title: De novo, heterozygous, loss‐of‐function mutations in SYNGAP1 cause a syndromic form of intellectual disability

abstract: De novo mutations (DNM) in SYNGAP1, encoding Ras/Rap GTPase-activating protein SynGAP, have been reported in individuals with nonsyndromic intellectual disability (ID). We identified 10 previously unreported individuals with SYNGAP1 DNM; seven via the Deciphering Developmental Disorders (DDD) Study, one through clinical analysis for copy number variation and the remaining two (monozygotic twins) via a research multi-gene panel analysis. Seven of the nine heterozygous mutations are likely to result in loss-of-function (3 nonsense; 3 frameshift; 1 whole gene deletion). The remaining two mutations, one of which affected the monozygotic twins, were missense variants. Each individual carrying a DNM in SYNGAP1 had moderate-to-severe ID and 7/10 had epilepsy; typically myoclonic seizures, absences or drop attacks. 8/10 had hypotonia, 5/10 had significant constipation, 7/10 had wide-based/unsteady gait, 3/10 had strabismus, and 2/10 had significant hip dysplasia. A proportion of the affected individuals had a similar, myopathic facial appearance, with broad nasal bridge, relatively long nose and full lower lip vermilion. A distinctive behavioral phenotype was also observed with aggressive/challenging behavior and significant sleep problems being common. 7/10 individuals had MR imaging of the brain each of which was reported as normal. The clinical features of the individuals reported here show significant overlap with those associated with 6p21.3 microdeletions, confirming that haploinsufficiency for SYNGAP1 is responsible for both disorders. © 2015 Wiley Periodicals, Inc.


title: VSA: An offline scheduling analyzer for Xen virtual machine monitor

abstract: Nowadays, it is an important trend in the system domain to use the software-based virtualization technology to build the execution environments (e.g., the Clouds). After introducing the virtualization layer, there exist two schedulers: One in the hypervisor and the other inside the Guest Operating System (GOS). To fully understand the virtualized system and identify the possible reasons for performance problems incurred by the virtualization technology, it is very important for the system administrators and engineers to know the scheduling behavior of the hypervisor, in addition to understanding the scheduler inside the GOS. In this paper, we develop a virtualization scheduling analyzer, called VSA, to analyze the trace data of the Xen virtual machine monitor. With VSA, one can easily obtain the scheduling data associated with virtual processors (i.e., VCPUs) and physical processors (i.e., PCPUs), and further conduct the scheduling analysis for a group of interacting VCPUs running in the same domain.


title: Handcrafted vs. non-handcrafted features for computer vision classification

abstract: Abstract This work presents a generic computer vision system designed for exploiting trained deep Convolutional Neural Networks (CNN) as a generic feature extractor and mixing these features with more traditional hand-crafted features. Such a system is a single structure that can be used for synthesizing a large number of different image classification tasks. Three substructures are proposed for creating the generic computer vision system starting from handcrafted and non-handcrafter features: i) one that remaps the output layer of a trained CNN to classify a different problem using an SVM; ii) a second for exploiting the output of the penultimate layer of a trained CNN as a feature vector to feed an SVM; and iii) a third for merging the output of some deep layers, applying a dimensionality reduction method, and using these features as the input to an SVM. The application of feature transform techniques to reduce the dimensionality of feature sets coming from the deep layers represents one of the main contributions of this paper. Three approaches are used for the non-handcrafted features: deep transfer learning features based on convolutional neural networks (CNN), principal component analysis network (PCAN), and the compact binary descriptor (CBD). For the handcrafted features, a wide variety of state-of-the-art algorithms are considered: Local Ternary Patterns, Local Phase Quantization, Rotation Invariant Co-occurrence Local Binary Patterns, Completed Local Binary Patterns, Rotated local binary pattern image, Globally Rotation Invariant Multi-scale Co-occurrence Local Binary Pattern, and several others. The computer vision system based on the proposed approach was tested on many different datasets, demonstrating the generalizability of the proposed approach thanks to the strong performance recorded. The Wilcoxon signed rank test is used to compare the different methods; moreover, the independence of the different methods is studied using the Q-statistic. To facilitate replication of our experiments, the MATLAB source code will be available at ( https://www.dropbox.com/s/bguw035yrqz0pwp/ElencoCode.docx?dl=0 ).


title: Efficient set intersection for inverted indexing

abstract: Conjunctive Boolean queries are a key component of modern information retrieval systems, especially when Web-scale repositories are being searched. A conjunctive query q is equivalent to a vqv-way intersection over ordered sets of integers, where each set represents the documents containing one of the terms, and each integer in each set is an ordinal document identifier. As is the case with many computing applications, there is tension between the way in which the data is represented, and the ways in which it is to be manipulated. In particular, the sets representing index data for typical document collections are highly compressible, but are processed using random access techniques, meaning that methods for carrying out set intersections must be alert to issues to do with access patterns and data representation. Our purpose in this article is to explore these trade-offs, by investigating intersection techniques that make use of both uncompressed “integer” representations, as well as compressed arrangements. We also propose a simple hybrid method that provides both compact storage, and also faster intersection computations for conjunctive querying than is possible even with uncompressed representations.


title: Dependability evaluation of cloud infrastructures

abstract: Cloud computing is a model that resources such as storage, applications and networking infrastructures can be offered as services over the internet. Cloud applications are becoming ever larger and more complex with high availability requirement. This paper presents a modeling strategy based on a hierarchical and heterogeneous modeling for planning of cloud infrastructures. The modeling strategy allows the selection of cloud infrastructures according to dependability and cost requirements. A case study based on virtual learning environments hosted on Eucalyptus platform is adopted to demonstrate the feasibility of the proposed modeling strategy.


title: Erect wheel-legged stair climbing robot for indoor service applications

abstract: In this paper, an automatic erect stair climbing mobile robot is developed for indoor service applications. The design of high center of mass, tilt axis near ground, and the triangular wheel-legged structure enable the robot to climb stairs in a dynamic and self-balancing way. The wheel-legged mechanism also keeps the advantages of differential-wheeled mobile platforms when moving on flat ground, such as easy to control, saving power, and zero turning radius. Moreover, the overall mechanical design fits the requirements for a service robot to have proper height and small footprint, which can make human-robot interaction more natural and comfortable in indoor environment. Since the perceptual and control system of the robot are both well integrated, we successfully demonstrate the stair-climbing function and prove that the design and implementation of our work are feasible and efficient. As a result, the robot is expected to be a prototype of universal platform for indoor mobile service robots in the future.


title: Social Media Influencer and Cyberbullying: A Lesson Learned from Preliminary Findings

abstract: Social media influencers generate large followers that include the enthusiastic engaged people who ::: value their views, as well as haters. While enjoying the opportunities to become the endorser of certain brands as a result of being famous, they are also bound by cyberbullying issues. Cyberbullying is not new, it has happened even before the social networking era. Nonetheless, the focus of cyberbullying related studies is usually school children or young adults. This study fills the gap by exploring the categories of cyberbullying faced by the social media influencers and the best practices to deal with the problem. We collected the data by using content analysis and interview techniques. The findings show that social media influencers face ::: (i) harassment, (ii) flaming, (iii) outing, (iv) ::: masquerading, (v) dissing, (vi) trolling and (vii) ::: catfishing as part of the cyberbullying by their ::: haters. We also found that the influencers’ best ::: practices to handle the situation differs based on the form of cyberbullying. The findings contribute to the existing literature on cyberbullying studies, particularly the lesson learned from the ::: cyberbullying towards social media influencers.


title: An ambidextrous perspective on business intelligence and analytics support in decision processes: Insights from a multiple case study

abstract: Abstract Providing data-centric decision support for organizational decision processes is a crucial but challenging task. Business intelligence and analytics (BI&A) equips analytics experts with the technological capabilities to support decision processes with reliable information and analytic insights, thus potentially raising the quality of managerial decision making. However, the very nature of organizational decision processes imposes conflicting task requirements regarding adaptability and rigor. This research proposes ambidexterity as a theoretical lens to investigate data-centric decision support. Based on an in-depth multiple case study of BI&A-supported decision processes, we identify and discuss tensions that arise from the conflicting task requirements and that pose a challenge for effective BI&A support. We also provide insights into tactics for managing these tensions and thus achieving ambidexterity. Additionally, we shed light on the relationship between ambidexterity and decision quality. Integrating the empirical findings from this research, we propose a theory of ambidexterity in decision support, which explains how such ambidexterity can be facilitated and how it affects decision outcomes. Finally, we discuss the study's implications for theory and practice.


title: Modelling Sentence Generation from Sum of Word Embedding Vectors as a Mixed Integer Programming Problem

abstract: Converting a sentence to a meaningful vector representation has uses in many NLP tasks, however very few methods allow that representation to be restored to a human readable sentence. Being able to generate sentences from the vector representations demonstrates the level of information maintained by the embedding representation – in this case a simple sum of word embeddings. We introduce such a method for moving from this vector representation back to the original sentences. This is done using a two stage process, first a greedy algorithm is utilised to convert the vector to a bag of words, and second a simple probabilistic language model is used to order the words to get back the sentence. To the best of our knowledge this is the first work to demonstrate quantitatively the ability to reproduce text from a large corpus based directly on its sentence embeddings.


title: PRE-FIGHT DETECTION Classification of Fighting Situations Using Heirachical AdaBoost

abstract: Abstract: This paper investigates the detection and classification of fighting and pre and post fighting events when viewed from a video camera. Specifically we investigate normal, pre, post and actual fighting sequences and classify them. A hierarchical AdaBoost classifier is described and results using this approach are pre- sented. We show it is possible to classify pre-fighting situations using such an approach and demonstrate how it can be used in the general case of continuous sequences.


