title: The Association between Health Behaviours and Academic Performance in Canadian Elementary School Students: A Cross-Sectional Study

abstract: Background: Establishing early healthy eating and physical activity behaviours is critical in supporting children’s long-term health and well-being. The objective of the current paper was to examine the association between health behaviours and academic performance in elementary school students in a school board in Nova Scotia, Canada. Methods: Our population-based study included students in grades 4–6 across 18 schools in a rural school board. Diet and physical activity were assessed through validated instruments. Academic performance measures were obtained from the school board for Mathematics and English Language Arts (ELA). Associations between health behaviours and academic performance were assessed using multilevel logistic regression. Results: Students with unhealthy lifestyle behaviours were more likely to have poor academic performance for both ELA and Mathematics compared to students with healthy lifestyle behaviours; associations were statistically significant for diet quality, physical activity, sugar-sweetened beverage consumption for ELA; and breakfast skipping, not being physically active at morning recess, and not being physically active after school for Mathematics. The effects of diet and physical activity were independent of each other and there was no interaction between the two exposures. Conclusions: Our findings suggest that support for healthy behaviours may help to improve academic outcomes of students.


MCAT Math Retrieval System for NTCIR-12 MathIR Task


title: Combining answer set programming with description logics for the Semantic Web

abstract: Towards the integration of rules and ontologies in the Semantic Web, we propose a combination of logic programming under the answer set semantics with the description logics SHIF(D) and SHOIN(D), which underly the Web ontology languages OWL Lite and OWL DL, respectively. This combination allows for building rules on top of ontologies but also, to a limited extent, building ontologies on top of rules. We introduce description logic programs (dl-programs), which consist of a description logic knowledge base L and a finite set of description logic rules (dl-rules) P. Such rules are similar to usual rules in logic programs with negation as failure, but may also contain queries to L, possibly default negated, in their bodies. We define Herbrand models for dl-programs, and show that satisfiable positive dl-programs have a unique least Her-brand model. More generally, consistent stratified dl-programs can be associated with a unique minimal Her-brand model that is characterized through iterative least Herbrand models. We then generalize the (unique) minimal Herbrand model semantics for positive and stratified dl-programs to a strong answer set semantics for all dl-programs, which is based on a reduction to the least model semantics of positive dl-programs. We also define a weak answer set semantics based on a reduction to the answer sets of ordinary logic programs. Strong answer sets are weak answer sets, and both properly generalize answer sets of ordinary normal logic programs. We then give fixpoint characterizations for the (unique) minimal Herbrand model semantics of positive and stratified dl-programs, and show how to compute these models by finite fixpoint iterations. Furthermore, we give a precise picture of the complexity of deciding strong and weak answer set existence for a dl-program.


title: On the impossibility of informationally efficient markets

abstract: If competitive equilibrium is defined as a situation in which prices are such that all arbitrage profits are eliminated, is it possible that a competitive economy always be in equilibrium? Clearly not, for then those who arbitrage make no (private) return from their (privately) costly activity. Hence the assumptions that all markets, including that for information, are always in equilibrium and always perfectly arbitraged are inconsistent when arbitrage is costly. We propose here a model in which there is an equilibrium degree of disequilibrium: prices reflect the information of informed individuals (arbitrageurs) but only partially, so that those who expend resources to obtain information do receive compensation. How informative the price system is depends on the number of individuals who are informed; but the number of individuals who are informed is itself an endogenous variable in the model. The model is the simplest one in which prices perform a well-articulated role in conveying information from the informed to the uninformed. When informed individuals observe information that the return to a security is going to be high, they bid its price up, and conversely when they observe information that the return is going to be low. Thus the price system makes publicly available the information obtained by informed individuals to the uninformed. In general, however, it does this imperfectly; this is perhaps lucky, for were it to do it perfectly , an equilibrium would not exist. In the introduction, we shall discuss the general methodology and present some conjectures concerning certain properties of the equilibrium. The remaining analytic sections of the paper are devoted to analyzing in detail an important example of our general model, in which our conjectures concerning the nature of the equilibrium can be shown to be correct. We conclude with a discussion of the implications of our approach and results, with particular emphasis on the relationship of our results to the literature on "efficient capital markets."


