title: Beauty and Brains: Detecting Anomalous Pattern Co-Occurrences

abstract: Our world is filled with both beautiful and brainy people, but how often does a Nobel Prize winner also wins a beauty pageant? Let us assume that someone who is both very beautiful and very smart is more rare than what we would expect from the combination of the number of beautiful and brainy people. Of course there will still always be some individuals that defy this stereotype; these beautiful brainy people are exactly the class of anomaly we focus on in this paper. They do not posses intrinsically rare qualities, it is the unexpected combination of factors that makes them stand out. ::: In this paper we define the above described class of anomaly and propose a method to quickly identify them in transaction data. Further, as we take a pattern set based approach, our method readily explains why a transaction is anomalous. The effectiveness of our method is thoroughly verified with a wide range of experiments on both real world and synthetic data.


title: NISP: Pruning Networks Using Neuron Importance Score Propagation

abstract: To reduce the significant redundancy in deep Convolutional Neural Networks (CNNs), most existing methods prune neurons by only considering statistics of an individual layer or two consecutive layers (e.g., prune one layer to minimize the reconstruction error of the next layer), ignoring the effect of error propagation in deep networks. In contrast, we argue that it is essential to prune neurons in the entire neuron network jointly based on a unified goal: minimizing the reconstruction error of important responses in the "final response layer" (FRL), which is the second-to-last layer before classification, for a pruned network to retrain its predictive power. Specifically, we apply feature ranking techniques to measure the importance of each neuron in the FRL, and formulate network pruning as a binary integer optimization problem and derive a closed-form solution to it for pruning neurons in earlier layers. Based on our theoretical analysis, we propose the Neuron Importance Score Propagation (NISP) algorithm to propagate the importance scores of final responses to every neuron in the network. The CNN is pruned by removing neurons with least importance, and then fine-tuned to retain its predictive power. NISP is evaluated on several datasets with multiple CNN models and demonstrated to achieve significant acceleration and compression with negligible accuracy loss.


title: The possibility of predicting learning performance using features of note taking activities and instructions in a blended learning environment

abstract: A formative assessment was introduced to a blended learning course in order to predict participant’s learning performance using measurements of their note taking activity and metrics of their attitudes. The lexical metrics were created by analyzing the contents of student’s notes from every class, and by measuring their characteristics. In the results of two types of regression analysis of these measurements, features of note taking activities were a significant source of information for predicting the scores of final exams. In regards to temporal changes in prediction performance during the course, the progress of learning and the typical changes in features of note taking activity were discussed. During the analyses, the effectiveness of the lecturer’s note taking instructions was also confirmed.


title: Effectiveness of shelterbelt with a non-uniform density distribution

abstract: Abstract The ecological project of shelterbelts in oasis–desert ecotone is a main measure to wind–sand damage, which is one of the most serious eco-environment problems in arid regions. The effectiveness of shelterbelts with a non-uniform density distribution, planted perpendicular to the prevailing wind direction, was studied through field observations. The results showed that the main factor influencing wind speed is the density distribution of a particular shelterbelt of a specifically given tree specie. In this study, the shelter effects of a sparse shelterbelt was compared with a dense shelterbelt and it corresponded with most previous reports, namely that dense shelterbelts resulted in a higher wind reduction whilst the sparse shelterbelts have a longer shelter distance at the leeward side of the belt. For shelterbelts with a non-uniform density distribution and wind blowing from high density to low density, wind speed changes will be similar to that of a high-density shelterbelt at both the windward and leeward sides of the shelterbelt. However, when the wind blows from the opposite direction, the flow fields change greatly as compared to an evenly distributed shelterbelt. A combined effect of density manifested, thus providing better shelter effects by balancing the reduction in wind speed with the shelter distance.


title: Specific reading disability (dyslexia): what have we learned in the past four decades?

abstract: We summarize some of the most important findings from research evaluating the hypothesized causes of specific reading disability (dyslexia) over the past four decades. After outlining components of reading ability, we discuss manifest causes of reading difficulties, in terms of deficiencies in component reading skills that might lead to such difficulties. The evidence suggests that inadequate facility in word identification due, in most cases, to more basic deficits in alphabetic coding is the basic cause of difficulties in learning to read. We next discuss hypothesized deficiencies in readingrelated cognitive abilities as underlying causes of deficiencies in component reading skills. The evidence in these areas suggests that, in most cases, phonological skills deficiencies associated with phonological coding deficits are the probable causes of the disorder rather than visual, semantic, or syntactic deficits, although reading difficulties in some children may be associated with general language deficits. Hypothesized deficits in general learning abilities (e.g., attention, association learning, cross-modal transfer etc.) and low-level sensory deficits have weak validity as causal factors in specific reading disability. These inferences are, by and large, supported by research evaluating the biological foundations of dyslexia. Finally, evidence is presented in support of the idea that many poor readers are impaired because of inadequate instruction or other experiential factors. This does not mean that biological factors are not relevant, because the brain and environment interact to produce the neural networks that support reading acquisition. We conclude with a discussion of the clinical implications of the research findings, focusing on the need for enhanced instruction.


title: Anaerobic biodegradation of sugar beet pulp

abstract: Sugar beet pulp is a by-product of sugar production and consists mainly of cellulose, hemicellulose and pectin. Its composition is suitable for biological degradation. A possible alternative for the utilization of this material (besides cattle feeding) can be anaerobic methanogenic degradation. It has an additional advantage--biogas production. Beet pulp was treated by a two-step anaerobic process. The first step consisted of hydrolysis and acidification. The second step was methanogenesis. In this paper, observation of the process of anaerobic degradation and determination of optimal parameters is discussed. A laboratory-scale model for sugar beet pulp anaerobic biodegradation was operated. Results of model performance have shown very good pulp digestion characteristics. In addition, high efficiency removal of organic matter was achieved. Methane yield was over 0.360 m3 kg(-1) dried pulp and excess sludge production was 0.094 g per gram COD added.


An Attention-based Regression Model for Grounding Textual Phrases in Images


title: Privacy Leakage in Mobile Online Social Networks

abstract: Mobile Online Social Networks (mOSNs) have recently grown in popularity. With the ubiquitous use of mobile devices and a rapid shift of technology and access to OSNs, it is important to examine the impact of mobile OSNs from a privacy standpoint. We present a taxonomy of ways to study privacy leakage and report on the current status of known leakages. We find that all mOSNs in our study exhibit some leakage of private information to third parties. Novel concerns include combination of new features unique to mobile access with the leakage in OSNs that we had examined earlier.


title: Self-efficacy in information security: Its influence on end users' information security practice behavior

abstract: The ultimate success of information security depends on appropriate information security practice behaviors by the end users. Based on social cognitive theory, this study models and tests relationships among self-efficacy in information security, security practice behavior and motivation to strengthen security efforts. This study also explores antecedents to individuals' self-efficacy beliefs in information security. Results provide support for the many hypothesized relationships. This study provides an initial step toward understanding of the applicability of social cognitive theory in a new domain of information security. The results suggest that simply listing what not to do and penalties associated with a wrong doing in the users' information security policy alone will have a limited impact on effective implementation of security measures. The findings may help information security professionals design security awareness programs that more effectively increase the self-efficacy in information security.


title: A CNN Based Transfer Learning Model for Automatic Activity Recognition from Accelerometer Sensors

abstract: Accelerometers are become ubiquitous and available in several devices such as smartphones, smartwaches, fitness trackers, and wearable devices. Accelerometers are increasingly used to monitor human activities of daily living in different contexts such as monitoring activities of persons with cognitive deficits in smart homes, and monitoring physical and fitness activities. Activity recognition is the most important core component in monitoring applications. Activity recognition algorithms require substantial amount of labeled data to produce satisfactory results under diverse circumstances. Several methods have been proposed for activity recognition from accelerometer data. However, very little work has been done on identifying connections and relationships between existing labeled datasets to perform transfer learning for new datasets. In this paper, we investigate deep learning based transfer learning algorithm based on convolutional neural networks (CNNs) that takes advantage of learned representations of activities of daily living from one dataset to recognize these activities in different other datasets characterized by different features including sensor modality, sampling rate, activity duration and environment. We experimentally validated our proposed algorithm on several existing datasets and demonstrated its performance and suitability for activity recognition.


title: Non-local RoIs for Instance Segmentation

abstract: We introduce the concept of Non-Local RoI (NL-RoI) Block as a generic and flexible module that can be seamlessly adapted into different Mask R-CNN heads for various tasks. Mask R-CNN treats RoIs (Regions of Interest) independently and performs the prediction based on individual object bounding boxes. However, the correlation between objects may provide useful information for detection and segmentation. The proposed NL-RoI Block enables each RoI to refer to all other RoIs' information, and results in a simple, low-cost but effective module. Our experimental results show that generalizations with NL-RoI Blocks can improve the performance of Mask R-CNN for instance segmentation on the Robust Vision Challenge benchmarks.


title: A Quality by design (QbD) approach on starch-based nanocapsules: A promising platform for topical drug delivery.