IMPLEMENTING ACCOUNTING SOFTWARE IN SMALL BUSINESSES IN NEW ZEALAND : AN EXPLORATORY INVESTIGATION


title: A Framework of Semantic Recommender System for e-Learning.

abstract: With the rapid increasing of learning objects (LOs) in a variety of media formats, it becomes quite difficult and complicated task for learners to find suitable LOs based on their needs and preferences. To support personalization, recommender systems can be used to assist learners in finding the appropriate LOs which will be needed for their learning. In this paper, we propose a framework of a semantic recommender system for e-learning in which it will assist learners to find and select the relevant LOs to their field of interest. The proposed framework utilizes the intra and extra semantic relationships between LOs and the learner’s needs to provide personalized recommendations for learners. The semantic recommendation algorithm is based on the extension of the query keywords by using the semantic relations, concepts and reasoning means in the domain ontology. The proposed system can be used to reduce the time and effort involved in finding suitable LOs, and thus, improves the quality of learning.


title: Shared mental models: a conceptual analysis

abstract: The notion of a shared mental model is well known in the literature regarding team work among humans. It has been used to explain team functioning. The idea is that team performance improves if team members have a shared understanding of the task that is to be performed and of the involved team work. We maintain that the notion of shared mental model is not only highly relevant in the context of human teams, but also for teams of agents and for human-agent teams. However, before we can start investigating how to engineer agents on the basis of the notion of shared mental model, we first have to get a better understanding of the notion, which is the aim of this paper. We do this by investigating which concepts are relevant for shared mental models, and modeling how they are related by means of UML. Through this, we obtain a mental model ontology. Then, we formally define the notion of shared mental model and related notions. We illustrate our definitions by means of an example.


title: Broadband stripline to microstrip transition with constant impedance field matching section for applications in multilayer planar technologies

abstract: This paper presents a broadband Stripline-to-Microstrip transition with constant impedance section. In the transition the structural parameters of transmission line have been used to provide smooth electromagnetic field matching together with providing unchanged characteristic impedance along transition section. This solution eliminates the necessity of impedance transformer. The transition enables easy access to stripline circuits situated on the internal layers of multilayer structures by use of typical microstrip terminals. The transition has a simple structure for the ease of fabrication with low cost. The measured performance exhibits flat frequency response over broad bandwidth. The insertion loss is less than 0.5 dB and a return loss is better than 25 dB within frequency range 1 GHz to 10 GHz


title: Scalable Pixel based Visual Data Exploration

abstract: Pixel-based visualization techniques have proven to be of high value in visual data exploration, since mapping data points to pixels not only allows the analysis and visualization of large data sets, but also provides an intuitive way to convert raw data into a graphical form that often fosters new insights, encouraging the formation and validation of new hypotheses to the end of better problem solving and gaining deeper domain knowledge. But the ever increasing mass of information leads to new challenges on pixel-based techniques and concepts, since the volume, complexity and dynamic nature of today's scientific and commercial data sets are beyond the capability of many of current presentation techniques. Most existing pixel based approaches do not scale well on such large data sets as visual representation suffers from the high number of relevant data points, that might be even higher than the available monitor resolution and does therefore not allow a direct mapping of all data points to pixels on the display. In this paper we focuses on ways to increase the scalability of pixel based approaches by integrating relevance driven techniques into the visualization process. We provide first examples for effective scalable pixel based visualizations of financial- and geo-spatial data.


title: Popup: automatic paper architectures from 3D models

abstract: Paper architectures are 3D paper buildings created by folding and cutting. The creation process of paper architecture is often labor-intensive and highly skill-demanding, even with the aid of existing computer-aided design tools. We propose an automatic algorithm for generating paper architectures given a user-specified 3D model. The algorithm is grounded on geometric formulation of planar layout for paper architectures that can be popped-up in a rigid and stable manner, and sufficient conditions for a 3D surface to be popped-up from such a planar layout. Based on these conditions, our algorithm computes a class of paper architectures containing two sets of parallel patches that approximate the input geometry while guaranteed to be physically realizable. The method is demonstrated on a number of architectural examples, and physically engineered results are presented.


title: The effects of cannabidiol (CBD) on cognition and symptoms in outpatients with chronic schizophrenia a randomized placebo controlled trial

abstract: Rationale ::: Preliminary evidence suggests that cannabidiol (CBD) may be effective in the treatment of neurodegenerative disorders; however, CBD has never been evaluated for the treatment of cognitive impairments associated with schizophrenia (CIAS).


title: Sandnet: network traffic analysis of malicious software

abstract: Dynamic analysis of malware is widely used to obtain a better understanding of unknown software. While existing systems mainly focus on host-level activities of malware and limit the analysis period to a few minutes, we concentrate on the network behavior of malware over longer periods. We provide a comprehensive overview of typical malware network behavior by discussing the results that we obtained during the analysis of more than 100,000 malware samples. The resulting network behavior was dissected in our new analysis environment called Sandnet that complements existing systems by focusing on network traffic analysis. Our in-depth analysis of the two protocols that are most popular among malware authors, DNS and HTTP, helps to understand and characterize the usage of these prevalent protocols.


title: Distance metric learning and feature combination for shape-based 3D model retrieval

abstract: This paper proposes a 3D model retrieval algorithm that employs an unsupervised distance metric learning with a combination of appearance-based features; two sets of local visual features and a set of global features. These visual features are extracted from range images rendered from multiple viewpoints about the 3D model to be compared. The local visual features are bag-of-features histograms of a set of Scale Invariant Feature Transform (SIFT) features by Lowe [7] sampled at either salient or dense and random points. The global visual feature is also a SIFT feature sampled at an image center. The proposed method then uses an unsupervised distance metric learning based on the Manifold Ranking (MR) [15] to compute distances between these features. However, the original MR may not be effective when applied to a set of features having certain distance distribution. We propose an empirical method to adjust the distance profile so that the MR becomes effective. Experiments showed that the retrieval algorithm using a linear combination of distances computed from the proposed set of features by using the modified MR performed well across multiple benchmarks having different characteristics.


title: Amaze: A Multiplayer Computer Game

abstract: Amaze relies solely on the V kernel for point-to-point communication. The game's techniques could work in a general class of distributed applications.


title: Cross-supervised synthesis of web-crawlers

abstract: A web-crawler is a program that automatically and systematically tracks the links of a website and extracts information from its pages. Due to the different formats of websites, the crawling scheme for different sites can differ dramatically. Manually customizing a crawler for each specific site is time consuming and error-prone. Furthermore, because sites periodically change their format and presentation, crawling schemes have to be manually updated and adjusted. In this paper, we present a technique for automatic synthesis of web-crawlers from examples. The main idea is to use hand-crafted (possibly partial) crawlers for some websites as the basis for crawling other sites that contain the same kind of information. Technically, we use the data on one site to identify data on another site. We then use the identified data to learn the website structure and synthesize an appropriate extraction scheme. We iterate this process, as synthesized extraction schemes result in additional data to be used for re-learning the website structure. We implemented our approach and automatically synthesized 30 crawlers for websites from nine different categories: books, TVs, conferences, universities, cameras, phones, movies, songs, and hotels.


title: Big Data Analytics in Mobile Cellular Networks

abstract: Mobile cellular networks have become both the generators and carriers of massive data. Big data analytics can improve the performance of mobile cellular networks and maximize the revenue of operators. In this paper, we introduce a unified data model based on the random matrix theory and machine learning. Then, we present an architectural framework for applying the big data analytics in the mobile cellular networks. Moreover, we describe several illustrative examples, including big signaling data, big traffic data, big location data, big radio waveforms data, and big heterogeneous data, in mobile cellular networks. Finally, we discuss a number of open research challenges of the big data analytics in the mobile cellular networks.


title: Online Convolutional Dictionary Learning

abstract: While a number of different algorithms have recently been proposed for convolutional dictionary learning, this remains an expensive problem. The single biggest impediment to learning from large training sets is the memory requirements, which grow at least linearly with the size of the training set since all existing methods are batch algorithms. The work reported here addresses this limitation by extending online dictionary learning ideas to the convolutional context.


title: Neural correlates of long-term intense romantic love

abstract: The present study examined the neural correlates of long-term intense romantic love using functional magnetic resonance imaging (fMRI). Ten women and 7 men married an average of 21.4 years underwent fMRI while viewing facial images of their partner. Control images included a highly familiar acquaintance; a close, long-term friend; and a low-familiar person. Effects specific to the intensely loved, long-term partner were found in: (i) areas of the dopamine-rich reward and basal ganglia system, such as the ventral tegmental area (VTA) and dorsal striatum, consistent with results from early-stage romantic love studies; and (ii) several regions implicated in maternal attachment, such as the globus pallidus (GP), substantia nigra, Raphe nucleus, thalamus, insular cortex, anterior cingulate and posterior cingulate. Correlations of neural activity in regions of interest with widely used questionnaires showed: (i) VTA and caudate responses correlated with romantic love scores and inclusion of other in the self; (ii) GP responses correlated with friendship-based love scores; (iii) hypothalamus and posterior hippocampus responses correlated with sexual frequency; and (iv) caudate, septum/fornix, posterior cingulate and posterior hippocampus responses correlated with obsession. Overall, results suggest that for some individuals the reward-value associated with a long-term partner may be sustained, similar to new love, but also involves brain systems implicated in attachment and pair-bonding.


