as a viable business solution for delivering broadband services to the end users. While in developing countries (like Bosnia and Herzegovina), such areas are widespread among suburban and rural topography, it is of great interest to provide preconditions for planning of these networks. In order to create preconditions for WiMAX planning, the most important problem that must be solved is its coverage prediction. The problem becomes more important considering the fact that, up to date, no globally verified tool for WiMAX coverage prediction is developed. With that in mind, within our research, some of existing empirical propagation models for WiMAX coverage planning are tested with respect to real measurement results conducted all over the world. Similar analysis, with proposed novel metric for models evaluation, is presented in authors' previous work [1], considering some of the most popular empirical propagation models, so far developed. However, in order to improve results obtained in [1], within this paper, additional set of empirical propagation model is tested, providing more general conclusion about the most appropriate empirical propagation model for 3.5 GHz WiMAX coverage planning.


title: Camera Style Adaptation for Person Re-identification

abstract: Being a cross-camera retrieval task, person re-identification suffers from image style variations caused by different cameras. The art implicitly addresses this problem by learning a camera-invariant descriptor subspace. In this paper, we explicitly consider this challenge by introducing camera style (CamStyle) adaptation. CamStyle can serve as a data augmentation approach that smooths the camera style disparities. Specifically, with CycleGAN, labeled training images can be style-transferred to each camera, and, along with the original training samples, form the augmented training set. This method, while increasing data diversity against over-fitting, also incurs a considerable level of noise. In the effort to alleviate the impact of noise, the label smooth regularization (LSR) is adopted. The vanilla version of our method (without LSR) performs reasonably well on few-camera systems in which over-fitting often occurs. With LSR, we demonstrate consistent improvement in all systems regardless of the extent of over-fitting. We also report competitive accuracy compared with the state of the art. Code is available at: https://github.com/zhunzhong07/CamStyle


title: Slaughterhouse Waste Co-Digestion - Experiences from 15 Years of Full-Scale Operation

abstract: At Tekniska Verken in Linkoping AB (TVAB) there is a long time experience of handling and producing biogas from large volumes of slaughterhouse waste. Experiences from research and development and ...


title: CSR: A Cloud-Assisted Speech Recognition Service for Personal Mobile Device

abstract: Automatic speech recognition (ASR) is a technology which converts the phrases or words spoken by human into text. As a mature technology, ASR has become an alternative input method on many mobile devices, complementing the other input methods operated by hands. Although the technology has been developed for years, the accuracy and computational complexity of ASR have prohibited ASR from being used as the primary input method on mobile devices. While speaker-dependent ASR (SD-ASR) technologies may be used to improve the recognition accuracy, the user is often reluctant to take a time-consuming training process needed to enable SD-ASR for each device. To overcome these problems, we propose a cloud-assisted speech recognition service and its infrastructural design, called CSR, which utilizes servers in the cloud to accelerate ASR, integrates SD-ASR technologies to improve the accuracy of ASR, and populate SD information to enable SD-ASR on multiple mobile device. We have built a prototype to observe the benefits of the CSR service and the issues in load balance, power consumption, and privacy on the client and the server. We show that the CSR service offers fast responses, good accuracy, high availability and good scalability in serving many concurrent users.


title: An Overview of Smart Contract: Architecture, Applications, and Future Trends

abstract: With the rapid development of cryptocurrency and its underlying blockchain technologies, platforms such as Ethereum and Hyperledger began to support various types of smart contracts. Smart contracts are computer protocols intended to digitally facilitate, verify, or enforce the negotiation or performance of a contract. Smart contracts have broad range of applications, such as financial services, prediction markets and Internet of Things (IoT), etc. However, there are still many challenges such as security issues and privacy disclosure that await future research. In this paper, we present a comprehensive overview on blockchain powered smart contracts. First, we give a systematic introduction for smart contracts, including the basic framework, operating mechanisms, platforms and programming languages. Second, application scenarios and existing challenges are discussed. Finally, we describe the recent advances of smart contract and present its future development trends, e.g., parallel blockchain. This paper is aimed at providing helpful guidance and reference for future research efforts.


title: Guaranteeing anonymity when sharing medical data, the Datafly System.

abstract: Abstract ::: We present a computer program named Datafly that maintains anonymity in medical data by automatically generalizing, substituting, and removing information as appropriate without losing many of the details found within the data. Decisions are made at the field and record level at the time of database access, so the approach can be used on the fly in role-based security within an institution, and in batch mode for exporting data from an institution. Often organizations release and receive medical data with all explicit identifiers, such as name, address and phone number, removed in the incorrect belief that patient confidentiality is maintained because the resulting data look anonymous; however, we show the remaining data can often be used to re-identify individuals by linking or matching the data to other databases or by looking at unique characteristics found in the fields and records of the database itself. When these less apparent aspects are taken into account, each released record can be made to ambiguously map to many possible people, providing a level of anonymity determined by the user.


title: Mobile product recognition

abstract: We present a mobile product recognition system for the camera-phone. By snapping a picture of a product with a camera-phone, the user can retrieve online information of the product. The product is recognized by an image-based retrieval system located on a remote server. Our database currently comprises more than one million entries, primarily products packaged in rigid boxes with printed labels, such as CDs, DVDs, and books. We extract low bit-rate descriptors from the query image and compress the location of the descriptors using location histogram coding on the camera-phone. We transmit the compressed query features, instead of a query image, to reduce the transmission delay. We use inverted index compression and fast geometric re-ranking on our database to provide a low delay image recognition response for large scale databases. Experimental timing results on different parts of the mobile product recognition system is reported in this work.


title: Applications of the Near-Field Techniques in EMC Investigations

abstract: A completely automatic near-field mapping system has been developed within the Research Institute for Electronic Embedded Systems (IRSEEM) in order to determine the electromagnetic field created by electronic systems and components. This test bench uses a 3-D positioning system of the probe to make accurate measurements. This paper presents some applications of the near-field techniques in EMC investigations. In the first part, near-field measurements are used to locate precisely the electromagnetic sources of a limiter device. In the second part, we present an equivalent model of the radiated emission of an integrated circuit. In the last part, the near-field test bench is used to characterize faults in a cable.