abstract: Exploring novel applications for approved excipients with a history of safe use in therapeutics is a smart strategy to obtain improved pharmaceutical products. The present study aimed at developing a novel starch-based nanoparticulate carrier system (StNC) for topical delivery of lipophilic bioactive molecules. The role of the different factors that affect the particle size distribution and zeta potential of StNC prepared by the emulsification-solvent evaporation method was assessed using a quality by design approach. An optimal formulation was selected and fully characterized in terms of molecular interactions (DSC and FTIR), morphology (TEM and AFM), as well as in vitro and in vivo biological properties, including biological sensitivity/irritation studies performed in human volunteers. Results show the surfactant and lipid contents play a major role in StNC particle size distribution. In addition, all tested formulations presented a zeta potential of ca. +33.6±6.7 mV, indicating a good physical stability, while revealing an excellent compromise between stability, safety and cosmeticity, evidencing that StNC are suitable nanocarriers for topical use. Finally, the design planning methodology has clearly shown its usefulness for optimizing the formulation, being also crucial for the understanding of StNC formation process. The StNC proved to be a promising formulation strategy and a potential nanocarrier for topical lipophilic bioactive molecules.


title: Personality Traits and Music Genres: What Do People Prefer to Listen To?

abstract: Personality-based personalized systems are increasingly gaining interest as personality traits has been shown to be a stable construct within humans. In order to provide a personality-based experience to the user, users' behavior, preferences, and needs in relation to their personality need to be investigated. Although for a technological mediated environment the search for these relationships is often new territory, there are findings from personality research of the real world that can be used in personalized systems. However, for these findings to be implementable, we need to investigate whether they hold in a technologically mediated environment. In this study we assess prior work on personality-based music genre preferences from traditional personality research. We analyzed a dataset consisting of music listening histories and personality scores of 1415 Last.fm users. Our results show agreements with prior work, but also important differences that can help to inform personalized systems.


title: NRGcoin: Virtual currency for trading of renewable energy in smart grids

abstract: In this paper we introduce a new decentralized digital currency, called NRGcoin. Prosumers in the smart grid trade locally produced renewable energy using NRGcoins, the value of which is determined on an open currency exchange market. Similar to Bitcoins, this currency offers numerous advantages over fiat currency, but unlike Bitcoins it is generated by injecting energy into the grid, rather than spending energy on computational power. In addition, we propose a novel trading paradigm for buying and selling green energy in the smart grid. Our mechanism achieves demand response by providing incentives to prosumers to balance their production and consumption out of their own self-interest. We study the advantages of our proposed currency over traditional money and environmental instruments, and explore its benefits for all parties in the smart grid.


title: Event-centric search and exploration in document collections

abstract: Textual data ranging from corpora of digitized historic documents to large collections of news feeds provide a rich source for temporal and geographic information. Such types of information have recently gained a lot of interest in support of different search and exploration tasks, e.g., by organizing news along a timeline or placing the origin of documents on a map. However, for this, temporal and geographic information embedded in documents is often considered in isolation. We claim that through combining such information into (chronologically ordered) event-like features interesting and meaningful search and exploration tasks are possible. In this paper, we present a framework for the extraction, exploration, and visualization of event information in document collections. For this, one has to identify and combine temporal and geographic expressions from documents, thus enriching a document collection by a set of normalized events. Traditional search queries then can be enriched by conditions on the events relevant to the search subject. Most important for our event-centric approach is that a search result consists of a sequence of events relevant to the search terms and not just a document hit-list. Such events can originate from different documents and can be further explored, in particular events relevant to a search query can be ordered chronologically. We demonstrate the utility of our framework by different (multilingual) search and exploration scenarios using a Wikipedia corpus.


title: Explaining ERP failure in a developing country: a Jordanian case study

abstract: Purpose – Enterprise resource planning (ERP) systems are increasingly being adopted by organisations in developing countries. As in industrialised countries, this adoption seems beset by significant rates of failure, leading to a large waste of investment and other resources. This paper seeks to understand why such ERP failure occurs.Design/methodology/approach – The paper moves beyond factor lists to make use of an overall “design‐reality gap” model. The model is applied to a case study of ERP failure in a Jordanian manufacturing firm, analysing the situation both before and during ERP implementation through a mix of interviews, observation and document analysis.Findings – The research finds sizeable gaps between the assumptions and requirements built into ERP system design, and the actual realities of the client organisation. It is these gaps – and the failure to close them during implementation – that underlie ERP project failure.Research limitations/implications – This study shows the relevance and ap...


title: Movement Skill Assessment of Typically Developing Preschool Children: A Review of Seven Movement Skill Assessment Tools

abstract: The importance of movement is often overlooked because it is such a natural part of human life. It is, however, crucial for a child’s physical, cognitive and social development. In addition, experiences support learning and development of fundamental movement skills. The foundations of those skills are laid in early childhood and essential to encourage a physically active lifestyle. Fundamental movement skill performance can be examined with several assessment tools. The choice of a test will depend on the context in which the assessment is planned. This article compares seven assessment tools which are often referred to in European or international context. It discusses the tools’ usefulness for the assessment of movement skill development in general population samples. After a brief description of each assessment tool the article focuses on contents, reliability, validity and normative data. A conclusion outline of strengths and weaknesses of all reviewed assessment tools focusing on their use in educational research settings is provided and stresses the importance of regular data collection of fundamental movement skill development among preschool children. ::: ::: ::: ::: Key points ::: ::: ::: This review discusses seven movement skill assessment tool’s test content, reliability, validity and normative samples. ::: ::: ::: The seven assessment tools all showed to be of great value. Strengths and weaknesses indicate that test choice will depend on specific purpose of test use. ::: ::: ::: Further data collection should also include larger data samples of able bodied preschool children. ::: ::: ::: Admitting PE specialists in assessment of fundamental movement skill performance among preschool children is recommended. ::: ::: ::: The assessment tool’s normative data samples would benefit from frequent movement skill performance follow-up of today’s children. ::: ::: ::: ::: ::: ::: ::: ::: Abbreviations ::: MOT 4-6: Motoriktest fur vier- bis sechsjahrige Kinder, ::: ::: M-ABC: Movement Assessment Battery for Children, PDMS: Peabody Development Scales, ::: ::: KTK: Korper-Koordinationtest fur Kinder, ::: ::: TGDM: Test of Gross Motor Development, ::: ::: MMT: Maastrichtse Motoriektest, ::: ::: BOTMP: Bruininks-Oseretsky Test of Motor Proficiency. ICC: intraclass correlation coefficient, ::: ::: NR: not reported, ::: ::: GM: gross motor, ::: ::: LV: long version, ::: ::: SV: short version, ::: ::: LF: long form, ::: ::: SF: short form, ::: ::: STV: subtest version, ::: ::: SEMs: standard errors of measurement, ::: ::: TMQ: Total Motor Quotient, ::: ::: TMC: Total Motor Composite, ::: ::: CSSA: Comprehensive Scales of Student Abilities ::: ::: MSEL: Mullen Scales of Early learning: AGS Edition ::: ::: AUC: Areas under curve ::: ::: BC: Battery composite ::: ::: ROC: Receiver operating characteristic


title: ISeeCube: visual analysis of gaze data for video

abstract: We introduce a new design for the visual analysis of eye tracking data recorded from dynamic stimuli such as video. ISeeCube includes multiple coordinated views to support different aspects of various analysis tasks. It combines methods for the spatiotemporal analysis of gaze data recorded from unlabeled videos as well as the possibility to annotate and investigate dynamic Areas of Interest (AOIs). A static overview of the complete data set is provided by a space-time cube visualization that shows gaze points with density-based color mapping and spatiotemporal clustering of the data. A timeline visualization supports the analysis of dynamic AOIs and the viewers' attention on them. AOI-based scanpaths of different viewers can be clustered by their Levenshtein distance, an attention map, or the transitions between AOIs. With the provided visual analytics techniques, the exploration of eye tracking data recorded from several viewers is supported for a wide range of analysis tasks.


title: Deblurring Face Images with Exemplars

abstract: The human face is one of the most interesting subjects involved in numerous applications. Significant progress has been made towards the image deblurring problem, however, existing generic deblurring methods are not able to achieve satisfying results on blurry face images. The success of the state-of-the-art image deblurring methods stems mainly from implicit or explicit restoration of salient edges for kernel estimation. When there is not much texture in the blurry image (e.g., face images), existing methods are less effective as only few edges can be used for kernel estimation. Moreover, recent methods are usually jeopardized by selecting ambiguous edges, which are imaged from the same edge of the object after blur, for kernel estimation due to local edge selection strategies. In this paper, we address these problems of deblurring face images by exploiting facial structures. We propose a maximum a posteriori (MAP) deblurring algorithm based on an exemplar dataset, without using the coarse-to-fine strategy or ad-hoc edge selections. Extensive evaluations against state-of-the-art methods demonstrate the effectiveness of the proposed algorithm for deblurring face images. We also show the extendability of our method to other specific deblurring tasks.


title: A Generalized Tree Matching Algorithm Considering Nested Lists for Web Data Extraction

abstract: This paper studies structured data extraction from Web pages. One of the effective methods is tree matching, which can detect template patterns from web pages used for extraction. However, one major limitation of existing tree matching algorithms is their inability to deal with embedded lists with repeated patterns. In the Web context, lists are everywhere, e.g., lists of products, jobs and publications. Due to the fact that lists in trees may have different lengths, the match score of the trees can be very low although they follow exactly the same template pattern. To make the matter worse, a list can have nested lists in it at any level. To solve this problem, existing research uses various heuristics to detect candidate lists first and then applies tree matching to generate data extraction patterns. This paper proposes a generalized tree matching algorithm by extending an existing tree matching algorithm with the ability to handle nested lists through a novel grammar generation algorithm. To the best of our knowledge, this is the first tree matching algorithm that is able to consider lists. In addition, it is well-known that there are two problem formulations for Web data extraction: (1) pattern generation based on multiple pages following the same template, and (2) pattern generation based on a single page containing lists of data instances following the same templates (each list may use a different template). These two problems are currently solved using different algorithms. The proposed (single) algorithm is able to solve both problems effectively. Extensive experiments show that the new algorithm outperforms the state-of-the-art existing systems for both problems considerably.


