title: Student Satisfaction, Performance, and Knowledge Construction in Online Collaborative Learning

abstract: A growing amount of research focuses on learning in group settings and more specifically on learning in computersupported collaborative learning (CSCL) settings. Studies on western students indicate that online collaboration enhances student learning achievement; however, few empirical studies have examined student satisfaction, performance, and knowledge construction through online collaboration from a cross-cultural perspective. This study examines satisfaction, performance, and knowledge construction via online group discussions of students in two different cultural contexts. Students were both first-year university students majoring in educational sciences at a Flemish university and a Chinese university. Differences and similarities of the two groups of students with regard to satisfaction, learning process, and achievement were analyzed.


title: Autostacker: a compositional evolutionary learning system

abstract: We introduce an automatic machine learning (AutoML) modeling architecture called Autostacker, which combines an innovative hierarchical stacking architecture and an Evolutionary Algorithm (EA) to perform efficient parameter search. Neither prior domain knowledge about the data nor feature preprocessing is needed. Using EA, Autostacker quickly evolves candidate pipelines with high predictive accuracy. These pipelines can be used as is or as a starting point for human experts to build on. Autostacker finds innovative combinations and structures of machine learning models, rather than selecting a single model and optimizing its hyperparameters. Compared with other AutoML systems on fifteen datasets, Autostacker achieves state-of-art or competitive performance both in terms of test accuracy and time cost.


title: Penile prosthesis implantation: past, present and future

abstract: Penile prosthesis implantation is the oldest effective treatment for erectile dysfunction. This review examines the past, present and future of penile prosthesis implantation. Advances in prosthetic design and implantation techniques have resulted today in devices that produce nearly normal flaccid and erect states, and have remarkable freedom from mechanical failure. The future of prosthetic design holds promises for even more improvements.


title: ZoKrates - Scalable Privacy-Preserving Off-Chain Computations

abstract: Scalability and privacy are two challenges for today's blockchain systems. Processing transactions at every node in the system limits the system's ability to scale. Furthermore, the requirement to publish all corporate or individual information for processing at every node, essentially making the data public, is - despite of all other advantages - often considered a major obstacle to blockchain adoption. In this paper, we make two main contributions to address these two problems: (i)To increase efficiency, we propose a processing model which employs noninteractive proofs to off-chain computations, thereby reducing on-chain computational efforts to the verification of correctness of execution rather than the execution itself. Due to the verifiable computation scheme's zero-knowledge property, private information used in the off-chain computation does not have to become public to verify correctness. (ii)We introduce ZoKrates, a toolbox to specify, integrate and deploy such off-chain computations. It consists of a domain-specific language, a compiler, and generators for proofs and verification Smart Contracts. ZoKrates hides significant complexity inherent to zero-knowledge proofs, provides a more familiar and higher level of programming abstractions to developers and enables circuit integration, hence fostering adoption.


title: Generation of Musical Sequences with Genetic Techniques

abstract: Biologically inspired computational methods have recently attracted much research interest in the field of computer music. This group of methods is a subset of computational intelligence, and is represented by neural networks (NNs), genetic algorithms (GAs), and genetic programming (GP). A common feature of these methods is that they all mimic biological processes: NNs realize the evolutionary device of learning from experience, as humans and other animals do, while GAs and GP are based upon procedures that imitate the laws of natural selection. Genetic algorithms have been shown to display distinct performance improvements compared to enumerative, calculus-based, and random searches of a given arbitrary search space (Goldberg 1989). This is achieved by combining aspects of these search methods to result in a ”guided random” search. The genetic algorithm samples points throughout the search space for their worth, and is ”blind” to any information regarding the search space apart from this measure of worth. This makes genetic search techniques more general and applicable to many search or optimization tasks, as long as an appropriate encoding scheme is employed. By using a population of candidate solutions, rather than single individuals, an inherent parallelism in the search process is apparent. This is because the search for an optimum solution is ”performed over genetic structures (building blocks) that can represent a number of possible so


title: Cockroft-Walton voltage multiplier combined with switched-coupled-inductor boost converter

abstract: A new scheme of a step-up converter with very high voltage gain is analyzed in this paper. The scheme is based on the combination of the switched-coupled-inductor boost converter and the diode-capacitor Cockoft-Walton multiplier. The scheme provides a soft commutation of the switch and the diodes. The paper analyzes the main modes of operation and obtained the formulas for determining the DC voltage gain, the boundary between continuous and discontinuous modes of operation and formulas to calculate the maximal voltage stresses on the transistor and on the diodes. The experimental results proved the theoretical expectations.


title: The Heritability of Gender Identity Disorder in a Child and Adolescent Twin Sample

abstract: The heritability and prevalence of the gender identity disorder (GID) was examined, as well as its comorbidity with separation anxiety and depression, in a nonretrospective study of child and adolescent twins. The parents of 314 twins (ages 4–17 years; 96 monozygotic pairs [MZ] and 61 dizygotic [DZ] pairs) completed the Coolidge Personality and Neuropsychological Inventory (CPNI) containing a six-item DSM-IV-based GID scale. Prevalence of clinically significant GID symptomatology in the twin sample was estimated to be 2.3%. Univariate model fitting analyses were conducted using an ordinal transformation of the GID scale. The model that best described the data included a significant additive genetic component accounting for 62% of the variance and a nonshared environmental component accounting for the remaining 38% of the variance. Results suggested no heterogeneity in the parameter estimates resulting from age. The correlation between GID and depression was modest, but significant (r = .20; P .05). Overall, the results support the hypothesis that there is a strong heritable component to GID. The findings may also imply that gender identity may be much less a matter of choice and much more a matter of biology.


Bandit processes and dynamic allocation indices


title: Learning Mid-level Filters for Person Re-identification

abstract: In this paper, we propose a novel approach of learning mid-level filters from automatically discovered patch clusters for person re-identification. It is well motivated by our study on what are good filters for person re-identification. Our mid-level filters are discriminatively learned for identifying specific visual patterns and distinguishing persons, and have good cross-view invariance. First, local patches are qualitatively measured and classified with their discriminative power. Discriminative and representative patches are collected for filter learning. Second, patch clusters with coherent appearance are obtained by pruning hierarchical clustering trees, and a simple but effective cross-view training strategy is proposed to learn filters that are view-invariant and discriminative. Third, filter responses are integrated with patch matching scores in RankSVM training. The effectiveness of our approach is validated on the VIPeR dataset and the CUHK01 dataset. The learned mid-level features are complementary to existing handcrafted low-level features, and improve the best Rank-1 matching rate on the VIPeR dataset by 14%.


title: Automatic Coding for Neonatal Jaundice From Free Text Data Using Ensemble Methods

abstract: This study explores the creation of a machine learning model to automatically identify whether a Neonatal Intensive Care Unit (NICU) patient was diagnosed with neonatal jaundice during a particular hospitalization based on their associated clinical notes. We develop a number of techniques for text preprocessing and feature selection and compare the effectiveness of different classification models. We show that using ensemble decision tree classification, both with AdaBoost and with bagging, outperforms support vector machines (SVM), the current state-of-the-art technique for neonatal jaundice coding.


title: Face Recognition Using Pyramid Histogram of Oriented Gradients and SVM

abstract: Face recognition has become an important issue in many applications such as security systems, credit card verification and criminal identification. The recently proposed shape descriptor, Pyramid Histogram of Oriented Gradients (PHOG) features, counts occurrences of gradient orientation in localized portions of an image and has been proved as an efficient tool for providing spatial distribution of edges. In this paper, we proposed to use pyramid histogram of oriented gradients (PHOG) as the features extracted for face recognition. The obtained feature vectors are fed up into Support Vector Machine (SVM) classifier for classification. Experiments carried out on UMIST Face Database databases that both frontal head images and rotated head images are investigated. The experimental results show this approach can be used to automatically identify the faces more effective.


title: Building a CDR analytics platform for real-time services

abstract: Growing real-time services require discovering transient customer needs in CDRs. In this paper, we propose a CDR analytics system architecture for event-based analytics processing in real time. This system is expected to allow marketing staff to inject analytics-based rules to trigger marketing campaigns. We also propose two implementations which are based on off-the-shelf computing tools.


title: Developing open source scientific practice

abstract: 3 Routine practice 8 3.1 Version control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.2 Execution automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.3 Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.4 Readability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.5 Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13


title: Is Video-Game Playing a Risk Factor for Pathological Gambling in Australian Adolescents?

abstract: Very little research has been conducted to examine the relationship between video-game playing and gambling in adolescence. In this study, 2,669 adolescents aged 13–17 years were surveyed to obtained details of their involvement in gambling and video-game playing as well as a measure of pathological gambling (the DSM-IV-J). The results showed that, the frequency of video game playing was significantly related to pathological gambling, but that the effect size was very small and largely accounted for by the greater popularity of both activities amongst boys. There was some evidence for stronger associations between technologically similar activities, namely arcade video games and an interest in gaming machines, but other factors discussed in the paper may also account for this association. In summary, the findings suggested that playing video-games is unlikely to be a significant risk factor for pathological gambling during adolescence.


