title: The minds of gods: A comparative study of supernatural agency

abstract: The present work is the first study to systematically compare the minds of gods by examining some of the intuitive processes that guide how people reason about them. By examining the Christian god and the spirit-masters of the Tyva Republic, it first confirms that the consensus view of the Christian god’s mind is one of omniscience with acute concern for interpersonal social behavior (i.e., moral behaviors) and that Tyvan spirit-masters are not as readily attributed with knowledge or concern of moral information. Then, it reports evidence of a moralization bias of gods’ minds; American Christians who believe that God is omniscient rate God as more knowledgeable of moral behaviors than nonmoral information. Additionally, Tyvans who do not readily report pro- or antisocial behavior among the things that spirit-masters care about will nevertheless rate spirit-masters’ knowledge and concern of moral information higher than nonmoral information. However, this knowledge is distributed spatially; the farther away from spirits’ place of governance a moral behavior takes place, the less they know and care about it. Finally, the wider the breadth of knowledge Tyvans attribute to spirit-masters, the more they attribute moral concern for behaviors that transpire beyond their jurisdiction. These results further demonstrate that there is a significant gulf between expressed beliefs and intuitive religious cognition and provides evidence for a moralization bias of gods’ minds.


title: Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges

abstract: Voluminous amounts of data have been produced, since the past decade as the miniaturization of Internet of things (IoT) devices increases. However, such data are not useful without analytic power. Numerous big data, IoT, and analytics solutions have enabled people to obtain valuable insight into large data generated by IoT devices. However, these solutions are still in their infancy, and the domain lacks a comprehensive survey. This paper investigates the state-of-the-art research efforts directed toward big IoT data analytics. The relationship between big data analytics and IoT is explained. Moreover, this paper adds value by proposing a new architecture for big IoT data analytics. Furthermore, big IoT data analytic types, methods, and technologies for big data mining are discussed. Numerous notable use cases are also presented. Several opportunities brought by data analytics in IoT paradigm are then discussed. Finally, open research challenges, such as privacy, big data mining, visualization, and integration, are presented as future research directions.


Wireless Location in IS – 95 CDMA Cellular Radio Systems


title: Queue Mining – Predicting Delays in Service Processes

abstract: Information systems have been widely adopted to support service processes in various domains, e.g., in the telecommunication, finance, and health sectors. Recently, work on process mining showed how management of these processes, and engineering of supporting systems, can be guided by models extracted from the event logs that are recorded during process operation. In this work, we establish a queueing perspective in operational process mining. We propose to consider queues as first-class citizens and use queueing theory as a basis for queue mining techniques. To demonstrate the value of queue mining, we revisit the specific operational problem of online delay prediction: using event data, we show that queue mining yields accurate online predictions of case delay.


title: Guided Learning for Bidirectional Sequence Classification

abstract: In this paper, we propose guided learning, a new learning framework for bidirectional sequence classification. The tasks of learning the order of inference and training the local classifier are dynamically incorporated into a single Perceptron like learning algorithm. We apply this novel learning algorithm to POS tagging. It obtains an error rate of 2.67% on the standard PTB test set, which represents 3.3% relative error reduction over the previous best result on the same data set, while using fewer features.


title: Honeypot Effectiveness in Different Categories of Attacks on Wireless Sensor Networks

abstract: Wireless sensor networks (WSN) are entering the daily life of many. Like wireless LAN before, these are a new playground for developers, but also offer new ways for misuse of this technology. For the improvement of security and to get more valuable data about new attacks, a honeypot for WSNs is in development. This paper presents an effectiveness analysis of a honeypot for WSN and shows detection capabilities in the categories of known and unknown attacks.


title: Extraction of evidence tables from abstracts of randomized clinical trials using a maximum entropy classifier and global constraints

abstract: Systematic use of the published results of randomized clinical trials is increasingly important in evidence-based medicine. In order to collate and analyze the results from potentially numerous trials, evidence tables are used to represent trials concerning a set of interventions of interest. An evidence table has columns for the patient group, for each of the interventions being compared, for the criterion for the comparison (e.g. proportion who survived after 5 years from treatment), and for each of the results. Currently, it is a labour-intensive activity to read each published paper and extract the information for each field in an evidence table. There have been some NLP studies investigating how some of the features from papers can be extracted, or at least the relevant sentences identified. However, there is a lack of an NLP system for the systematic extraction of each item of information required for an evidence table. We address this need by a combination of a maximum entropy classifier, and integer linear programming. We use the later to handle constraints on what is an acceptable classification of the features to be extracted. With experimental results, we demonstrate substantial advantages in using global constraints (such as the features describing the patient group, and the interventions, must occur before the features describing the results of the comparison).


title: Mental Transformation Skill in Young Children: The Role of Concrete and Abstract Motor Training

abstract: We examined the effects of three different training conditions, all of which involve the motor system, on kindergarteners' mental transformation skill. We focused on three main questions. First, we asked whether training that involves making a motor movement that is relevant to the mental transformation-either concretely through action (action training) or more abstractly through gestural movements that represent the action (move-gesture training)-resulted in greater gains than training using motor movements irrelevant to the mental transformation (point-gesture training). We tested children prior to training, immediately after training (posttest), and 1 week after training (retest), and we found greater improvement in mental transformation skill in both the action and move-gesture training conditions than in the point-gesture condition, at both posttest and retest. Second, we asked whether the total gain made by retest differed depending on the abstractness of the movement-relevant training (action vs. move-gesture), and we found that it did not. Finally, we asked whether the time course of improvement differed for the two movement-relevant conditions, and we found that it did-gains in the action condition were realized immediately at posttest, with no further gains at retest; gains in the move-gesture condition were realized throughout, with comparable gains from pretest-to-posttest and from posttest-to-retest. Training that involves movement, whether concrete or abstract, can thus benefit children's mental transformation skill. However, the benefits unfold differently over time-the benefits of concrete training unfold immediately after training (online learning); the benefits of more abstract training unfold in equal steps immediately after training (online learning) and during the intervening week with no additional training (offline learning). These findings have implications for the kinds of instruction that can best support spatial learning.


title: A Scalable Framework for Provisioning Large-Scale IoT Deployments

abstract: Internet of Things (IoT) devices are usually considered external application dependencies that only provide data or process and execute simple instructions. The recent emergence of IoT devices with embedded execution environments allows practitioners to deploy and execute custom application logic directly on the device. This approach fundamentally changes the overall process of designing, developing, deploying, and managing IoT systems. However, these devices exhibit significant differences in available execution environments, processing, and storage capabilities. To accommodate this diversity, a structured approach is needed to uniformly and transparently deploy application components onto a large number of heterogeneous devices. This is especially important in the context of large-scale IoT systems, such as in the smart city domain. In this article, we present LEONORE, an infrastructure toolset that provides elastic provisioning of application components on resource-constrained and heterogeneous edge devices in large-scale IoT deployments. LEONORE supports push-based as well as pull-based deployments. To improve scalability and reduce generated network traffic between cloud and edge infrastructure, we present a distributed provisioning approach that deploys LEONORE local nodes within the deployment infrastructure close to the actual edge devices. We show that our solution is able to elastically provision large numbers of devices using a testbed based on a real-world industry scenario.


title: Investigations on non-planar wideband electro-magnetic dipole antenna with dielectric superstrate

abstract: This paper presents the effect of the dielectric superstrates on bandwidth and gain of Electro-Magnetic Dipole (EMD) antenna without and loaded with superstrates. It is found that there is a slight degradation in the performance of the antenna when the superstrate is touching the antenna i.e height of the superstrate above the EMD (H =0 mm). Further, it is also observed that the degraded performance characteristics of the EMD antenna can be improved by placing the dielectric superstrates above the EMD at optimum height (H) = H opt . The EMD antenna without dielectric superstrate achieves an impedance bandwidth of 3.354 GHz (SWR< 2) at 3.20 GHz, and loaded with dielectric superstrates which shows that the resonate frequency is decreased and achieved impedance bandwidth is 3.351GHz (SWR< 2) at 3.37 GHz. As the dielectric constant of the superstrate increases, it has been observed that the center frequency 3.39 GHz and gain is decreased to 7.40 dB from 7.52 dB. As the height of the superstrate is increased the performance of the antenna improves and at particular optimum height, the gain and bandwidth for all the superstrates will be closer to the free space radiation conditions of the EMD antenna without superstate. However, the return-loss for all dielectric constants of the superstrates is more than −18.60 dB which is well above the acceptable limit. The variation in return-loss is within acceptable limits. This antenna is stable radiation pattern with low cross polarization, low back radiation, nearly identical E- and H-plane patterns. There is a good agreement between simulated and measured results.


title: A Practical Randomized CP Tensor Decomposition

abstract: The CANDECOMP/PARAFAC (CP) decomposition is a leading method for the analysis of multiway data. The standard alternating least squares algorithm for the CP decomposition (CP-ALS) involves a series ...