title: The Effect of Collective Attention on Controversial Debates on Social Media

abstract: We study the evolution of long-lived controversial debates as manifested on Twitter from 2011 to 2016. Specifically, we explore how the structure of interactions and content of discussion varies with the level of collective attention, as evidenced by the number of users discussing a topic. Spikes in the volume of users typically correspond to external events that increase the public attention on the topic - as, for instance, discussions about 'gun control' often erupt after a mass shooting. This work is the first to study the dynamic evolution of polarized online debates at such scale. By employing a wide array of network and content analysis measures, we find consistent evidence that increased collective attention is associated with increased network polarization and network concentration within each side of the debate; and overall more uniform lexicon usage across all users.


title: Approximation Algorithms for Degree-Constrained Minimum-Cost Network-Design Problems

abstract: We study network-design problems with two different design objectives: the total cost of the edges and nodes in the network and the maximum degree of any node in the network. A prototypical example is the degree-constrained node-weighted Steiner tree problem: We are given an undirected graph G(V,E) , with a nonnegative integral function d that specifies an upper bound d(v) on the degree of each vertex v ∈ V in the Steiner tree to be constructed, nonnegative costs on the nodes, and a subset of k nodes called terminals. The goal is to construct a Steiner tree T containing all the terminals such that the degree of any node v in T is at most the specified upper bound d(v) and the total cost of the nodes in T is minimum. Our main result is a bicriteria approximation algorithm whose output is approximate in terms of both the degree and cost criteria—the degree of any node v ∈ V in the output Steiner tree is O(d(v) log k) and the cost of the tree is O(log k) times that of a minimum-cost Steiner tree that obeys the degree bound d(v) for each node v . Our result extends to the more general problem of constructing one-connected networks such as generalized Steiner forests. We also consider the special case in which the edge costs obey the triangle inequality and present simple approximation algorithms with better performance guarantees.


title: Learning saliency-based visual attention: A review

abstract: Humans and other primates shift their gaze to allocate processing resources to a subset of the visual input. Understanding and emulating the way that human observers free-view a natural scene has both scientific and economic impact. It has therefore attracted the attention from researchers in a wide range of science and engineering disciplines. With the ever increasing computational power, machine learning has become a popular tool to mine human data in the exploration of how people direct their gaze when inspecting a visual scene. This paper reviews recent advances in learning saliency-based visual attention and discusses several key issues in this topic.


title: Android ION Hazard: the Curse of Customizable Memory Management System

abstract: ION is a unified memory management interface for Android that is widely used on virtually all ARM based Android devices. ION attempts to achieve several ambitious goals that have not been simultaneously achieved before (not even on Linux). Different from managing regular memory in the system, ION is designed to share and manage memory with special constraints, e.g., physically contiguous memory. Despite the great flexibility and performance benefits offered, such a critical subsystem, as we discover, unfortunately has flawed security assumptions and designs. In this paper, we systematically analyze ION related vulnerabilities from conceptual root causes to detailed implementation decisions. Since ION is often customized heavily for different Android devices, the specific vulnerabilities often manifest themselves differently. By conducting a range of runtime testing as well as static analysis, we are able to uncover a large number of serious vulnerabilities on the latest Android devices (e.g., Nexus 6P running Android 6.0 and 7.0 preview) such as denial-of-service and dumping memory from the system and arbitrary applications (e.g., email content, passwords). Finally, we offer suggestions on how to redesign the ION subsystem to eliminate these flaws. We believe that the lessons learned can help guide the future design of similar memory management subsystems.


title: PWM Plus Secondary-Side Phase-Shift Controlled Soft-Switching Full-Bridge Three-Port Converter for Renewable Power Systems

abstract: In this paper, a pulsewidth modulation (PWM) plus secondary-side phase-shift (SSPS) controlled full-bridge three-port converter (FB-TPC) is proposed for a renewable power system, which is derived by integrating two interleaved buck/boost circuits (IBBC) and a SSPS full-bridge converter (FBC), to interface the energy source, energy storage element, and output load simultaneously. The two switching legs are shared by the IBBC and FBC, which improves the active devices sharing ratio and power density. With PWM plus SSPS control scheme, decoupled power control can be realized and the converter can achieve wide input voltage range power conversion. In addition, zero-voltage switching (ZVS) of both the primary and secondary switches are achieved in a wide range. Furthermore, the circulating current can be greatly eliminated, which helps to reduce conduction losses. Moreover, the voltage stresses on the secondary rectifier are effectively suppressed since the power switches and diodes are clamped to output voltage. The operation principles, output characteristics, ZVS performances, and control strategy are analyzed in detail. Experimental results of a 600-W prototype are provided to verify the effectiveness and the advantages of the proposed converter.


title: Recent advances in DNA assembly technologies

abstract: DNA assembly is one of the most important foundational technologies for synthetic biology and metabolic engineering. Since the development of the restriction digestion and ligation method in the early 1970s, a significant amount of effort has been devoted to developing better DNA assembly methods with higher efficiency, fidelity, and modularity, as well as simpler and faster protocols. This review will not only summarize the key DNA assembly methods and their recent applications, but also highlight the innovations in assembly schemes and the challenges in automating the DNA assembly methods.


title: Evaluation Framework for IEEE 802.15.4 and IEEE 802.11 for Smart Cities

abstract: Jemula802 is a simulator developed by Disney Research for the standard IEEE 802.11, more specifically 802.11a/g. Jemula802 is used for simulations on scenarios related with Theme Park deployments. This paper describes the progress of the design and implementation of the IEEE 802.15.4 over Jemula. Jemula802.15.4-adaptation transforms Jemula802 (simulator of IEEE 802.11 standard) into the IEEE 802.15.4 standard simulator. IEEE 802.15.4, standard for low-rate wireless personal area networks (LR-WPANs) that specifies the physical layer and medium access control that is the base for upper layer protocols like Zigbee, MiWi and WirelessHART. Thereby, Jemula is offering a simulator and emulation framework to evaluate and compare deployments based on IEEE 802.11 (WiFi) and IEEE 802.15.4 (Wireless Sensor Networks). In addition, Jemula offers the visualization libraries for theme parks that can be also applied for smart cities. Therefore, Jemula can be considered as a evaluation framework for IEEE 802.15.4 and IEEE 802.11 for Smart Cities.


title: Survey of Spatio-Temporal Databases

abstract: Spatio-temporal databases aim to support extensions to existing models of Spatial Information Systems (SIS) to include time in order to better describe our dynamic environment. Although interest into this area has increased in the past decade, a number of important issues remain to be investigated. With the advances made in temporal database research, we can expect a more unified approach towards aspatial temporal data in SIS and a wider discussion on spatio-temporal data models. This paper provides an overview of previous achievements within the field and highlights areas currently receiving or requiring further investigation.


title: A systematic comparative evaluation of biclustering techniques

abstract: BackgroundBiclustering techniques are capable of simultaneously clustering rows and columns of a data matrix. These techniques became very popular for the analysis of gene expression data, since a gene can take part of multiple biological pathways which in turn can be active only under specific experimental conditions. Several biclustering algorithms have been developed in the past recent years. In order to provide guidance regarding their choice, a few comparative studies were conducted and reported in the literature. In these studies, however, the performances of the methods were evaluated through external measures that have more recently been shown to have undesirable properties. Furthermore, they considered a limited number of algorithms and datasets.ResultsWe conducted a broader comparative study involving seventeen algorithms, which were run on three synthetic data collections and two real data collections with a more representative number of datasets. For the experiments with synthetic data, five different experimental scenarios were studied: different levels of noise, different numbers of implanted biclusters, different levels of symmetric bicluster overlap, different levels of asymmetric bicluster overlap and different bicluster sizes, for which the results were assessed with more suitable external measures. For the experiments with real datasets, the results were assessed by gene set enrichment and clustering accuracy.ConclusionsWe observed that each algorithm achieved satisfactory results in part of the biclustering tasks in which they were investigated. The choice of the best algorithm for some application thus depends on the task at hand and the types of patterns that one wants to detect.


title: Bifidobacteria and Their Role as Members of the Human Gut Microbiota

abstract: Members of the genus Bifidobacterium are among the first microbes to colonise the human gastrointestinal tract and are believed to exert positive health benefits on their host. Due to their purported health-promoting properties, bifidobacteria have been incorporated into many functional foods as active ingredients. Bifidobacteria naturally occur in a range of ecological niches that are either directly or indirectly connected to the animal gastrointestinal tract, such as the human oral cavity, the insect gut and sewage. To be able to survive in these particular ecological niches, bifidobacteria must possess specific adaptations to be competitive. Determination of genome sequences has revealed genetic attributes that may explain bifidobacterial ecological fitness, such as metabolic abilities, evasion of the host adaptive immune system and colonisation of the host through specific appendages. However, genetic modification is crucial toward fully elucidating the mechanisms by which bifidobacteria exert their adaptive abilities and beneficial properties. In this review we provide an up to date summary of the general features of bifidobacteria, whilst paying particular attention to the metabolic abilities of this species. We also describe methods that have allowed successful genetic manipulation of bifidobacteria.