title: SemTK: An Ontology-first, Open Source Semantic Toolkit for Managing and Querying Knowledge Graphs

abstract: The relatively recent adoption of Knowledge Graphs as an enabling technology in multiple high-profile artificial intelligence and cognitive applications has led to growing interest in the Semantic Web technology stack. Many semantics-related tools, however, are focused on serving experts with a deep understanding of semantic technologies. For example, triplification of relational data is available but there is no open source tool that allows a user unfamiliar with OWL/RDF to import data into a semantic triple store in an intuitive manner. Further, many tools require users to have a working understanding of SPARQL to query data. Casual users interested in benefiting from the power of Knowledge Graphs have few tools available for exploring, querying, and managing semantic data. We present SemTK, the Semantics Toolkit, a user-friendly suite of tools that allow both expert and non-expert semantics users convenient ingestion of relational data, simplified query generation, and more. The exploration of ontologies and instance data is performed through SPARQLgraph, an intuitive web-based user interface in SemTK understandable and navigable by a lay user. The open source version of SemTK is available at this http URL


title: User browsing models: relevance versus examination

abstract: There has been considerable work on user browsing models for search engine results, both organic and sponsored. The click-through rate (CTR) of a result is the product of the probability of examination (will the user look at the result) times the perceived relevance of the result (probability of a click given examination). Past papers have assumed that when the CTR of a result varies based on the pattern of clicks in prior positions, this variation is solely due to changes in the probability of examination. We show that, for sponsored search results, a substantial portion of the change in CTR when conditioned on prior clicks is in fact due to a change in the relevance of results for that query instance, not just due to a change in the probability of examination. We then propose three new user browsing models, which attribute CTR changes solely to changes in relevance, solely to changes in examination (with an enhanced model of user behavior), or to both changes in relevance and examination. The model that attributes all the CTR change to relevance yields substantially better predictors of CTR than models that attribute all the change to examination, and does only slightly worse than the model that attributes CTR change to both relevance and examination. For predicting relevance, the model that attributes all the CTR change to relevance again does better than the model that attributes the change to examination. Surprisingly, we also find that one model might do better than another in predicting CTR, but worse in predicting relevance. Thus it is essential to evaluate user browsing models with respect to accuracy in predicting relevance, not just CTR.


title: Stability of photovoltaic and wind turbine grid-connected inverters for a large set of grid impedance values

abstract: The aim of this paper is to analyze the stability problems of grid connected inverters used in distributed generation. Complex controllers (e.g., multiple rotating dq-frames or resonant-based) are often required to compensate low frequency grid voltage background distortion and an LCL-filter is usually adopted for the high frequency one. The possible wide range of grid impedance values (distributed generation is suited for remote areas with radial distribution plants) challenge the stability and the effectiveness of the LCL-filter-based current controlled system. It has been found out and it will be demonstrated in this paper that the use of active damping helps to stabilise the system in respect to many different kinds of resonances. The use of active damping results in an easy plug-in feature of the generation system in a vast range of grid conditions and in a more flexible operation of the overall system able to manage sudden grid changes. In the paper, a vast measurement campaign made on a single-phase system and on a three-phase system used as scale prototypes for photovoltaic and wind turbines, respectively, validate the analysis.


title: Refactoring for generalization using type constraints

abstract: Refactoring is the process of applying behavior-preserving transformations (called "refactorings") in order to improve a program's design. Associated with a refactoring is a set of preconditions that must be satisfied to guarantee that program behavior is preserved, and a set of source code modifications. An important category of refactorings is concerned with generalization (e.g., Extract Interface for re-routing the access to a class via a newly created interface, and Pull Up Members for moving members into a superclass). For these refactorings, both the preconditions and the set of allowable source code modifications depend on interprocedural relationships between types of variables. We present an approach in which type constraints are used to verify the preconditions and to determine the allowable source code modifications for a number of generalization-related refactorings. This work is implemented in the standard distribution of Eclipse (see www.eclipse.org).


title: Mobile cloud computing security using cryptographic hash function algorithm

abstract: The use of cloud computing in the year 2008 is 42.8 million and it increased up to 998 million users in the year 2014. In other hand, the growth is not followed by increasing of user trust in cloud based data management especially in business sector. Some companies is not interested in adopting cloud computing mobile service due to risks of security and privacy. In cloud computing mobile service, some security gap and misuse of data ownership policy by service provider and other unconcern parties are still found. This thread is becoming an obstacle in adapting mobile cloud computing paradigm, the ease is not comparable to company's data misuse thread by other parties. So that, mobile cloud computing security system needs to construct which can lessen the risks of security and privacy. The use of complicated keys will complicate the parties which has no authority in accessing privately company's data. This security system will use hash function algorithm which is symmetric so that decrypting process in cloud system become more complicated and its expected that company's data will be better protected. With this system, the system mobile cloud computing users both corporate and individuals can manage business resources online quickly and safely from the risks of data leakage.


title: Surface Reconstruction Based on Computer Stereo Vision Using Structured Light Projection

abstract: Abstract—Object reconstruction is one of the most important topics in computer vision due to its wide field of application. Reverse engineering has an increasing need for reconstruction of stereo parts. A system for reconstruction three dimensional (3-D) object’s surface from its 2-D views using a coded structured light combining with the epipolar geometry that exists between the stereo image pair is presented. A set of multiple gray coded patterns and a set of sine wave patterns are combined to form a single "Gray Code Composite Pattern" and a single "Phase Shift Composite Pattern" respectively. These two structured light patterns are projected onto the target object through a DLP projector and the distorted strips patterns produced by its surface are captured by two CCD cameras. By demodulating the distorted patterns and adding epipolar constraints the match points between the image pair are found out and then the target object’s 3-D point’s coordinates on the surface are obtained. The system shows excellent linearity and the results shows good resolution.


title: From Extractive to Abstractive Summarization: A Journey

abstract: The availability of large documentsummary corpora have opened up new possibilities for using statistical text generation techniques for abstractive summarization. Progress in Extractive text summarization has become stagnant for a while now and in this work we compare the two possible alternates to it. We present an argument in favor of


title: 3D Printed Hair: Fused Deposition Modeling of Soft Strands, Fibers, and Bristles

abstract: We introduce a technique for furbricating 3D printed hair, fibers and bristles, by exploiting the stringing phenomena inherent in 3D printers using fused deposition modeling. Our approach offers a range of design parameters for controlling the properties of single strands and also of hair bundles. We further detail a list of post-processing techniques for refining the behavior and appearance of printed strands. We provide several examples of output, demonstrating the immediate feasibility of our approach using a low cost, commodity printer. Overall, this technique extends the capabilities of 3D printing in a new and interesting way, without requiring any new hardware.


title: Deep Matching and Validation Network: An End-to-End Solution to Constrained Image Splicing Localization and Detection

abstract: Image splicing is a very common image manipulation technique that is sometimes used for malicious purposes. A splicing detec- tion and localization algorithm usually takes an input image and produces a binary decision indicating whether the input image has been manipulated, and also a segmentation mask that corre- sponds to the spliced region. Most existing splicing detection and localization pipelines suffer from two main shortcomings: 1) they use handcrafted features that are not robust against subsequent processing (e.g., compression), and 2) each stage of the pipeline is usually optimized independently. In this paper we extend the formulation of the underlying splicing problem to consider two input images, a query image and a potential donor image. Here the task is to estimate the probability that the donor image has been used to splice the query image, and obtain the splicing masks for both the query and donor images. We introduce a novel deep convolutional neural network architecture, called Deep Matching and Validation Network (DMVN), which simultaneously localizes and detects image splicing. The proposed approach does not depend on handcrafted features and uses raw input images to create deep learned representations. Furthermore, the DMVN is end-to-end op- timized to produce the probability estimates and the segmentation masks. Our extensive experiments demonstrate that this approach outperforms state-of-the-art splicing detection methods by a large margin in terms of both AUC score and speed.


title: Violence, abuse, and crime exposure in a national sample of children and youth.

abstract: OBJECTIVE ::: The objective of this research was to obtain national estimates of exposure to the full spectrum of the childhood violence, abuse, and crime victimizations relevant to both clinical practice and public-policy approaches to the problem. ::: ::: ::: METHODS ::: The study was based on a cross-sectional national telephone survey that involved a target sample of 4549 children aged 0 to 17 years. ::: ::: ::: RESULTS ::: A clear majority (60.6%) of the children and youth in this nationally representative sample had experienced at least 1 direct or witnessed victimization in the previous year. Almost half (46.3%) had experienced a physical assault in the study year, 1 in 4 (24.6%) had experienced a property offense, 1 in 10 (10.2%) had experienced a form of child maltreatment, 6.1% had experienced a sexual victimization, and more than 1 in 4 (25.3%) had been a witness to violence or experienced another form of indirect victimization in the year, including 9.8% who had witnessed an intrafamily assault. One in 10 (10.2%) had experienced a victimization-related injury. More than one third (38.7%) had been exposed to 2 or more direct victimizations, 10.9% had 5 or more, and 2.4% had 10 or more during the study year. ::: ::: ::: CONCLUSIONS ::: The scope and diversity of child exposure to victimization is not well recognized. Clinicians and researchers need to inquire about a larger spectrum of victimization types to identify multiply victimized children and tailor prevention and interventions to the full range of threats that children face.