title: Reduced Complexity LDPC Decoding using Forced Convergence

abstract: Low-density parity-check (LDPC) codes have recently emerged as a viable option for forward error correction (FEC) in future wireless communications systems. One factor influencing the decision of whether to use a specific FEC technique in a system is its suitability for real time systems – and hence, its computational complexity. In this paper, we present a novel LDPC decoding architecture, where we use a threshold rule to decide whether a variable node of the LDPC decoder should update its information in subsequent iterations of the decoding process, or be considered as converged. We show that by choosing this threshold appropriately, decoding complexity can be significantly reduced with only negligible deterioration in error correction performance.


title: Women's clitoris, vagina, and cervix mapped on the sensory cortex: fMRI evidence.

abstract: INTRODUCTION ::: The projection of vagina, uterine cervix, and nipple to the sensory cortex in humans has not been reported. ::: ::: ::: AIMS ::: The aim of this study was to map the sensory cortical fields of the clitoris, vagina, cervix, and nipple, toward an elucidation of the neural systems underlying sexual response. ::: ::: ::: METHODS ::: Using functional magnetic resonance imaging (fMRI), we mapped sensory cortical responses to clitoral, vaginal, cervical, and nipple self-stimulation. For points of reference on the homunculus, we also mapped responses to the thumb and great toe (hallux) stimulation. ::: ::: ::: MAIN OUTCOME MEASURES ::: The main outcome measures used for this study were the fMRI of brain regions activated by the various sensory stimuli. ::: ::: ::: RESULTS ::: Clitoral, vaginal, and cervical self-stimulation activated differentiable sensory cortical regions, all clustered in the medial cortex (medial paracentral lobule). Nipple self-stimulation activated the genital sensory cortex (as well as the thoracic) region of the homuncular map. ::: ::: ::: CONCLUSION ::: The genital sensory cortex, identified in the classical Penfield homunculus based on electrical stimulation of the brain only in men, was confirmed for the first time in the literature by the present study in women applying clitoral, vaginal, and cervical self-stimulation, and observing their regional brain responses using fMRI. Vaginal, clitoral, and cervical regions of activation were differentiable, consistent with innervation by different afferent nerves and different behavioral correlates. Activation of the genital sensory cortex by nipple self-stimulation was unexpected, but suggests a neurological basis for women's reports of its erotogenic quality.


title: Superstrate-Enhanced Ultrawideband Tightly Coupled Array With Resistive FSS

abstract: An efficient low-profile ultrawideband tightly coupled array employing a resistive frequency selective surface (FSS) and a superstrate is introduced. The FSS suppresses destructive ground plane interference, resulting in an increase in the array bandwidth by a factor greater than two. A superstrate is used to alleviate losses due to the resistive FSS. It is shown that a properly designed superstrate reduces losses by about 2.5 dB. The proposed array, which employs tightly coupled bowtie elements, achieves very low profile (0.055λlow) , 21:1 bandwidth, and radiation efficiency greater than 73% across the band. This paper presents the analysis and design of the array, particularly the FSS and superstrate. To validate the design, measurements of a prototype array are presented as well.


A review of the design methods of complex topology structures for 3D printing


title: Weakly Supervised Object Boundaries

abstract: State-of-the-art learning based boundary detection methods require extensive training data. Since labelling object boundaries is one of the most expensive types of annotations, there is a need to relax the requirement to carefully annotate images to make both the training more affordable and to extend the amount of training data. In this paper we propose a technique to generate weakly supervised annotations and show that bounding box annotations alone suffice to reach high-quality object boundaries without using any object-specific boundary annotations. With the proposed weak supervision techniques we achieve the top performance on the object boundary detection task, outperforming by a large margin the current fully supervised state-of-theart methods.


Judgment Before Emotion: People Access Moral Evaluations Faster than Affective States.


title: Studies of fuel dispersion after pin failure: Analysis of assumed blockage accidents for the MYRRHA–FASTEF critical core

abstract: Abstract The present work has been carried out in the framework of the European FP7 project SEARCH, in which the MYRRHA demonstrator reactor is designed to be able to operate both in ADS mode and in critical mode using lead–bismuth eutectic (LBE) as primary coolant. According to the project task definition, the pin failure and fuel dispersion scenarios in severe accidents had to be extensively studied for reactor safety analysis. In this paper, the unprotected severe transients analyses for the MYRRHA–FASTEF critical core were performed using the SIMMER-III code. The aim of the current work is to obtain a deeper understanding of core material redistribution processes after pin damage. Since the fuel has almost the same density as the coolant, its pellets, chunks and particles will essentially be carried by the coolant flow, thus moving upwards out of the core and redistributing into the upper pool region and peripheral structures. Starting the simulations from the steady state configuration, relevant parameters reflect good agreement with the design operational conditions. For the transients, the most severe accident scenario proposed, that may possibly lead to pin failure and furthermore core damage, is the unprotected blockage accident (UBA). The calculation results show that after pin failure, the mobile fuel starts to re-distribute. In the meantime, the reactor stabilizes to shut-down status because of the fuel loss. Our results show that the blockage propagation is impossible thanks to the gap between fuel subassemblies.


title: Automatic Verb Classification Based on Statistical Distributions of Argument Structure

abstract: Automatic acquisition of lexical knowledge is critical to a wide range of natural language processing tasks. Especially important is knowledge about verbs, which are the primary source of relational information in a sentence---the predicate-argument structure that relates an action or state to its participants (i.e., who did what to whom). In this work, we report on supervised learning experiments to automatically classify three major types of English verbs, based on their argument structure--specifically, the thematic roles they assign to participants. We use linguistically-motivated statistical indicators extracted from large annotated corpora to train the classifier, achieving 69.8% accuracy for a task whose baseline is 34%, and whose expert-based upper bound we calculate at 86.5%. A detailed analysis of the performance of the algorithm and of its errors confirms that the proposed features capture properties related to the argument structure of the verbs. Our results validate our hypotheses that knowledge about thematic relations is crucial for verb classification, and that it can be gleaned from a corpus by automatic means. We thus demonstrate an effective combination of deeper linguistic knowledge with the robustness and scalability of statistical techniques.


title: Research on a kind of energy interconnection communication network architecture based on the technology of SDN

abstract: In this paper, we make use of the software defined network (SDN) technology's characteristic of separating the function of forwarding and control to concentrate control function in the control layer (comprised by SDN controllers), and design a kind of communication network architecture for energy interconnection. Firstly, we give the concept of Intelligent Energy Controller (IEC), and make it as the device to arrange information from underlying network and implement commands from upper layer. Then, we describe the architecture and running process of the system, by introducing in detail the design of the architecture and the flow of information flowing in the network and how to process them. At the end, we analyze the benefits and hidden dangers of the architecture, to reveal the advantages and disadvantages of the architecture, as a reference to related researches.


title: PubTator: a web-based text mining tool for assisting biocuration

abstract: Manually curating knowledge from biomedical literature into structured databases is highly expensive and time-consuming, making it difficult to keep pace with the rapid growth of the literature. There is therefore a pressing need to assist biocuration with automated text mining tools. Here, we describe PubTator, a web-based system for assisting biocuration. PubTator is different from the few existing tools by featuring a PubMed-like interface, which many biocurators find familiar, and being equipped with multiple challenge-winning text mining algorithms to ensure the quality of its automatic results. Through a formal evaluation with two external user groups, PubTator was shown to be capable of improving both the efficiency and accuracy of manual curation. PubTator is publicly available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator/.


title: Conserving self-control strength.

abstract: Individuals may be motivated to limit their use of self-control resources, especially when they have depleted some of that resource. Expecting to need self-control strength in the future should heighten the motivation to conserve strength. In 4 experiments, it was found that depleted participants who anticipated exerting self-control in the future performed more poorly in an intervening test of self-control than participants who were not depleted, and more poorly than those who did not expect to exert self-control in the future. Conversely, those who conserved strength performed better on tasks that they conserved the strength for as compared with those who did not conserve. The underlying economic or conservation of resource model sheds some light on the operation of self-control strength.


title: Failure data analysis of a large-scale heterogeneous server environment

abstract: The growing complexity of hardware and software mandates the recognition of fault occurrence in system deployment and management. While there are several techniques to prevent and/or handle faults, there continues to be a growing need for an in-depth understanding of system errors and failures and their empirical and statistical properties. This understanding can help evaluate the effectiveness of different techniques for improving system availability, in addition to developing new solutions. In this paper, we analyze the empirical and statistical properties of system errors and failures from a network of nearly 400 heterogeneous servers running a diverse workload over a year. While improvements in system robustness continue to limit the number of actual failures to a very small fraction of the recorded errors, the failure rates are significant and highly variable. Our results also show that the system error and failure patterns are comprised of time-varying behavior containing long stationary intervals. These stationary intervals exhibit various strong correlation structures and periodic patterns, which impact performance but also can be exploited to address such performance issues.


title: A novel dual-polarized dielectric resonator antenna for 3.5 GHz LTE base station

abstract: A novel dual-polarized dielectric resonator antenna (DRA) for 3.5 GHz LTE base station is presented in this paper. In the design, a hybrid feeding technique is used to obtain high-isolation and low cross-polarization. The bandwidths of both ports are increased by digging a symmetric hole in the dielectric resonator (DR). In addition, the symmetric radiation patterns could be obtained because of the strictly symmetric configuration of the antenna. Simulated result demonstrates that, the impedance bandwidths (VSWR 34dB) and low cross-polarization level (>30dB) are also achieved over the entire operating bandwidth. The antenna has symmetric radiation patterns and 90 ± 5° half-power beamwidths in both ports.