title: Meta-Analysis of Action Video Game Impact on Perceptual, Attentional, and Cognitive Skills

abstract: The ubiquity of video games in today's society has led to significant interest in their impact on the brain and behavior and in the possibility of harnessing games for good. The present meta-analyses focus on one specific game genre that has been of particular interest to the scientific community—action video games, and cover the period 2000–2015. To assess the long-lasting impact of action video game play on various domains of cognition, we first consider cross-sectional studies that inform us about the cognitive profile of habitual action video game players, and document a positive average effect of about half a standard deviation (g = 0.55). We then turn to long-term intervention studies that inform us about the possibility of causally inducing changes in cognition via playing action video games, and show a smaller average effect of a third of a standard deviation (g = 0.34). Because only intervention studies using other commercially available video game genres as controls were included, this latter result highlights the fact that not all games equally impact cognition. Moderator analyses indicated that action video game play robustly enhances the domains of top-down attention and spatial cognition, with encouraging signs for perception. Publication bias remains, however, a threat with average effects in the published literature estimated to be 30% larger than in the full literature. As a result, we encourage the field to conduct larger cohort studies and more intervention studies, especially those with more than 30 hours of training. Public Significance Statement : Understanding the effects of action video game play is essential given that (a) a large number of individuals regularly spend many hours on these types of games, and (b) proponents are offering suites of video games that are claimed to change behavior or enhance cognition. The 2 meta-analyses in this paper present the current status of this field, concluding that playing action video games has some positive effects on improving cognitive skills. This review also identifies some limitations of current research.


Value of buffy coat examination in early diagnosis of neonatal septicemia.


Torque Control of a KUKA youBot Arm


title: Comparison of contemporary CMOS ring oscillators

abstract: This work presents measured data and analysis, accurately comparing three, four-stage ring oscillators. The delay cell topologies considered here include the linear source coupled, and two saturating types. Each oscillator is fabricated concurrently in a 1.8 V, 0.18 /spl mu/m CMOS process and is characterized for phase noise, power consumption and tuning range.


Reconciling Multiple Matches for the Signature-Based Application Identification


title: ADAPTIVE GAME MECHANICS FOR LEARNING PURPOSES - Making Serious Games Playable and Fun

abstract: This paper investigates adaptive games mechanics and how to implement them. First, a comprehensive review of existing adaptive models is presented. Next, we propose a new adaptive model, which combines dynamic difficulty adaptation, the player’s performance, and adaptive flow. An implementation of these new adaptive mechanics is presented in the form of a simple serious game called Number to Number Combat. This game was released freely on the internet in order to be tested by the gaming community. It has shown very promising results that will help us to improve our adaptive model.


title: Individual Tree Delineation in Windbreaks Using Airborne-Laser-Scanning Data and Unmanned Aerial Vehicle Stereo Images

abstract: This letter is aimed to compare the performance of canopy height models (CHMs) derived from airborne laser scanning (ALS) data and unmanned aerial vehicle (UAV) stereo images in the extraction of individual tree height and crown size. Treetops were identified using the local maximum algorithm from the Gaussian filtered CHMs. A parabola fitting was used to determine the crown size. Factors affecting the delineation results, such as point cloud density and the spatial distribution and growing status of trees, were analyzed. The results showed that the UAV stereo images, together with the ALS-derived digital elevation model (DEM), can achieve better performance than ALS data alone based on our data set. The match ratio between delineated and field-measured trees varied significantly, with the highest ratio of 66.94% obtained by UAV in the young aspen forest and the lowest ratio of 33.76% obtained by ALS in the old forest. Aside from the influence of point density, this letter also shed light on the important role that the spatial distribution and growing status of trees play in the delineation of individual trees. To conclude, integrating UAV stereo images with the ALS-derived DEM is effective in delineating individual tree attributes in small-scale windbreaks, which provides some suggestions for the future management of agriculture land.


title: Towards blockchain-based intelligent transportation systems

abstract: Blockchain, widely known as one of the disruptive technologies emerged in recent years, is experiencing rapid development and has the full potential of revolutionizing the increasingly centralized intelligent transportation systems (ITS) in applications. Blockchain can be utilized to establish a secured, trusted and decentralized autonomous ITS ecosystem, creating better usage of the legacy ITS infrastructure and resources, especially effective for crowdsourcing technology. This paper conducts a preliminary study of Blockchain-based ITS (B2ITS). We outline an ITS-oriented, seven-layer conceptual model for blockchain, and on this basis address the key research issues in B2ITS. We consider that blockchain is one of the secured and trusted architectures for building the newly developed parallel transportation management systems (PtMS) , and thereby discuss the relationship between B2ITS and PtMS. Finally, we present a case study for blockchain-based realtime ride-sharing services. In our viewpoint, B2ITS represents the future trend of ITS research and practice, and this paper is aimed at stimulating further effort and providing helpful guidance and reference for future research works.


title: Anomalous magnetic field activity during a bioenergy healing experiment

abstract: A few studies have reported magnetic fi eld changes during bio- energy healing. In a pilot experiment, we examined magnetic fi eld activity during hands-on healing and distant healing of mice with experimentally in- duced tumors. During healing sessions, we observed distinct magnetic fi eld oscillations adjacent to the mice cages, which were similar in appearance to those reported by Zimmerman (1985). The magnetic fi eld oscillations began as 20-30 Hz oscillations, slowing to 8-9 Hz, and then to less than 1 Hz, at which point the oscillations reversed and increased in frequency, with an over- all symmetrical appearance resembling a "chirp wave." The waves ranged from 1-8 milliGauss peak-to-peak in strength and 60-120 sec in duration. Evidence to date suggests that bioenergy healing may be detectable with DC gaussmeters, although we have not ruled out an artifactual basis for the oscil- lations reported here.


title: Hybrid Beamforming for Massive MIMO: A Survey

abstract: Hybrid multiple-antenna transceivers, which combine large-dimensional analog pre/postprocessing with lower-dimensional digital processing, are the most promising approach for reducing the hardware cost and training overhead in massive MIMO systems. This paper provides a comprehensive survey of the various incarnations of such structures that have been proposed in the literature. We provide a taxonomy in terms of the required channel state information (CSI), namely whether the processing adapts to the instantaneous or the average (second-order) CSI; while the former provides somewhat better signal-to-noise and interference ratio (SNIR), the latter has much lower overhead for CSI acquisition. We furthermore distinguish hardware structures of different complexities. Finally, we point out the special design aspects for operation at millimeter-wave frequencies.


title: Silver-loaded seaweed-based cellulosic fiber improves epidermal skin physiology in atopic dermatitis: safety assessment, mode of action and controlled, randomized single-blinded exploratory in vivo study

abstract: Please cite this paper as: Silver-loaded seaweed-based cellulosic fibre improves epidermal skin physiology in atopic dermatitis: safety assessment, mode of action and controlled, randomized single-blinded exploratory in vivo study. Experimental Dermatology 2010; 19: e9–e15. ::: ::: ::: ::: Abstract ::: ::: Background: The epidermal part of the skin is the major interface between the internal body and the external environment. The skin has a specific physiology and is to different degrees adapted for protection against multiple exogenous stress factors. Clothing is the material with the longest and most intensive contact to human skin. It plays a critical role especially in inflammatory dermatoses or skin conditions with an increased susceptibility of bacterial and fungal infections like atopic dermatitis. Previously, we have shown a dose-dependent antibacterial and antifungal activity of silver-loaded seaweed-based cellulosic fibres. ::: ::: ::: ::: Aim of the study: We studied the mode of action of silver-loaded seaweed-based cellulosic fiber and performed a broad safety assessment. The principal aim was to analyse the effects of wearing the textile on epidermal skin physiology in 37 patients with atopic dermatitis in a controlled, randomized single-blinded in vivo study. Furthermore, the sensitization potential was tested in a patch test in 111 panellists. ::: ::: ::: ::: Results: We could demonstrate in vitro a dose-dependent scavenging of induced reactive oxygen species by silver-loaded seaweed-based cellulosic fibers. Safety assessment of these fibres showed no detectable release of silver ions. Furthermore, ex vivo assessment after 24 h application both in healthy volunteers and patients with atopic dermatitis by sequential tape stripping and subsequently raster electron microscopy and energy dispersive microanalysis analysis revealed no detectable amounts of silver in any of stratum corneum layers. Serum analysis of silver showed no detectable levels. The in vivo patch testing of 111 volunteers revealed no sensitization against different SeaCell® Active (SeaCell GmbH, Rudolstadt, Germany) containing fabrics. The in vivo study on 37 patients with known atopic dermatitis and mild-to-moderate eczema on their arms were randomly assigned to either silver-loaded seaweed fibre T-shirts or to cotton T-shirts for 8 weeks. A significant reduction in Staphylococcus aureus colonization was detectable for the silver T-shirts compared with cotton T-shirts without any changes in non-pathogenic surface bacteria colonization. Furthermore, a more pronounced improvement in barrier function (transepidermal water loss) was observed in mildly involved eczema areas during the first 4 weeks of the study. Stratum corneum hydration and surface pH improved in both treatment groups over time. ::: ::: ::: ::: Conclusion: The tested silver-loaded seaweed fibre can be regarded as safe and seams to be suited for application in bio-active textiles in atopic dermatitis based on its positive in vivo activity.


title: Zintl phases for thermoelectric devices.