title: A real-time Java chip-multiprocessor

abstract: Chip-multiprocessors are an emerging trend for embedded systems. In this article, we introduce a real-time Java multiprocessor called JopCMP. It is a symmetric shared-memory multiprocessor, and consists of up to eight Java Optimized Processor (JOP) cores, an arbitration control device, and a shared memory. All components are interconnected via a system on chip bus. The arbiter synchronizes the access of multiple CPUs to the shared main memory. In this article, three different arbitration policies are presented, evaluated, and compared with respect to their real-time and average-case performance: a fixed priority, a fair-based, and a time-sliced arbiter. Tasks running on different CPUs of a chip-multiprocessor (CMP) influence each others' execution times when accessing a shared memory. Therefore, the system needs an arbiter that is able to limit the worst-case execution time of a task running on a CPU, even though tasks executing simultaneously on other CPUs access the main memory. Our research shows that timing analysis is in fact possible for homogeneous multiprocessor systems with a shared memory. The timing analysis of tasks, executing on the CMP using time-sliced memory arbitration, leads to viable worst-case execution time bounds. The time-sliced arbiter divides the memory access time into equal time slots, one time slot for each CPU. This memory arbitration scheme allows for a calculation of upper bounds of Java application worst-case execution times, depending on the number of CPUs, the time slot size, and the memory access time. Examples of worst-case execution time calculation are presented, and the analyzed results of a real-world application task are compared to measured execution time results. Finally, we evaluate the tradeoffs when using a time-predictable solution compared to using average-case optimized chip-multiprocessors, applying three different benchmarks. These experiments are carried out by executing the programs on the CMP prototype.


title: Tracking down performance variation against source code evolution

abstract: Little is known about how software performance evolves across software revisions. The severity of this situation is high since (i) most performance variations seem to happen accidentally and (ii) addressing a performance regression is challenging, especially when functional code is stacked on it. This paper reports an empirical study on the performance evolution of 19 applications, totaling over 19 MLOC. It took 52 days to run our 49 benchmarks. By relating performance variation with source code revisions, we found out that: (i) 1 out of every 3 application revisions introduces a performance variation, (ii) performance variations may be classified into 9 patterns, (iii) the most prominent cause of performance regression involves loops and collections. We carefully describe the patterns we identified, and detail how we addressed the numerous challenges we faced to complete our experiment.


title: Separating the polynomial-time hierarchy by oracles

abstract: We present exponential lower bounds on the size of depth-k Boolean circuits for computing certain functions. These results imply that there exists an oracle set A such that, relative to A, all the levels in the polynomial-time hierarchy are distinct, i.e., ΣkP,A is properly contained in Σk+1P,A for all k.


title: Design and experimental evaluation of the hydraulically actuated prototype leg of the HyQ robot

abstract: This paper focuses on the design and experimental evaluation of a hydraulically actuated robot leg. The evaluation of the leg prototype is an important milestone in the development of HyQ, a Hydraulically actuated Quadruped robot. The prototype features two rotary joints actuated by hydraulic cylinders and has a mass of 4.5kg. We performed several experiments with the leg prototype attached to a vertical slider to tests the robustness of the mechanical design and the hydraulic actuation system. Besides the experimental evaluation of the hydraulic components, we also extensively studied the sensor data of the leg during periodic hopping. The results show that hydraulic actuation is suitable for legged robots because of its high power-to-weight ratio, fast response and ability to cope with high impact force peaks. Furthermore, we compare the cylinder force data obtained by the load cell with the calculated value based on the cylinder pressures to analyze if it is possible to eliminate this sensory system redundancy in the future. Through these studies, weaknesses of the design were identified and suggestions on how to improve them are presented.


Process modeling


title: Understanding SaaS adoption from the perspective of organizational users: A tripod readiness model

abstract: We examine factors that influence how ready organizational users are to adopt SaaS.SaaS adoption depends on technological, organizational and environmental readiness.Survey study in China support the importance of each aspect of SaaS Readiness.Different aspects of SaaS Readiness vary in priorities at decision and use stages. As an innovation that revolutionizes application delivery based on cloud-computing, software-as-a-service (SaaS) has seen a tremendous growth during the last few years. However, its diffusion is not evenly distributed: some organizational users are open to SaaS but others are still hesitant despite the huge cost saving it may bring. The behavioral impacts of SaaS are far-reaching and the new socio-technical phenomenon deserves a close look. Based on the literature review, this study proposes a tripod model of SaaS Readiness that suggests that organizational users need to get prepared from technological, organizational and environmental aspects for the adoption of SaaS. The empirical results support that all three aspects are important for SaaS adoption yet their influences vary across psychological and overt outcome variables.


title: Adolescent Alcoholism and Drug Addiction: The Experience of Parents

abstract: Alcoholism and drug addiction have marked impacts on the ability of families to function. Much of the literature has been focused on adult members of a family who present with substance dependency. There is limited research into the effects of adolescent substance dependence on parenting and family functioning; little attention has been paid to the parents’ experience. This qualitative study looks at the parental perspective as they attempted to adapt and cope with substance dependency in their teenage children. The research looks into family life and adds to family functioning knowledge when the identified client is a youth as opposed to an adult family member. Thirty-one adult caregivers of 21 teenagers were interviewed, resulting in eight significant themes: (1) finding out about the substance dependence problem; (2) experiences as the problems escalated; (3) looking for explanations other than substance dependence; (4) connecting to the parent’s own history; (5) trying to cope; (6) challenges of getting help; (7) impact on siblings; and (8) choosing long-term rehabilitation. Implications of this research for clinical practice are discussed.


title: Retrieving landmark and non-landmark images from community photo collections

abstract: State of the art data mining and image retrieval in community photo collections typically focus on popular subsets, e.g. images containing landmarks or associated to Wikipedia articles. We propose an image clustering scheme that, seen as vector quantization compresses a large corpus of images by grouping visually consistent ones while providing a guaranteed distortion bound. This allows us, for instance, to represent the visual content of all thousands of images depicting the Parthenon in just a few dozens of scene maps and still be able to retrieve any single, isolated, non-landmark image like a house or graffiti on a wall. Starting from a geo-tagged dataset, we first group images geographically and then visually, where each visual cluster is assumed to depict different views of the the same scene. We align all views to one reference image and construct a 2D scene map by preserving details from all images while discarding repeating visual features. Our indexing, retrieval and spatial matching scheme then operates directly on scene maps. We evaluate the precision of the proposed method on a challenging one-million urban image dataset.


title: A common basis for modelling service-oriented and event-driven architecture

abstract: Component based approaches to Enterprise Architecture (EA) include Service Oriented Architecture (SOA) and Event Driven Architecture (EDA). Model-based approaches to EA support SOA in terms of components and services expressed as interfaces and messages. However, there are few modelbased approaches that support EDA even though SOA and EDA are both based on components. UML has components, however there is no support for events and no support for component patterns (or templates). This paper describes a simple extension to UML that supports both SOA and EDA. Components have both operation and event interfaces. The modelling language is implemented using a higher-order simulation language where templates are defined as functions over component definitions. The languages are described using a case study that has been implemented in Java.


title: Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts ∗

abstract: We present an ensemble method for concept drift that dynamically creates and removes weighted experts in response to changes in performance. The method, dynamic weighted majority (*DWM*), uses four mechanisms to cope with concept drift: It trains online learners of the ensemble, it weights those learners based on their performance, it removes them, also based on their performance, and it adds new experts based on the global performance of the ensemble. After an extensive evaluation---consisting of five experiments, eight learners, and thirty data sets that varied in type of target concept, size, presence of noise, and the like---we concluded that *DWM* outperformed other learners that only incrementally learn concept descriptions, that maintain and use previously encountered examples, and that employ an unweighted, fixed-size ensemble of experts.


title: Learning document structure for retrieval and classification

abstract: In this paper, we present a method for the retrieval of document images with chosen layout characteristics. The proposed method is based on statistics of patch-codewords over different regions of image. We begin with a set of wanted and a random set of unwanted images representative of a large heterogeneous collection. We then use raw-image patches extracted from the unlabeled images to learn a codebook. To model the spatial relationships between patches, the image is recursively partitioned horizontally and vertically, and a histogram of patch-codewords is computed in each partition. The resulting set of features give a high precision and recall for the retrieval of hand-drawn and machine-print table-documents, and unconstrained mixed form-type documents, when trained using a random forest classifier. We compare our method to the spatial-pyramid method, and show that the proposed approach for learning layout characteristics is competitive for document images.


title: Laminar Flow Activates Peroxisome Proliferator-Activated Receptor-γ in Vascular Endothelial Cells

abstract: Background— Steady laminar flow is atheroprotective, in part because of its antiinflammatory effects on vascular endothelial cells (ECs). We studied the activation of peroxisome proliferator-activated receptor-γ (PPARγ) in ECs in response to laminar flow and the associated antiinflammatory effect. Methods and Results— Using flow channel with cultured ECs, we found that laminar flow activated the PPARγ-mediated PPAR-responsive element (PPRE) activity and increased the mRNA encoding CD36, a PPARγ-targeted gene. Analysis of the CD36 promoter revealed that PPRE was required for flow activation. Laminar flow induced the GAL-PPARγ-LBD fusion protein, which suggests that flow activation of PPARγ was ligand dependent. The pharmaceutical inhibitors of phospholipase A2 (PLA2) and cytochrome P450 epoxygenases (CYP450s) were able to block the laminar flow-activated PPARγ. We also showed that lipid extracts from flow media contained ligands for the activation of PPARγ in other cell types. This paracrine activation exe...