title: Cloudlet-based Efficient Data Collection in Wireless Body Area Networks

abstract: Abstract Wireless Body Area Networks (WBANs) have developed as an effective solution for a wide range of healthcare, military and sports applications. Most of the proposed works studied efficient data collection from individual and traditional WBANs. Cloud computing is a new computing model that is continuously evolving and spreading. This paper presents a novel cloudlet-based efficient data collection system in WBANs. The goal is to have a large scale of monitored data of WBANs to be available at the end user or to the service provider in reliable manner. A prototype of WBANs, including Virtual Machine (VM) and Virtualized Cloudlet (VC) has been proposed for simulation characterizing efficient data collection in WBANs. Using the prototype system, we provide a scalable storage and processing infrastructure for large scale WBANs system. This infrastructure will be efficiently able to handle the large size of data generated by the WBANs system, by storing these data and performing analysis operations on it. The proposed model is fully supporting for WBANs system mobility using cost effective communication technologies of WiFi and cellular which are supported by WBANs and VC systems. This is in contrast of many of available mHealth solutions that is limited for high cost communication technology, such as 3G and LTE. Performance of the proposed prototype is evaluated via an extended version of CloudSim simulator. It is shown that the average power consumption and delay of the collected data is tremendously decreased by increasing the number of VMs and VCs.


title: Interpersonal Emotion Regulation: Implications for Affiliation, Perceived Support, Relationships, and Well-Being

abstract: [Correction Notice: An Erratum for this article was reported in Vol 115(4) of Journal of Personality and Social Psychology (see record 2018-45703-004). In the article, there were several errors in Appendices A and B. The formatting of Appendix A rendered it inaccessible to the reader, the volume and page range for the Williams et al. citation was not updated to reflect final publication information, and two e-mail addresses were not correct. Those e-mail addresses should appear as wcwill@alumni.stanford.edu and jzaki@stanford.edu. For Appendix B, the John et al. (2008) citation should appear directly below the Prosociality heading. The online version of this article has been corrected.] People often recruit social resources to manage their emotions, a phenomenon known as interpersonal emotion regulation (IER). Despite its importance, IER’s psychological structure remains poorly understood. We propose that two key dimensions describe IER: (a) individuals’ tendency to pursue IER in response to emotional events, and (b) the efficacy with which they perceive IER improves their emotional lives. To probe these dimensions, we developed the Interpersonal Regulation Questionnaire (IRQ), a valid and reliable measure of individual differences in IER. Factor analyses of participants’ responses confirmed tendency and efficacy as independent dimensions of IER (Study 1; N = 285), and demonstrated independence between how individuals engage with IER in response to negative, versus positive, emotion. In Study 2 (N = 347), we found that individuals high in IER tendency and efficacy are more emotionally expressive, empathetic, and socially connected. Two subsequent studies highlighted behavioral consequences of IER dimensions: people high in IER tendency sought out others more often following experimentally induced emotion (Study 3; N = 400), and individuals high in IER efficacy benefitted more from social support after real-world emotional events (Study 4; N = 787). Finally, a field study of social networks in freshman dormitories revealed that individuals high in IER tendency and efficacy developed more supportive relationships during the first year of college (Study 5; N = 193). These data (a) identify distinct dimensions underlying IER, (b) demonstrate that these dimensions can be stably measured and separated from related constructs, and (c) reveal their implications for relationships and well-being. (PsycINFO Database Record (c) 2018 APA, all rights reserved)


title: Segmentation and Classification of Skin Lesions for Disease Diagnosis

abstract: In this paper, a novel approach for automatic segmentation and classification of skin lesions is proposed. Initially, skin images are filtered to remove unwanted hairs and noise and then the segmentation process is carried out to extract lesion areas. For segmentation, a region growing method is applied by automatic initialization of seed points. The segmentation performance is measured with different well known measures and the results are appreciable. Subsequently, the extracted lesion areas are represented by color and texture features. SVM and k-NN classifiers are used along with their fusion for the classification using the extracted features. The performance of the system is tested on our own dataset of 726 samples from 141 images consisting of 5 different classes of diseases. The results are very promising with 46.71% and 34% of F-measure using SVM and k-NN classifier respectively and with 61% of F-measure for fusion of SVM and k-NN.


A comparative evaluation of systems for scalable linear algebra-based analytics


title: Anomaly Detection Using Program Control Flow Graph Mining From Execution Logs

abstract: We focus on the problem of detecting anomalous run-time behavior of distributed applications from their execution logs. Specifically we mine templates and template sequences from logs to form a control flow graph (cfg) spanning distributed components. This cfg represents the baseline healthy system state and is used to flag deviations from the expected behavior of runtime logs. The novelty in our work stems from the new techniques employed to: (1) overcome the instrumentation requirements or application specific assumptions made in prior log mining approaches, (2) improve the accuracy of mined templates and the cfg in the presence of long parameters and high amount of interleaving respectively, and (3) improve by orders of magnitude the scalability of the cfg mining process in terms of volume of log data that can be processed per day. We evaluate our approach using (a) synthetic log traces and (b) multiple real-world log datasets collected at different layers of application stack. Results demonstrate that our template mining, cfg mining, and anomaly detection algorithms have high accuracy. The distributed implementation of our pipeline is highly scalable and has more than 500 GB/day of log data processing capability even on a 10 low-end VM based (Spark + Hadoop) cluster. We also demonstrate the efficacy of our end-to-end system using a case study with the Openstack VM provisioning system.


title: Cyborg beast: a low-cost 3d-printed prosthetic hand for children with upper-limb differences

abstract: BackgroundThere is an increasing number of children with traumatic and congenital hand amputations or reductions. Children's prosthetic needs are complex due to their small size, constant growth, and psychosocial development. Families’ financial resources play a crucial role in the prescription of prostheses for their children, especially when private insurance and public funding are insufficient. Electric-powered (i.e., myoelectric) and body-powered (i.e., mechanical) devices have been developed to accommodate children’s needs, but the cost of maintenance and replacement represents an obstacle for many families. Due to the complexity and high cost of these prosthetic hands, they are not accessible to children from low-income, uninsured families or to children from developing countries. Advancements in computer-aided design (CAD) programs, additive manufacturing, and image editing software offer the possibility of designing, printing, and fitting prosthetic hands devices at a distance and at very low cost. The purpose of this preliminary investigation was to describe a low-cost three-dimensional (3D)-printed prosthetic hand for children with upper-limb reductions and to propose a prosthesis fitting methodology that can be performed at a distance.ResultsNo significant mean differences were found between the anthropometric and range of motion measurements taken directly from the upper limbs of subjects versus those extracted from photographs. The Bland and Altman plots show no major bias and narrow limits of agreements for lengths and widths and small bias and wider limits of agreements for the range of motion measurements. The main finding of the survey was that our prosthetic device may have a significant potential to positively impact quality of life and daily usage, and can be incorporated in several activities at home and in school.ConclusionsThis investigation describes a low-cost 3D-printed prosthetic hand for children and proposes a distance fitting procedure. The Cyborg Beast prosthetic hand and the proposed distance-fitting procedures may represent a possible low-cost alternative for children in developing countries and those who have limited access to health care providers. Further studies should examine the functionality, validity, durability, benefits, and rejection rate of this type of low-cost 3D-printed prosthetic device.


title: Skin detection based on image color segmentation with histogram and K-means clustering

abstract: Skin detection is a crucial pre-processing step for finding human faces in images. The challenging task is to find a reliable, yet efficient method for detection of skin region(s). In this paper, we proposed a new, simple and efficient method for skin detection based on image segmentation of different color spaces, and simple clustering technique (K-means) for clustering similar pixels on an image. Three K-means input features are used : a) two components from two different color spaces (Hue, Cr, Cb), b) positions of pixels on an image and c) rough estimation of skin pixels obtained from skin-color based detection. Our approach showed promising results on human images from different ethnicities, with simple background and high illumination. The computational cost of the method has been very low, since no training data is required. Results indicate that the method is suitable as a pre-processing step for some supervised method for advanced human skin segmentation and detection.


title: Biogas production from maize grains and maize silage

abstract: The main objective of this work was anaerobic digestion of maize grains and maize silage and biogas production from these crops. Maize grains were treated using one-stage and two-stage anaerobic techniques; using hydrolysis and acidification as the first stage and methanogenesis as the second stage. Processing nonacidified maize grains in an anaerobic reactor is more stable, though the anaerobic degradation start-up period is longer, specific production of biogas is lower and excess sludge production is higher as from acidified maize grains. Maximum specific biogas production was 0.72 m 3 ·kg -1 of volatile suspended solids – VSS (nonacidified maize) (at 35oC) and 0.770 m 3 ·kg -1 VSS (acidified maize) during anaerobic digestion of maize grains. At average yield of 9 t·ha


title: Discovery of Online Shopping Patterns Across Websites

abstract: In the online world, customers can easily navigate to different online stores to make purchases. The products purchased on one site are often associated with product purchases on other sites e.g., a hotel reservation on one site and a car rental on another site. Whereas market basket analysis is often used to discover associations among products for brick-and-mortar stores, it is rarely applied in the online setting where consumers navigate among different online stores to buy products. We define online shopping patterns and develop two novel methods to perform market basket analysis across websites. While this research is motivated by online shopping applications, our contribution is mainly methodological. The two methods we develop in this paper can not only be used to identify various online shopping patterns across sites and products but can also be applied to settings where patterns exist across different dimensions. Experiments on both synthetic data and real online shopping data demonstrate the effectiveness of our methods.