abstract: By converting waste heat into electricity and improving the efficiency of refrigeration systems, thermoelectric devices could play a significant role in solving today's energy problems. Increasing the thermoelectric efficiency (as measured by the thermoelectric material's figure-of-merit, zT) is critical to the development of this technology. Complex Zintl phases, in particular, make ideal candidates for thermoelectric materials because the necessary "electron-crystal, phonon-glass" properties can be engineered with an understanding of the Zintl chemistry. A recent example is the discovery that Yb(14)MnSb(11), a transition metal Zintl compound, has twice the zT as the material currently in use at NASA. This perspective outlines a strategy to discover new high zT materials in Zintl phases, and presents results pointing towards the success of this approach.


title: On-Body Transmission at 5.2 GHz: Simulations Using FDTD With a Time Domain Huygens' Technique

abstract: On-body communications systems are heavily influenced by the effects of the human body and cannot be analyzed without taking these into account. This, however, leads to a large problem which is a challenge to model. In this paper a transmission scenario is modeled which includes transmit and receive cavity backed slot (CBS) antennas on the body of a male adult. The slots are backed with cavities in order to get unidirectional radiation patterns and decrease the near field body interaction of the slots. The scenario is simulated with finite difference time domain (FDTD) method in combination with a time domain Huygens' (TDH) technique in order to reduce the computational load. A cylindrical numerical phantom including 15 different tissue types is modeled to represent the upper body of a male. The predicted transmission coefficient, return loss and radiation patterns obtained using the TDH method are shown to be in agreement with the resource intensive direct FDTD approach but are obtained in approximately 30% of the time.


title: Some considerations on the fine structure of rhabdomeric photoreceptors in the amphioxus, Branchiostoma lanceolatum (Cephalochordata).

abstract: This study confirms the close relationship of two of the photoreceptive cells of the amphioxus central nervous system, the Hesse and Joseph cells. Both cells have rhbdoms located at the cell surface and possess 9 + 0 cilia that are not related to the rhabdom structure. The rhabdom of Joseph cells is enclosed by thin glial lamellae containing gliofilaments, whereas that of Hesse cells is associated with a pigmentary cell, the two cell types forming an eyecup. The pigment cell is mostly filled of pigment granules and has a 9 + 2 cilium. A third photoreceptive cell of the lancelet, the lamellate cell, is of ciliary type. The significance of the presence of rhabdomeric and ciliary photoreceptors in the lancelet brain is discussed.


title: Similarity-Based Retrieval and Automatic Adaptation of Semantic Workflows

abstract: The increasing demand for individual and more flexible process models and workflows asks for new intelligent process-oriented information systems. Such systems should, among other things, support domain experts in the creation and adaptation of process models or workflows. For this purpose, repositories of best practice workflows are an important means as they collect valuable experiential knowledge that can be reused in various ways. In this chapter we present process-oriented case-based reasoning (POCBR) as a method to support the creation and adaptation of workflows based on such knowledge. We provide a general introduction to process-oriented case-based reasoning and present a concise view of the POCBR methods we developed during the past ten years. This includes graph-based representation of semantic workflows, semantic workflow similarity, similarity-based retrieval, and workflow adaptation based on automatically learned adaptation knowledge. Finally, we sketch several application domains such as traditional business processes, social workflows, and cooking workflows.


title: Direct observation of the budding and fusion of an enveloped virus by video microscopy of viable cells

abstract: Video-enhanced microscopy and digital image processing were used to observe the assembly, budding, and fusion of Respiratory Syncytial virus. Viral filaments were seen to bud from the plasma membrane of viable infected cells to a final length of 5-10 micron with an average speed of elongation of 110-250 nm/s. The rapidity of viral assembly and its synchronous occurrence (leading to the production of several viral particles per minute from the same surface domain) suggests a directed process of recruitment of viral components to an area selected for virus maturation. Virions were also seen to adsorb to the cell surface, and to fuse with the plasma membrane. These are the first real time observations of viral morphogenesis and penetration which are crucial events in the infectious cycle of enveloped viruses.


title: A Taxonomy of Faults for Wireless Sensor Networks

abstract: Over the last decade, Wireless Sensor Networks (WSN) went from being a promising technology to the main enabler of countless Internet of Things applications in all types of areas. In industry, WSNs are now used for monitoring and controlling industrial processes, with the benefits of low installation costs, self-organization, self-configuration, and added functionality. Nevertheless, despite the fact that base WSN technologies are quite stable and subject to standardization, they have kept one of their main characteristics: fault-proneness. As a result, in recent years considerable effort has been made in order to provide mechanisms that increase the availability, reliability and maintainability of this type of networks. In this context, a whole range of techniques such as fault detection, fault identification and fault diagnosis used in other research fields are now being applied to WSNs. Unfortunately, this has not led to a consistent, comprehensive WSN fault taxonomy that can be used to characterize and/or classify faults. Neglecting the importance of WSN fault characterization (e.g., when using supervised algorithms for anomaly detection) may lead to bad classifiers and, consequently, bad fault handling procedures and/or tools. In this paper, we start by reviewing base fault management concepts and techniques that can be applied to WSNs. We then proceed to propose and present a comprehensive WSN fault taxonomy that can be used not only in general purpose WSNs but also in Industrial WSNs. Finally, the proposed taxonomy is validated by applying it to an extensive set of faults described in the literature.


title: The Enterprise System Experience— From Adoption to Success

abstract: 173 For some 20 years after John Dearden wrote these words, history proved him right. Today, however, there is a booming market for software packages claiming to provide a total, integrated solution to companies’ information-processing needs. Even companies that choose not to adopt such packages are pursuing aggressive strategies of systems integration by redeveloping custom software and adopting technologies such as data warehousing. Integrated enterprise systems deserve serious research attention because of their great potential for financial, technical, managerial, human, and strategic benefits, costs, and risks. This chapter provides a theoretical framework for analyzing, both retrospectively and prospectively, the business value of enterprise systems. We first describe the historical context in which enterprise systems emerged. Next we identify the key characteristics of enterprise systems, discuss the reasons companies do and do not adopt them, and summarize arguments about why enterprise systems are an important topic for research. We then analyze enterprise systems in terms of the concept of success. We argue that the many facets of success create difficulties for


title: Working Memory Benefits Creative Insight, Musical Improvisation, and Original Ideation Through Maintained Task-Focused Attention

abstract: Anecdotes from creative eminences suggest that executive control plays an important role in creativity, but scientific evidence is sparse. Invoking the Dual Pathway to Creativity Model, the authors hypothesize that working memory capacity (WMC) relates to creative performance because it enables persistent, focused, and systematic combining of elements and possibilities (persistence). Study 1 indeed showed that under cognitive load, participants performed worse on a creative insight task. Study 2 revealed positive associations between time-on-task and creativity among individuals high but not low in WMC, even after controlling for general intelligence. Study 3 revealed that across trials, semiprofessional cellists performed increasingly more creative improvisations when they had high rather than low WMC. Study 4 showed that WMC predicts original ideation because it allows persistent (rather than flexible) processing. The authors conclude that WMC benefits creativity because it enables the individual to maintain attention focused on the task and prevents undesirable mind wandering.


Mobile Augmented Reality System for Construction Site Visualization


title: Automatic In-Text Keyword Tagging based on Information Retrieval

abstract: Abstract: As shown in Wikipedia, tagging or cross-linking through major keywords in a document collection improves not only the readability of documents but also responsive and adaptive navigation among related documents. In recent years, the Semantic Web has increased the importance of social tagging as a key feature of the Web 2.0 and, as its crucial phenotype, Tag Cloud has emerged to the public. In this paper we provide an efficient method of automated in-text keyword tagging based on large-scale controlled term collection or keyword dictionary, where the computational complexity of O(mN) – if a pattern matching algorithm is used – can be reduced to O(mlogN) – if an Information Retrieval technique is adopted – while m is the length of target document and N is the total number of candidate terms to be tagged. The result shows that automatic in-text tagging with keywords filtered by Information Retrieval speeds up to about 6 ~ 40 times compared with the fastest pattern matching algorithm.


title: Conscious and non-conscious components of superstitious beliefs in judgment and decision-making

abstract: Despite the large impact that superstitious beliefs have on the marketplace, we currently know very little about their implications for consumer judgment and decision making. We document the existence of the influence of superstitious beliefs on consumer behavior and specify their conscious and nonconscious underlying properties. In particular, we show that superstitious beliefs have a robust influence on product satisfaction and decision making under risk. However, these effects are only observed when superstitious beliefs are allowed to work nonconsciously. Using a process-dissociation task, we further demonstrate the distinct conscious versus nonconscious components of the effect of superstition on decision making under risk. (c) 2007 by JOURNAL OF CONSUMER RESEARCH, Inc..


title: Teacher-Student Curriculum Learning

abstract: We propose Teacher-Student Curriculum Learning (TSCL), a framework for automatic curriculum learning, where the Student tries to learn a complex task and the Teacher automatically chooses subtasks from a given set for the Student to train on. We describe a family of Teacher algorithms that rely on the intuition that the Student should practice more those tasks on which it makes the fastest progress, i.e. where the slope of the learning curve is highest. In addition, the Teacher algorithms address the problem of forgetting by also choosing tasks where the Student's performance is getting worse. We demonstrate that TSCL matches or surpasses the results of carefully hand-crafted curricula in two tasks: addition of decimal numbers with LSTM and navigation in Minecraft. Using our automatically generated curriculum enabled to solve a Minecraft maze that could not be solved at all when training directly on solving the maze, and the learning was an order of magnitude faster than uniform sampling of subtasks.