title: Detection of collimation field in digital radiography using Frobenius norm of Hessian

abstract: Collimators are used to focus the radiation beam to a certain anatomical region in order to reduce the patient dose. Redundant shielded region should be removed before diagnostic image processing, as it can impair image enhancement algorithms. Manual selection of irradiated image region is impractical and should be automated. In this paper we propose a Frobenius norm of Hessian based algorithm for automatic collimation field detection. Algorithm validation was performed through, among others, maximum absolute difference between manually annotated and automatically determined collimation region. Our algorithm was tested on a database that contains 680 images and the percentage of correctly annotated regions is 96.03 %.


title: IoT device used for air pollution campaign to encourage cycling habit in inverleith neighborhood

abstract: This paper describes a collaboration research that have been accomplished together between Design Informatics Program at The University of Edinburgh and The City of Edinburgh Council (CEC) in Inverleith. CEC has several priority plans listed in 2014–2017 local community plan. One of that priorities is to improve cycling habit within the society. This project was intended to motivate people to do more cycling by creating awareness about air pollution issues. The utilization of Internet of Things (IoT devices), secondary data analysis and physical data visualization were methodology approaches used in this project. This paper explains workflow of the project, including data collection and analysis, participatory design workshop, field measurement and physical data visualization. Based on the analysis of data in this research, a series of physical installation was deployed as the outcome of this research.


title: Air-Writing Recognition—Part I: Modeling and Recognition of Characters, Words, and Connecting Motions

abstract: Air-writing refers to writing of linguistic characters or words in a free space by hand or finger movements. Air-writing differs from conventional handwriting; the latter contains the pen-up-pen-down motion, while the former lacks such a delimited sequence of writing events. We address air-writing recognition problems in a pair of companion papers. In Part I, recognition of characters or words is accomplished based on six-degree-of-freedom hand motion data. We address air-writing on two levels: motion characters and motion words. Isolated air-writing characters can be recognized similar to motion gestures although with increased sophistication and variability. For motion word recognition in which letters are connected and superimposed in the same virtual box in space, we build statistical models for words by concatenating clustered ligature models and individual letter models. A hidden Markov model is used for air-writing modeling and recognition. We show that motion data along dimensions beyond a 2-D trajectory can be beneficially discriminative for air-writing recognition. We investigate the relative effectiveness of various feature dimensions of optical and inertial tracking signals and report the attainable recognition performance correspondingly. The proposed system achieves a word error rate of 0.8% for word-based recognition and 1.9% for letter-based recognition. We also subjectively and objectively evaluate the effectiveness of air-writing and compare it with text input using a virtual keyboard. The words per minute of air-writing and virtual keyboard are 5.43 and 8.42, respectively.


title: Compact MIMO Antenna for Portable Devices in UWB Applications

abstract: A compact multiple-input-multiple-output (MIMO) antenna with a small size of 26×40 mm2 is proposed for portable ultrawideband (UWB) applications. The antenna consists of two planar-monopole (PM) antenna elements with microstrip-fed printed on one side of the substrate and placed perpendicularly to each other to achieve good isolation. To enhance isolation and increase impedance bandwidth, two long protruding ground stubs are added to the ground plane on the other side and a short ground strip is used to connect the ground planes of the two PMs together to form a common ground. Simulation and measurement are used to study the antenna performance in terms of reflection coefficients at the two input ports, coupling between the two input ports, radiation pattern, realized peak gain, efficiency and envelope correlation coefficient for pattern diversity. Results show that the MIMO antenna has an impedance bandwidth of larger than 3.1-10.6 GHz, low mutual coupling of less than -15 dB, and a low envelope correlation coefficient of less than 0.2 across the frequency band, making it a good candidate for portable UWB applications.


title: A Dual-Source Approach for 3D Human Pose Estimation from a Single Image

abstract: In this work we address the challenging problem of 3D human pose estimation from single images. Recent approaches learn deep neural networks to regress 3D pose directly from images. One major challenge for such methods, however, is the collection of training data. Specifically, collecting large amounts of training data containing unconstrained images annotated with accurate 3D poses is infeasible. We therefore propose to use two independent training sources. The first source consists of accurate 3D motion capture data, and the second source consists of unconstrained images with annotated 2D poses. To integrate both sources, we propose a dual-source approach that combines 2D pose estimation with efficient 3D pose retrieval. To this end, we first convert the motion capture data into a normalized 2D pose space, and separately learn a 2D pose estimation model from the image data. During inference, we estimate the 2D pose and efficiently retrieve the nearest 3D poses. We then jointly estimate a mapping from the 3D pose space to the image and reconstruct the 3D pose. We provide a comprehensive evaluation of the proposed method and experimentally demonstrate the effectiveness of our approach, even when the skeleton structures of the two sources differ substantially.


title: The CMU Pose, Illumination, and Expression (PIE) database

abstract: Between October 2000 and December 2000, we collected a database of over 40,000 facial images of 68 people. Using the CMU (Carnegie Mellon University) 3D Room, we imaged each person across 13 different poses, under 43 different illumination conditions, and with four different expressions. We call this database the CMU Pose, Illumination and Expression (PIE) database. In this paper, we describe the imaging hardware, the collection procedure, the organization of the database, several potential uses of the database, and how to obtain the database.


title: String similarity measures and joins with synonyms

abstract: A string similarity measure quantifies the similarity between two text strings for approximate string matching or comparison. For example, the strings "Sam" and "Samuel" can be considered similar. Most existing work that computes the similarity of two strings only considers syntactic similarities, e.g., number of common words or q-grams. While these are indeed indicators of similarity, there are many important cases where syntactically different strings can represent the same real-world object. For example, "Bill" is a short form of "William". Given a collection of predefined synonyms, the purpose of the paper is to explore such existing knowledge to evaluate string similarity measures more effectively and efficiently, thereby boosting the quality of string matching. In particular, we first present an expansion-based framework to measure string similarities efficiently while considering synonyms. Because using synonyms in similarity measures is, while expressive, computationally expensive (NP-hard), we propose an efficient algorithm, called selective-expansion, which guarantees the optimality in many real scenarios. We then study a novel indexing structure called SI-tree, which combines both signature and length filtering strategies, for efficient string similarity joins with synonyms. We develop an estimator to approximate the size of candidates to enable an online selection of signature filters to further improve the efficiency. This estimator provides strong low-error, high-confidence guarantees while requiring only logarithmic space and time costs, thus making our method attractive both in theory and in practice. Finally, the results from an empirical study of the algorithms verify the effectiveness and efficiency of our approach.


title: Medical Image Segmentation Using New Hybrid Level-Set Method

abstract: In this paper, a new hybrid medical image segmentation method in the level-set framework is proposed. The method uses both the objectpsilas boundary and region information to achieve robust and accurate segmentation results. The boundary information can help to detect the precise location of the target object and the region information can help to prevent the boundary leakage problem. Experimental results on a synthetic image as well as real 2D and 3D medical images are also shown in the paper with the emphasis on the comparisons between the new hybrid method and the Chan-Vese method.


title: Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

abstract: In this work, we propose to apply trust region optimization to deep reinforcement learning using a recently proposed Kronecker-factored approximation to the curvature. We extend the framework of natural policy gradient and propose to optimize both the actor and the critic using Kronecker-factored approximate curvature (K-FAC) with trust region; hence we call our method Actor Critic using Kronecker-Factored Trust Region (ACKTR). To the best of our knowledge, this is the first scalable trust region natural gradient method for actor-critic methods. It is also a method that learns non-trivial tasks in continuous control as well as discrete control policies directly from raw pixel inputs. We tested our approach across discrete domains in Atari games as well as continuous domains in the MuJoCo environment. With the proposed methods, we are able to achieve higher rewards and a 2- to 3-fold improvement in sample efficiency on average, compared to previous state-of-the-art on-policy actor-critic methods. Code is available at this https URL


title: Assessment method of object-oriented programming languages for web development

abstract: The article describes an approach to evaluating the effectiveness of modern technologies for web development. Performance of different languages (C++, C#, JavaScript, PHP) is analyzed in the context of their suitability for the web. The method for determination of technology efficiency is considered in view of its cost, performance, size of community, the learning complexity and hardware requirements to the server. The validity of performance indicators is substantiated by real popularity of considered technologies. Author regards the prospects of server-side JavaScript with Node.js platform for web development.