title: Development: Estimating Quick–to-Market Software

abstract: Developers can use this new sizing metric called Web Objects and an adaptation of the Cocomo II model called WebMo to more accurately estimate Web based software development effort and duration. Based on work with over 40 projects, these estimation tools are especially useful for quick-to-market development efforts.


title: Big data in mobile social networks: a QoE-oriented framework

abstract: Due to the rapid development of mobile social networks, mobile big data play an important role in providing mobile social users with various mobile services. However, as mobile big data have inherent properties, current MSNs face a challenge to provide mobile social user with a satisfactory quality of experience. Therefore, in this article, we propose a novel framework to deliver mobile big data over content- centric mobile social networks. At first, the characteristics and challenges of mobile big data are studied. Then the content-centric network architecture to deliver mobile big data in MSNs is presented, where each datum consists of interest packets and data packets, respectively. Next, how to select the agent node to forward interest packets and the relay node to transmit data packets are given by defining priorities of interest packets and data packets. Finally, simulation results show the performance of our framework with varied parameters.


title: Evolving scalable and modular adaptive networks with Developmental Symbolic Encoding

abstract: Evolutionary neural networks, or neuroevolution, appear to be a promising way to build versatile adaptive systems, combining evolution and learning. One of the most challenging problems of neuroevolution is finding a scalable and robust genetic representation, which would allow to effectively grow increasingly complex networks for increasingly complex tasks. In this paper we propose a novel developmental encoding for networks, featuring scalability, modularity, regularity and hierarchy. The encoding allows to represent structural regularities of networks and build them from encapsulated and possibly reused subnetworks. These capabilities are demonstrated on several test problems. In particular for parity and symmetry problems we evolve solutions, which are fully general with respect to the number of inputs. We also evolve scalable and modular weightless recurrent networks capable of autonomous learning in a simple generic classification task. The encoding is very flexible and we demonstrate this by evolving networks capable of learning via neuromodulation. Finally, we evolve modular solutions to the retina problem, for which another well known neuroevolution method—HyperNEAT—was previously shown to fail. The proposed encoding outperformed HyperNEAT and Cellular Encoding also in another experiment, in which certain connectivity patterns must be discovered between layers. Therefore we conclude the proposed encoding is an interesting and competitive approach to evolve networks.


Manuscript Matcher: A Content and Bibliometrics-based Scholarly Journal Recommendation System


title: Semantic Similarity of Short Texts in Languages with a Deficient Natural Language Processing Support

abstract: Measuring the semantic similarity of short texts is a noteworthy problem since short texts are widely used on the Internet, in the form of product descriptions or captions, image and webpage tags, news headlines, etc. This paper describes a methodology which can be used to create a software system capable of determining the semantic similarity of two given short texts. The proposed LInSTSS approach is particularly suitable for application in situations when no large, publicly available, electronic linguistic resources can be found for the desired language. We describe the basic working principles of the system architecture we propose, as well as the stages of its construction and use. Also, we explain the procedure used to generate a paraphrase corpus which is then utilized in the evaluation process. Finally, we analyze the evaluation results obtained from a system created for the Serbian language, and we discuss possible improvements which would increase system accuracy.


title: Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies

abstract: The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008–2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0.


title: Unifying Map and Landmark Based Representations for Visual Navigation

abstract: This works presents a formulation for visual navigation that unifies map based spatial reasoning and path planning, with landmark based robust plan execution in noisy environments. Our proposed formulation is learned from data and is thus able to leverage statistical regularities of the world. This allows it to efficiently navigate in novel environments given only a sparse set of registered images as input for building representations for space. Our formulation is based on three key ideas: a learned path planner that outputs path plans to reach the goal, a feature synthesis engine that predicts features for locations along the planned path, and a learned goal-driven closed loop controller that can follow plans given these synthesized features. We test our approach for goal-driven navigation in simulated real world environments and report performance gains over competitive baseline approaches.


title: Improving Authorship Attribution: Optimizing Burrows' Delta Method*

abstract: Abstract Burrows' Delta Method (Burrows, 2002) is a leading method of authorship attribution. It can be used to shortlist potential authors from a list or to even identify potential authors. The technique has been extended by Hoover (2004a, 2006). In this investigation, we look at the choice of words for the word vector used, the size of the word vector, the similarity measure and the impact of corpus choice on the accuracy of text classification. Our results show a word frequency vector of between 200 and 300 words give the most accurate results (Aldridge, 2007). We also demonstrate a dramatic improvement in accuracy by adapting Burrows' Delta to the cosine similarity measure. Additionally, our results indicate areas where the word vector can be optimized still further for more accurate results.


title: The Potential Energy of an Autoencoder

abstract: Autoencoders are popular feature learning models, that are conceptually simple, easy to train and allow for efficient inference. Recent work has shown how certain autoencoders can be associated with an energy landscape, akin to negative log-probability in a probabilistic model, which measures how well the autoencoder can represent regions in the input space. The energy landscape has been commonly inferred heuristically, by using a training criterion that relates the autoencoder to a probabilistic model such as a Restricted Boltzmann Machine (RBM). In this paper we show how most common autoencoders are naturally associated with an energy function, independent of the training procedure, and that the energy landscape can be inferred analytically by integrating the reconstruction function of the autoencoder. For autoencoders with sigmoid hidden units, the energy function is identical to the free energy of an RBM, which helps shed light onto the relationship between these two types of model. We also show that the autoencoder energy function allows us to explain common regularization procedures, such as contractive training, from the perspective of dynamical systems. As a practical application of the energy function, a generative classifier based on class-specific autoencoders is presented.


title: BCC: Reducing False Aborts in Optimistic Concurrency Control with Low Cost for In-Memory Databases

abstract: The Optimistic Concurrency Control (OCC) method has been commonly used for in-memory databases to ensure transaction serializability --- a transaction will be aborted if its read set has been changed during execution. This simple criterion to abort transactions causes a large proportion of false positives, leading to excessive transaction aborts. Transactions aborted false-positively (i.e. false aborts) waste system resources and can significantly degrade system throughput (as much as 3.68x based on our experiments) when data contention is intensive. ::: ::: Modern in-memory databases run on systems with increasingly parallel hardware and handle workloads with growing concurrency. They must efficiently deal with data contention in the presence of greater concurrency by minimizing false aborts. This paper presents a new concurrency control method named Balanced Concurrency Control (BCC) which aborts transactions more carefully than OCC does. BCC detects data dependency patterns which can more reliably indicate unserializable transactions than the criterion used in OCC. The paper studies the design options and implementation techniques that can effectively detect data contention by identifying dependency patterns with low overhead. To test the performance of BCC, we have implemented it in Silo and compared its performance against that of the vanilla Silo system with OCC and two-phase locking (2PL). Our extensive experiments with TPC-W-like, TPC-C-like and YCSB workloads demonstrate that when data contention is intensive, BCC can increase transaction throughput by more than 3x versus OCC and more than 2x versus 2PL; meanwhile, BCC has comparable performance with OCC for workloads with low data contention.


title: Multi-target tracking by on-line learned discriminative appearance models

abstract: We present an approach for online learning of discriminative appearance models for robust multi-target tracking in a crowded scene from a single camera. Although much progress has been made in developing methods for optimal data association, there has been comparatively less work on the appearance models, which are key elements for good performance. Many previous methods either use simple features such as color histograms, or focus on the discriminability between a target and the background which does not resolve ambiguities between the different targets. We propose an algorithm for learning a discriminative appearance model for different targets. Training samples are collected online from tracklets within a time sliding window based on some spatial-temporal constraints; this allows the models to adapt to target instances. Learning uses an Ad-aBoost algorithm that combines effective image descriptors and their corresponding similarity measurements. We term the learned models as OLDAMs. Our evaluations indicate that OLDAMs have significantly higher discrimination between different targets than conventional holistic color histograms, and when integrated into a hierarchical association framework, they help improve the tracking accuracy, particularly reducing the false alarms and identity switches.


title: Mary Ann Liebert, Inc. Research on Presence in Virtual Reality: A Survey

abstract: Virtual Reality (VR) is starting to be used in psychological therapy around the world. However, a thorough understanding of the reason why VR is effective and what effect it has on the human psyche is still missing. Most research on this subject is related to the concept of presence. This paper gives an up-to-date overview of research in this diverse field. It starts with the most prevailing definitions and theories on presence, most of which attribute special roles for the mental process of attention and for mental models of the virtual space. A review of the phenomena thought to be effected by presence shows that there is still a strong need for research on this subject because little conclusive evidence exists regarding the relationship between presence and phenoma such as emotional responses to virtual stimuli. An investigation shows there has been substantial research for developing methods for measuring presence and research regarding factors that contribute to presence. Knowledge of these contributing factors can play a vital role in development of new VR applications, but key knowledge elements in this area are still missing.