title: Widget based automated rigging of bipedal character with custom manipulators

abstract: In this research paper we present an automated rigging system for a humanoid (biped) character with complete customization and control options according to the need of the character rigger and the animator. The rigging requirements check list has been proposed for each body part by analyzing various live natural human motions and then summarizing them. The system initially provides widgets that represent human skeletal joints, which can be easily manipulated and modified according to the character type and body height. The system takes the widgets positions from the 3D space and creates the joint based skeletal structure automatically using procedural programming and then followed by creating a complete bipedal rig with custom manipulators to satisfy the animators need in a production environment.


title: Music Mood Detection Based On Audio And Lyrics With Deep Neural Net

abstract: We consider the task of multimodal music mood prediction based on the audio signal and the lyrics of a track. We reproduce the implementation of traditional feature engineering based approaches and propose a new model based on deep learning. We compare the performance of both approaches on a database containing 18,000 tracks with associated valence and arousal values and show that our approach outperforms classical models on the arousal detection task, and that both approaches perform equally on the valence prediction task. We also compare the a posteriori fusion with fusion of modalities optimized simultaneously with each unimodal model, and observe a significant improvement of valence prediction. We release part of our database for comparison purposes.


title: Constructing Unrestricted Adversarial Examples with Generative Models

abstract: Adversarial examples are typically constructed by perturbing an existing data point within a small matrix norm, and current defense methods are focused on guarding against this type of attack. In this paper, we propose unrestricted adversarial examples, a new threat model where the attackers are not restricted to small norm-bounded perturbations. Different from perturbation-based attacks, we propose to synthesize unrestricted adversarial examples entirely from scratch using conditional generative models. Specifically, we first train an Auxiliary Classifier Generative Adversarial Network (AC-GAN) to model the class-conditional distribution over data samples. Then, conditioned on a desired class, we search over the AC-GAN latent space to find images that are likely under the generative model and are misclassified by a target classifier. We demonstrate through human evaluation that unrestricted adversarial examples generated this way are legitimate and belong to the desired class. Our empirical results on the MNIST, SVHN, and CelebA datasets show that unrestricted adversarial examples can bypass strong adversarial training and certified defense methods designed for traditional adversarial attacks.


title: Predicting Success in Dialogue

abstract: Task-solving in dialogue depends on the linguistic alignment of the interlocutors, which Pickering & Garrod (2004) have suggested to be based on mechanistic repetition effects. In this paper, we seek confirmation of this hypothesis by looking at repetition in corpora, and whether repetition is correlated with task success. We show that the relevant repetition tendency is based on slow adaptation rather than short-term priming and demonstrate that lexical and syntactic repetition is a reliable predictor of task success given the first five minutes of a taskoriented dialogue.


title: Building RDF Ontologies from Semi-Structured Legal Documents

abstract: The increasing interest in the context of e-government requires intelligent techniques for legal information and knowledge management. In this paper we describe a system that, given a number of legal paper documents, automatically transforms them into suitable RDF statements, using several ontological and linguistic knowledge levels. Although we describe a general methodology for a number of application domains, our system is particularly suitable for the notary realm.


Enabling Blockchain Innovations with Pegged


title: A Review of Cell Equalization Methods for Lithium Ion and Lithium Polymer Battery Systems

abstract: Lithium-based battery technology offers performance advantages over traditional battery technologies at the cost of increased monitoring and controls overhead. Multiple-cell Lead-Acid battery packs can be equalized by a controlled overcharge, eliminating the need to periodically adjust individual cells to match the rest of the pack. Lithium-based based batteries cannot be equalized by an overcharge, so alternative methods are required. This paper discusses several cell-balancing methodologies. Active cell balancing methods remove charge from one or more high cells and deliver the charge to one or more low cells. Dissipative techniques find the high cells in the pack, and remove excess energy through a resistive element until their charges match the low cells. This paper presents the theory of charge balancing techniques and the advantages and disadvantages of the presented methods.


title: Categorization of Anomalies in Smart Manufacturing Systems to Support the Selection of Detection Mechanisms

abstract: An important issue in anomaly detection in smart manufacturing systems is the lack of consistency in the formal definitions of anomalies, faults, and attacks. The term anomaly is used to cover a wide range of situations that are addressed by different types of solutions. In this letter, we categorize anomalies in machines, controllers, and networks along with their detection mechanisms, and unify them under a common framework to aid in the identification of potential solutions. The main contribution of the proposed categorization is that it allows the identification of gaps in anomaly detection in smart manufacturing systems.


title: Management of Distributed Energy Resources in IEC 61850 using Web Services on devices

abstract: The adoption of standardized Web Service communication protocols from manufacturing and automation industries carries a number of currently unused potentials for energy distribution processes. This work addresses potential advantages of such an approach, particularly highlighting benefits for medium-term, wide-spread use of Distributed Energy Resources (DER). Therefore a new communication protocol mapping for IEC 61850 is introduced based on the Devices Profile for Web Services (DPWS). Other than currently specified mappings, this approach benefits from intrinsic compliance to WS-* protocols. It brings along numerous advantages like plug and play field deployment of Intelligent Electrical Devices (IED) and more efficient asset management through seamless enterprise IT integration. Next to detailing this new mapping in theory, an increasingly important DER use case scenario based on fully electric vehicles (EV) is conducted as proof of concept. This use case also serves as evaluation scenario focusing on latency and scalability issues of the discovery process of dynamic DERs.


title: Understanding User Beliefs About Algorithmic Curation in the Facebook News Feed

abstract: People are becoming increasingly reliant on online socio-technical systems that employ algorithmic curation to organize, select and present information. We wanted to understand how individuals make sense of the influence of algorithms, and how awareness of algorithmic curation may impact their interaction with these systems. We investigated user understanding of algorithmic curation in Facebook's News Feed, by analyzing open-ended responses to a survey question about whether respondents believe their News Feeds show them every post their Facebook Friends create. Responses included a wide range of beliefs and causal inferences, with different potential consequences for user behavior in the system. Because user behavior is both input for algorithms and constrained by them, these patterns of belief may have tangible consequences for the system as a whole.


Fuzzy & Datamining based Disease Prediction Using K-NN Algorithm


title: LOGIC, PROGRAMMING AND PROLOG (2ED)

abstract: Copyright c ©2000, Ulf Nilsson and Jan MaAluszynski. The book may be downloaded and printed for personal use only provided that the text (1) is not altered in any way, and (2) is accompanied by this copyright notice. The book may also be copied and distributed in paper-form for non-profit use only. No other form of distribution is allowed. It is not allowed to distribute the book electronically.


title: Strip-type Grid Array Antenna with a Two-Layer Rear-Space Structure

abstract: A strip-type grid array antenna is analyzed using the finite-difference time-domain method. The space between the grid array and the ground plane, designated as the rear space, is filled with a dielectric layer and an air layer (two-layer rear-space structure). A VSWR bandwidth of approximately 13% is obtained with a maximum directivity of approximately 18 dB. The grid array exhibits a narrow beam with low side lobes. The cross-polarization component is small (?25 dB), as desired.


title: Detection of Individual Tree Crowns in Airborne Lidar Data

abstract: Laser scanning provides a good means to collect information on forest stands. This paper presents an approach to delineate single trees automatically in small footprint light detection and ranging (lidar) data in deciduous and mixed temperate forests. In rasterized laser data possible tree tops are detected with a local maximum filter. Afterwards the crowns are delineated with a combination of a pouring algorithm, knowledge-based assumptions on the shape of trees, and a final detection of the crown-edges by searching vectors starting from the trees’ tops. The segmentation results are assessed by comparison with terrestrial measured crown projections and with photogrammetrically delineated trees. The segmentation algorithm works well for coniferous stands. However, the implemented method tends to merge crowns in dense stands of deciduous trees.


title: Deriving TF-IDF as a fisher kernel

abstract: The Dirichlet compound multinomial (DCM) distribution has recently been shown to be a good model for documents because it captures the phenomenon of word burstiness, unlike standard models such as the multinomial distribution. This paper investigates the DCM Fisher kernel, a function for comparing documents derived from the DCM. We show that the DCM Fisher kernel has components that are similar to the term frequency (TF) and inverse document frequency (IDF) factors of the standard TF-IDF method for representing documents. Experiments show that the DCM Fisher kernel performs better than alternative kernels for nearest-neighbor document classification, but that the TF-IDF representation still performs best.


title: An innovative augmented reality educational platform using Gamification to enhance lifelong learning and cultural education

abstract: This paper proves that Life-long learning and Cultural Education can be supported through the use of technological advances and techniques which so far were extensively used in Games and immersive applications. In order to promote learning, this research proposal focuses on Augmented Reality (AR) and on Gamification for the creation of an educational AR book. The suggested learning subject is Science aimed at children between the ages of 10-12 years old, although the platform can be easily applied also to Culture, Arts and History. In our project, users interact with a virtual laboratory and are able to perform experiments and complete challenges through gaming so as to expand and test their knowledge. Using AR and gamification techniques, the aim is to deliver a more comprehensive understanding of the subject matter while at the same time engage learners and increase their enjoyment during the learning process. By engaging learners in cultural subjects, the cultural heritage is delivered to next generations and remains alive.


title: Applications of big data to smart cities

abstract: Many governments are considering adopting the smart city concept in their cities and implementing big data applications that support smart city components to reach the required level of sustainability and improve the living standards. Smart cities utilize multiple technologies to improve the performance of health, transportation, energy, education, and water services leading to higher levels of comfort of their citizens. This involves reducing costs and resource consumption in addition to more effectively and actively engaging with their citizens. One of the recent technologies that has a huge potential to enhance smart city services is big data analytics. As digitization has become an integral part of everyday life, data collection has resulted in the accumulation of huge amounts of data that can be used in various beneficial application domains. Effective analysis and utilization of big data is a key factor for success in many business and service domains, including the smart city domain. This paper reviews the applications of big data to support smart cities. It discusses and compares different definitions of the smart city and big data and explores the opportunities, challenges and benefits of incorporating big data applications for smart cities. In addition it attempts to identify the requirements that support the implementation of big data applications for smart city services. The review reveals that several opportunities are available for utilizing big data in smart cities; however, there are still many issues and challenges to be addressed to achieve better utilization of this technology.