title: Biomechanical analysis of the effect of congruence, depth and radius on the stability ratio of a simplistic ‘ball-and-socket’ joint model

abstract: OBJECTIVES ::: The bony shoulder stability ratio (BSSR) allows for quantification of the bony stabilisers in vivo. We aimed to biomechanically validate the BSSR, determine whether joint incongruence affects the stability ratio (SR) of a shoulder model, and determine the correct parameters (glenoid concavity versus humeral head radius) for calculation of the BSSR in vivo. ::: ::: ::: METHODS ::: Four polyethylene balls (radii: 19.1 mm to 38.1 mm) were used to mould four fitting sockets in four different depths (3.2 mm to 19.1mm). The SR was measured in biomechanical congruent and incongruent experimental series. The experimental SR of a congruent system was compared with the calculated SR based on the BSSR approach. Differences in SR between congruent and incongruent experimental conditions were quantified. Finally, the experimental SR was compared with either calculated SR based on the socket concavity or plastic ball radius. ::: ::: ::: RESULTS ::: The experimental SR is comparable with the calculated SR (mean difference 10%, sd 8%; relative values). The experimental incongruence study observed almost no differences (2%, sd 2%). The calculated SR on the basis of the socket concavity radius is superior in predicting the experimental SR (mean difference 10%, sd 9%) compared with the calculated SR based on the plastic ball radius (mean difference 42%, sd 55%). ::: ::: ::: CONCLUSION ::: The present biomechanical investigation confirmed the validity of the BSSR. Incongruence has no significant effect on the SR of a shoulder model. In the event of an incongruent system, the calculation of the BSSR on the basis of the glenoid concavity radius is recommended.Cite this article: L. Ernstbrunner, J-D. Werthel, T. Hatta, A. R. Thoreson, H. Resch, K-N. An, P. Moroder. Biomechanical analysis of the effect of congruence, depth and radius on the stability ratio of a simplistic 'ball-and-socket' joint model. Bone Joint Res 2016;5:453-460. DOI: 10.1302/2046-3758.510.BJR-2016-0078.R1.


title: Eye-Gaze Tracking Method Driven by Raspberry PI Applicable in Automotive Traffic Safety

abstract: This paper comes as a response to the fact that, lately, more and more accidents are caused by people who fall asleep at the wheel. Eye tracking is one of the most important aspects in driver assistance systems since human eyes hold much in-formation regarding the driver's state, like attention level, gaze and fatigue level. The number of times the subject blinks will be taken into account for identification of the subject's drowsiness. Also the direction of where the user is looking will be estimated according to the location of the user's eye gaze. The developed algorithm was implemented on a Raspberry Pi board in order to create a portable system. The main determination of this project is to conceive an active eyetracking based system, which focuses on the drowsiness detection amongst fatigue related deficiencies in driving.


title: Breathe easy: Model and control of human respiration for computer animation

abstract: In this paper, we detail an anatomically inspired, physically based model of the human torso designed for the visual simulation of respiration using a mixed system of rigid and deformable parts. Motion related to breath is a signature movement of the human body and an indicator for life but it has been largely overlooked by the graphics community. A novel composition of biological components is necessary to capture the key characteristics of breathing motion visible in the human trunk because the movement is generated fundamentally through the combination of both rigid bone and soft tissue. Our approach uses a simple physically based muscle element which is used throughout to drive the motion of the ribs and diaphragm as well as in other muscles, like those of the abdomen, to produce passive resistance. In addition, we describe an implementation of a straightforward method for preserving incompressible volume in deformable bodies to use in approximating the motion of the abdomen related to breath. Through the careful construction of this anatomically based torso, control for respiration becomes the generation of periodic contraction signals for a minimal set of two muscle groups. We show the flexibility of our approach through the animation of several breathing styles using our system.


Information Asset Valuation Method for Information Technology Security Risk Assessment


Vulvar melanoma with urethral invasion and bladder metastases – a case report and review of the literature


title: Consequences of changing biodiversity

abstract: Human alteration of the global environment has triggered the sixth major extinction event in the history of life and caused widespread changes in the global distribution of organisms. These changes in biodiversity alter ecosystem processes and change the resilience of ecosystems to environmental change. This has profound consequences for services that humans derive from ecosystems. The large ecological and societal consequences of changing biodiversity should be minimized to preserve options for future solutions to global environmental problems.


title: An accident prediction approach based on XGBoost

abstract: As an important threat to public security, urban fire accident causes huge economic loss and catastrophic collapse. Predicting and analyzing the interior rule of urban fire accident from its appearance needed to be solved in the field. In this paper, we propose a new urban fire accident prediction approach based on XGBoost. The method determines the predictive indexes in a quantitative and qualitative way from different characteristics in various kinds of fire accidents. For screening the features we need, we adopt the feature selection algorithm based on association rules. For data cleaning, we use a method based on Box-Cox transformation that transforms the continual response variables from the feature space for removing the dependencies on unobservable errors and the predictor variable to some extent. Then we use the data to train the model based on XGBoost to obtain the best prediction accuracy. Experiments show that the method provides a feasible solution to urban fire accident prediction. The method contributes to improving the public security situation, we have added the method and related model to the City in a box™, Shenzhen Aerospace Smart City System Technology Co., Ltd.


title: Why do i keep interrupting myself?: environment, habit and self-interruption

abstract: Self-interruptions account for a significant portion of task switching in information-centric work contexts. However, most of the research to date has focused on understanding, analyzing and designing for external interruptions. The causes of self-interruptions are not well understood. In this paper we present an analysis of 889 hours of observed task switching behavior from 36 individuals across three high-technology information work organizations. Our analysis suggests that self-interruption is a function of organizational environment and individual differences, but also external interruptions experienced. We find that people in open office environments interrupt themselves at a higher rate. We also find that people are significantly more likely to interrupt themselves to return to solitary work associated with central working spheres, suggesting that self-interruption occurs largely as a function of prospective memory events. The research presented contributes substantially to our understanding of attention and multitasking in context.


A Genetic Algorithm Approach to Dynamic Job Shop Scheduling Problem


title: An Efficiency-Optimal Control Method for Mono-Inverter Dual-PMSM Systems

abstract: Current research and development programs in aviation are promoting new technological advances to make electrical systems cheaper, lighter, and more reliable. Mono-inverter dual-permanent magnet synchronous motor (MIDPMSM) systems tend to share the static converters in electro-mechanical systems by connecting motors in parallel. However several studies have shown that the efficiency of this configuration is lower than those with a traditional Mono-inverter mono-permanent magnet synchronous motor (PMSM) configuration, especially when unequal load torques are applied. In this paper, we propose an efficiency-optimized control method for MIDPMSM systems, which has been designed using rigorous, mathematical calculations. The determinacy, stability, and optimal efficiency of the system, which are essential in an aviation application, are explained in detail. This new control structure is simply making current single PMSM systems easy to upgrade to support the MIDPMSM system. The experiment has demonstrated not only feasibility, but also significant improvements in performance.


title: Architecting systems-of-systems and their constituents: A case study applying Industry 4.0 in the construction domain

abstract: The development of system‐of‐systems (SoS) requires a continuous interplay between design decisions on the SoS level and those on the level of its constituent systems (CS), which often preexist and ...


title: A Stackelberg Game Approach Toward Socially-Aware Incentive Mechanisms for Mobile Crowdsensing

abstract: Mobile crowdsensing has shown great potential in addressing large-scale data sensing problems by allocating sensing tasks to pervasive mobile users. The mobile users will participate in a crowdsensing platform if they can receive a satisfactory reward. In this paper, to effectively and efficiently recruit a sufficient number of mobile users, i.e., participants, we investigate an optimal incentive mechanism of a crowdsensing service provider. We apply a two-stage Stackelberg game to analyze the participation level of the mobile users and the optimal incentive mechanism of the crowdsensing service provider using backward induction. In order to motivate the participants, the incentive mechanism is designed by taking into account the social network effects from the underlying mobile social domain. We derive the analytical expressions for the discriminatory incentive as well as the uniform incentive mechanisms. To fit into practical scenarios, we further formulate a Bayesian Stackelberg game with incomplete information to analyze the interaction between the crowdsensing service provider and mobile users, where the social structure information, i.e., the social network effects, is uncertain. The existence and uniqueness of the Bayesian Stackelberg equilibrium is analytically validated by identifying the best response strategies of the mobile users. The numerical results corroborate the fact that the network effects significantly stimulate a higher mobile participation level and greater revenue for the crowdsensing service provider. In addition, the social structure information helps the crowdsensing service provider achieve greater revenue gain.


title: Cannabidiol as a Therapeutic Alternative for Post-traumatic Stress Disorder: From Bench Research to Confirmation in Human Trials

abstract: Post-traumatic stress disorder (PTSD) is characterized by poor adaptation to a traumatic experience. This disorder affects approximately 10% of people at some point in life. Current pharmacological therapies for PTSD have been shown to be inefficient and produce considerable side effects. Since the discovery of the involvement of the endocannabinoid (eCB) system in emotional memory processing, pharmacological manipulation of eCB signaling has become a therapeutic possibility for the treatment of PTSD. Cannabidiol (CBD), a phytocannabinoid constituent of Cannabis sativa without the psychoactive effects of Δ9-tetrahydrocannabinol, has gained particular attention. Preclinical studies in different rodent behavioral models have shown that CBD can both facilitate the extinction of aversive memories and block their reconsolidation, possibly through potentialization of the eCB system. These results, combined with the currently available pharmacological treatments for PTSD being limited, necessitated testing CBD use with the same therapeutic purpose in humans as well. Indeed, as observed in rodents, recent studies have confirmed the ability of CBD to alter important aspects of aversive memories in humans and promote significant improvements in the symptomatology of PTSD. The goal of this review was to highlight the potential of CBD as a treatment for disorders related to inappropriate retention of aversive memories, by assessing evidence from preclinical to human experimental studies.