title: Data visualization with multidimensional scaling

abstract: We discuss methodology for multidimensional scaling (MDS) and its implementation in two software systems, GGvis and XGvis. MDS is a visualization technique for proximity data, that is, data in the form of N × N dissimilarity matrices. MDS constructs maps (“configurations,” “embeddings”) in IRk by interpreting the dissimilarities as distances. Two frequent sources of dissimilarities are high-dimensional data and graphs. When the dissimilarities are distances between high-dimensional objects, MDS acts as a (often nonlinear) dimension-reduction technique. When the dissimilarities are shortest-path distances in a graph, MDS acts as a graph layout technique. MDS has found recent attention in machine learning motivated by image databases (“Isomap”). MDS is also of interest in view of the popularity of “kernelizing” approaches inspired by Support Vector Machines (SVMs; “kernel PCA”).This article discusses the following general topics: (1) the stability and multiplicity of MDS solutions; (2) the analysis of struc...


title: Co-creation experiences: The next practice in value creation

abstract: Consumers today have more choices of products and services than ever before, but they seem dissatisfied. Firms invest in greater product variety but are less able to differentiate themselves. Growth and value creation have become the dominant themes for managers. In this paper, we explain this paradox. The meaning of value and the process of value creation are rapidly shifting from a product- and firm-centric view to personalized consumer experiences. Informed, networked, empowered, and active consumers are increasingly co-creating value with the firm. The interaction between the firm and the consumer is becoming the locus of value creation and value extraction. As value shifts to experiences, the market is becoming a forum for conversation and interactions between consumers, consumer communities, and firms. It is this dialogue, access, transparency, and understanding of risk-benefits that is central to the next practice in value creation.


title: Social Media Image Analysis for Public Health

abstract: Several projects have shown the feasibility to use emph{textual} social media data to track public health concerns, such as temporal influenza patterns or geographical obesity patterns. In this paper, we look at whether geo-tagged emph{images} from Instagram also provide a viable data source. Especially for "lifestyle" diseases, such as obesity, drinking or smoking, images of social gatherings could provide information that is not necessarily shared in, say, tweets. In this study, we explore whether (i) tags provided by the users and (ii) annotations obtained via automatic image tagging are indeed valuable for studying public health. We find that both user-provided and machine-generated tags provide information that can be used to infer a county's health statistics. Whereas for most statistics user-provided tags are better features, for predicting excessive drinking machine-generated tags such as "liquid' and "glass' yield better models. This hints at the potential of using machine-generated tags to study substance abuse.


title: Bridging the gap between extractive and abstractive summaries: Creation and evaluation of coherent extracts from heterogeneous sources

abstract: In our work, we present a corpus of heterogeneous documents for summarization to address the issue that information seekers usually face a range of different types of information sources. A second issue we address, is the summary type, as most manual summaries are abstractive, whereas automatic methods mainly create extractive summaries, which are hard to compare to each other using standard evaluation methods. Therefore, we suggest a multi-step process for creating \emph{coherent extracts}, which are based on information taken directly from the source documents, but minimally redacted and meaningfully ordered to form a coherent text. Our qualitative and quantitative evaluation show that quantitative results are not sufficient to judge the quality of a summary and that other quality criteria, such as coherence, should also be taken into account. We find that our corpus is of high quality and that it has the potential to bridge the gap between reference corpora of abstracts and automatic methods producing extracts. Our corpus is available to the research community for further development.


title: Energy Storage in Flywheels : An Overview Le stockage d ’ énergie dans les volants : Aperçu

abstract: In a deregulated power market with increasing penetration of distributed generators and renewable sources, energy storage becomes a necessity. Renewable energy sources are characterized by a fluctuating and intermittent nature, which simply means that energy may be available when it is not needed, and vice versa. Energy storage devices can help rectify the mismatch between generation and demand at any loading condition. Such devices can also provide some ancillary services, such as frequency regulation, voltage support, power quality improvement, transmission congestion relief, and system upgrade deferral. This paper presents an overview of the flywheel as a promising energy storage element. Electrical machines used with flywheels are surveyed along with their control techniques. Loss minimization and bearing system development are introduced. In addition, power system applications of flywheels are summarized. Finally, some outstanding technical difficulties are recommended for future research work.


title: Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence

abstract: The rapid growth of Location-based Social Networks (LBSNs) provides a great opportunity to satisfy the strong demand for personalized Point-of-Interest (POI) recommendation services. However, with the tremendous increase of users and POIs, POI recommender systems still face several challenging problems: (1) the hardness of modeling non-linear user-POI interactions from implicit feedback; (2) the difficulty of incorporating context information such as POIs' geographical coordinates. To cope with these challenges, we propose a novel autoencoder-based model to learn the non-linear user-POI relations, namely \textit{SAE-NAD}, which consists of a self-attentive encoder (SAE) and a neighbor-aware decoder (NAD). In particular, unlike previous works equally treat users' checked-in POIs, our self-attentive encoder adaptively differentiates the user preference degrees in multiple aspects, by adopting a multi-dimensional attention mechanism. To incorporate the geographical context information, we propose a neighbor-aware decoder to make users' reachability higher on the similar and nearby neighbors of checked-in POIs, which is achieved by the inner product of POI embeddings together with the radial basis function (RBF) kernel. To evaluate the proposed model, we conduct extensive experiments on three real-world datasets with many state-of-the-art baseline methods and evaluation metrics. The experimental results demonstrate the effectiveness of our model.


title: Discovering Petri Nets From Event Logs

abstract: As information systems are becoming more and more intertwined with the operational processes they support, multitudes of events are recorded by today’s information systems. The goal of process mining is to use such event data to extract process related information, e.g., to automatically discover a process model by observing events recorded by some system or to check the conformance of a given model by comparing it with reality. In this article, we focus on process discovery, i.e., extracting a process model from an event log. We focus on Petri nets as a representation language, because of the concurrent and unstructured nature of real-life processes. The goal is to introduce several approaches to discover Petri nets from event data (notably the α-algorithm, state-based regions, and language-based regions). Moreover, important requirements for process discovery are discussed. For example, process mining is only meaningful if one can deal with incompleteness (only a fraction of all possible behavior is observed) and noise (one would like to abstract from infrequent random behavior). These requirements reveal significant challenges for future research in this domain.


title: The process-oriented event model (PoEM): a conceptual model for industrial events

abstract: The paper presents a comprehensive theoretical framework for modeling events and semantics of event processing at a level of abstraction that captures the different processes in industrial applications but is not limited to a specific application domain. The model, called Process-oriented Event Model (PoEM), provides a formal approach to model real-world entities and their interrelationships, and specifies the process of moving from data streams to event detection to event-based goal planning. The model links event detection to states, actions, and roles, enabling event notification, filtering, context awareness and escalation. PoEM defines event and non-event concepts and combines information from them to build an event processing workflow. Usage of the PoEM model is illustrated in case studies from the oil and gas industry and maritime piracy events.


title: Identifying Argumentation Schemes in Genetics Research Articles

abstract: This paper presents preliminary work on identification of argumentation schemes, i.e., identifying premises, conclusion and name of argumentation scheme, in arguments for scientific claims in genetics research articles. The goal is to develop annotation guidelines for creating corpora for argumentation mining research. This paper gives the specification of ten semantically distinct argumentation schemes based on analysis of argumentation in several journal articles. In addition, it presents an empirical study on readers’ ability to recognize some of the argumentation schemes.


title: Location based services using geographical information systems

abstract: One of the most obvious technologies behind Location Based Services (LBS) is positioning, with the most widely recognized system being the Global Positioning System (GPS). There are, however, other means of positioning in addition to GPS. These other technologies are network based positioning and typically rely on various means of triangulation of the signal from cell sites serving a mobile phone. In addition, the serving cell site can be used as a fix for location of the user. More and more organizations have started to apply location-based services over Wireless LANs to enable improvements to applications. Generally, a LBS keeps track of the position of users on the network as they roam through the facility. A central system collects and integrates this position information to drive additional functions that identify the position of users in relation to the facility and pertinent areas, such as information on hospitals and medical centers, booths, emergency centers, stores, police stations, gas stations, etc. LBSs offer personalized services to the subscribers based on their current position using Global Navigation Satellite System (GNSS), Geographic Information System (GIS) and Wireless Communications and Networking technologies. LBSs offer modern world the tool for efficient management and continuous control. More and more people involve LBS in their industry and day-to-day life to better achieve their goals. In this paper, we present a review of LBS using Geographical Information Systems and two applications that we developed. These are automatic vehicle location, which can provide better management and control for a moving fleet of vehicles and police traffic information system.