title: Webpage Depth Viewability Prediction Using Deep Sequential Neural Networks

abstract: Display advertising is the most important revenue source for publishers in the online publishing industry. The ad pricing standards are shifting to a new model in which ads are paid only if they are viewed. Consequently, an important problem for publishers is to predict the probability that an ad at a given page depth will be shown on a user's screen for a certain dwell time. This paper proposes deep learning models based on Long Short-Term Memory (LSTM) to predict the viewability of any page depth for any given dwell time. The main novelty of our best model consists in the combination of bi-directional LSTM networks, encoder-decoder structure, and residual connections. The experimental results over a dataset collected from a large online publisher demonstrate that the proposed LSTM-based sequential neural networks outperform the comparison methods in terms of prediction performance.


title: Coordination of Directional Overcurrent Relays Using Seeker Algorithm

abstract: Coordination of directional overcurrent relays in a multiloop subtransmission or distribution network is formulated as an optimization problem. In this paper, the coordination of directional overcurrent relays is formulated as a mixed-integer nonlinear programming problem and is then solved by a new seeker optimization technique. Based on the act of human searching, in the proposed seeker technique, the search direction and step length are determined in an adaptive way. The proposed method is implemented in three different test cases. The results are compared with previously proposed analytic and evolutionary approaches.


title: Thermal modeling of friction stir welding in a moving coordinate system and its validation

abstract: A three-dimensional heat transfer model for friction stir welding (FSW) is presented in this paper; a moving coordinate is introduced to reduce the difficulty of modeling the moving tool. Heat input from the tool shoulder and the tool pin are considered in the model. The finite difference method was applied in solving the control equations. A non-uniform grid mesh is generated for the calculation. FSW experiments have been done to validate the calculated results. The calculated results are in good agreement with the experimental results. The calculation result also shows that preheat to the workpiece is beneficial to FSW.


Fuzzy position-velocity control of underactuated finger of FTN robot hand


title: An evaluation of the innovative potentials of a HIV pilot exploring medical pluralism in rural South Africa

abstract: This article reflects on an internal evaluation undertaken to estimate the potentials of a community-university pilot project to be developed into a bonafide innovation that can be applied at scale. The focus of the community-university partnership has been to reduce the unintended consequences of medical pluralism on the HIV and AIDS epidemic in Waterberg district, Limpopo Province, South Africa. Despite promising outputs from the partnership – including an increase in adherence to antiretroviral therapy and a reduction in stigma among traditionalists living with HIV – the partnership wished to establish whether further funding should be applied for to take the pilot from its current prototype status to a more established innovation. In order to evaluate the innovative potentials of the pilot, the opportunity vacuum model of innovation was adapted and applied. The findings indicate that (1) the application of the opportunity vacuum model of innovation to evaluate the potentials of the pilot to be developed into a bonafide innovation was fit for purpose and (2) the pilot contains the key ingredients that are associated with innovations in the making. The discussion reflects on the social potentials of the pilot to contribute to 90-90-90 from a global, national and local perspective. The reflection concludes by suggesting that the opportunity vacuum model of innovation is a versatile heuristic that could be applied in other contexts and the community-university pilot represents a nascent innovation which has sufficient potential to justify further development. Keywords : Adjacent possible; community-university partnership; innovation; HIV/makgoma conflation; opportunity vacuum model


A latent class multidimensional IRT model approach to select most informative items within standardised students ’ tests for school restitution


title: Managing information systems security: a soft approach

abstract: The increasing rate of reported computer security problems suggests that the highly structured and technical traditional approaches to the management of IS security do not appear to be successful. This paper presents the Orion Strategy, a participative approach to the planning and management of information security in organisations. The details of this approach are discussed along with an overview of its pilot implementation within an Australian organisation. The findings from the study indicate that a high level of user participation in the planning and management of security results in raised awareness of security issues and an ownership of responsibility for the successful operations of chosen security measures.


title: Decoding the "Free/Open Source (F/OSS) Software Puzzle"a survey of theoretical and empirical contributions

abstract: F/OSS software has been described by many as a puzzle. In the past five years, it has stimulated the curiosity of scholars in a variety of fields, including economics, law, psychology, anthropology and computer science, so that the number of contributions on the subject has increased exponentially. The purpose of this paper is to provide a sufficiently comprehensive account of these contributions in order to draw some general conclusions on the state of our understanding of the phenomenon and identify directions for future research. The exercise suggests that what is puzzling about F/OSS is not so much the fact that people freely contribute to a good they make available to all, but rather the complexity of its institutional structure and its ability to organizationally evolve over time.


title: PUFatt: Embedded platform attestation based on novel processor-based PUFs

abstract: Software-based attestation schemes aim at proving the integrity of code and data residing on a platform to a verifying party. However, they do not bind the hardware characteristics to the attestation protocol and are vulnerable to impersonation attacks. We present PUFatt, a new automatable method for linking software-based attestation to intrinsic device characteristics by means of a novel processor-based Physically Unclonable Function, which enables secure timed (and even) remote attestation particularly suitable for embedded and low-cost devices. Our proof-of-concept implementation on FPGA demonstrates the effectiveness, applicability and practicability of the approach.