title: Automatic Medical Concept Extraction from Free Text Clinical Reports, a New Named Entity Recognition Approach

abstract: Actually in the Hospital Information Systems, there is a wide range of clinical information representation from the Electronic Health Records (EHR), and most of the information contained in clinical reports is written in natural language free text. In this context, we are researching the problem of automatic clinical named entities recognition from free text clinical reports. We are using Snomed-CT (Systematized Nomenclature of Medicine – Clinical Terms) as dictionary to identify all kind of clinical concepts, and thus the problem we are considering is to map each clinical entity named in a free text report with its Snomed-CT unique ID. More in general, we are developed a new approach for the named entity recognition (NER) problem in specific domains, and we have applied it to recognize clinical concepts in free text clinical reports. In our approach we apply two types of NER approaches, dictionary-based and machine learning-based. We use a specific domain dictionary-based gazetteer (using Snomed-CT to get the standard clinical code for the clinical concept), and the main approach that we introduce is using a unsupervised shallow learning neural network, word2vec from Mikolov et al., to represent words as vectors, and then making the recognition based on the distance between candidates and dictionary terms. We have applied our approach on a Dataset with 318.585 clinical reports in Spanish from the emergency service of the Hospital “Rafael Mendez” from Lorca (Murcia) Spain, and preliminary results are encouraging.


title: Improvements in Muscle Symmetry in Children with Cerebral Palsy After Equine-Assisted Therapy (Hippotherapy)

abstract: Objective: To evaluate the effect of hippotherapy (physical therapy utilizing the movement of a horse) on muscle activity in children with spastic cerebral palsy. Design: Pretest/post-test control group. Setting/location: Therapeutic Riding of Tucson (TROT), Tucson, AZ. Subjects: Fifteen (15) children ranging from 4 to 12 years of age diagnosed with spastic cerebral palsy. Interventions: Children meeting inclusion criteria were randomized to either 8 minutes of hippotherapy or 8 minutes astride a stationary barrel. Outcome measures: Remote surface electromyography (EMG) was used to measure muscle activity of the trunk and upper legs during sitting, standing, and walking tasks before and after each intervention. Results: After hippotherapy, significant improvement in symmetry of muscle activity was noted in those muscle groups displaying the highest asymmetry prior to hippotherapy. No significant change was noted after sitting astride a barrel. Conclusions: Eight minutes of hippotherapy, but not stationary...


title: Triangulating Social Multimedia Content for Event Localization using Flickr and Twitter

abstract: © 2014 John Wiley & Sons Ltd. The analysis of social media content for the extraction of geospatial information and event-related knowledge has recently received substantial attention. In this article we present an approach that leverages the complementary nature of social multimedia content by utilizing heterogeneous sources of social media feeds to assess the impact area of a natural disaster. More specifically, we introduce a novel social multimedia triangulation process that uses both Twitter and Flickr content in an integrated two-step process: Twitter content is used to identify toponym references associated with a disaster; this information is then used to provide approximate orientation for the associated Flickr imagery, allowing us to delineate the impact area as the overlap of multiple view footprints. In this approach, we practically crowdsource approximate orientations from Twitter content and use this information to orient Flickr imagery accordingly and identify the impact area through viewshed analysis and viewpoint integration. This approach enables us to avoid computationally intensive image analysis tasks associated with traditional image orientation, while allowing us to triangulate numerous images by having them pointed towards the crowdsourced toponym location. The article presents our approach and demonstrates its performance using a real-world wildfire event as a representative application case study.


title: Classification of Four-Class Motor Imagery Employing Single-Channel Electroencephalography

abstract: With advances in brain-computer interface (BCI) research, a portable few- or single-channel BCI system has become necessary. Most recent BCI studies have demonstrated that the common spatial pattern (CSP) algorithm is a powerful tool in extracting features for multiple-class motor imagery. However, since the CSP algorithm requires multi-channel information, it is not suitable for a few- or single-channel system. In this study, we applied a short-time Fourier transform to decompose a single-channel electroencephalography signal into the time-frequency domain and construct multi-channel information. Using the reconstructed data, the CSP was combined with a support vector machine to obtain high classification accuracies from channels of both the sensorimotor and forehead areas. These results suggest that motor imagery can be detected with a single channel not only from the traditional sensorimotor area but also from the forehead area.


title: Ensemble of Data-Driven Prognostic Algorithms With Weight Optimization and K-Fold Cross Validation

abstract: The traditional data-driven prognostic approach is to construct multiple candidate algorithms using a training data set, evaluate their respective performance using a testing data set, and select the one with the best performance while discarding all the others. This approach has three shortcomings: (i) the selected standalone algorithm may not be robust, i.e., it may be less accurate when the real data acquired after the deployment differs from the testing data; (ii) it wastes the resources for constructing the algorithms that are discarded in the deployment; (iii) it requires the testing data in addition to the training data, which increases the overall expenses for the algorithm selection. To overcome these drawbacks, this paper proposes an ensemble data-driven prognostic approach which combines multiple member algorithms with a weightedsum formulation. Three weighting schemes, namely, the accuracy-based weighting, diversity-based weighting and optimization-based weighting, are proposed to determine the weights of member algorithms for data-driven prognostics. The k-fold cross validation (CV) is employed to estimate the prediction error required by the weighting schemes. Two case studies were employed to demonstrate the effectiveness of the proposed prognostic approach. The results suggest that the ensemble approach with any weighting scheme gives more accurate RUL predictions compared to any sole algorithm and that the optimization-based weighting scheme gives the best overall performance among the three weighting schemes. †


abstract: Compassion Fatigue as a Threat to Ethical Practice: Identification, Personal and Workplace Prevention/Management Strategies Vicki Lachman; MEDSURG Nursing


title: Attribute Couplet Attacks and Privacy Preservation in Social Networks

abstract: The emerging of social networks, e.g., Facebook, Twitter, and Instagram, has eventually changed the way in which we live. Social networks are acquiring and storing a significant amount of profile information and daily activities of over billions of active users. The data sets, drawn from the social networks, are becoming great sources for exploring and attracting huge interests from different research communities. However, publishing social network data sets have also raised serious security and privacy risks; it is highly likely that they will be targeted by the hackers due to their substantially commercial values. To deal with the problem of privacy leakage, a number of attack models and corresponding privacy preserving solutions have been proposed recently. A common approach is to anonymize the identities of the users when publishing the data sets while we argue that the remaining relationship is sufficient to identify an anonymized user. In this paper, we define a new type of attack as attribute couplet attack . The attribute couplet attack facilitates the relationship of a couplet of anonymous nodes (i.e., a pair of users) and some limited background information to unveil the protected identities. To achieve privacy-preservation under attribute couplet attacks, we propose a new anonymity concept as k-couplet anonymity. A social network data set satisfies the k-couplet anonymity if, for any pair of nodes, there exist at least the other $k-1$ couplets sharing the same attributes. Then, we design and implement two heuristic algorithms to promote the k-couplet anonymity. Furthermore, we design an approximate algorithm for multiple-attribute social networks to realize the k-couplet anonymity. The evaluation results on multiple data sets demonstrate that the privacy and utility of the social network data sets can be well preserved, when incorporating the proposed k-couplet anonymity and the associate heuristic algorithms.


title: Generating Query Suggestions to Support Task-Based Search

abstract: We address the problem of generating query suggestions to support users in completing their underlying tasks (which motivated them to search in the first place). Given an initial query, these query suggestions should provide a coverage of possible subtasks the user might be looking for. We propose a probabilistic modeling framework that obtains keyphrases from multiple sources and generates query suggestions from these keyphrases. Using the test suites of the TREC Tasks track, we evaluate and analyze each component of our model.


title: TrustWalker: a random walk model for combining trust-based and item-based recommendation

abstract: Collaborative filtering is the most popular approach to build recommender systems and has been successfully employed in many applications. However, it cannot make recommendations for so-called cold start users that have rated only a very small number of items. In addition, these methods do not know how confident they are in their recommendations. Trust-based recommendation methods assume the additional knowledge of a trust network among users and can better deal with cold start users, since users only need to be simply connected to the trust network. On the other hand, the sparsity of the user item ratings forces the trust-based approach to consider ratings of indirect neighbors that are only weakly trusted, which may decrease its precision. In order to find a good trade-off, we propose a random walk model combining the trust-based and the collaborative filtering approach for recommendation. The random walk model allows us to define and to measure the confidence of a recommendation. We performed an evaluation on the Epinions dataset and compared our model with existing trust-based and collaborative filtering methods.


title: Breast lesions detection in digital mammography: An automated pre-diagnosis

abstract: Breast cancer is one of the most common causes of death in the female population worldwide and one of the most prevalent cancers among other types of cancer. An early and adequate diagnosis is a key factor for an appropriate treatment, increasing the probability of survival. In order to enhance the efficiency and effectiveness of a diagnosis, an image analysis system was implemented; its purpose was to provide support for radiologists in detection of lesions from mammograms. Image segmentation techniques were carried out to find breast lesions within the mammograms in the region of interest (ROI), which is related to the area where breast density is concentrated. Breast density is defined as the brightest part on the mammographic image and it is composed by glandular and adipose tissue where breast lesions are likely to be exposed. This study provides a methodology divided in two main segmentation techniques: 1) a region growing technique and 2) split and merge technique. This study also gives a complete description of image analysis and the tools used in it.


title: Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks

abstract: Deep convolutional neural networks (CNNs) are widely used in modern AI systems for their superior accuracy but at the cost of high computational complexity. The complexity comes from the need to simultaneously process hundreds of filters and channels in the high-dimensional convolutions, which involve a significant amount of data movement. Although highly-parallel compute paradigms, such as SIMD/SIMT, effectively address the computation requirement to achieve high throughput, energy consumption still remains high as data movement can be more expensive than computation. Accordingly, finding a dataflow that supports parallel processing with minimal data movement cost is crucial to achieving energy-efficient CNN processing without compromising accuracy. In this paper, we present a novel dataflow, called row-stationary (RS), that minimizes data movement energy consumption on a spatial architecture. This is realized by exploiting local data reuse of filter weights and feature map pixels, i.e., activations, in the high-dimensional convolutions, and minimizing data movement of partial sum accumulations. Unlike dataflows used in existing designs, which only reduce certain types of data movement, the proposed RS dataflow can adapt to different CNN shape configurations and reduces all types of data movement through maximally utilizing the processing engine (PE) local storage, direct inter-PE communication and spatial parallelism. To evaluate the energy efficiency of the different dataflows, we propose an analysis framework that compares energy cost under the same hardware area and processing parallelism constraints. Experiments using the CNN configurations of AlexNet show that the proposed RS dataflow is more energy efficient than existing dataflows in both convolutional (1.4× to 2.5×) and fully-connected layers (at least 1.3× for batch size larger than 16). The RS dataflow has also been demonstrated on a fabricated chip, which verifies our energy analysis.