title: Stream Compilation for Real-Time Embedded Multicore Systems

abstract: Multicore systems have not only become ubiquitous in the desktop and server worlds, but are also becoming the standard in the embedded space. Multicore offers programability and flexibility over traditional ASIC solutions. However, many of the advantages of switching to multicore hinge on the assumption that software development is simpler and less costly than hardware development. However, the design and development of correct, high-performance, multi-threaded programs is a difficult challenge for most programmers. Stream programming is one model that has wide applicability in the multimedia, signal processing, and networking domains. Streaming is convenient for developers because it separates the creation of actors, or functions that operate on packets of data, from the flow of data through the system. However, stream compilers are generally ineffective for embedded systems because they do not handle strict resource or timing constraints. Specifically, real-time deadlines and memory size limitations are not handled by conventional stream partitioning and scheduling techniques. This paper introducesthe SPIR compiler that orchestrates the execution of streaming applications with strict memory and timing constraints. Software defined radio or SDR is chosen as the application space to illustrate the effectiveness of the compiler for mapping applications onto the IBM Cell platform.


title: WaCo: A Wake-Up Radio COOJA Extension for Simulating Ultra Low Power Radios

abstract: Radio communication remains the primary battery consuming activity in wireless systems. Advances in MAC protocols have enabled significant lifetime improvements, but in systems with low data rate, idle listening, and other communication artifacts can begin to dominate costs. One proposal to combat this is the addition of a second, extremely low power radio component that is always-on. As a consequence of the extremely low power, such radios are incapable of decoding general data, and thus are often delegated the task of listening for a trigger, leading to the terminology wake-up radio , as this extremely low power radio is used to wake up a higher power radio, which is then used for data communication. While wake-up technology has been steadily evolving over the last decade in the hardware arena, few protocols have been developed to exploit it. In this work, we present WaCo , our wake-up radio COOJA extension that allows exploration of the capabilities of the wake-up radio from the desktop environment. We also use our extended simulator to concretely show the potential benefits of the wake-up radio hardware with two, standard data collection protocols. Our results simultaneously confirm that wake-up technology has tremendous potential and that our simulator extension provides an effective mechanism for such exploration.


title: REPAIRING NON-MANIFOLD TRIANGLE MESHES USING SIMULATED ANNEALING

abstract: In the field of reverse engineering one often faces the problem of repairing triangulations with holes, intersecting triangles, Mobius-band-like structures or other artifacts. In this paper we present a novel approach for generating manifold triangle meshes from such incomplete or imperfect triangulations. Even for heavily damaged triangulations, representing closed surfaces with arbitrary genus, our algorithm results in correct manifold triangle meshes. The algorithm is based on a randomized optimization technique from probability calculus called simulated annealing.


title: A Machine Learning Perspective on Predictive Coding with PAQ8

abstract: PAQ8 makes use of several simple machine learning models and algorithms. We show how understanding PAQ8 enables us to improve the algorithms. We also present a broad range of new applications of PAQ8 to machine learning tasks including language modeling and adaptive text prediction, adaptive game playing, classification, and lossy compression using features acquired via unsupervised learning.


title: An improved microcontroller-based sensorless brushless DC (BLDC) motor drive for automotive applications

abstract: The direct back EMF detection method previously described in a sensorless BLDC motor drive system J, Shao et al., (2002) synchronously samples the motor back EMF during the PWM off time without the need to sense or re-construct the motor neutral. Since this direct back EMF sensing scheme requires a minimum PWM off time to sample the back EMF signal, the duty cycle is limited to something less than 100%. In the paper, an improved direct back EMF detection scheme which samples the motor back EMF synchronously during either the PWM on time or the PWM off time is proposed to overcome the problem. In this paper, some techniques for automotive applications, such as motor rotation detection, and current sensing are proposed as well. Experimental results are presented.


title: Case Method Fast-Track: A Rad Approach

abstract: From the Book: ::: In writing this book I tried to achieve two different objectives. The first objective was to provide an overview of fast-track, the techniques it applies, and particularly the management challenges it presents. The second objective was to provide a handbook for project managers running fast-track projects. Putting these together might have endangered the second, and more pragmatic objective. This I did not want to do, so the book is split into two distinct parts. Chapters 1 to 3 deal with fast-track in general, Chapter 4 to 7 deal with the life-cycle of a project in more detail, identifying the tasks and their deliverable, the techniques and the tools that support them. These later chapters refer back to the relevant sections of the first part of the book so the descriptions of specific techniques are all gathered together in Chapter 3 and not scattered and duplicated through subsequent chapters. ::: I hope that this structure will mean a casual reader can gain an understanding of the fast-track approach quickly from the first part of the book, and the practitioner can have an organized and compact reference for daily use from the second part. ::: In assembling the second part I frequently found myself adding an extra step or an additional task, for completeness. The result is that, while not all projects will need every step of every task, they are all necessary under some circumstances. Every task is included in a project on the basis of what it produces. If a particular project has no need of the result, for example, documentation of test results, then it should not be produced and the task should be omitted. During the initial planning of a project the template project plan offeredin Chapters 4 to 6 should be assessed and tailored for the project, cutting out the unnecessary tasks and steps. Do not be afraid to prune. The questions to ask are: "Do I need this deliverable to manage the project?" and "Does the sponsor value this deliverable above its price?" I have used Oracle tools throughout in examples, and in particular I have recommended an approach that exploits the ability of the Oracle CASE tools to define rules once then reuse them many times. Some CASE tools do not exploit reusability in this way, but the trend in development technology is towards greater reusability so it seems very appropriate here. ::: Dai Clegg ::: July 1994


title: ContextVP: Fully Context-Aware Video Prediction.

abstract: Video prediction models based on convolutional networks, recurrent networks, and their combinations often result in blurry predictions. We identify an important contributing factor for imprecise predictions that has not been studied adequately in the literature: blind spots, i.e., lack of access to all relevant past information for accurately predicting the future. To address this issue, we introduce a fully context-aware architecture that captures the entire available past context for each pixel using Parallel Multi-Dimensional LSTM units and aggregates it using blending units. Our model outperforms a strong baseline network of 20 recurrent convolutional layers and yields state-of-the-art performance for next step prediction on three challenging real-world video datasets: Human 3.6M, Caltech Pedestrian, and UCF-101. Moreover, it does so with fewer parameters than several recently proposed models, and does not rely on deep convolutional networks, multi-scale architectures, separation of background and foreground modeling, motion flow learning, or adversarial training. These results highlight that full awareness of past context is of crucial importance for video prediction.


title: Embracing diversity in community healthcare settings: developing a client-centred approach to weaning support

abstract: Embracing diversity within a multi-ethnic and multi-faith population is an ongoing challenge for healthcare practitioners who seek to deliver patient centred care. This paper presents an account of a pilot scheme intended to provide client-centred help with weaning to 30 British Bangladeshi families through a specially trained support worker (infant feeding advisor). The infant feeding advisor visited each family at their home a minimum of five times during the weaning period, the final visit being when the child was one year old. Following the completion of the intervention, each family was visited by the project health visitor to evaluate progress. Evaluation showed that, in the majority of families, the pilot scheme was helpful. Parents’ responses appeared to indicate that one of the benefits of employing infant feeding advisors who were empathetic and knowledgeable about the culture of the families, and who had the relevant language skills, was a more effective exchange of health information and improved dialogue between the client and practitioner. Women, both mothers and grandmothers, showed an increased acceptance and use of health information, suggesting that the encouragement of a client-centred approach should be given priority in the development of new services. This pilot demonstrates that the development of patient centred services achieves not only improvements in health but also increased awareness of health issues among a diverse population.


title: Content-based citation analysis: The next generation of citation analysis

abstract: Traditional citation analysis has been widely applied to detect patterns of scientific collaboration, map the landscapes of scholarly disciplines, assess the impact of research outputs, and observe knowledge transfer across domains. It is, however, limited, as it assumes all citations are of similar value and weights each equally. Content-based citation analysis (CCA) addresses a citation’s value by interpreting each one based on its context at both the syntactic and semantic levels. This paper provides a comprehensive overview of CAA research in terms of its theoretical foundations, methodical approaches, and example applications. In addition, we highlight how increased computational capabilities and publicly available full-text resources have opened this area of research to vast possibilities, which enable deeper citation analysis, more accurate citation prediction, and increased knowledge discovery.


title: Information theoretic framework of trust modeling and evaluation for ad hoc networks

abstract: The performance of ad hoc networks depends on cooperation and trust among distributed nodes. To enhance security in ad hoc networks, it is important to evaluate trustworthiness of other nodes without centralized authorities. In this paper, we present an information theoretic framework to quantitatively measure trust and model trust propagation in ad hoc networks. In the proposed framework, trust is a measure of uncertainty with its value represented by entropy. We develop four Axioms that address the basic understanding of trust and the rules for trust propagation. Based on these axioms, we present two trust models: entropy-based model and probability-based model, which satisfy all the axioms. Techniques of trust establishment and trust update are presented to obtain trust values from observation. The proposed trust evaluation method and trust models are employed in ad hoc networks for secure ad hoc routing and malicious node detection. A distributed scheme is designed to acquire, maintain, and update trust records associated with the behaviors of nodes' forwarding packets and the behaviors of making recommendations about other nodes. Simulations show that the proposed trust evaluation system can significantly improve the network throughput as well as effectively detect malicious behaviors in ad hoc networks.