AN EXPLORATORY STUDY ON THE DETERMINANTS OF SMARTPHONE APP PURCHASE


title: Fast Automatic Step Size Estimation for Gradient Descent Optimization of Image Registration

abstract: Fast automatic image registration is an important prerequisite for image-guided clinical procedures. However, due to the large number of voxels in an image and the complexity of registration algorithms, this process is often very slow. Stochastic gradient descent is a powerful method to iteratively solve the registration problem, but relies for convergence on a proper selection of the optimization step size. This selection is difficult to perform manually, since it depends on the input data, similarity measure and transformation model. The Adaptive Stochastic Gradient Descent (ASGD) method is an automatic approach, but it comes at a high computational cost. In this paper, we propose a new computationally efficient method (fast ASGD) to automatically determine the step size for gradient descent methods, by considering the observed distribution of the voxel displacements between iterations. A relation between the step size and the expectation and variance of the observed distribution is derived. While ASGD has quadratic complexity with respect to the transformation parameters, fast ASGD only has linear complexity. Extensive validation has been performed on different datasets with different modalities, inter/intra subjects, different similarity measures and transformation models. For all experiments, we obtained similar accuracy as ASGD. Moreover, the estimation time of fast ASGD is reduced to a very small value, from 40 s to less than 1 s when the number of parameters is 105, almost 40 times faster. Depending on the registration settings, the total registration time is reduced by a factor of 2.5–7 $\times$ for the experiments in this paper.


title: An Analysis of Statistical and Syntactic Phrases

abstract: As the amount of textual information available through the World Wide Web grows, there is a growing need for high-precision IR systems that enable a user to find useful information from the masses of available textual data. Phrases have traditionally been regarded as precision-enhancing devices and have proved useful as content-identifiers in representing documents. In this study, we compare the usefulness of phrases recognized using linguistic methods and those recognized by statistical techniques. We focus in particular on high-precision retrieval. We discover that once a good basic ranking scheme is being used, the use of phrases does not have a major effect on precision at high ranks. Phrases are more useful at lower ranks where the connection between documents and relevance is more tenuous. Also, we find that the syntactic and statistical methods for recognizing phrases yield comparable performance.


title: Greedy Sampling of Graph Signals

abstract: Sampling is a fundamental topic in graph signal processing, having found applications in estimation, clustering, and video compression. In contrast to traditional signal processing, the irregularity of the signal domain makes selecting a sampling set non-trivial and hard to analyze. Indeed, though conditions for graph signal interpolation from noiseless samples exist, they do not lead to a unique sampling set. The presence of noise makes choosing among these sampling sets a hard combinatorial problem. Although greedy sampling schemes are commonly used in practice, they have no performance guarantee. This work takes a twofold approach to address this issue. First, universal performance bounds are derived for the Bayesian estimation of graph signals from noisy samples. In contrast to currently available bounds, they are not restricted to specific sampling schemes and hold for any sampling sets. Second, this paper provides near-optimal guarantees for greedy sampling by introducing the concept of approximate submodularity and updating the classical greedy bound. It then provides explicit bounds on the approximate supermodularity of the interpolation mean-square error showing that it can be optimized with worst-case guarantees using greedy search even though it is not supermodular. Simulations illustrate the derived bound for different graph models and show an application of graph signal sampling to reduce the complexity of kernel principal component analysis.


title: Multiple memory systems and consciousness.

abstract: Abstract This Introduction to the Special Issue on Human Memory discusses some of the recent and current developments in the study of human memory from the neuropsychological perspective. A problem of considerable current interest, that of multiple memory systems, is a problem in classification. Much of the evidence for it is derived from clinical and experimental observations of dissociations between performances in memory tasks. The distinction between short-term and long-term memory is considered as an example of classification by dissociation. Current conceptualizations of multiple long-term memory systems are reviewed from the vantage point that distinguishes among three major kinds of memory--episodic, semantic, and procedural. These systems are briefly described and compared, and current views concerning the relation between them are discussed. The role of consciousness in memory is raised against the backdrop of the suggestion that it may be necessary to differentiate among several kinds of consciousness.


title: DeepNorm-A Deep Learning Approach to Text Normalization

abstract: This paper presents an simple yet sophisticated approach to the challenge by Sproat and Jaitly (2016)- given a large corpus of written text aligned to its normalized spoken form, train an RNN to learn the correct normalization function. Text normalization for a token seems very straightforward without it's context. But given the context of the used token and then normalizing becomes tricky for some classes. We present a novel approach in which the prediction of our classification algorithm is used by our sequence to sequence model to predict the normalized text of the input token. Our approach takes very less time to learn and perform well unlike what has been reported by Google (5 days on their GPU cluster). We have achieved an accuracy of 97.62 which is impressive given the resources we use. Our approach is using the best of both worlds, gradient boosting - state of the art in most classification tasks and sequence to sequence learning - state of the art in machine translation. We present our experiments and report results with various parameter settings.


title: External Compensation of Nonuniform Electrical Characteristics of Thin-Film Transistors and Degradation of OLED Devices in AMOLED Displays

abstract: The variation of electrical characteristics of polycrystalline-silicon thin-film transistor (TFT) and degradation of organic light-emitting-diode (OLED) device cause nonuniform intensity of luminance and image sticking in active-matrix OLED (AMOLED) displays. An external compensation method that senses and compensates variations of threshold voltage and mobility of TFTs and degradation of OLED device is proposed. The effect of the external compensation method on AMOLED pixel is experimentally verified by measuring the luminance of OLEDs and the electrical characteristics of TFTs in AMOLED pixels.


title: Magnetic nanochain integrated microfluidic biochips

abstract: Microfluidic biochips hold great potential for liquid analysis in biomedical research and clinical diagnosis. However, the lack of integrated on-chip liquid mixing, bioseparation and signal transduction presents a major challenge in achieving rapid, ultrasensitive bioanalysis in simple microfluidic configurations. Here we report magnetic nanochain integrated microfluidic chip built upon the synergistic functions of the nanochains as nanoscale stir bars for rapid liquid mixing and as capturing agents for specific bioseparation. The use of magnetic nanochains enables a simple planar design of the microchip consisting of flat channels free of common built-in components, such as liquid mixers and surface-anchored sensing elements. The microfluidic assay, using surface-enhanced Raman scattering nanoprobes for signal transduction, allows for streamlined parallel analysis of multiple specimens with greatly improved assay kinetics and delivers ultrasensitive identification and quantification of a panel of cancer protein biomarkers and bacterial species in 1 μl of body fluids within 8 min.


title: Prediction of Tensile Property of Hydrogenated Ti600 Titanium Alloy Using Artificial Neural Network

abstract: An artificial neural network (ANN) model has been developed to analyze and predict the correlation between tensile property and hydrogenation temperature and hydrogen content of hydrogenated Ti600 titanium alloy. The input parameters of the neural network model are hydrogenation temperature and hydrogen content. The output is ultimate tensile strength. The accuracy of ANN model was tested by the testing data samples. The prediction capability of ANN model was compared with the multiple linear regression approach and response surface method. The combined influence of inputs on the tensile property is also simulated using ANN model. It is found that excellent performance of the ANN model was achieved, and the results showed good agreement with experimental data. Moreover, the developed ANN model can be used as a tool to control the tensile property of titanium alloys.


title: The relationship between frequency of Facebook use, participation in Facebook activities, and student engagement

abstract: Educators and others are interested in the effects of social media on college students, with a specific focus on the most popular social media website-Facebook. Two previous studies have examined the relationship between Facebook use and student engagement, a construct related to positive college outcomes. However, these studies were limited by their evaluation of Facebook usage and how they measured engagement. This paper fills a gap in the literature by using a large sample (N = 2368) of college students to examine the relationship between frequency of Facebook use, participation in Facebook activities, and student engagement. Student engagement was measured in three ways: a 19-item scale based on the National Survey of Student Engagement, time spent preparing for class, and time spent in co-curricular activities. Results indicate that Facebook use was significantly negatively predictive of engagement scale score and positively predictive of time spent in co-curricular activities. Additionally, some Facebook activities were positively predictive of the dependent variables, while others were negatively predictive.


title: A Half-Turn Transformer With Symmetry Magnetic Flux for High-Frequency-Isolated DC/DC Converters

abstract: A half-turn transformer with symmetrical magnetic flux is proposed for high-frequency-isolated dc/dc converters. The transformer structure can reduce the minimum number of turns from 1 to 0.5. By adopting a novel winding and circuit configuration, a symmetrical magnetic flux is realized to make full utilization of the ferrite core. A simple EI core, which is easily manufacture and commercially available, can be used to make the half-turn transformer. The operation principles of the transformer are analyzed and simulated. The performance of the transformer is experimentally evaluated on a 750-kHz, 380/12-V, 600-W LLC converter using gallium nitride devices with a peak efficiency of 98%.


title: Distributed dialogue policies for multi-domain statistical dialogue management

abstract: Statistical dialogue systems offer the potential to reduce costs by learning policies automatically on-line, but are not designed to scale to large open-domains. This paper proposes a hierarchical distributed dialogue architecture in which policies are organised in a class hierarchy aligned to an underlying knowledge graph. This allows a system to be deployed using a modest amount of data to train a small set of generic policies. As further data is collected, generic policies can be adapted to give in-domain performance. Using Gaussian process-based reinforcement learning, it is shown that within this framework generic policies can be constructed which provide acceptable user performance, and better performance than can be obtained using under-trained domain specific policies. It is also shown that as sufficient in-domain data becomes available, it is possible to seamlessly improve performance, without subjecting users to unacceptable behaviour during the adaptation period and without limiting the final performance compared to policies trained from scratch.