A S ] 8 M ay 2 01 8 Capsule Networks for Low Resource Spoken Language Understanding


title: Efficient end-to-end learning for quantizable representations

abstract: Embedding representation learning via neural networks is at the core foundation of modern similarity based search. While much effort has been put in developing algorithms for learning binary hamming code representations for search efficiency, this still requires a linear scan of the entire dataset per each query and trades off the search accuracy through binarization. To this end, we consider the problem of directly learning a quantizable embedding representation and the sparse binary hash code end-to-end which can be used to construct an efficient hash table not only providing significant search reduction in the number of data but also achieving the state of the art search accuracy outperforming previous state of the art deep metric learning methods. We also show that finding the optimal sparse binary hash code in a mini-batch can be computed exactly in polynomial time by solving a minimum cost flow problem. Our results on Cifar-100 and on ImageNet datasets show the state of the art search accuracy in precision@k and NMI metrics while providing up to 98X and 478X search speedup respectively over exhaustive linear search. The source code is available at https://github.com/maestrojeong/Deep-Hash-Table-ICML18


title: A Novel, Simple Interpretation of Nesterov's Accelerated Method as a Combination of Gradient and Mirror Descent

abstract: First-order methods play a central role in large-scale convex optimization. Despite their various forms of descriptions and many applications, such methods mostly and fundamentally rely on two basic types of analyses: gradient-descent analysis, which yields primal progress, and mirror-descent analysis, which yields dual progress. In this paper, we observe that the performances of these two analyses are complementary, so that faster algorithms can be designed by coupling the two analyses, and their corresponding descent steps. In particular, we show in this paper how to obtain a conceptually simple reinterpretation of Nesterov’s accelerated gradient method [Nes83, Nes04, Nes05]. Nesterov’s method is the optimal first-order method for the class of smooth convex optimization problems. However, the proof of the fast convergence of Nesterov’s method has no clear interpretation and is regarded by some as relying on an ‘algebraic trick’. We apply our novel insights to express Nesterov’s algorithm as a coupling of gradient descent and mirror descent, and as a result, the convergence proof can be understood as some natural combination of the two underlying convergence analyses. We believe that this complementary view of the two types of analysis may not only facilitate the study of Nesterov’s method in a white-box manner so as to apply it to problems outside its original scope, but also let us design better first-order methods in a conceptually easier way.


title: Design and development of a portable virtual reality headset

abstract: This paper describes the design and the development of hardware and software of a portable Virtual Reality Headset. The portability of the device is achieved using a small credit-card sized, yet a powerful computer, Raspberry Pi®. It is responsible for the execution of the main software and display of the rendered images on the screen. The Inertial Measurement Unit (IMU) present in the device tracks the user's head movements and communicates with Raspberry Pi using the Arduino® platform. This technology focuses on developing a dedicated hardware platform for the Virtual Reality purposes. Throughout the design phase of the project, cost was kept minimal without compromising on the performance of the system.


title: Efficient exploration with Double Uncertain Value Networks

abstract: This paper studies directed exploration for reinforcement learning agents by tracking uncertainty about the value of each available action. We identify two sources of uncertainty that are relevant for exploration. The first originates from limited data (parametric uncertainty), while the second originates from the distribution of the returns (return uncertainty). We identify methods to learn these distributions with deep neural networks, where we estimate parametric uncertainty with Bayesian drop-out, while return uncertainty is propagated through the Bellman equation as a Gaussian distribution. Then, we identify that both can be jointly estimated in one network, which we call the Double Uncertain Value Network. The policy is directly derived from the learned distributions based on Thompson sampling. Experimental results show that both types of uncertainty may vastly improve learning in domains with a strong exploration challenge.


A Comb Method to Render ECC Resistant against Side Chiannel Attacks,” http://eprint.iacr.org/2004/342.pdf


title: CloudWatcher: Network security monitoring using OpenFlow in dynamic cloud networks (or: How to provide security monitoring as a service in clouds?)

abstract: Cloud computing is becoming a popular paradigm. Many recent new services are based on cloud environments, and a lot of people are using cloud networks. Since many diverse hosts and network configurations coexist in a cloud network, it is essential to protect each of them in the cloud network from threats. To do this, basically, we can employ existing network security devices, but applying them to a cloud network requires more considerations for its complexity, dynamism, and diversity. In this paper, we propose a new framework, CloudWatcher, which provides monitoring services for large and dynamic cloud networks. This framework automatically detours network packets to be inspected by pre-installed network security devices. In addition, all these operations can be implemented by writing a simple policy script, thus, a cloud network administrator is able to protect his cloud network easily. We have implemented the proposed framework, and evaluated it on different test network environments.


title: Situational Awareness for security adaptation in Industrial Control Systems

abstract: Situational Awareness (SA) offers an analysed view of system's security posture. Securing Industrial Control Systems (ICSs) and critical infrastructures requires timely and correct SA. System administrators make decisions and modify security mechanisms based on SA information. In this paper, we envision how security adaptation can facilitate administrators' work in the ICS protection. Security adaptation is not widely applied in ICS context. Moreover, existing security adaptation approaches concentrate on recognition of an adaptation need, i.e,. building situational awareness, instead of security decision making. Therefore, we present steps to create a security adaptation plan, and apply fuzzy set theory and linguistic relations for decision making, when SA information indicates that required security is not reached.


title: Asymmetric boosting

abstract: A cost-sensitive extension of boosting, denoted as asymmetric boosting, is presented. Unlike previous proposals, the new algorithm is derived from sound decision-theoretic principles, which exploit the statistical interpretation of boosting to determine a principled extension of the boosting loss. Similarly to AdaBoost, the cost-sensitive extension minimizes this loss by gradient descent on the functional space of convex combinations of weak learners, and produces large margin detectors. It is shown that asymmetric boosting is fully compatible with AdaBoost, in the sense that it becomes the latter when errors are weighted equally. Experimental evidence is provided to demonstrate the claims of cost-sensitivity and large margin. The algorithm is also applied to the computer vision problem of face detection, where it is shown to outperform a number of previous heuristic proposals for cost-sensitive boosting (AdaCost, CSB0, CSB1, CSB2, asymmetric-AdaBoost, AdaC1, AdaC2 and AdaC3).


title: Forward osmosis for concentration of anaerobic digester centrate

abstract: The nutrient-rich liquid stream produced during the dewatering of digested biomass (i.e., the centrate) is commonly mixed with the influent raw wastewater at wastewater treatment facilities. This increases the nitrogen and phosphorus loading on biological processes, increases operating costs, and in some cases, results in increased nutrient concentrations in the final effluent. Forward osmosis (FO) is a membrane treatment process that was investigated at bench scale to determine its feasibility to concentrate centrate under both batch and continuous operating conditions. The continuous bench-scale system used FO as pretreatment for reverse osmosis (RO). Results demonstrated that high water flux and high nutrient rejection could be achieved. The combined FO/RO process exhibited sustainable flux over an extended time period. A mathematical model was developed in order to determine the specific energy, power, and membrane area requirements for a larger-scale centrate treatment process. Modeling results indicated that to optimize power and membrane area requirements, the system should be operated at approximately 70% water recovery.


title: Mining StackOverflow to filter out off-topic IRC discussion

abstract: Internet Relay Chat (IRC) is a commonly used tool by Open Source developers. Developers use IRC channels to discuss programming related problems, but much of the discussion is irrelevant and off-topic. Essentially if we treat IRC discussions like email messages, and apply spam filtering, we can try to filter out the spam (the off-topic discussions) from the ham (the programming discussions). Yet we need labelled data that unfortunately takes time to curate. To avoid costly cur ration in order to filter out off-topic discussions, we need positive and negative data-sources. On-line discussion forums, such as Stack Overflow, are very effective for solving programming problems. By engaging in open-data, Stack Overflow data becomes a powerful source of labelled text regarding programming. This work shows that we can train classifiers using Stack Overflow posts as positive examples of on-topic programming discussion. You Tube video comments, notorious for their lack of quality, serve as training set of off-topic discussion. By exploiting these datasets, accurate classifiers can be built, tested and evaluated that require very little effort for end-users to deploy and exploit.