Big-ETL : Extracting-Transforming-Loading Approach for Big Data


title: Efficient Implementation of the Italiano Algorithms for Updating the Transitive Closure on Associative Parallel Processors

abstract: We propose a simple data structure for an efficient implementation of the Italiano algorithms for the dynamic updating of the transitive closure of a directed graph represented as adjacency matrix on a model of associative (or content addressable) parallel processors with vertical processing (the STAR-machine). Associative versions of the Italiano algorithms are represented as procedures DeleteArc1 and InsertArc1. We prove the correctness of these procedures and evaluate their time and space complexity. We also present the main advantages of associative versions of the Italiano algorithms.


title: Simultaneous Transfer and Doping of CVD-Grown Graphene by Fluoropolymer for Transparent Conductive Films on Plastic

abstract: Chemical doping can decrease sheet resistance of graphene while maintaining its high transparency. We report a new method to simultaneously transfer and dope chemical vapor deposition grown graphene onto a target substrate using a fluoropolymer as both the supporting and doping layer. Solvent was used to remove a significant fraction of the supporting fluoropolymer, but residual polymer remained that doped the graphene significantly. This contrasts with a more widely used supporting layer, polymethylmethacrylate, which does not induce significant doping during transfer. The fluoropolymer doping mechanism can be explained by the rearrangement of fluorine atoms on the graphene basal plane caused by either thermal annealing or soaking in solvent, which induces ordered dipole moments near the graphene surface. This simultaneous transfer and doping of the graphene with a fluoropolymer increases the carrier density significantly, and the resulting monolayer graphene film exhibits a sheet resistance of ∼320 Ω/sq...


title: Topology-regularized universal vector autoregression for traffic forecasting in large urban areas

abstract: Fast paced growth in urban areas will soon drive traffic forecasting systems obsolete.Next generation systems should be: topological, modular, scalable, online & nonlinear.The proposed method with such properties has low network wide generalization error.Method outperforms baselines and univariate equivalents over two large area datasets.The topological design adjacency matrix is pivotal & requires expert domain knowledge. Autonomous vehicles are soon to become ubiquitous in large urban areas, encompassing cities, suburbs and vast highway networks. In turn, this will bring new challenges to the existing traffic management expert systems. Concurrently, urban development is causing growth, thus changing the network structures. As such, a new generation of adaptive algorithms are needed, ones that learn in real-time, capture the multivariate nonlinear spatio-temporal dependencies and are easily adaptable to new data (e.g. weather or crowdsourced data) and changes in network structure, without having to retrain and/or redeploy the entire system.We propose learning Topology-Regularized Universal Vector Autoregression (TRU-VAR) and examplify deployment with of state-of-the-art function approximators. Our expert system produces reliable forecasts in large urban areas and is best described as scalable, versatile and accurate. By introducing constraints via a topology-designed adjacency matrix (TDAM), we simultaneously reduce computational complexity while improving accuracy by capturing the non-linear spatio-temporal dependencies between timeseries. The strength of our method also resides in its redundancy through modularity and adaptability via the TDAM, which can be altered even while the system is deployed. The large-scale network-wide empirical evaluations on two qualitatively and quantitatively different datasets show that our method scales well and can be trained efficiently with low generalization error.We also provide a broad review of the literature and illustrate the complex dependencies at intersections and discuss the issues of data broadcasted by road network sensors. The lowest prediction error was observed for TRU-VAR, which outperforms ARIMA in all cases and the equivalent univariate predictors in almost all cases for both datasets. We conclude that forecasting accuracy is heavily influenced by the TDAM, which should be tailored specifically for each dataset and network type. Further improvements are possible based on including additional data in the model, such as readings from different metrics.


title: Secure time synchronization service for sensor networks

abstract: In this paper, we analyze attacks on existing time synchronization protocols for wireless sensor networks. We propose a secure time synchronization toolbox to counter these attacks. This toolbox includes protocols for secure pairwise and group synchronization of nodes that lie in each other's power ranges and of nodes that are separated by multiple hops. We provide an in-depth analysis of security and energy overhead of the proposed protocols.


title: Robust Uncalibrated Stereo Rectification with Constrained Geometric Distortions (USR-CGD)

abstract: A novel algorithm for uncalibrated stereo image-pair rectification under the constraint of geometric distortion, called USR-CGD, is presented in this work. Although it is straightforward to define a rectifying transformation (or homography) given the epipolar geometry, many existing algorithms have unwanted geometric distortions as a side effect. To obtain rectified images with reduced geometric distortions while maintaining a small rectification error, we parameterize the homography by considering the influence of various kinds of geometric distortions. Next, we define several geometric measures and incorporate them into a new cost function for parameter optimization. Finally, we propose a constrained adaptive optimization scheme to allow a balanced performance between the rectification error and the geometric error. Extensive experimental results are provided to demonstrate the superb performance of the proposed USR-CGD method, which outperforms existing algorithms by a significant margin.


title: Image Retargetability

abstract: Real-world applications could benefit from the ability to automatically retarget an image to different aspect ratios and resolutions while preserving its visually and semantically important content. However, not all images can be equally processed. This study introduces the notion of image retargetability to describe how well a particular image can be handled by content-aware image retargeting. We propose to learn a deep convolutional neural network to rank photo retargetability, in which the relative ranking of photo retargetability is directly modeled in the loss function. Our model incorporates the joint learning of meaningful photographic attributes and image content information, which can facilitate the regularization of the complicated retargetability rating problem. To train and analyze this model, we collect a dataset that contains retargetability scores and meaningful image attributes assigned by six expert raters. The experiments demonstrate that our unified model can generate retargetability rankings that are highly consistent with human labels. To further validate our model, we show the applications of image retargetability in retargeting method selection, retargeting method assessment and generating a photo collage.


title: Toward an integrated framework for innovation in service: A resource-based view and dynamic capabilities approach

abstract: Organizations today desire strategies that place them on the frontiers of service innovation. In this research, we provide a novel research framework that incorporates resources and capabilities and how they affect elements of service innovation. This research draws on the resource-based view of the firm to define firm's internal resources and outside (i.e., relational) capabilities. Because firms compete in a dynamically changing market, we introduce dynamic service capabilities as three distinct processes that allow resources and relational capabilities to affect components of service innovation. Three propositions are introduced in addition to specific examples of promising future empirical work using service-oriented firms. This study aims to contribute to service innovation through a theoretical foundation in which we posit how firms develop their service innovation through a dynamic service capabilities framework. Contributions to theory and practice are discussed in addition to limitations to the study.