title: Bioinspired Programming of Memory Devices for Implementing an Inference Engine

abstract: Cognitive tasks are essential for the modern applications of electronics, and rely on the capability to perform inference. The Von Neumann bottleneck is an impor- tant issue for such tasks, and emerging memory devices offer an opportunity to overcome this issue by fusing computing and memory, in nonvolatile instant ON/OFF systems. A vision for accomplishing this is to use brain-inspired architectures, which excel at inference and do not differentiate between computing and memory. In this work, we use a neuroscience-inspired model of learning, spike-timing-dependent plasticity, to devel- op a bioinspired approach for programming memory devices, which naturally gives rise to an inference engine. The method is then adapted to different memory devices, including multi- valued memories (cumulative memristive device, phase- change memory) and stochastic binary memories (conductive bridge memory, spin transfer torque magnetic tunnel junction). By means of system-level simulations, we investigate several applications, including image recognition and pattern detec- tion within video and auditory data. We compare the results of the different devices. Stochastic binary devices require the use of redundancy, the extent of which depends tremendously on the considered task. A theoretical analysis allows us to understand how the various devices differ, and ties the inference engine to the machine learning algorithm of expec- tation-maximization. Monte Carlo simulations demonstrate an exceptional robustness of the inference engine with respect to device variations and other issues. A theoretical analysis explains the roots of this robustness. These results highlight a possible new bioinspired paradigm for programming emerging memory devices, allowing the natural learning of a complex inference engine. The physics of the memory devices plays an active role. The results open the way for a reinvention of the role of memory, when solving inference tasks.


title: Recommendation in heterogeneous information networks with implicit user feedback

abstract: Recent studies suggest that by using additional user or item relationship information when building hybrid recommender systems, the recommendation quality can be largely improved. However, most such studies only consider a single type of relationship, e.g., social network. Notice that in many applications, the recommendation problem exists in an attribute-rich heterogeneous information network environment. In this paper, we study the entity recommendation problem in heterogeneous information networks. We propose to combine various relationship information from the network with user feedback to provide high quality recommendation results. The major challenge of building recommender systems in heterogeneous information networks is to systematically define features to represent the different types of relationships between entities, and learn the importance of each relationship type. In the proposed framework, we first use meta-path-based latent features to represent the connectivity between users and items along different paths in the related information network. We then define a recommendation model with such latent features and use Bayesian ranking optimization techniques to estimate the model. Empirical studies show that our approach outperforms several widely employed implicit feedback entity recommendation techniques.


title: Planning, learning and coordination in multiagent decision processes

abstract: There has been a growing interest in AI in the design of multiagent systems, especially in multiagent cooperative planning. In this paper, we investigate the extent to which methods from single-agent planning and learning can be applied in multiagent settings. We survey a number of different techniques from decision-theoretic planning and reinforcement learning and describe a number of interesting issues that arise with regard to coordinating the policies of individual agents. To this end, we describe multiagent Markov decision processes as a general model in which to frame this discussion. These are special n-person cooperative games in which agents share the same utility function. We discuss coordination mechanisms based on imposed conventions (or social laws) as well as learning methods for coordination. Our focus is on the decomposition of sequential decision processes so that coordination can be learned (or imposed) locally, at the level of individual states. We also discuss the use of structured problem representations and their role in the generalization of learned conventions and in approximation.


Deconvolutional Networks for Feature Learning


title: A Novel Scalable DBSCAN Algorithm with Spark

abstract: DBSCAN is a well-known clustering algorithmwhich is based on density and is able to identify arbitraryshaped clusters and eliminate noise data. However, parallelization of DBSCAN is a challenging work because based on MPIor OpenMP environments, there exist the issues of lack of fault-tolerance and there is no guarantee that workload is balanced. Moreover, programming with MPI requires data scientists tohave an advanced experience to handle communication betweennodes which is a big challenge. We present a new parallel DBSCAN algorithm using thenew big data framework Spark. In order to reduce searchtime, we apply kd-tree in our algorithm. More specifically, wepropose a novel approach to avoid communication betweenexecutors so that we can locally obtain partial clusters moreefficiently. Based on Java API, we select appropriate datastructures carefully: Using Queue to contain neighbors of thedata point, and using Hashtable when checking the statusof and processing the data points. In addition, we use otheradvanced features from Spark to make our implementationmore effective. We implement the algorithm in Java andevaluate its scalability by using different number of processingcores. Our experiments demonstrate that the algorithm wepropose scales up very well. Using data sets containing up to1 million high-dimensional points, we show that our proposedalgorithm achieves speedups up to 6 using 8 cores (10k), 10using 32 cores (100k), and 137 using 512 cores (1m). Anotherexperiment using 10k data points is conducted and the resultshows that the algorithm with MapReduce achieves speedupsto 1.3 using 2 cores, 2.0 using 4 cores, and 3.2 using 8 cores.


title: Performance Evaluation Methodology for Historical Document Image Binarization

abstract: Document image binarization is of great importance in the document image analysis and recognition pipeline since it affects further stages of the recognition process. The evaluation of a binarization method aids in studying its algorithmic behavior, as well as verifying its effectiveness, by providing qualitative and quantitative indication of its performance. This paper addresses a pixel-based binarization evaluation methodology for historical handwritten/machine-printed document images. In the proposed evaluation scheme, the recall and precision evaluation measures are properly modified using a weighting scheme that diminishes any potential evaluation bias. Additional performance metrics of the proposed evaluation scheme consist of the percentage rates of broken and missed text, false alarms, background noise, character enlargement, and merging. Several experiments conducted in comparison with other pixel-based evaluation measures demonstrate the validity of the proposed evaluation scheme.


title: New Measures of Well-Being

abstract: We present new measures of well-being to assess the following concepts: 1. Psychological Well-Being (PWB); 2. Positive Feelings, Negative Feelings, and the balance between the two (SPANE-P, N, B); and 3. Positive Thinking. The PWB scale is a short 8–item summary survey of the person’s self-perceived functioning in important areas such as relationships, self-esteem, purpose and meaning, and optimism. The scale is substantially correlated with other psychological well-being scales, but is briefer. The scale provides a single overall psychological well-being score and does not yield scores for various components of well-being. The Scale of Positive and Negative Experience (SPANE) yields a score for positive experience and feelings (6 items), a score for negative experience and feelings (6 items), and the two can be combined to create an experience balance score. This 12-item brief scale has a number of desirable features compared to earlier measures of positive and negative feelings. In particular, the scale assesses with a few items a broad range of negative and positive experiences and feelings, not just those of a certain type, and is based on the frequency of feelings during the past month. A scale to measure Positive Thinking is also presented. Basic psychometric statistics are presented for the scales based on 573 college students at five universities.


title: Competitive adsorption and state of charge of halide ions in monolayer oxide film growth processes at Pt anodes

abstract: The competitive electrodeposition of the monolayer of surface oxide on Pt electrodes is studied in the presence of co-adsorbed halide ions (X–= Cl–, Br–, I–), down to very low concentrations. These experiments give information essential to the understanding of the state of the surface of Pt anodes at which halogen evolution is occurring from aqueous halide ion solutions and hence provide a basis for interpretation of the kinetics of, and electrocatalysis in, anodic evolution of halogens, especially Cl2. The isotherms for competitive electrochemical adsorption of OH and O species in the surface oxide film, relative to that of X– ions, are evaluated quantitatively.Cl– selectively blocks the initially deposited OH monolayer at Pt while Br– and I– block the formation of the surface oxide non-selectively over a wide potential range, indicating a difference of the state of charge (‘electrosorption valency’) of adsorbed Br– and I–, in relation to that of adsorbed Cl–. This is confirmed by the forms of the competitive adsorption isotherms: Cl– gives linear-logarithmic behaviour in [Cl–], indicating strong lateral interactions, while Br– and I– effects conform more to Langmuir behaviour.A method for quantitatively treating the competitive adsorption, giving information on lateral interaction effects in the surface oxide film with co-adsorbed X– ions, is given in terms of a ‘differentiated’ adsorption isotherm function.


title: Real time power estimation and thread scheduling via performance counters

abstract: Estimating power consumption is critical for hardware and software developers, and of the latter, particularly for OS programmers writing process schedulers. However, obtaining processor and system power consumption information can be non-trivial. Simulators are time consuming and prone to error. Power meters report whole-system consumption, but cannot give per-processor or per-thread information. More intrusive hardware instrumentation is possible, but such solutions are usually employed while designing the system, and are not meant for customer use. Given these difficulties, plus the current availability of some form of performance counters on virtually all platforms (even though such counters were initially designed for system bring-up, and not intended for general programmer consumption), we analytically derive functions for real-time estimation of processor and system power consumption using performance counter data on real hardware. Our model uses data gathered from microbenchmarks that capture potential application behavior. The model is independent of our test benchmarks, and thus we expect it to be well suited for future applications. We target chip multiprocessors, analyzing effects of shared resources and temperature on power estimation, leveraging our model to implement a simple, power-aware thread scheduler. The NAS and SPEC-OMP benchmarks shows a median error of 5.8% and 3.9%, respectively. SPEC 2006 shows a marginally higher median error of 7.2%.