Location-based services and GIS in perspective


Product Avatar as Digital Counterpart of a Physical Individual Product: Literature Review and Implications in an Aircraft


title: Reinforcement Learning and Markov Decision Processes

abstract: Situated in between supervised learning and unsupervised learning, the paradigm of reinforcement learning deals with learning in sequential decision making problems in which there is limited feedback. This text introduces the intuitions and concepts behind Markov decision processes and two classes of algorithms for computing optimal behaviors: reinforcement learning and dynamic programming. First the formal framework of Markov decision process is defined, accompanied by the definition of value functions and policies. The main part of this text deals with introducing foundational classes of algorithms for learning optimal behaviors, based on various definitions of optimality with respect to the goal of learning sequential decisions. Additionally, it surveys efficient extensions of the foundational algorithms, differing mainly in the way feedback given by the environment is used to speed up learning, and in the way they concentrate on relevant parts of the problem. For both model-based and model-free settings these efficient extensions have shown useful in scaling up to larger problems.


title: THE USE OF THE STABLE FREE RADICAL DIPHENYLPICRYLHYDRAZYL (DPPH) FOR ESTIMATING ANTIOXIDANT ACTIVITY

abstract: Molyneux, P. The use of the stable free radical diphenylpicrylhydrazyl (DPPH) for estimating antioxidant activity


title: Language and the perception of Space, Motion and Time

abstract: This literature review analyzes articles on the perception of space, motion and time across speakers of typologically different languages (L1s). The purpose of this analysis is to explore evidence of native language influence on speakers’ perception and conceptualization of these cognitive domains. The analysis revealed that although languages may not encode all the cognitive aspects of space and motion, there is no difference among speakers with regard to the conceptualization and perception of these two domains. On the other hand, language-specific encodings of time seem to influence the speakers’ perception of this domain, so it was concluded that language may affect abstract thought in general. Possible consequences for second language acquisition are discussed.


title: Performance evaluation of score level fusion in multimodal biometric systems

abstract: In a multimodal biometric system, the effective fusion method is necessary for combining information from various single modality systems. In this paper the performance of sum rule-based score level fusion and support vector machines (SVM)-based score level fusion are examined. Three biometric characteristics are considered in this study: fingerprint, face, and finger vein. We also proposed a new robust normalization scheme (Reduction of High-scores Effect normalization) which is derived from min-max normalization scheme. Experiments on four different multimodal databases suggest that integrating the proposed scheme in sum rule-based fusion and SVM-based fusion leads to consistently high accuracy. The performance of simple sum rule-based fusion preceded by our normalization scheme is comparable to another approach, likelihood ratio-based fusion [8] (Nandakumar et al., 2008), which is based on the estimation of matching scores densities. Comparison between experimental results on sum rule-based fusion and SVM-based fusion reveals that the latter could attain better performance than the former, provided that the kernel and its parameters have been carefully selected.


title: Anomaly Detection Using GANs for Visual Inspection in Noisy Training Data

abstract: The detection and the quantification of anomalies in image data are critical tasks in industrial scenes such as detecting micro scratches on product. In recent years, due to the difficulty of defining anomalies and the limit of correcting their labels, research on unsupervised anomaly detection using generative models has attracted attention. Generally, in those studies, only normal images are used for training to model the distribution of normal images. The model measures the anomalies in the target images by reproducing the most similar images and scoring image patches indicating their fit to the learned distribution. This approach is based on a strong presumption; the trained model should not be able to generate abnormal images. However, in reality, the model can generate abnormal images mainly due to noisy normal data which include small abnormal pixels, and such noise severely affects the accuracy of the model. Therefore, we propose a novel anomaly detection method to distort the distribution of the model with existing abnormal images. The proposed method detects pixel-level micro anomalies with a high accuracy from 1024x1024 high resolution images which are actually used in an industrial scene. In this paper, we share experimental results on open datasets, due to the confidentiality of the data.


title: An investigation into compassion fatigue and self-compassion in acute medical care hospital nurses: a mixed methods study

abstract: In the acute medical care hospital setting, nursing the sick and dying is both physically and emotionally demanding, making acute medical care hospital nurses more vulnerable to Compassion Fatigue (CF) or Secondary Traumatic Stress (STS). If not addressed in its earliest stages, CF can adversely change the caregiver’s ability to provide compassionate care. It has been shown that Self-Compassion (SC) can be beneficial for the caregiver, with corresponding benefits for the individual needing care. However, the relationship of this attribute to CF in the acute medical care hospital nursing setting has not been intensively studied. This study explores the environmental and psychosocial factors affecting the prevalence and levels of CF in acute medical care hospital nursing staff and whether SC can be used as a coping strategy that enables nurses to mitigate the prevalence and levels of CF. Using a mixed-methods study design, acute medical care hospital nurses were surveyed using a demographic/work-related questionnaire, the Secondary Traumatic Stress Scale (used to measure CF), the Self-Compassion Scale, questions requiring a narrative written response and semi-structured informal interviews. One-way ANOVA was conducted to explore the impact of work-related and demographic characteristics on levels of SC and CF. Pearson correlation co-efficient (r value) was used to explore the relationship between CF and SC and lastly, multiple regression was used to discover whether a predictive relationship existed between SC and CF. The interviews were recorded, transcribed verbatim and subjected to thematic analysis. The results of this study have exposed the prevalence of CF in acute medical care nurses and it’s damaging effects. And has also confirmed that SC can have a moderating effect on CF and an ability to be predictive of CF. Factors that influence the distress and vulnerability to CF were also identified, as well as the personal approaches used, whether successful or not, to enhance levels of SC and the ‘barrier’s to achieving it. This unique study could lead to care environments encouraging the self-compassion and well-being of their acute medical care nursing staff and become places where threat cultures, weak leadership, an emphasis on task rather than process and a feeling of being undervalued, do not monopolise.


title: A miniaturized dual-frequency Wilkinson power divider using defected ground structure

abstract: In this paper, we apply a defected ground structure (DGS) to design a compact dual-frequency Wilkinson power divider (WPD). As the necessary area for dual-frequency Wilkinson power divider has a considerable increment toward single frequency WPD, we have to reduce its size especially in lower frequencies. With using dumbbell DGS, both designed operating frequencies are significantly lowered and consequently 49% size reduction for both operating frequencies is achieved. Simulated and measured results are presented and show good agreement.


title: Production of long term steroid-producing granulosa cell cultures by cell hybridization.

abstract: Primary cultures of rat ovarian granulosa cells have been used extensively to study hormonal regulation of cellular function. To date, no long term cultures of ovarian cells which retain their differentiated functions have been developed. Hypoxanthine guanine phosphoribosyl transferase-deficient simian virus 40-transformed rat ovarian granulosa cells were fused with freshly prepared rat granulosa cells using inactivated Sendai virus. Putative hybrid cell strains obtained after selection in medium containing hypoxanthine, aminopterin, and thymidine were analyzed for progesterone synthesis. Neither the original simian virus 40-transformed granulosa cell nor its hypoxanthine guanine phosphoribosyl transferase-deficient derivative produced progesterone, but three of the hybrid strains produced progesterone at basal levels and in response to dibutyryl cAMP. One of these strains produced progesterone in a dose-responsive fashion when exposed to prostaglandin E2, cholera toxin, dibutyryl cAMP, and 2-chloroadenosine. Cell strains obtained by hybridization were remarkably similar to primary cultures of granulosa cells with respect to both the magnitude and temporal aspects of progesterone production in response to dibutyryl cAMP.


title: Learning Filter Banks Using Deep Learning For Acoustic Signals

abstract: Designing appropriate features for acoustic event recognition tasks is an active field of research. Expressive features should both improve the performance of the tasks and also be interpret-able. Currently, heuristically designed features based on the domain knowledge requires tremendous effort in hand-crafting, while features extracted through deep network are difficult for human to interpret. In this work, we explore the experience guided learning method for designing acoustic features. This is a novel hybrid approach combining both domain knowledge and purely data driven feature designing. Based on the procedure of log Mel-filter banks, we design a filter bank learning layer. We concatenate this layer with a convolutional neural network (CNN) model. After training the network, the weight of the filter bank learning layer is extracted to facilitate the design of acoustic features. We smooth the trained weight of the learning layer and re-initialize it in filter bank learning layer as audio feature extractor. For the environmental sound recognition task based on the Urban- sound8K dataset, the experience guided learning leads to a 2% accuracy improvement compared with the fixed feature extractors (the log Mel-filter bank). The shape of the new filter banks are visualized and explained to prove the effectiveness of the feature design process.