title: Feature-based similarity search in 3D object databases

abstract: The development of effective content-based multimedia search systems is an important research issue due to the growing amount of digital audio-visual information. In the case of images and video, the growth of digital data has been observed since the introduction of 2D capture devices. A similar development is expected for 3D data as acquisition and dissemination technology of 3D models is constantly improving. 3D objects are becoming an important type of multimedia data with many promising application possibilities. Defining the aspects that constitute the similarity among 3D objects and designing algorithms that implement such similarity definitions is a difficult problem. Over the last few years, a strong interest in methods for 3D similarity search has arisen, and a growing number of competing algorithms for content-based retrieval of 3D objects have been proposed. We survey feature-based methods for 3D retrieval, and we propose a taxonomy for these methods. We also present experimental results, comparing the effectiveness of some of the surveyed methods.


title: Towards a neural language model for signature extraction from forensic logs

abstract: Signature extraction is a critical preprocessing step in forensic log analysis because it enables sophisticated analysis techniques to be applied to logs. Currently, most signature extraction frameworks either use rule-based approaches or handcrafted algorithms. Rule-based systems are error-prone and require high maintenance effort. Hand-crafted algorithms use heuristics and tend to work well only for specialized use cases. In this paper we present a novel approach to extract signatures from forensic logs that is based on a neural language model. This language model learns to identify mutable and non-mutable parts in a log message. We use this information to extract signatures. Neural language models have shown to work extremely well for learning complex relationships in natural language text. We experimentally demonstrate that our model can detect which parts are mutable with an accuracy of 86.4%. We also show how extracted signatures can be used for clustering log lines.


title: Distributed Task Allocation of Multiple Robots: A Control Perspective

abstract: The problem of dynamic task allocation in a distributed network of redundant robot manipulators for path-tracking with limited communications is investigated in this paper, where ${k}$ fittest ones in a group of ${n}$ redundant robot manipulators with ${n>k}$ are allocated to execute an object tracking task. The problem is essentially challenging in view of the interplay of manipulator kinematics and the dynamic competition for activation among manipulators. To handle such an intricate problem, a distributed coordination control law is developed for the dynamic task allocation among multiple redundant robot manipulators with limited communications and with the aid of a consensus filter. In addition, a theorem and its proof are presented for guaranteeing the convergence and stability of the proposed distributed control law. Finally, an illustrative example is provided and analyzed to substantiate the efficacy of the proposed control law.


title: Learning Structured Natural Language Representations for Semantic Parsing

abstract: We introduce a neural semantic parser that converts natural language utterances to intermediate representations in the form of predicate-argument structures, which are induced with a transition system and subsequently mapped to target domains. The semantic parser is trained end-to-end using annotated logical forms or their denotations. We obtain competitive results on various datasets. The induced predicate-argument structures shed light on the types of representations useful for semantic parsing and how these are different from linguistically motivated ones.


title: SmartCampus: A user-centric testbed for Internet of Things experimentation

abstract: The Internet of Things (IoT) is not only about improving business processes, but has also the potential to profoundly impact the life of many citizens. Likewise the IoT can provide an useful tool for the longitudinal observation of human behavior and the understanding of behavioral patterns that can inform further IoT technology design. Today experimentation with IoT technologies is predominately carried out in lab based testbeds. There is however an emerging need for increased realism of the experimentation environment, as well as involvement of real end users into the experimentation lifecycle. In this paper we present SmartCampus, a user centric experimental research facility for IoT technologies. The current testbed deployment is focused on Smart Buildings, a key building block for cities of the future. Unlike current lab based testbeds, SmartCampus deeply embeds heterogeneous IoT devices as a programmable experimentation substrate in a real life office environment and makes flexible experimentation with real end users possible. We present the architecture realization of the current facility and underlying considerations that motivated its design. Using several recent experimental use cases, we demonstrate the usefulness of such experimental facilities for user-centric IoT research.


title: Coping through emotional approach: Scale construction and validation.

abstract: Four studies demonstrate the psychometric adequacy and validity of scales designed to assess coping through emotional approach. In separate undergraduate samples, exploratory and confirmatory factor analyses of dispositional (Study 1) and situational (Study 3) coping item sets yielded 2 distinct emotional approach coping factors: emotional processing (i.e., active attempts to acknowledge and understand emotions) and emotional expression. The 2 scales yielded high internal consistency and test-retest reliability, as well as convergent and discriminant validity. A study (Study 2) of young adults and their parents established the scales' interjudge reliabilities. Longitudinal (Study 3) and experimental (Study 4) research supported the predictive validity of the emotional approach coping scales with regard to adjustment to stressful encounters. Findings highlight the utility of functionalist theories of emotion as applied to coping theory.


title: Elements of a hybrid simulation model: A case study of the blood supply chain in low- and middle-income countries

abstract: A hybrid simulation model is a simulation model that is formed from at least two different simulation modelling methods (e.g. discrete event, system dynamics, agent-based). The use of different simulation modelling methods in one model requires modellers to specify additional model elements. This paper discusses three elements, namely, the modules, module interfaces and updating rules. Each module may use a different simulation method. The interface between modules defines the information that will be passed between them (including aggregation and disaggregation). The updating rules define how the information sent by one module affects other modules. These three elements are explained using a case study of a blood supply chain simulation model for low- and middle-income countries (LMIC) which has different characteristics and challenges in comparison to the typical blood supply chain in high-income countries (HIC).