title: Lessons from Dutch IT-outsourcing success and failure

abstract: We present the findings from a Dutch field study of a representative sample of 30 outsourcing deals totalling to more than 100 million Euro, where both customers, corresponding IT-outsourcing providers and their intermediaries (if present) participated. Of the 30 deals, 18 were successful. As the sample is representative for the 700 Dutch IT-outsourcing deals between 2007-2010, the Dutch success rate was about 60% at that time. The main objective of the study was to examine what made some a success and some a failure and how to influence the outcome if possible. For a number of well-known factors we investigated whether they discriminate between IT-outsourcing success and failure in the early phase of service delivery and determined their impact on the chance on a successful deal. We investigated controllable factors to increase the odds during sourcing and rigid factors as a warning sign before closing a deal.Based on 253 interviews with 516 different questions we collected 28 thousand data points (which took several FTEs). From the data and the perceived failure or success of the closed deals we investigated the discriminative power of the determinants (ex post). We found three statistically significant controllable factors that discriminated in an early phase between failure and success. They are: working according to the transition plan, demand management and, to our surprise, communication within the supplier organisation (so not between client and supplier). These factors also turned out to be the only significant factors for a (logistic) model predicting the chance of a successful IT-outsourcing. Improving demand management and internal communication at the supplier increases the odds the most, sticking to the transition plan only modestly. Other controllable factors were not significant in our study. They are: managing the business case, transfer of staff or assets, retention of expertise and communication within the client organisation. Of the rigid factors, the motive to outsource, cultural differences, and the type of work were insignificant. The motive of the supplier was significant: short-term motivations like increasing profit margins or business volume decreased the chance of success while long-term motivations like increasing market share or becoming a player increased the success rate. From the data we inferred that the degree of experience with sourcing did not show to be a convincing factor of success. Hiring sourcing consultants (intermediaries) worked contra-productive: it lowered chances of success. This illustrates that you better not outsource outsourcing.


title: Architecture-scale human-assisted additive manufacturing

abstract: Recent digital fabrication tools have opened up accessibility to personalized rapid prototyping; however, such tools are limited to product-scale objects. The materials currently available for use in 3D printing are too fine for large-scale objects, and CNC gantry sizes limit the scope of printable objects. In this paper, we propose a new method for printing architecture-scale objects. Our proposal includes three developments: (i) a construction material consisting of chopsticks and glue, (ii) a handheld chopstick dispenser, and (iii) a printing guidance system that uses projection mapping. The proposed chopstickglue material is cost effective, environmentally sustainable, and can be printed more quickly than conventional materials. The developed handheld dispenser enables consistent feeding of the chopstickglue material composite. The printing guidance system --- consisting of a depth camera and a projector --- evaluates a given shape in real time and indicates where humans should deposit chopsticks by projecting a simple color code onto the form under construction. Given the mechanical specifications of the stickglue composite, an experimental pavilion was designed as a case study of the proposed method and built without scaffoldings and formworks. The case study also revealed several fundamental limitations, such as the projector does not work in daylight, which requires future investigations.


title: Effects of Partial and Acute Total Sleep Deprivation on Performance across Cognitive Domains, Individuals and Circadian Phase

abstract: Background: Cognitive performance deteriorates during extended wakefulness and circadian phase misalignment, and some individuals are more affected than others. Whether performance is affected similarly across cognitive domains, or whether cognitive processes involving Executive Functions are more sensitive to sleep and circadian misalignment than Alertness and Sustained Attention, is a matter of debate. Methodology/Principal Findings: We conducted a 2 6 12-day laboratory protocol to characterize the interaction of repeated partial and acute total sleep deprivation and circadian phase on performance across seven cognitive domains in 36 individuals (18 males; mean 6 SD of age=27.664.0 years). The sample was stratified for the rs57875989 polymorphism in PER3, which confers cognitive susceptibility to total sleep deprivation. We observed a deterioration of performance during both repeated partial and acute total sleep deprivation. Furthermore, prior partial sleep deprivation led to poorer cognitive performance in a subsequent total sleep deprivation period, but its effect was modulated by circadian phase such that it was virtually absent in the evening wake maintenance zone, and most prominent during early morning hours. A significant effect of PER3 genotype was observed for Subjective Alertness during partial sleep deprivation and on n-back tasks with a high executive load when assessed in the morning hours during total sleep deprivation after partial sleep loss. Overall, however, Subjective Alertness and Sustained Attention were more affected by both partial and total sleep deprivation than other cognitive domains and tasks including n-back tasks of Working Memory, even when implemented with a high executive load. Conclusions/Significance: Sleep loss has a primary effect on Sleepiness and Sustained Attention with much smaller effects on challenging Working Memory tasks. These findings have implications for understanding how sleep debt and circadian rhythmicity interact to determine waking performance across cognitive domains and individuals.


title: The Antecedents of Customer Self-Disclosure to Online Virtual Advisors

abstract: This study proposes and tests a model of the antecedents to customer self-disclosure to an online virtual advisor during the requirements elicitation stage of a skin care product. In such a context, a customer is required to provide personal information for the advisor to identify a suitable skin care product. Consistent with the view of self-disclosure as a form of social exchange, we propose that a customer’s intention to self-disclose is affected by the extent to which she believes the disclosures will enable her to obtain a better product, and her perceived information misuse risk. Furthermore, we posit that the customer’s perception of the advisor’s responsiveness acts both as an antecedent to her disclosure intentions, as well as her perceived benefits and costs. The results of an experimental investigation support the theoretical model.


title: Food-borne parasitic zoonoses in the Philippines.

abstract: A number of food-borne parasitic zoonoses have been recorded in the Philippines and include echinostomiasis, artyfechinostomosis, fascioliasis, heterophydiasis, carneophallosis, clonorchiasis, paragonimiasis, taeniasis, echinococcosis/hydatidosis, diphyllobothriosis/spirometrosis and sparganosis, intestinal capillariasis, gnathostomiasis, angiostrongylosis, toxoplasmosis and sarcosporidiosis. Some are now rarely observed while others continue to be public health problems. Many are endemic in certain areas of the Philippines because of the habit of consuming raw or partly cooked fish, snails, crustaceans, and meat. Artyfechinostomosis caused by Artyfechinostomum malayanum is a recently recognized problem in the Philippines and is reported in man and pigs. Human infection results from ingesting raw or partly cooked the freshwater snail, Bullastra cumingiana which serves as second intermediate host. More information on the epidemiology, transmission including the animal hosts involved locally are still needed for some of these problems. Human infection with many of these diseases can be prevented by changing the food habits, but this requires aggressive health education campaigns.


Typology of Distributed Ledger based Business Models


title: A closer look at batch size in mini-batch training of deep auto-encoders

abstract: In deep learning community, gradient based methods are typically employed to train the proposed models. These methods generally operate in a mini-batch training manner wherein a small fraction of the training data is invoked to compute an approximative gradient. It is reported that models trained with large batch are prone to generalize worse than those trained with small batch. Several inspiring works are conducted to figure out the underlying reason of this phenomenon, but almost all of them focus on classification tasks. In this paper, we investigate the influence of batch size on regression task. More specifically, we tested the generalizability of deep auto-encoder trained with varying batch size and checked some well-known measures relating to model generalization. Our experimental results lead to three conclusions. First, there exist no obvious generalization gap in regression model such as auto-encoders. Second, with a same train loss as target, small batch generally lead to solutions closer to the starting point than large batch. Third, spectral norm of weight matrices is closely related to generalizability of the model, but different layers contribute variously to the generalization performance.


title: A cascade filter for pulse wave baseline drift elimination

abstract: Analyzing the pulse wave is an important method to diagnosis the status of human health. Generally the directly acquired signal contains varying degrees of baseline drift which is usually caused by breathing and physical movement, it mostly influences the accuracy of signal analysis and must be eliminated. This article presented a cascade filter framework to remove the baseline drift of pulse wave. The first stage zero phase filter (ZPF) roughly reduced the trend of the baseline drift. The second stage of the removed method considered the signal features. Through the pulse waveform reference points' accurate location and cubic spline estimation, we completely eliminated the rest drift. Compared with the other methods, e.g. wavelet-based filtering, morphological filtering, empirical mode decomposition (EMD) filtering, the experimental result showed that the proposed method had not only better effect, but also small calculation cost, and the scheme was more suitable for actual application.


title: The topography of high-order human object areas

abstract: Abstract Cortical topography is one of the most fundamental organizing principles of cortical areas. One such topography – eccentricity mapping – is present even in high-order, ventral stream visual areas. Within these areas, different object categories have specific eccentricity biases. In particular, faces, letters and words appear to be associated with central visual-field bias, whereas buildings are associated with a peripheral one. We propose that resolution needs are an important factor in organizing object representations: objects whose recognition depends on analysis of fine detail will be associated with central-biased representations, whereas objects whose recognition entails large-scale integration will be more peripherally biased.


title: The Future of Ad Blocking: An Analytical Framework and New Techniques

abstract: We present a systematic study of ad blocking - and the associated "arms race" - as a security problem. We model ad blocking as a state space with four states and six state transitions, which correspond to techniques that can be deployed by either publishers or ad blockers. We argue that this is a complete model of the system. We propose several new ad blocking techniques, including ones that borrow ideas from rootkits to prevent detection by anti-ad blocking scripts. Another technique uses the insight that ads must be recognizable by humans to comply with laws and industry self-regulation. We have built prototype implementations of three of these techniques, successfully blocking ads and evading detection. We systematically evaluate our proposed techniques, along with existing ones, in terms of security, practicality, and legality. We characterize the order of growth of the development effort required to create/maintain ad blockers as a function of the growth of the web. Based on our state-space model, our new techniques, and this systematization, we offer insights into the likely "end game" of the arms race. We challenge the widespread assumption that the arms race will escalate indefinitely, and instead identify a combination of evolving technical and legal factors that will determine the outcome.