title: Metal/insulator/semiconductor carrier selective contacts for photovoltaic cells

abstract: A novel solar cell structure using metal/insulator (TiO 2 and NiO)/semiconductor (MIS) structures as carrier selective contacts on both Si and GaAs is proposed. These cells do not use any diffused p-n junction to separate the carriers. Simulations show that these cells can provide a very high open circuit voltage. Using MIS contacts a three terminal multi-material solar cell composed of two independently operating subcells of GaAs and Si is introduced. Since no current matching is required, a combined efficiency of 39% can be achieved. This technology can enable high efficiency solar cells at a low cost.


title: Novel method for planar microstrip antenna matching impedance

abstract: Because all microstrip antennas have to be matched to the standard generator impedance or load, the input impedance matching method for antenna is particularly important. In this paper a new methodology in achieving matching impedance of a planar microstrip antenna for wireless application is described. The method is based on embedding an Interdigital capacitor. The fine results obtained by using a microstrip Interdigital capacitor for matching antenna impedance led to an efficient method to improve array antenna performance. In fact, a substantial saving on the whole surfaces as well as enhancement of the gain, the directivity and the power radiated was achieved.


title: Analyzing Social and Communication Network Structures of Social Bots and Humans

abstract: Recently, several journalistic accounts have suggested that Twitter is becoming a bellwether for mis- and disinformation due to the pervasiveness of bots. These bots are either automated or semi-automated. Understanding the intent and usage of these bots has piqued the scientific curiosity among researchers. To that effect, in this study, we analyze the role of bots in two distinct categories of real-world events, i.e., natural disasters and sports. We collected over 1.2 million tweets that were generated by nearly 800,000 users for Hurricane Harvey, Hurricane Irma, Hurricane Maria, and Mexico Earthquake. We corroborate our analysis by examining bots that engaged with the 2018 Winter Olympics. We collected over 1.4 million tweets generated by nearly 700,000 users based on the hashtags #Olympics2018 and #PyeongChang2018. We examined the social and communication network of bots and humans for the aforementioned events. Our results show distinctive patterns in the network structures of bots when compared with that of humans. Content analysis of the tweets further revealed that bots used hashtags more uniformly than humans, across all the events.


title: Methodology for IT Governance Assessment and Design

abstract: IT Governance has become an increasingly important process in delivering business value from IT. A number of IT governance frameworks have been proposed as the MIT — CISR framework and COBIT. However, there is no proposal of a systematic methodology for IT Governance assessment and design, considering the possibility of joint usage of multiple frameworks. This paper presents a systematic methodology for IT governance assessment and design, specified through different phases, their activities and outcomes. This methodology is applied in a real case showing the practical benefit from its usage.


title: Torque Ripple Reduction of a Direct-Drive Permanent-Magnet Synchronous Machine by Material-Efficient Axial Pole Pairing

abstract: This paper investigates a material-efficient axial pole pairing method for torque ripple reduction in a direct-drive outer-rotor surface-mounted permanent-magnet synchronous machine. The effects of the magnet pole arc width on the torque ripple characteristics of the machine are first established by both analytical and 2-D finite element approaches. Furthermore, the effectiveness of the axial pole pairing technique in mitigating the machine cogging torque, back electromotive force harmonics, and overall torque quality is comprehensively examined. Finally, 3-D finite element analysis and experiments are carried out to validate the proposed approach, and the results show that axial pole pairing can be cost efficiently implemented in terms of magnet material usage and assembly.


title: Information Security Policy Compliance: An Empirical Study of Ethical Ideology

abstract: Information security policy compliance (ISP) is one of the key concerns that face organizations today. Although technical and procedural measures help improve information security, there is an increased need to accommodate human, social and organizational factors. Despite the plethora of studies that attempt to identify the factors that motivate compliance behavior or discourage abuse and misuse behaviors, there is a lack of studies that investigate the role of ethical ideology per se in explaining compliance behavior. The purpose of this research is to investigate the role of ethics in explaining Information Security Policy (ISP) compliance. In that regard, a model that integrates behavioral and ethical theoretical perspectives is developed and tested. Overall, analyses indicate strong support for the validation of the proposed theoretical model.


title: Video Event Understanding Using Natural Language Descriptions

abstract: Human action and role recognition play an important part in complex event understanding. State-of-the-art methods learn action and role models from detailed spatio temporal annotations, which requires extensive human effort. In this work, we propose a method to learn such models based on natural language descriptions of the training videos, which are easier to collect and scale with the number of actions and roles. There are two challenges with using this form of weak supervision: First, these descriptions only provide a high-level summary and often do not directly mention the actions and roles occurring in a video. Second, natural language descriptions do not provide spatio temporal annotations of actions and roles. To tackle these challenges, we introduce a topic-based semantic relatedness (SR) measure between a video description and an action and role label, and incorporate it into a posterior regularization objective. Our event recognition system based on these action and role models matches the state-of-the-art method on the TRECVID-MED11 event kit, despite weaker supervision.


title: Morphology and phylogeny of Agmasoma penaei (Microsporidia) from the type host, Litopenaeus setiferus, and the type locality, Louisiana, USA.

abstract: Since June 2012, samples of wild caught white shrimp, Litopenaeus setiferus, from the Gulf of Mexico, Plaquemines and Jefferson Parishes (Louisiana, USA) with clinical signs of microsporidiosis have been delivered to the Louisiana Aquatic Diagnostic Laboratory for identification. Infection was limited predominantly to female gonads and was caused by a microsporidium producing roundish pansporoblasts with eight spores (3.6×2.1 μm) and an anisofilar (2-3+4-6) polar filament. These features allowed identification of the microsporidium as Agmasoma penaei Sprague, 1950. Agmasoma penaei is known as a microsporidium with world-wide distribution, causing devastating epizootic disease among wild and cultured shrimps. This paper provides molecular and morphological characterisation of A. penaei from the type host and type locality. Comparison of the novel ssrDNA sequence of A. penaei from Louisiana, USA with that of A. penaei from Thailand revealed 95% similarity, which suggests these geographical isolates are two different species. The A. penaei sequences did not show significant homology to any other examined taxon. Phylogenetic reconstructions using the ssrDNA and alpha- and beta-tubulin sequences supported its affiliation with the Clade IV Terresporidia sensu Vossbrink 2005, and its association with parasites of fresh and salt water crustaceans of the genera Artemia, Daphnia and Cyclops.


title: A small-sized wall-climbing robot for anti-terror scout

abstract: This paper presents a small-sized wall climbing robot for anti-terror and rescue scout tasks, which takes advantage of a method called critical suction. In this paper the critical suction mechanism is discussed in theory. Furthermore, the fluid model of the robot's suction system is set up using the fluid network theory. According to the dynamic response equation of the negative pressure inner the suction cup, the robot suction process on the wall is simulated. Finally, the results of the experiments for adsorbing on the sorts of walls proved this type of robot can be adsorbed on the surfaces.


title: A novel nonvolatile memory with spin torque transfer magnetization switching: spin-ram

abstract: A novel nonvolatile memory utilizing spin torque transfer magnetization switching (STS), abbreviated spin-RAM hereafter, is presented for the first time. The spin-RAM is programmed by magnetization reversal through an interaction of a spin momentum-torque-transferred current and a magnetic moment of memory layers in magnetic tunnel junctions (MTJs), and therefore an external magnetic field is unnecessary as that for a conventional MRAM. This new programming mode has been accomplished owing to our tailored MTJ, which has an oval shape of 100 times 150 nm. The memory cell is based on a 1-transistor and a 1-MTJ (ITU) structure. The 4kbit spin-RAM was fabricated on a 4 level metal, 0.18 mum CMOS process. In this work, writing speed as high as 2 ns, and a write current as low as 200 muA were successfully demonstrated. It has been proved that spin-RAM possesses outstanding characteristics such as high speed, low power and high scalability for the next generation universal memory


title: Propagation characteristics of intra-body communications for body area networks

abstract: Body area networks (BAN) will become increasingly important in the future personal communication systems. We believe that intra-body communications (IBC) based on near field electromagnetic waves is a suitable solution for BAN. This paper presents experimental measurements of the frequency and time domain responses in order to investigate the transmission characteristics of the human body as a conductor of low power high frequency radio signals (up to 3GHz). In the measurements, several distances between transmitter and receiver (up to a long distance from the head to the foot) and six test people (to study the possible variation results due to physiological body effects) have been considered. Besides analyzing the intra-body channel response, we also aim to define a suitable frequency range for IBC. Finally, this paper also presents an initial evaluation study of several digital modulation schemes in order to investigate the optimal technique for IBC.


title: Analysis of cell-target encounter by random filopodial projections

abstract: Dynamic projections from the surface of many motile cell types provide for variable contact with the extracellular environment and can be important in regulating cell migration events. For example, during nerve development and regeneration, the sensory motile tip of the axon exhibits long, slender filopodia projecting from the growth cone periphery. Extension and retraction of these filopodia continually remodel the points of contact between axon and surroundings. Experimental studies show that filopodial contact with specific extracellular features can guide subsequent growth cone migration, suggesting a potentially important means of engineering nerve growth to repair nerve injury or construct biological neural networks. A simulation model is presented of the dynamic filopodial structure on the nerve growth cone based on recent experimental characterization. The model is analyzed to obtain quantitative relationships between average filopodial characteristics, which are commonly measured experimental quantities, and the underlying parameters of individual filopodium dynamics. It is then applied to simulate encounter between a growth cone and its target due to filopodial dynamics alone. Filopodial contribution to growth cone-target encounter is summarized in terms of a mean encounter time that is reminiscent of a first passage time for a diffusing particle. The parametric relationships in this study provide a basis for further investigation of filopodial-mediated mechanisms in nerve growth and other cellular processes.