title: Relation Between Burnout Syndrome and Job Satisfaction Among Mental Health Workers

abstract: AIM ::: To identify predictors of burnout syndrome, such as job satisfaction and manifestations of occupational stress, in mental health workers. ::: ::: ::: METHOD ::: The study included a snowball sample of 174 mental health workers in Croatia. The following measurement instruments were used: Maslach Burnout Inventory, Manifestations of Occupational Stress Survey, and Job Satisfaction Survey. We correlated dimensions of burnout syndrome with job satisfaction and manifestations of occupational stress dimensions. We also performed multiple regression analysis using three dimensions of burnout syndrome--emotional exhaustion, depersonalization, and personal accomplishment. ::: ::: ::: RESULTS ::: Stepwise multiple regression analysis showed that pay and rewards satisfaction (beta=-0.37), work climate (beta=-0.18), advancement opportunities (beta=0.17), the degree of psychological (beta=0.41), and physical manifestations of occupational stress (beta=0.29) were significant predictors of emotional exhaustion (R=0.76; F=30.02; P<0.001). The frequency of negative emotional and behavioral reactions toward patients and colleagues (beta=0.48), psychological (beta=0.27) and physical manifestations of occupational stress (beta=0.24), and pay and rewards satisfaction (beta=0.22) were significant predictors of depersonalization (R=0.57; F=13,01; P<0.001). Satisfaction with the work climate (beta=-0.20) was a significant predictor of lower levels of personal accomplishment (R=0.20; F=5.06; P<0.005). ::: ::: ::: CONCLUSION ::: Mental health workers exhibited a moderate degree of burnout syndrome, but there were no significant differences regarding their occupation. Generally, both dimensions of job satisfaction and manifestations of occupational stress proved to be relevant predictors of burnout syndrome.


title: Action MACH a spatio-temporal Maximum Average Correlation Height filter for action recognition

abstract: In this paper we introduce a template-based method for recognizing human actions called action MACH. Our approach is based on a maximum average correlation height (MACH) filter. A common limitation of template-based methods is their inability to generate a single template using a collection of examples. MACH is capable of capturing intra-class variability by synthesizing a single Action MACH filter for a given action class. We generalize the traditional MACH filter to video (3D spatiotemporal volume), and vector valued data. By analyzing the response of the filter in the frequency domain, we avoid the high computational cost commonly incurred in template-based approaches. Vector valued data is analyzed using the Clifford Fourier transform, a generalization of the Fourier transform intended for both scalar and vector-valued data. Finally, we perform an extensive set of experiments and compare our method with some of the most recent approaches in the field by using publicly available datasets, and two new annotated human action datasets which include actions performed in classic feature films and sports broadcast television.


title: An Iterative MapReduce Based Frequent Subgraph Mining Algorithm

abstract: Frequent subgraph mining (FSM) is an important task for exploratory data analysis on graph data. Over the years, many algorithms have been proposed to solve this task. These algorithms assume that the data structure of the mining task is small enough to fit in the main memory of a computer. However, as the real-world graph data grows, both in size and quantity, such an assumption does not hold any longer. To overcome this, some graph database-centric methods have been proposed in recent years for solving FSM; however, a distributed solution using MapReduce paradigm has not been explored extensively. Since, MapReduce is becoming the de- facto paradigm for computation on massive data, an efficient FSM algorithm on this paradigm is of huge demand. In this work, we propose a frequent subgraph mining algorithm called MIRAGE which uses an iterative MapReduce based framework. MIRAGE is complete as it returns all the frequent subgraphs for a given user-defined support, and it is efficient as it applies all the optimizations that the latest FSM algorithms adopt. Our experiments with real life and large synthetic datasets validate the effectiveness of MIRAGE for mining frequent subgraphs from large graph datasets. The source code of MIRAGE is available from www.cs.iupui.edu/alhasan/software/


title: Validation and application of real-time polymerase chain reaction assays for representative rumen bacteria

abstract: Real-time polymerase chain reaction (PCR) assays for 11 representative rumen bacterial species were validated. The sensitivity was tested by using the serially diluted target 16S rDNA from respective bacterial species. The recovery of the target DNA and the assay reproducibility were determined using DNA from rumen fluid spiked with different quantities of the target. Minimum detection levels for the target were 10-100 copies in pure culture. The recovery of the added target ranged from 82.4 to 116.6%. The intra- and inter-assay variations of each assay were <9.4 and <12.6%, respectively. Therefore, the real-time PCR assays evaluated in the present study are considered to be sufficiently reliable for monitoring all 11 bacterial species in the rumen. The assays were then applied to the monitoring of the bacterial species attached to ruminally incubated rice straw. Among the monitored fibrolytic species, Fibrobacter succinogenes was found to be the most dominant, accounting for 2.61% of total bacteria after 24 h incubation. Selenomonas ruminantium and Streptococcus bovis, non-fibrolytics, were detected on the rice straw at 8.96% and 1.16% of total bacteria, respectively. Such high levels of non-fibrolytics on the plant fiber suggest a synergistic relationship between fibrolytics and non-fibrolytics.


title: Designing an FPGA-Accelerated Homomorphic Encryption Co-Processor

abstract: In this paper we report on our advances designing and implementing an FPGA-based computation accelerator as part of a Homomorphic Encryption Processing Unit (HEPU) co-processor. This hardware accelerator technology improves the practicality of computing on encrypted data by reducing the computational bottlenecks of lattice encryption primitives that support homomorphic encryption schemes. We focus on accelerating the Chinese Remainder Transform (CRT) and inverse Chinese Remainder Transform (iCRT) for power-of-2 cyclotomic rings, but also accelerate other basic ring arithmetic such as Ring Addition, Ring Subtraction and Ring Multiplication. We instantiate this capability in a Xilinx Virtex-7 FPGA that can attach to a host computer through either a PCI-Express port or Ethernet. We focus our experimental performance analysis on the NTRU-based LTV Homomorphic Encryption scheme. This is a leveled homomorphic encryption scheme, but our accelerator is compatible with other lattice-based schemes and recent improved bootstrapping designs to support arbitrary depth computation. We experimentally compare performance with a reference software implementations of the CRT and iCRT bottlenecks and when used in a practical application of encrypted string comparison.


title: Hidden Markov Models for Speech Recognition

abstract: The use of hidden Markov models for speech recognition has become predominant in the last several years, as evidenced by the number of published papers and talks at major speech conferences. The reasons this method has become so popular are the inherent statistical (mathematically precise) framework; the ease and availability of training algorithms for cstimating the parameters of the models from finite training sets of speech data; the flexibility of the resulting recognition system in which one can easily change the size, type, or architecture of the models to suit particular words, sounds, and so forth; and the ease of implementation of the overall recognition system. In this expository article, we address the role of statistical methods in this powerful technology as applied to speech recognition and discuss a range of theoretical and practical issues that are as yet unsolved in terms of their importance and their effect on performance for different system implementations.