Combining multiple evidence from different types of thesaurus for query expansion


title: Performance analysis and receiver architectures of DCF77 radio-controlled clocks

abstract: DCF77 is a longwave radio transmitter located in Germany. Atomic clocks generate a 77.5-kHz carrier which is amplitude- and phase-modulated to broadcast the official time. The signal is used by industrial and consumer radio-controlled clocks. DCF77 faces competition from the Global Positioning System (GPS) which provides higher accuracy time. Still, DCF77 and other longwave time services worldwide remain popular because they allow indoor reception at lower cost, lower power, and sufficient accuracy. Indoor longwave reception is challenged by signal attenuation and electromagnetic interference from an increasing number of devices, particularly switched-mode power supplies. This paper introduces new receiver architectures and compares them with existing detectors and time decoders. Simulations and analytical calculations characterize the performance in terms of bit error rate and decoding probability, depending on input noise and narrowband interference. The most promising detector with maximum-likelihood time decoder displays the time in less than 60 s after powerup and at a noise level of E(b)/N(0) = 2.7 dB, an improvement of 20 dB over previous receivers. A field-programmable gate array-based demonstration receiver built for the purposes of this paper confirms the capabilities of these new algorithms. The findings of this paper enable future high-performance DCF77 receivers and further study of indoor longwave reception.


title: Towards a Framework for the Evaluation of Machine Compositions

abstract: We outline a framework within which machine compositions may be evaluated objectively. In particular, the framework allows statements about those compositions to be refuted on the basis of empirical experimentation. We consider this to be fundamental if we wish to evaluate the degree to which our programs achieve their compositional aims. Furthermore, a review of the literature reveals that this is a largely ignored aspect of research into algorithmic composition. Our framework involves four components: specifying the compositional aims; inducing a critic from a set of example musical phrases; composing music that satisfies the critic; and evaluating specific claims about the compositions in experiments using human subjects. We describe a system which exemplifies these four stages and which demonstrates the practicality of the framework. Finally, the application of the framework to the evaluation of musical creativity is discussed and directions for future research are suggested.


title: Social Influence Locality for Modeling Retweeting Behaviors

abstract: We study an interesting phenomenon of social influence locality in a large microblogging network, which suggests that users' behaviors are mainly influenced by close friends in their ego networks. We provide a formal definition for the notion of social influence locality and develop two instantiation functions based on pairwise influence and structural diversity. The defined influence locality functions have strong predictive power. Without any additional features, we can obtain a F1-score of 71.65% for predicting users' retweet behaviors by training a logistic regression classifier based on the defined functions. Our analysis also reveals several intriguing discoveries. For example, though the probability of a user retweeting a microblog is positively correlated with the number of friends who have retweeted the microblog, it is surprisingly negatively correlated with the number of connected circles that are formed by those friends.


title: Parameterless Optimal Approximate Message Passing

abstract: Iterative thresholding algorithms are well-suited for high-dimensional problems in sparse recovery and compressive sensing. The performance of this class of algorithms depends heavily on the tuning of certain threshold parameters. In particular, both the final reconstruction error and the convergence rate of the algorithm crucially rely on how the threshold parameter is set at each step of the algorithm. In this paper, we propose a parameter-free approximate message passing (AMP) algorithm that sets the threshold parameter at each iteration in a fully automatic way without either having an information about the signal to be reconstructed or needing any tuning from the user. We show that the proposed method attains both the minimum reconstruction error and the highest convergence rate. Our method is based on applying the Stein unbiased risk estimate (SURE) along with a modified gradient descent to find the optimal threshold in each iteration. Motivated by the connections between AMP and LASSO, it could be employed to find the solution of the LASSO for the optimal regularization parameter. To the best of our knowledge, this is the first work concerning parameter tuning that obtains the fastest convergence rate with theoretical guarantees.


title: Leakage in data mining: formulation, detection, and avoidance

abstract: Deemed "one of the top ten data mining mistakes", leakage is essentially the introduction of information about the data mining target, which should not be legitimately available to mine from. In addition to our own industry experience with real-life projects, controversies around several major public data mining competitions held recently such as the INFORMS 2010 Data Mining Challenge and the IJCNN 2011 Social Network Challenge are evidence that this issue is as relevant today as it has ever been. While acknowledging the importance and prevalence of leakage in both synthetic competitions and real-life data mining projects, existing literature has largely left this idea unexplored. What little has been said turns out not to be broad enough to cover more complex cases of leakage, such as those where the classical i.i.d. assumption is violated, that have been recently documented. In our new approach, these cases and others are explained by explicitly defining modeling goals and analyzing the broader framework of the data mining problem. The resulting definition enables us to derive general methodology for dealing with the issue. We show that it is possible to avoid leakage with a simple specific approach to data management followed by what we call a learn-predict separation, and present several ways of detecting leakage when the modeler has no control over how the data have been collected.


title: Condition Monitoring for Device Reliability in Power Electronic Converters: A Review

abstract: Condition monitoring (CM) has already been proven to be a cost effective means of enhancing reliability and improving customer service in power equipment, such as transformers and rotating electrical machinery. CM for power semiconductor devices in power electronic converters is at a more embryonic stage; however, as progress is made in understanding semiconductor device failure modes, appropriate sensor technologies, and signal processing techniques, this situation will rapidly improve. This technical review is carried out with the aim of describing the current state of the art in CM research for power electronics. Reliability models for power electronics, including dominant failure mechanisms of devices are described first. This is followed by a description of recently proposed CM techniques. The benefits and limitations of these techniques are then discussed. It is intended that this review will provide the basis for future developments in power electronics CM.


BusMonitor: A Hypervisor-Based Solution for Memory Bus Covert Channels


title: Modular Decomposition in Visuomotor Learning

abstract: The principle of 'divide-and-conquer', the decomposition of a complex task into simpler subtasks each learned by a separate module, has been proposed as a computational strategy during learning1–3. We explore the possibility that the human motor system uses such a modular decomposition strategy to learn the visuomotor map, the relationship between visual inputs and motor outputs. Using a virtual reality system, subjects were exposed to opposite prism-like visuomotor remappings—discrepancies between actual and visually perceived hand locations— for movements starting from two distinct locations. Despite this conflicting pairing between visual and motor space, subjects learned the two starting-point-dependent visuomotor mappings and the generalization of this learning to intermediate starting locations demonstrated an interpolation of the two learned maps. This interpolation was a weighted average of the two learned visuomotor mappings, with the weighting sigmoidally dependent on starting location, a prediction made by a computational model of modular learning known as the "mixture of experts"1. These results provide evidence that the brain may employ a modular decomposition strategy during learning.


title: Sound and Immersion in the First-Person Shooter: Mixed Measurement of the Player's Sonic Experience

abstract: One of the aims of modern First-Person Shooter (FPS) design is to provide an immersive experience to the player. This paper examines the role of sound in enabling such immersion and argues that, even in 'realism' FPS games, it may be achieved sonically through a focus on caricature rather than realism. The paper utilizes and develops previous work in which both a conceptual framework for the design and analysis of run and gun FPS sound is developed and the notion of the relationship between player and FPS soundscape as an acoustic ecology is put forward (Grimshaw and Schott 2007a; Grimshaw and Schott 2007b). Some problems of sound practice and sound reproduction in the game are highlighted and a conceptual solution is proposed.


title: Fast MPEG-CDVS Encoder with GPU-CPU Hybrid Computing

abstract: The compact descriptors for visual search (CDVS) standard from ISO/IEC moving pictures experts group has succeeded in enabling the interoperability for efficient and effective image retrieval by standardizing the bitstream syntax of compact feature descriptors. However, the intensive computation of a CDVS encoder unfortunately hinders its widely deployment in industry for large-scale visual search. In this paper, we revisit the merits of low complexity design of CDVS core techniques and present a very fast CDVS encoder by leveraging the massive parallel execution resources of graphics processing unit (GPU). We elegantly shift the computation-intensive and parallel-friendly modules to the state-of-the-arts GPU platforms, in which the thread block allocation as well as the memory access mechanism are jointly optimized to eliminate performance loss. In addition, those operations with heavy data dependence are allocated to CPU for resolving the extra but non-necessary computation burden for GPU. Furthermore, we have demonstrated the proposed fast CDVS encoder can work well with those convolution neural network approaches which enables to leverage the advantages of GPU platforms harmoniously, and yield significant performance improvements. Comprehensive experimental results over benchmarks are evaluated, which has shown that the fast CDVS encoder using GPU-CPU hybrid computing is promising for scalable visual search.


title: A Wireless Multi-Channel Recording System for Freely Behaving Mice and Rats

abstract: To understand the neural basis of behavior, it is necessary to record brain activity in freely moving animals. Advances in implantable multi-electrode array technology have enabled researchers to record the activity of neuronal ensembles from multiple brain regions. The full potential of this approach is currently limited by reliance on cable tethers, with bundles of wires connecting the implanted electrodes to the data acquisition system while impeding the natural behavior of the animal. To overcome these limitations, here we introduce a multi-channel wireless headstage system designed for small animals such as rats and mice. A variety of single unit and local field potential signals were recorded from the dorsal striatum and substantia nigra in mice and the ventral striatum and prefrontal cortex simultaneously in rats. This wireless system could be interfaced with commercially available data acquisition systems, and the signals obtained were comparable in quality to those acquired using cable tethers. On account of its small size, light weight, and rechargeable battery, this wireless headstage system is suitable for studying the neural basis of natural behavior, eliminating the need for wires, commutators, and other limitations associated with traditional tethered recording systems.