title: High-Availability at Massive Scale: Building Google’s Data Infrastructure for Ads

abstract: Google’s Ads Data Infrastructure systems run the multi-billion dollar ads business at Google. High availability and strong consistency are critical for these systems. While most distributed systems handle machine-level failures well, handling datacenter-level failures is less common. In our experience, handling datacenter-level failures is critical for running true high availability systems. Most of our systems (e.g. Photon, F1, Mesa) now support multi-homing as a fundamental design property. Multi-homed systems run live in multiple datacenters all the time, adaptively moving load between datacenters, with the ability to handle outages of any scale completely transparently.


title: Investigating the effects of encumbrance on one- and two- handed interactions with mobile devices

abstract: In this paper, we investigate the effects of encumbrance (carrying typical objects such as shopping bags during interaction) and walking on target acquisition on a touchscreen mobile phone. Users often hold objects and use mobile devices at the same time and we examined the impact encumbrance has on one- and two- handed interactions. Three common input postures were evaluated: two-handed index finger, one-handed preferred thumb and two-handed both thumbs, to assess the effects on performance of carrying a bag in each hand while walking. The results showed a significant decrease in targeting performance when users were encumbered. For example, input accuracy dropped to 48.1% for targeting with the index finger when encumbered, while targeting error using the preferred thumb to input was 4.2mm, an increase of 40% compared to unencumbered input. We also introduce a new method to evaluate the user's preferred walking speed when interacting - PWS&I, and suggest future studies should use this to get a more accurate measure of the user's input performance.


title: Bee-honey and yogurt: a novel mixture for treating patients with vulvovaginal candidiasis during pregnancy

abstract: OBJECTIVE ::: To evaluate the clinical and mycological cure rates of a novel mixture consisting of Bee-honey and yogurt compared to local antifungal agents for treating patients with vulvo-vaginal candidiasis (VVC) during pregnancy. ::: ::: ::: MATERIALS AND METHODS ::: This is a prospective comparative study which included 129 patients with VVC during pregnancy. The participants were allocated into study group (n = 82) who received a mixture of Bee-honey and yogurt vaginally and control group (n = 47) who received local anti-fungal agents. The Chi-square test was used to evaluate the clinical and mycological cure rates and the side-effects of both modes of therapy. ::: ::: ::: RESULTS ::: The clinical cure rate was significantly higher in the study than the control group (87.8 vs. 72.3%, respectively) while the mycological cure rate was higher in the control than the study group (91.5 vs. 76.9%, respectively). Both types of therapy were favorably tolerated by most of the patients. Side effects were reported only in 24.3 and 29.7% of patients in group I and II, respectively (p < 0.05). ::: ::: ::: CONCLUSIONS ::: The mixture of Bee-honey and yogurt produced a high clinical cure rate and a reasonable mycological cure rate. It can be used as a complementary or an alternative to antifungal agents especially in patients with VVC during pregnancy.


title: Implementation of Brain Breaks® in the Classroom and Effects on Attitudes toward Physical Activity in a Macedonian School Setting

abstract: The purpose of this study was to explore the effects of Brain Break® activities on interest and motivation for physical activity among schoolchildren and the contribution of such activities on learning for health and holistic development. The study sample was comprised of 283 participants, primary school students from 3rd to 5th grades from two public schools in the Republic of Macedonia. Six experimental and six control groups were included in the study. Interventions in classroom settings—based Brain Break® video exercises were introduced in the experimental group during a period of three months. Students’ attitudes toward physical activity were tested using a self-report survey instrument entitled “Attitudes toward Physical Activity Scale (APAS)” before and after intervention. Applied factor analyses were completed and the results of these analysis support APAS validity and the successful use of this application in the measurement of the learning experience, self-awareness, self-efficacy, and self-confidence in developing physical fitness. Learning was enhanced by using video exercises. Information presented in this paper is meaningful for the promotion of better exercise habits and the holistic approach to better health by using personal motivation and motivation provided by others. The results from repeated ANCOVA suggest positive effects of the applied Brain Break® video exercises as an interventional program. The study confirms the effect of application of Brain Break® video exercises on children’s attitudes for physical activity, motivation for PA, internalization of movement habits as personal good.


title: Optimization of photovoltaic farm under partial shading effects using artificial intelligent based matrix switch controller

abstract: Partial shading on photovoltaic array due to weather conditions reduces the energy generated and worsening the entire photovoltaic farm efficiency. The purpose of this research was to electric physically reconflgurate PV string through switch matrix by control of smart controller. It is great to find current and voltage at TCT point approaching work of range from MPPT, despite being awarded shading effect.


title: A Cyber-Physical Experimentation Environment for the Security Analysis of Networked Industrial Control Systems

abstract: Although many studies address the security of Networked Industrial Control Systems (NICSs), today we still lack an efficient way to conduct scientific experiments that measure the impact of attacks against both the physical and the cyber parts of these systems. This paper presents an innovative framework for an experimentation environment that can reproduce concurrently physical and cyber systems. The proposed approach uses an emulation testbed based on Emulab to recreate cyber components and a real-time simulator, based on Simulink, to recreate physical processes. The main novelty of the proposed framework is that it provides a set of experimental capabilities that are missing from other approaches, e.g. safe experimentation with real malware, flexibility to use different physical processes. The feasibility of the approach is confirmed by the development of a fully functional prototype, while its applicability is proven through two case studies of industrial systems from the electrical and chemical domain.


title: CapStones and ZebraWidgets: sensing stacks of building blocks, dials and sliders on capacitive touch screens

abstract: Recent research proposes augmenting capacitive touch pads with tangible objects, enabling a new generation of mobile applications enhanced with tangible objects, such as game pieces and tangible controllers. In this paper, we extend the concept to capacitive tangibles consisting of multiple parts, such as stackable gaming pieces and tangible widgets with moving parts. We achieve this using a system of wires and connectors inside each block that causes the capacitance of the bottom-most block to reflect the entire assembly. We demonstrate three types of tangibles, called CapStones, Zebra Dials and Zebra Sliders that work with current consumer hardware and investigate what designs may become possible as touchscreen hardware evolves.


title: Better streaming algorithms for clustering problems

abstract: We study clustering problems in the streaming model, where the goal is to cluster a set of points by making one pass (or a few passes) over the data using a small amount of storage space. Our main result is a randomized algorithm for the k--Median problem which produces a constant factor approximation in one pass using storage space O(k poly log n). This is a significant improvement of the previous best algorithm which yielded a 2O(1/e) approximation using O(ne) space. Next we give a streaming algorithm for the k--Median problem with an arbitrary distance function. We also study algorithms for clustering problems with outliers in the streaming model. Here, we give bicriterion guarantees, producing constant factor approximations by increasing the allowed fraction of outliers slightly.


title: Polygonal chain intersection

abstract: Abstract In order to find all intersections among polygonal chains, this paper presents a procedure consisting of two phases: (1) splitting polygonal chains into a minimal number of monotone chains and (2) finding intersections among the monotone chains. For the first phase, we suggest an optimal algorithm splitting polygonal chains into a minimal number of monotone chains, with an O( n+r log r) time complexity, where n is the number of line-segments in the polygonal chains and r is the number of C-subchains. For the second phase, we extend Bentley–Ottmann's sweep-line algorithm and the time complexity is O(( n+k )log m) , where k is the number of intersections, and m is the minimal number of monotone chains.


title: Combining belief propagation and successive cancellation list decoding of polar codes on a GPU platform

abstract: The decoding performance of polar codes strongly depends on the decoding algorithm used, while also the decoder throughput and its latency mainly depend on the decoding algorithm. In this work, we implement the powerful successive cancellation list (SCL) decoder on a GPU and identify the bottlenecks of this algorithm with respect to parallel computing and its difficulties. The inherent serial decoding property of the SCL algorithm naturally limits the achievable speed-up gains on GPUs when compared to CPU implementations. In order to increase the decoding throughput, we use a hybrid decoding scheme based on the belief propagation (BP) decoder, which can be intra and inter-frame parallelized. The proposed scheme combines excellent decoding performance and high throughput within the signal-to-noise ratio (SNR) region of interest.


Life Skills in Occupational Therapy


title: Real-time acoustic source localization in noisy environments for human-robot multimodal interaction

abstract: Interaction between humans involves a plethora of sensory information, both in the form of explicit communication as well as more subtle unconsciously perceived signals. In order to enable natural human-robot interaction, robots will have to acquire the skills to detect and meaningfully integrate information from multiple modalities. In this article, we focus on sound localization in the context of a multi-sensory humanoid robot that combines audio and video information to yield natural and intuitive responses to human behavior, such as directed eye-head movements towards natural stimuli. We highlight four common sound source localization algorithms and compare their performance and advantages for real-time interaction. We also briefly introduce an integrated distributed control framework called DVC, where additional modalities such as speech recognition, visual tracking, or object recognition can easily be integrated. We further describe the way the sound localization module has been integrated in our humanoid robot, CB.


title: Assessing mental health issues on college campuses: preliminary findings from a pilot study

abstract: A significant fraction of college students suffer from serious mental health issues including depression, anxiety, self-harm and suicidal thought. The prevalence and severity of these issues among college students also appear to increase over time. However, most of these issues often remain undiagnosed, and as a result, untreated. One of the main reasons of this gap between illness and treatment results from the lack of reliable data over time. While health care services in college campuses have been focusing on detection of illness onset and appropriate interventions, their tools are mostly manual surveys which often fail to capture the granular details of contexts and behaviors which might provide important cues about illness onset. To overcome the limitations of these manual tools, we deployed a smartphone based tool or unobtrusive and continuous data collection from 22 students during an academic semester. In this paper, we present the preliminary findings from our study about assessing mental health on college campuses using passively sensed smartphone data.