title: Understanding and Discovering Deliberate Self-harm Content in Social Media

abstract: Studies suggest that self-harm users found it easier to discuss self-harm-related thoughts and behaviors using social media than in the physical world. Given the enormous and increasing volume of social media data, on-line self-harm content is likely to be buried rapidly by other normal content. To enable voices of self-harm users to be heard, it is important to distinguish self-harm content from other types of content. In this paper, we aim to understand self-harm content and provide automatic approaches to its detection. We first perform a comprehensive analysis on self-harm social media using different input cues. Our analysis, the first of its kind in large scale, reveals a number of important findings. Then we propose frameworks that incorporate the findings to discover self-harm content under both supervised and unsupervised settings. Our experimental results on a large social media dataset from Flickr demonstrate the effectiveness of the proposed frameworks and the importance of our findings in discovering self-harm content.


title: Adapting GOMS to model human-robot interaction

abstract: A formal interaction modeling technique known as Goals, Operators, Methods, and Selection rules (GOMS) is well-established in human-computer interaction as a cost-effective way of evaluating designs without the participation of end users. This paper explores the use of GOMS for evaluating human-robot interaction. We provide a case study in the urban search-and-rescue domain and raise issues for developing GOMS models that have not been previously addressed. Further, we provide rationale for selecting different types of GOMS modeling techniques to help the analyst model human-robot interfaces.


title: Thermal management of electric machines

abstract: Electric machines have broadly been used in many industries including the transportation industry. With the evolving trend of electrification in transportation, electric machines with higher power density and higher efficiency are demanded and, thus, more stringent thermal management requirements are needed for electrified vehicle applications. This study comprehensively presents various important aspects of thermal management in electric machines with the main focus on transportation applications. Design considerations, challenges, and methods for enhanced thermal management are discussed. Fundamental thermal properties of common materials are presented and sources of losses in various parts of machines are explained. Furthermore, typical cooling techniques and thermal analysis approaches for electric machines are reviewed in detail. This study will serve as a reference guideline for machine designers, who are interested in thermal management, and for thermal researchers working on electric machines.


title: Ultrastructural study of two rumen fungi:Piromonas communis andSphaeromonas communis

abstract: Two species of strictly anaerobic rumen fungi,Sphaeromonas communis andPiromonas communis, were examined by light and electron microscopy (scanning and transmission). Although morphologically different (vegetative body, number of flagella per zoospore), the ultrastructure of these two microorganisms was rather similar to that ofNeocallimastix frontalis andN. patriciarum. Two types of organelles were regularly found, i.e., isolated or associated ribosomes in the form of aggregates and hydrogenosome-like organelles with an amorphous content that may be involved in energy generation for these mitochondria-free strictly anaerobic fungi. UnlikeN. frontalis, the distribution of organelles was homogenous.


High‐intensity ultrasound and hydrodynamic cavitation as powerful treament for biomass conversion


title: Informing sequential clinical decision-making through reinforcement learning: an empirical study

abstract: This paper highlights the role that reinforcement learning can play in the optimization of treatment policies for chronic illnesses. Before applying any off-the-shelf reinforcement learning methods in this setting, we must first tackle a number of challenges. We outline some of these challenges and present methods for overcoming them. First, we describe a multiple imputation approach to overcome the problem of missing data. Second, we discuss the use of function approximation in the context of a highly variable observation set. Finally, we discuss approaches to summarizing the evidence in the data for recommending a particular action and quantifying the uncertainty around the Q-function of the recommended policy. We present the results of applying these methods to real clinical trial data of patients with schizophrenia.


title: Robust image hash in Radon transform domain for authentication

abstract: Image authentication has become an emergency issue in the digital world as it can be easily tampered with the image editing techniques. In this paper, a novel robust hashing method for image authentication is proposed. The reported scheme first performs Radon transform (RT) on the image, and calculates the moment features which are invariant to translation and scaling in the projection space. Then discrete Fourier transform (DFT) is applied on the moment features to resist rotation. Finally, the magnitude of the significant DFT coefficients is normalized and quantized as the image hash bits. Experimental results show that the proposed algorithm can tolerate almost all the typical image processing manipulations, including JPEG compression, geometric distortion, blur, addition of noise, and enhancement. Compared with other approaches in the literature, the reported method is more effective for image authentication in terms of detection performance and the hash size.


DeepSymmetry: Joint Symmetry and Depth Estimation using Deep Neural Networks


title: Towards expert finding by leveraging relevant categories in authority ranking

abstract: How to improve authority ranking is a crucial research problem for expert finding. In this paper, we propose a novel framework for expert finding based on the authority information in the target category as well as the relevant categories. First, we develop a scalable method for measuring the relevancy between categories through topic models. Then, we provide a link analysis approach for ranking user authority by considering the information in both the target category and the relevant categories. Finally, the extensive experiments on two large-scale real-world Q&A data sets clearly show that the proposed method outperforms the baseline methods with a significant margin.


title: Strategic issues in customer relationship management (CRM) implementation

abstract: The number of customer relationship management (CRM) implementations has grown dramatically in recent years. However, few academic studies of the issues associated with the implementation of the concept are available. This paper offers a modest contribution through the analysis of a case study of a CRM implementation at a UK‐based manufacturing company. The case study illustrates that CRM is a complex and holistic concept, organised around business processes and the integration of information technologies. The study also highlights that implementing CRM requires effective leadership, sourcing, targeting and evaluation strategies.


title: Short-term load forecasting based on an adaptive hybrid method

abstract: This paper aims to develop a load forecasting method for short-term load forecasting, based on an adaptive two-stage hybrid network with self-organized map (SOM) and support vector machine (SVM). In the first stage, a SOM network is applied to cluster the input data set into several subsets in an unsupervised manner. Then, groups of 24 SVMs for the next day's load profile are used to fit the training data of each subset in the second stage in a supervised way. The proposed structure is robust with different data types and can deal well with the nonstationarity of load series. In particular, our method has the ability to adapt to different models automatically for the regular days and anomalous days at the same time. With the trained network, we can straightforwardly predict the next-day hourly electricity load. To confirm the effectiveness, the proposed model has been trained and tested on the data of the historical energy load from New York Independent System Operator.


title: Intelligent Virtual Environments-A State-ofthe-art Report

abstract: The impetus for this field comes from a number of directions. Firstly, as the amount of processing power available for rendering has increased, it has now become feasible to devote a little of it to aspects of a VE beyond visual realism. However visually appealing a VE is, if it is static and empty of change and behaviour, the immersive experience is of limited interest. Cities without people cannot make the user feel a real sense of presence. While animation can create dynamic interest, its pre-scripted nature runs against the interactional freedom a VE gives users and can only hold their interest for a limited time.


title: Automotive grade MIMO antenna setup and performance evaluation for LTE-communications

abstract: Multiple-input-multiple-output (MIMO) is a technique to achieve high data rates in mobile communication networks and is used by the Long-Term Evolution (LTE) standard. The integration of a two antenna MIMO system for usage in worldwide LTE networks into a conventional automotive roof top antenna is discussed. In-situ antenna measurement results of the final LTE MIMO configuration are shown. Furthermore initial test results of the integrated LTE antenna system in an 800 MHz live network are presented and a comparison to a reference system consisting of two quarter-wavelength monopole antennas with ideal spatial separation is provided.


title: Efficient Processing of Spatio-Temporal-Textual Queries

abstract: Devices with built-in GPS (e.g. smartphones) are producing a huge amount of data objects with spatial, temporal and textual information. For example, a significant part of Twitter messages sent from smartphones has spatial location (latitude and longitude), temporal information (timestamp) and textual information (the message itself). Therefore, there is a growing interest for new approaches that are able to select the data objects that are spatially, temporally and textually relevant from huge datasets. In this paper, we specify the spatio-temporal-textual query that returns the relevant data objects considering these three criteria simultaneously, presenting new indexes and algorithms to process such query efficiently. The proposed approaches are evaluated taking real datasets, potentially providing more accurate results.


A Survey of Sentiment Lexicons


title: Supporting Online Material Island Biogeography Reveals the Deep History of SIV

abstract: Simian immunodeficiency virus (SIV) lineages have been identified that are endemic to Bioko Island. The time the island formed offers a geological time scale calibration point for dating the most recent common ancestor of SIV. The Bioko viruses cover the whole range of SIV genetic diversity, and each Bioko SIV clade is most closely related to viruses circulating in hosts of the same genus on the African mainland rather than to SIVs of other Bioko species. Our phylogeographic approach establishes that SIV is ancient and at least 32,000 years old. Our conservative calibration point and analyses of gene sequence saturation and dating bias suggest it may be much older.