Data Privacy through Optimal k-Anonymization


abstract: A hierarchical model of approach and avoidance achievement motivation was proposed and tested in a college classroom. Mastery, performance-approach, and performance-avoidance goals were assessed and their antecedents and consequences examined. Results indicated that mastery goals were grounded in achievement motivation and high competence expectancies; performance-avoidance goals, in fear of failure and low competence expectancies; and performance-approach goals, in ach.ievement motivation, fear of failure, and high competence expectancies. Mastery goals facilitated intrinsic motivation, performance-approach goals enhanced graded performance, and performanceavoidance goals proved inimical to both intrinsic motivation and graded performance. The proposed model represents an integration of classic and contemporary approaches to the study of achievement motivation. Achievement motivation is a ubiquitous feature of daily life. In the classroom, at the workplace, and on the ballfield individuals strive to be competent in their effortful activities. In the past decade, many theorists have utilized a social-cognitive, achievement goal approach in accounting for individuals' competence-relevant strivings. "Achievement goal" is commonly defined as the purpose of task engagement (Maehr, 1989), and the specific type of goal adopted is posited to create a framework for how individuals interpret, experience, and act in their achievement pursuits (Dweck, 1986; Nicholls, 1989). Achievement goal theorists commonly identify two distinct orientations toward competence: a performance goal focused on the demonstration of competence relevant to others, and a mastery goal focused on the development of competence and task mastery (Ames & Archer, 1987; for similar conceptualizations with different nomenclature see Dweck, 1986; NichoUs, 1984). The adoption of a performance goal is hypothesized to produce susceptibility to a "helpless" pattern of responses in achievement settings (e.g., a preference for easy or difficult tasks, withdrawal of effort in the face of failure, and decreased task enjoyment), whereas the adoption of a mastery goal is presumed to lead to a "mastery" motivational pattern (e.g., a preference for moderately challenging tasks, persistence in the face of failure, and enhanced task enjoyment; Ames, 1992; Dweck & Leggett, 1988; Nicholls, 1989). Most achievement goal theorists conceptualize both perfor


title: A probabilistic neural network for earthquake magnitude prediction

abstract: A probabilistic neural network (PNN) is presented for predicting the magnitude of the largest earthquake in a pre-defined future time period in a seismic region using eight mathematically computed parameters known as seismicity indicators. The indicators considered are the time elapsed during a particular number (n) of significant seismic events before the month in question, the slope of the Gutenberg-Richter inverse power law curve for the n events, the mean square deviation about the regression line based on the Gutenberg-Richter inverse power law for the n events, the average magnitude of the last n events, the difference between the observed maximum magnitude among the last n events and that expected through the Gutenberg-Richter relationship known as the magnitude deficit, the rate of square root of seismic energy released during the n events, the mean time or period between characteristic events, and the coefficient of variation of the mean time. Prediction accuracies of the model are evaluated using three different statistical measures: the probability of detection, the false alarm ratio, and the true skill score or R score. The PNN model is trained and tested using data for the Southern California region. The model yields good prediction accuracies for earthquakes of magnitude between 4.5 and 6.0. The PNN model presented in this paper complements the recurrent neural network model developed by the authors previously, where good results were reported for predicting earthquakes with magnitude greater than 6.0.


title: Semi-Supervised Learning of the Electronic Health Record for Phenotype Stratification

abstract: Patient interactions with health care providers result in entries to electronic health records (EHRs). EHRs were built for clinical and billing purposes but contain many data points about an individual. Mining these records provides opportunities to extract electronic phenotypes, which can be paired with genetic data to identify genes underlying common human diseases. This task remains challenging: high quality phenotyping is costly and requires physician review; many fields in the records are sparsely filled; and our definitions of diseases are continuing to improve over time. Here we develop and evaluate a semi-supervised learning method for EHR phenotype extraction using denoising autoencoders for phenotype stratification. By combining denoising autoencoders with random forests we find classification improvements across multiple simulation models and improved survival prediction in ALS clinical trial data. This is particularly evident in cases where only a small number of patients have high quality phenotypes, a common scenario in EHR-based research. Denoising autoencoders perform dimensionality reduction enabling visualization and clustering for the discovery of new subtypes of disease. This method represents a promising approach to clarify disease subtypes and improve genotype-phenotype association studies that leverage EHRs. GRAPHICAL ABSTRACT HIGHLIGHTS Denoising autoencoders (DAs) can model electronic health records. Semi-supervised learning with DAs improves ALS patient survival predictions. DAs improve patient cluster visualization through dimensionality reduction.


title: Analysis and Application of Discrete Halbach Magnet Array With Unequal Arc Lengths and Unequally Changed Magnetization Directions

abstract: This paper presents a general process to analyze and optimize the performance for an arbitrary discrete Halbach magnet array which is “unnormal” on magnetization direction and arc length of each segment. Besides the original type, a new arrangement of Halbach array is proposed, which does not have main PM segments. Two-dimensional field analytical models for predicting the air-gap field distribution of both arrangements are offered and validated by an FEA method. Then, a multi-objective optimizer was applied for different requirements, and comparison between normal and optimized 3-segment Halbach array is given to verify the effect of optimization. At last, some potential applications and follow-up study are discussed.


title: Online model selection and learning by multikernel adaptive filtering

abstract: We propose an efficient multikernel adaptive filtering algorithm with double regularizers, providing a novel pathway towards online model selection and learning. The task is the challenging nonlinear adaptive filtering under no knowledge about a suitable kernel. Under this limited-knowledge assumption on an underlying model of a system of interest, many possible kernels are employed and one of the regularizers, a block l 1 norm for kernel groups, contributes to selecting a proper model (relevant kernels) in online and adaptive fashion, preventing a nonlinear filter from overfitting to noisy data. The other regularizer is the block l 1 norm for data groups, contributing to updating the dictionary adaptively. As the resulting cost function contains two nonsmooth (but proximable) terms, we approximate the latter regularizer by its Moreau envelope and apply the adaptive proximal forwardbackward splitting method to the approximated cost function. Numerical examples show the efficacy of the proposed algorithm.


A two-level investigation of information systems outsourcing


title: Operational data augmentation in classifying single aerial images of animals

abstract: In deep learning, data augmentation is important to increase the amount of training images to obtain higher classification accuracies. Most data-augmentation methods adopt the use of the following techniques: cropping, mirroring, color casting, scaling and rotation for creating additional training images. In this paper, we propose a novel data-augmentation method that transforms an image into a new image containing multiple rotated copies of the original image in the operational classification stage. The proposed method creates a grid of n×n cells, in which each cell contains a different randomly rotated image and introduces a natural background in the newly created image. This algorithm is used for creating new training and testing images, and enhances the amount of information in an image. For the experiments, we created a novel dataset with aerial images of cows and natural scene backgrounds using an unmanned aerial vehicle, resulting in a binary classification problem. To classify the images, we used a convolutional neural network (CNN) architecture and compared two loss functions (Hinge loss and cross-entropy loss). Additionally, we compare the CNN to classical feature-based techniques combined with a k-nearest neighbor classifier or a support vector machine. The results show that the pre-trained CNN with our proposed data-augmentation technique yields significantly higher accuracies than all other approaches.


title: Distance functions on digital pictures

abstract: Abstract This paper describes algorithms for computing various functions on a digital picture which depend on the distance to a given subset of the picture. The algorithms involve local operations which are performed repeatedly, “in parallel”, on every picture element and its immediate neighbors. Applications to the detection of “clusters” and “regularities” in a picture, and to the dissection of a region into “pieces”, are also described.


Process Mining and Robotic Process Automation: A Perfect Match


title: Time Series Analysis of Clickstream Logs from Online Courses

abstract: Due to the rapidly rising popularity of Massive Open Online Courses (MOOCs), there is a growing demand for scalable automated support technologies for student learning. Transferring traditional educational resources to online contexts has become an increasingly relevant problem in recent years. For learning science theories to be applicable, educators need a way to identify learning behaviors of students which contribute to learning outcomes, and use them to design and provide personalized intervention support to the students. Click logs are an important source of information about students' learning behaviors, however current literature has limited understanding of how these behaviors are represented within click logs. In this project, we have exploited the temporal dynamics of student behaviors both to do behavior modeling via graphical modeling approaches and to do performance prediction via recurrent neural network approaches in order to first identify student behaviors and then use them to predict their final outcome in the course. Our experiments showed that the long short-term memory (LSTM) model is capable of learning long-term dependencies in a sequence and outperforms other strong baselines in the prediction task. Further, these sequential approaches to click log analysis can be successfully imported to other courses when used with results obtained from graphical model behavior modeling.