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title: "Breaking"Disasters: Predicting and Characterizing the Global News Value of Natural and Man-made Disasters

abstract: Due to their often unexpected nature, natural and man-made disasters are difficult to monitor and detect for journalists and disaster management response teams. Journalists are increasingly relying on signals from social media to detect such stories in their early stage of development. Twitter, which features a vast network of local news outlets, is a major source of early signal for disaster detection. Journalists who work for global desks often follow these sources via Twitter's lists, but have to comb through thousands of small-scale or low-impact stories to find events that may be globally relevant. These are events that have a large scope, high impact, or potential geo-political relevance. We propose a model for automatically identifying events from local news sources that may break on a global scale within the next 24 hours. The results are promising and can be used in a predictive setting to help journalists manage their sources more effectively, or in a descriptive manner to analyze media coverage of disasters. Through the feature evaluation process, we also address the question: "what makes a disaster event newsworthy on a global scale?" As part of our data collection process, we have created a list of local sources of disaster/accident news on Twitter, which we have made publicly available.


title: Massive Data Analysis: Tasks, Tools, Applications, and Challenges

abstract: In this study, we provide an overview of the state-of-the-art technologies in programming, computing, and storage of the massive data analytics landscape. We shed light on different types of analytics that can be performed on massive data. For that, we first provide a detailed taxonomy on different analytic types along with examples of each type. Next, we highlight technology trends of massive data analytics that are available for corporations, government agencies, and researchers. In addition, we enumerate several instances of opportunities that exist for turning massive data into knowledge. We describe and position two distinct case studies of massive data analytics that are being investigated in our research group: recommendation systems in e-commerce applications; and link discovery to predict unknown association of medical concepts. Finally, we discuss the lessons we have learnt and open challenges faced by researchers and businesses in the field of massive data analytics.


title: Basic protocols in quantum reinforcement learning with superconducting circuits

abstract: Superconducting circuit technologies have recently achieved quantum protocols involving closed feedback loops. Quantum artificial intelligence and quantum machine learning are emerging fields inside quantum technologies which may enable quantum devices to acquire information from the outer world and improve themselves via a learning process. Here we propose the implementation of basic protocols in quantum reinforcement learning, with superconducting circuits employing feedback- loop control. We introduce diverse scenarios for proof-of-principle experiments with state-of-the-art superconducting circuit technologies and analyze their feasibility in presence of imperfections. The field of quantum artificial intelligence implemented with superconducting circuits paves the way for enhanced quantum control and quantum computation protocols.


title: Luzzu -- A Framework for Linked Data Quality Assessment

abstract: With the increasing adoption and growth of the Linked Open Data cloud [9], with RDFa, Microformats and other ways of embedding data into ordinary Web pages, and with initiatives such as schema.org, the Web is currently being complemented with a Web of Data. Thus, the Web of Data shares many characteristics with the original Web of Documents, which also varies in quality. This heterogeneity makes it challenging to determine the quality of the data published on the Web and to subsequently make this information explicit to data consumers. The main contribution of this article is LUZZU, a quality assessment framework for Linked Open Data. Apart from providing quality metadata and quality problem reports that can be used for data cleaning, LUZZU is extensible: third party metrics can be easily plugged-in the framework. The framework does not rely on SPARQL endpoints, and is thus free of all the problems that come with them, such as query timeouts. Another advantage over SPARQL based qual- ity assessment frameworks is that metrics implemented in LUZZU can have more complex functionality than triple matching. Using the framework, we performed a quality assessment of a number of statistical linked datasets that are available on the LOD cloud. For this evaluation, 25 metrics from ten different dimensions were implemented.


title: The Role Of Grounding In Collaborative Learning Tasks

abstract: Collaborative learning tasks involve interaction between multiple participants, who thus need to maintain some degree of mutual understanding. The process by which this is accomplished is termed grounding . The way in which collaboration, grounding and learning take place is largely determined by the task, the situation and the tools available. This paper discusses relations between grounding, collaboration and learning, drawing on research from two main areas: the Language Sciences and Cultural-Historical Activity Theory ("CHAT"). We build a unifying perspective of mutual understanding mediated by material and semiotic tools that can be used for analysis as well as for design of collaborative learning tasks, especially those that are carried out via computermediated communication. We illustrate the perspective with reference to a particular computermediated collaborative learning situation in the domain of physics.


title: A computer approach to content analysis: studies using the General Inquirer system

abstract: The General Inquirer is an IBM 7090 program system that was developed at Harvard in the spring of 1961 for content analysis research problems in the behavioral sciences. The first part of this paper describes this system and how it has been used. During the summer of 1962, the General Inquirer was merged with the Hunt Concept Learner to produce a method for automatic theme analysis. The second part of this paper discusses the rationale behind this development and some recent signs of its future promise.


title: Generate Identity-Preserving Faces by Generative Adversarial Networks

abstract: Generating identity-preserving faces aims to generate various face images keeping the same identity given a target face image. Although considerable generative models have been developed in recent years, it is still challenging to simultaneously acquire high quality of facial images and preserve the identity. Here we propose a compelling method using generative adversarial networks (GAN). Concretely, we leverage the generator of trained GAN to generate plausible faces and FaceNet as an identity-similarity discriminator to ensure the identity. Experimental results show that our method is qualified to generate both plausible and identity-preserving faces with high quality. In addition, our method provides a universal framework which can be realized in various ways by combining different face generators and identity-similarity discriminator.