Heuristic Algorithms for Solving the Resource-Constrained Project Scheduling Problem : Classification and Computational Analysis


Joint Embedding Models for Textual and Social Analysis


title: New Scientific Paradigms for Probiotics and Prebiotics

abstract: The inaugural meeting of the International Scientific Association for Probiotics and Prebiotics (ISAPP) was held May 3 to May 5 2002 in London, Ontario, Canada. A group of 63 academic and industrial scientists from around the world convened to discuss current issues in the science of probiotics and prebiotics. ISAPP is a non-profit organization comprised of international scientists whose intent is to strongly support and improve the levels of scientific integrity and due diligence associated with the study, use, and application of probiotics and prebiotics. In addition, ISAPP values its role in facilitating communication with the public and healthcare providers and among scientists in related fields on all topics pertinent to probiotics and prebiotics. It is anticipated that such efforts will lead to development of approaches and products that are optimally designed for the improvement of human and animal health and well being. This article is a summary of the discussions, conclusions, and recommendations made by 8 working groups convened during the first ISAPP workshop focusing on the topics of: definitions, intestinal flora, extra-intestinal sites, immune function, intestinal disease, cancer, genetics and genomics, and second generation prebiotics.


title: Intrusion detection in SCADA systems using machine learning techniques

abstract: Modern Supervisory Control and Data Acquisition (SCADA) systems are essential for monitoring and managing electric power generation, transmission and distribution. In the age of the Internet of Things, SCADA has evolved into big, complex and distributed systems that are prone to conventional in addition to new threats. So as to detect intruders in a timely and efficient manner a real time detection mechanism, capable of dealing with a range of forms of attacks is highly salient. Such a mechanism has to be distributed, low cost, precise, reliable and secure, with a low communication overhead, thereby not interfering in the industrial system’s operation. ::: ::: ::: In this commentary two distributed Intrusion Detection Systems (IDSs) which are able to detect attacks that occur in a SCADA system are proposed, both developed and evaluated for the purposes of the CockpitCI project. The CockpitCI project proposes an architecture based on real-time Perimeter Intrusion Detection System (PIDS), which provides the core cyber-analysis and detection capabilities, being responsible for continuously assessing and protecting the electronic security perimeter of each CI. Part of the PIDS that was developed for the purposes of the CockpitCI project, is the OCSVM module. During the duration of the project two novel OCSVM modules were developed and tested using datasets from a small-scale testbed that was created, providing the means to mimic a SCADA system operating both in normal conditions and under the influence of cyberattacks. ::: ::: ::: The first method, namely K-OCSVM, can distinguish real from false alarms using the OCSVM method with default values for parameters ν and σ combined with a recursive K-means clustering method. The K-OCSVM is very different from all similar methods that required pre-selection of parameters with the use of cross-validation or other methods that ensemble outcomes of one class classifiers. Building on the K-OCSVM and trying to cope with the high requirements that were imposed from the CockpitCi project, both in terms of accuracy and time overhead, a second method, namely IT-OCSVM is presented. IT-OCSVM method is capable of performing outlier detection with high accuracy and low overhead within a temporal window, adequate for the nature of SCADA systems. ::: ::: ::: The two presented methods are performing well under several attack scenarios. Having to balance between high accuracy, low false alarm rate, real time communication requirements and low overhead, under complex and usually persistent attack situations, a combination of several techniques is needed. Despite the range of intrusion detection activities, it has been proven that half of these have human error at their core. An increased empirical and theoretical research into human aspects of cyber security based on the volumes of human error related incidents can enhance cyber security capabilities of modern systems. In order to strengthen the security of SCADA systems, another solution is to deliver defence in depth by layering security controls so as to reduce the risk to the assets being protected.


title: Characterizing and Detecting Hateful Users on Twitter

abstract: Most current approaches to characterize and detect hate speech focus on \textit{content} posted in Online Social Networks. They face shortcomings to collect and annotate hateful speech due to the incompleteness and noisiness of OSN text and the subjectivity of hate speech. These limitations are often aided with constraints that oversimplify the problem, such as considering only tweets containing hate-related words. In this work we partially address these issues by shifting the focus towards \textit{users}. We develop and employ a robust methodology to collect and annotate hateful users which does not depend directly on lexicon and where the users are annotated given their entire profile. This results in a sample of Twitter's retweet graph containing $100,386$ users, out of which $4,972$ were annotated. We also collect the users who were banned in the three months that followed the data collection. We show that hateful users differ from normal ones in terms of their activity patterns, word usage and as well as network structure. We obtain similar results comparing the neighbors of hateful vs. neighbors of normal users and also suspended users vs. active users, increasing the robustness of our analysis. We observe that hateful users are densely connected, and thus formulate the hate speech detection problem as a task of semi-supervised learning over a graph, exploiting the network of connections on Twitter. We find that a node embedding algorithm, which exploits the graph structure, outperforms content-based approaches for the detection of both hateful ($95\%$ AUC vs $88\%$ AUC) and suspended users ($93\%$ AUC vs $88\%$ AUC). Altogether, we present a user-centric view of hate speech, paving the way for better detection and understanding of this relevant and challenging issue.


title: Discriminative Multi-modal Feature Fusion for RGBD Indoor Scene Recognition

abstract: RGBD scene recognition has attracted increasingly attention due to the rapid development of depth sensors and their wide application scenarios. While many research has been conducted, most work used hand-crafted features which are difficult to capture high-level semantic structures. Recently, the feature extracted from deep convolutional neural network has produced state-of-the-art results for various computer vision tasks, which inspire researchers to explore incorporating CNN learned features for RGBD scene understanding. On the other hand, most existing work combines rgb and depth features without adequately exploiting the consistency and complementary information between them. Inspired by some recent work on RGBD object recognition using multi-modal feature fusion, we introduce a novel discriminative multi-modal fusion framework for rgbd scene recognition for the first time which simultaneously considers the inter-and intra-modality correlation for all samples and meanwhile regularizing the learned features to be discriminative and compact. The results from the multimodal layer can be back-propagated to the lower CNN layers, hence the parameters of the CNN layers and multimodal layers are updated iteratively until convergence. Experiments on the recently proposed large scale SUN RGB-D datasets show that our method achieved the state-of-the-art without any image segmentation.


Coumarin: Chemical and pharmacological profile.


title: Osmosis is not driven by water dilution.

abstract: There is a misconception among plant scientists that osmosis is driven by the tendency of solutes to dilute water. In this opinion article, we discuss the quantitative and qualitative failures of this view, and go on to review the correct kinetic picture of osmosis as it appears in physics textbooks.


title: Comparison and Evaluation of Methods for Liver Tumor Classification from CT Datasets

abstract: This paper proposes an automatic system for early detection of liver diseases from Computed tomography (CT) images. The general Computer Aided Diagnosis (CAD) system, including liver diagnosis can be done by segmenting a liver and lesion, extracting features and classify disease whether it is hepatoma or hemangioma. To segment a liver from CT abdominal images histogram analyzer and morphological operation is used. Then to extract a lesion from liver Fuzzy c-mean (FCM) clustering is used. In feature extraction biorthogonal wavelet, Gray-level co-occurrence matrix (GLCM) and fast discrete curvelet transform (FDCT) techniques are used. The textural information obtained was used to train various neural network such as Back propagation Neural Network (BPN), Probabilistic Neural Network (PPN) and Cascade feed forward BPN (CFBPN).The outcome obtained from neural networks are compared with each other to find best combination of features and neural network.


title: Multitargeted molecular docking study of plant-derived natural products on phosphoinositide-3 kinase pathway components

abstract: Phosphoinositide-3 kinase (PI3K) signaling pathway comprises of a cornucopia of protein molecules capable of regulating numerous cellular events, including cell survival, cell cycle regulation, angiogenesis, and apoptosis. Deregulation of PI3K downstream signaling is a phenomenon commonly seen in various types of cancer and also held responsible for poor prognosis and resistance to chemotherapy. Targeting PI3K signaling pathway has become a new and promising strategy in combating cancer. In the present study, PI3K signaling components PI3K, PDK1, Akt, and mTOR were chosen and 51 natural compounds along with 17 reference compounds were selected as ligand with the aid of PubMed published literature search. Ligands were docked to protein molecules by using Maestro 9.3 (Schrodinger Inc.). It was discovered in this study that compounds myricetin, quercetin, morin, luteolin, and emodin yielded excellent dock score with the proteins concluded with the help of docking free energy. The remarkable feature of these compounds are their various pharmacodynamics and pharmacokinetic characteristics, many of which are in accordance with the “Lipinski’s Rule of five”. The docking study carried out is an endeavor to portray the docking of these compounds with the proteins, to summarize the various Gscore, hydrogen bond, electrostatic bond, and to chart out various factors that are decisive for and also govern the protein–ligand interactions.


title: Security testing for Android mHealth apps

abstract: Mobile health (mHealth) apps are an ideal tool for monitoring and tracking long-term health conditions; they are becoming incredibly popular despite posing risks to personal data privacy and security. In this paper, we propose a testing method for Android mHealth apps which is designed using a threat analysis, considering possible attack scenarios and vulnerabilities specific to the domain. To demonstrate the method, we have applied it to apps for managing hypertension and diabetes, discovering a number of serious vulnerabilities in the most popular applications. Here we summarise the results of that case study, and discuss the experience of using a testing method dedicated to the domain, rather than out-of-the-box Android security testing methods. We hope that details presented here will help design further, more automated, mHealth security testing tools and methods.


title: Interactive 3D architectural modeling from unordered photo collections

abstract: We present an interactive system for generating photorealistic, textured, piecewise-planar 3D models of architectural structures and urban scenes from unordered sets of photographs. To reconstruct 3D geometry in our system, the user draws outlines overlaid on 2D photographs. The 3D structure is then automatically computed by combining the 2D interaction with the multi-view geometric information recovered by performing structure from motion analysis on the input photographs. We utilize vanishing point constraints at multiple stages during the reconstruction, which is particularly useful for architectural scenes where parallel lines are abundant. Our approach enables us to accurately model polygonal faces from 2D interactions in a single image. Our system also supports useful operations such as edge snapping and extrusions. ::: Seamless texture maps are automatically generated by combining multiple input photographs using graph cut optimization and Poisson blending. The user can add brush strokes as hints during the texture generation stage to remove artifacts caused by unmodeled geometric structures. We build models for a variety of architectural scenes from collections of up to about a hundred photographs.


title: On segmentation of eCommerce queries

abstract: In this paper, we present QSEGMENT, a real-life query segmentation system for eCommerce queries. QSEGMENT uses frequency data from the query log which we call buyers' data and also frequency data from product titles what we call sellers' data. We exploit the taxonomical structure of the marketplace to build domain specific frequency models. Using such an approach, QSEGMENT performs better than previously described baselines for query segmentation. Also, we perform a large scale evaluation by using an unsupervised IR metric which we refer to as user-intent-score. We discuss the overall architecture of QSEGMENT as well as various use cases and interesting observations around segmenting eCommerce queries.


title: LegUp: An open-source high-level synthesis tool for FPGA-based processor/accelerator systems

abstract: It is generally accepted that a custom hardware implementation of a set of computations will provide superior speed and energy-efficiency relative to a software implementation. However, the cost and difficulty of hardware design is often prohibitive, and consequently, a software approach is used for most applications. In this paper, we introduce a new high-level synthesis tool called LegUp that allows software techniques to be used for hardware design. LegUp accepts a standard C program as input and automatically compiles the program to a hybrid architecture containing an FPGA-based MIPS soft processor and custom hardware accelerators that communicate through a standard bus interface. In the hybrid processor/accelerator architecture, program segments that are unsuitable for hardware implementation can execute in software on the processor. LegUp can synthesize most of the C language to hardware, including fixed-sized multi-dimensional arrays, structs, global variables and pointer arithmetic. Results show that the tool produces hardware solutions of comparable quality to a commercial high-level synthesis tool. We also give results demonstrating the ability of the tool to explore the hardware/software co-design space by varying the amount of a program that runs in software vs. hardware. LegUp, along with a set of benchmark C programs, is open source and freely downloadable, providing a powerful platform that can be leveraged for new research on a wide range of high-level synthesis topics.


title: The silence of biogeography

abstract: Modern biogeography now encompasses an impressive diversity of patterns and phenomena of the geography of nature, providing insights fundamental to understanding the forces influencing the spatial and temporal dynamics of biological diversity. However, rather than praise our discipline for its great breadth of visions, our purpose here is to point out our glaring oversight of a potentially transformative frontier in the geography of nature. A new, emerging area called soundscape ecology, if guided by the principles of biogeography, holds the promise of ‘opening the ears’ of our field and providing fresh perspectives on fundamental problems being addressed by biogeographers.


title: Biologically realistic primitives for engineered morphogenesis

abstract: Finding ways to engineer morphogenesis in biological systems, to direct the development of a multicellular organism according to desired specifications, will require both high-level understanding of organizing principles in such systems and low-level understanding of how basic tools can be reliably implemented in real cells. Past work has assumed low-level capabilities appropriate to computing agents but not necessarily to biology. Here I discuss potential ways of implementing low-level primitives based on capabilities for which evidence exists in biological systems, with the goal of developing a basis for engineering developmental processes that will be realizable in wetware. I focus on the use of biologically realistic morphogen gradients to produce structures of desired size, provide positional information, and trigger genetic cascades that lead to the growth of more complex structures.


title: Deep convolutional neural networks for detection of rail surface defects

abstract: In this paper, we propose a deep convolutional neural network solution to the analysis of image data for the detection of rail surface defects. The images are obtained from many hours of automated video recordings. This huge amount of data makes it impossible to manually inspect the images and detect rail surface defects. Therefore, automated detection of rail defects can help to save time and costs, and to ensure rail transportation safety. However, one major challenge is that the extraction of suitable features for detection of rail surface defects is a non-trivial and difficult task. Therefore, we propose to use convolutional neural networks as a viable technique for feature learning. Deep convolutional neural networks have recently been applied to a number of similar domains with success. We compare the results of different network architectures characterized by different sizes and activation functions. In this way, we explore the efficiency of the proposed deep convolutional neural network for detection and classification. The experimental results are promising and demonstrate the capability of the proposed approach.


title: Annotation and Classification of Sentence-level Revision Improvement

abstract: Studies of writing revisions rarely focus on revision quality. To address this issue, we introduce a corpus of between-draft revisions of student argumentative essays, annotated as to whether each revision improves essay quality. We demonstrate a potential usage of our annotations by developing a machine learning model to predict revision improvement. With the goal of expanding training data, we also extract revisions from a dataset edited by expert proofreaders. Our results indicate that blending expert and non-expert revisions increases model performance, with expert data particularly important for predicting low-quality revisions.


title: Decision models for record linkage

abstract: The process of identifying record pairs that represent the same real-world entity in multiple databases, commonly known as record linkage, is one of the important steps in many data mining applications. In this paper, we address one of the sub-tasks in record linkage, i.e., the problem of assigning record pairs with an appropriate matching status. Techniques for solving this problem are referred to as decision models. Most existing decision models rely on good training data, which is, however, not commonly available in real-world applications. Decision models based on unsupervised machine learning techniques have recently been proposed. In this paper, we review several existing decision models and then propose an enhancement to cluster-based decision models. Experimental results show that our proposed decision model achieves the same accuracy of existing models while significantly reducing the number of record pairs required for manual review. The proposed model also provides a mechanism to trade off the accuracy with the number of record pairs required for clerical review.


title: Advances in geometry and reflectance acquisition (course notes)

abstract: The demands for digital 3D content for numerous applications such as video games, movies, virtual prototyping, advertisement, virtual reality, augmented reality or even digital preservation of cultural heritage objects has led to a need for efficient and accurate automatic acquisition systems for both geometry and reflectance. This short course sheds light on the acquisition ecosystem with three of its most important components that consist in accurate acquisition methods for geometry and reflectance as well as strategies towards an efficient acquisition pipeline to fulfill the demands of industry with respect to mass digitization of 3D contents. In this context, this course provides a thorough overview of the standard methods for the acquisition of both geometry and reflectance of surfaces with different types of reflectance behavior ranging from diffuse over opaque to specular surfaces or even translucent and transparent surfaces. These standard acquisition techniques are - by design - only well-suited for a limited range of surface materials and, hence, not adequate if no prior information with respect to the surface reflectance behavior is available. For this reason, we also discuss recent advances towards an efficient, fully automatic acquisition in the scope of the concluding remarks.


title: SLIK: scalable low-latency indexes for a key-value store

abstract: Many large-scale key-value storage systems sacrifice features like secondary indexing and/or consistency in favor of scalability or performance. This limits the ease and efficiency of application development on such systems. Implementing secondary indexing in a large-scale memory based system is challenging because the goals for low latency, high scalability, consistency and high availability often conflict with each other. This paper shows how a large-scale key-value storage system can be extended to provide secondary indexes while meeting those goals. The architecture, called SLIK, enables multiple secondary indexes for each table. SLIK represents index B+ trees using objects in the underlying key-value store. It allows indexes to be partitioned and distributed independently of the data in tables while providing reasonable consistency guarantees using a lightweight ordered write approach. Our implementation of this design on RAMCloud (a main memory key-value store) performs indexed reads in 11 µs and writes in 30 µs. The architecture supports indexes spanning thousands of nodes, and provides linear scalability for throughput.


title: Letter-Based Speech Recognition with Gated ConvNets

abstract: In this paper we introduce a new speech recognition system, leveraging a simple letter-based ConvNet acoustic model. The acoustic model requires -- only audio transcription for training -- no alignment annotations, nor any forced alignment step is needed. At inference, our decoder takes only a word list and a language model, and is fed with letter scores from the -- acoustic model -- no phonetic word lexicon is needed. Key ingredients for the acoustic model are Gated Linear Units and high dropout. We show near state-of-the-art results in word error rate on the LibriSpeech corpus using log-mel filterbanks, both on the "clean" and "other" configurations.


Philosophy and Biography


title: TEXTUAL ENTAILMENT USING LEXICAL AND SYNTACTIC SIMILARITY

abstract: A two-way Textual Entailment (TE) recognition system that uses lexical and syntactic features has been described in this paper. The TE system is rule based that uses lexical and syntactic similarities. The important lexical similarity features that are used in the present system are: WordNet based uni-gram match, bi-gram match, longest common sub-sequence, skip-gram, stemming. In the syntactic TE system, the important features used are: subject-subject comparison, subject-verb comparison, object-verb comparison and cross subject-verb comparison. The system has been separately trained on each development corpus released as part of the Recognising Textual Entailment (RTE) competitions RTE-1, RTE-2, RTE-3 and RTE-5 and tested on the respective RTE test sets. No separate development data was released in RTE-4. The evaluation results on each test set are compared with the RTE systems that participated in the respective RTE competitions with lexical and syntactic approaches.


Probabilistic Part-of-Speech Tagging Using Decision Trees