title: Multi-objective Reinforcement Learning with Continuous Pareto Frontier Approximation Supplementary Material

abstract: This paper is about learning a continuous approximation of the Pareto frontier in Multi-Objective Markov Decision Problems (MOMDPs). We propose a policy-based approach that exploits gradient information to generate solutions close to the Pareto ones. Differently from previous policy-gradient multi-objective algorithms, where n optimization routines are used to have n solutions, our approach performs a single gradient-ascent run that at each step generates an improved continuous approximation of the Pareto frontier. The idea is to exploit a gradient-based approach to optimize the parameters of a function that defines a manifold in the policy parameter space so that the corresponding image in the objective space gets as close as possible to the Pareto frontier. Besides deriving how to compute and estimate such gradient, we will also discuss the non-trivial issue of defining a metric to assess the quality of the candidate Pareto frontiers. Finally, the properties of the proposed approach are empirically evaluated on two interesting MOMDPs.


title: Consortium Blockchain for Secure Energy Trading in Industrial Internet of Things

abstract: In industrial Internet of things (IIoT), peer-to-peer (P2P) energy trading ubiquitously takes place in various scenarios, e.g., microgrids, energy harvesting networks, and vehicle-to-grid networks. However, there are common security and privacy challenges caused by untrusted and nontransparent energy markets in these scenarios. To address the security challenges, we exploit the consortium blockchain technology to propose a secure energy trading system named energy blockchain. This energy blockchain can be widely used in general scenarios of P2P energy trading getting rid of a trusted intermediary. Besides, to reduce the transaction limitation resulted from transaction confirmation delays on the energy blockchain, we propose a credit-based payment scheme to support fast and frequent energy trading. An optimal pricing strategy using Stackelberg game for credit-based loans is also proposed. Security analysis and numerical results based on a real dataset illustrate that the proposed energy blockchain and credit-based payment scheme are secure and efficient in IIoT.


title: Interpretable Time Series Classification using All-Subsequence Learning and Symbolic Representations in Time and Frequency Domains

abstract: The time series classification literature has expanded rapidly over the last decade, with many new classification approaches published each year. The research focus has mostly been on improving the accuracy and efficiency of classifiers, while their interpretability has been somewhat neglected. Classifier interpretability has become a critical constraint for many application domains and the introduction of the 'right to explanation' GDPR EU legislation in May 2018 is likely to further emphasize the importance of explainable learning algorithms. In this work we analyse the state-of-the-art for time series classification, and propose new algorithms that aim to maintain the classifier accuracy and efficiency, but keep interpretability as a key design constraint. We present new time series classification algorithms that advance the state-of-the-art by implementing the following three key ideas: (1) Multiple resolutions of symbolic approximations: we combine symbolic representations obtained using different parameters; (2) Multiple domain representations: we combine symbolic approximations in time (e.g., SAX) and frequency (e.g., SFA) domains; (3) Efficient navigation of a huge symbolic-words space: we adapt a symbolic sequence classifier named SEQL, to make it work with multiple domain representations (e.g., SAX-SEQL, SFA-SEQL), and use its greedy feature selection strategy to effectively filter the best features for each representation. We show that a multi-resolution multi-domain linear classifier, SAX-SFA-SEQL, achieves a similar accuracy to the state-of-the-art COTE ensemble, and to a recent deep learning method (FCN), but uses a fraction of the time required by either COTE or FCN. We discuss the accuracy, efficiency and interpretability of our proposed algorithms. To further analyse the interpretability aspect of our classifiers, we present a case study on an ecology benchmark.


On the Foundations of Relaxation Labeling Processes


title: Video S ~ wization by Cue Stipfification

abstract: Abstract : A video sequence can be represented as a trajectory curve in a high dimensional feature space. This video curve can be analyzed by tools similar to those developed for planar curves. In particular, the classic binary curve splitting algorithm has been found to be a useful tool for video analysis. With a splitting condition that checks the dimensionality of the curve segment being split, the video curve can be recursively simplified and represented as a tree structure, and the frames that are found to be junctions between curve segments at different levels of the tree can be used as keyframes to summarize the video sequences at different levels of detail. These keyframes can be combined in various spatial and temporal configurations for browsing purposes. We describe a simple video player that displays the keyframes sequentially and lets the user change the summarization level on the fly with a slider. We also describe an approach to automatically selecting a summarization level that provides a concise and representative set of keyframes.


title: Improving Credit Risk Prediction in Online Peer-to-Peer (P2P) Lending Using Imbalanced Learning Techniques

abstract: Peer-to-peer (P2P) lending is a global trend of financial markets that allow individuals to obtain and concede loans without having financial institutions as a strong proxy. As many real-world applications, P2P lending presents an imbalanced characteristic, where the number of creditworthy loan requests is much larger than the number of non-creditworthy ones. In this work, we wrangle a real-world P2P lending data set from Lending Club, containing a large amount of data gathered from 2007 up to 2016. We analyze how supervised classification models and techniques to handle class imbalance impact creditworthiness prediction rates. Ensembles, cost-sensitive and sampling methods are combined and evaluated along logistic regression, decision tree, and bayesian learning schemes. Results show that, in average, sampling techniques outperform ensembles and cost sensitive approaches.


title: Collateral damage: insights into bacterial mechanisms that predispose host cells to cancer

abstract: Infections are estimated to contribute to 20% of all human tumours. Viruses are known to induce cell transformation, but evidence has also linked bacteria, such as Helicobacter pylori and Salmonella enterica subsp. enterica serovar Typhi, to different cancer types. In addition, Chlamydia trachomatis, Fusobacterium nucleatum and Bacteroides fragilis are associated with the development of cancer, although a causal relationship has not yet been established. Bacterial effectors such as colibactin and the virulence factor cytotoxin-associated gene A (CagA) can promote cancer directly by influencing host cell signalling cascades, such as the WNT and ataxia-telangiectasia mutated (ATM) pathways, or indirectly by inducing tissue damage and inflammatory responses. In this Review, we discuss how bacterial pathogens interact with host cells to contribute to the development of cancer.


title: Robotics Technology in Mental Health Care

abstract: This chapter discusses the present and potential future use of robotics technology in mental healthcare practice. Robots have been used to help treat people with autism, dementia, and other cognitive impairments, have helped to provide companionship to people experiencing loneliness, and have been used to help improve how people with disabilities are treated by clinicians. Robotics technology has also been used in novel ways to diagnose and study schizophrenia in naturalistic interactive social settings. Despite these exciting technological advances, it is critical that robot designers and mental health professionals insist on rigorous randomized controlled clinical trials (RCTs) before deploying robots into mental healthcare practice. While it is unlikely most robots would cause direct harm to clients or practitioners, adopting this technology before the evidence base is developed runs the risk of displacing proven interventions with less effective or ineffective treatments.


title: Discovering the $k$ Representative Skyline Over a Sliding Window

abstract: A representative skyline contains $k$ skyline points that can represent its corresponding full skyline. The existing measuring criteria of $k$ representative skylines are specifically designed for static data, and they cannot effectively handle streaming data. In this paper, we focus on the problem of calculating the $k$ representative skyline over data streams. First, we propose a new criterion to choose $k$ skyline points as the $k$ representative skyline for data stream environments, termed the $k$ largest dominance skyline ( $k$ -LDS), which is representative to the entire data set and is highly stable over the streaming data. Second, we propose an efficient exact algorithm, called Prefix-based Algorithm (PBA), to solve the $k$ -LDS problem in a 2-dimensional space. The time complexity of PBA is only $\mathcal {O}((M-k)\times k)$ where $M$ is the size of the full skyline set. Third, the $k$ -LDS problem for a $d$ -dimensional ( $d\ge 3$ ) space turns out to be very complex. Therefore, a greedy algorithm is designed to answer $k$ -LDS queries. To further accelerate the calculation, we propose a $\epsilon$ -greedy algorithm which can achieve an approximate factor of $\frac{1}{(1+\epsilon)}(1-\frac{1}{\sqrt{e}})$ . Experimental results on both synthetic and real-world data show that our $k$ -LDS significantly outperforms its competitors in data stream environments. Furthermore, we demonstrate that the proposed $\epsilon$ -greedy algorithm can solve $k$ -LDS efficiently and with a competitive accuracy.


title: A collaborative filtering approach to mitigate the new user cold start problem

abstract: The new user cold start issue represents a serious problem in recommender systems as it can lead to the loss of new users who decide to stop using the system due to the lack of accuracy in the recommendations received in that first stage in which they have not yet cast a significant number of votes with which to feed the recommender system's collaborative filtering core. For this reason it is particularly important to design new similarity metrics which provide greater precision in the results offered to users who have cast few votes. This paper presents a new similarity measure perfected using optimization based on neural learning, which exceeds the best results obtained with current metrics. The metric has been tested on the Netflix and Movielens databases, obtaining important improvements in the measures of accuracy, precision and recall when applied to new user cold start situations. The paper includes the mathematical formalization describing how to obtain the main quality measures of a recommender system using leave-one-out cross validation.


title: Anorectal malformation with congenital absence of vagina: a case report and review of the literature

abstract: Congenital anorectal malformations are relatively common, and associated genitourinary malformations have been reported in up to 40% of patients. Uterovaginal malformations are also not rare in children. They are known to occur with increased frequency in children with anorectal malformations, but the diagnosis may still be difficult. We report herein one such case of anorectal malformation associated with congenital total absence of the vagina in which the uterovaginal malformation was not diagnosed until the operative repair of the anorectal malformation. The operative procedure was thereafter directed, along with the anorectoplasty, towards restoring a functional uterovaginal tract. Review of the literature revealed that such a diagnosis is unsuspected or delayed in more than half of affected patients. Furthermore, these patients present with many diagnostic and therapeutic problems. Our report highlights the need to be aware of this condition to allow for an earlier diagnosis and appropriate operative treatment.


title: A decade of adaptive governance scholarship: synthesis and future directions

abstract: Adaptive governance is an emergent form of environmental governance that is increasingly called upon by scholars and practitioners to coordinate resource management regimes in the face of the complexity and uncertainty associated with rapid environmental change. Although the term “adaptive governance” is not exclusively applied to the governance of social-ecological systems, related research represents a significant outgrowth of literature on resilience, social-ecological systems, and environmental governance. We present a chronology of major scholarship on adaptive governance, synthesizing efforts to define the concept and identifying the array of governance concepts associated with transformation toward adaptive governance. Based on this synthesis, we define adaptive governance as a range of interactions between actors, networks, organizations, and institutions emerging in pursuit of a desired state for social-ecological systems. In addition, we identify and discuss ambiguities in adaptive governance scholarship such as the roles of adaptive management, crisis, and a desired state for governance of social-ecological systems. Finally, we outline a research agenda to examine whether an adaptive governance approach can become institutionalized under current legal frameworks and political contexts. We suggest a further investigation of the relationship between adaptive governance and the principles of good governance; the roles of power and politics in the emergence of adaptive governance; and potential interventions such as legal reform that may catalyze or enhance governance adaptations or transformation toward adaptive governance.


title: Symbolic road marking recognition using convolutional neural networks

abstract: This paper investigates the use of Convolutional Neural Networks for classification of painted symbolic road markings. Previous work on road marking recognition is mostly based on either template matching or on classical feature extraction followed by classifier training which is not always effective and based on feature engineering. However, with the rise of deep neural networks and their success in ADAS systems, it is natural to investigate the suitability of CNN for road marking recognition. Unlike others, our focus is solely on road marking recognition and not detection; which has been extensively explored and conventionally based on MSER feature extraction of the IPM images. We train five different CNN architectures with variable number of convolution/max-pooling and fully connected layers, and different resolution of road mark patches. We use a publicly available road marking data set and incorporate data augmentation to enhance the size of this data set which is required for training deep nets. The augmented data set is randomly partitioned in 70% and 30% for training and testing. The best CNN network results in an average recognition rate of 99.05% for 10 classes of road markings on the test set.