title: An Evaluation of the Challenges of Multilingualism in Data Warehouse Development

abstract: In this paper we discuss Business Intelligence and define what is meant by support for Multilingualism in a Business Intelligence reporting context. We identify support for Multilingualism as a challenging issue which has implications for data warehouse design and reporting performance. Data warehouses are a core component of most Business Intelligence systems and the star schema is the approach most widely used to develop data warehouses and dimensional Data Marts. We discuss the way in which Multilingualism can be supported in the Star Schema and identify that current approaches have serious limitations which include data redundancy and data manipulation, performance and maintenance issues. We propose a new approach to enable the optimal application of multilingualism in Business Intelligence. The proposed approach was found to produce satisfactory results when used in a proof-of-concept environment. Future work will include testing the approach in an enterprise environment.


title: Contraction of the Abdominal Muscles Associated With Movement of the Lower Limb

abstract: BACKGROUND AND PURPOSE ::: Activity of the trunk muscles is essential for maintaining stability of the lumbar spine because of the unstable structure of that portion of the spine. A model involving evaluation of the response of the lumbar multifidus and abdominal muscles to leg movement was developed to evaluate this function. ::: ::: ::: SUBJECTS ::: To examine this function in healthy persons, 9 male and 6 female subjects (mean age = 20.6 years, SD = 2.3) with no history of low back pain were studied. ::: ::: ::: METHODS ::: Fine-wire and surface electromyography electrodes were used to record the activity of selected trunk muscles and the prime movers for hip flexion, abduction, and extension during hip movements in each of those directions. ::: ::: ::: RESULTS ::: Trunk muscle activity occurring prior to activity of the prime mover of the limb was associated with hip movement in each direction. The transversus abdominis (TrA) muscle was invariably the first muscle that was active. Although reaction time for the TrA and oblique abdominal muscles was consistent across movement directions, reaction time for the rectus abdominis and multifidus muscles varied with the direction of limb movement. ::: ::: ::: CONCLUSION AND DISCUSSION ::: Results suggest that the central nervous system deals with stabilization of the spine by contraction of the abdominal and multifidus muscles in anticipation of reactive forces produced by limb movement. The TrA and oblique abdominal muscles appear to contribute to a function not related to the direction of these forces.


title: Synthetic aperture radar signal processing in parallel using GPGPU

abstract: In this work an efficient parallel implementation of the Chirp Scaling Algorithm for Synthetic Aperture Radar processing is presented. The architecture selected for the implementation is the general purpose graphic processing unit, as it is well suited for scientific applications and real-time implementation of algorithms. The analysis of a first implementation led to several improvements which resulted in an important speed-up. Details of the issues found are explained, and the performance improvement of their correction explicitly shown.


A model of intercultural communication competence


title: Towards foveated rendering for gaze-tracked virtual reality

abstract: Foveated rendering synthesizes images with progressively less detail outside the eye fixation region, potentially unlocking significant speedups for wide field-of-view displays, such as head mounted displays, where target framerate and resolution is increasing faster than the performance of traditional real-time renderers. To study and improve potential gains, we designed a foveated rendering user study to evaluate the perceptual abilities of human peripheral vision when viewing today's displays. We determined that filtering peripheral regions reduces contrast, inducing a sense of tunnel vision. When applying a postprocess contrast enhancement, subjects tolerated up to 2× larger blur radius before detecting differences from a non-foveated ground truth. After verifying these insights on both desktop and head mounted displays augmented with high-speed gaze-tracking, we designed a perceptual target image to strive for when engineering a production foveated renderer. Given our perceptual target, we designed a practical foveated rendering system that reduces number of shades by up to 70% and allows coarsened shading up to 30° closer to the fovea than Guenter et al. [2012] without introducing perceivable aliasing or blur. We filter both pre- and post-shading to address aliasing from undersampling in the periphery, introduce a novel multiresolution- and saccade-aware temporal antialising algorithm, and use contrast enhancement to help recover peripheral details that are resolvable by our eye but degraded by filtering. We validate our system by performing another user study. Frequency analysis shows our system closely matches our perceptual target. Measurements of temporal stability show we obtain quality similar to temporally filtered non-foveated renderings.


title: Computational Neuroscience Offers Hints for More General Machine Learning

abstract: Machine Learning has traditionally focused on narrow artificial intelligence - solutions for specific problems. Despite this, we observe two trends in the state-of-the-art: One, increasing architectural homogeneity in algorithms and models. Two, algorithms having more general application: New techniques often beat many benchmarks simultaneously. We review the changes responsible for these trends and look to computational neuroscience literature to anticipate future progress.


title: Monopole-Like and Boresight Pattern Reconfigurable Antenna

abstract: A novel pattern reconfigurable antenna is proposed to achieve two different radiation patterns by controlling the bias voltage of switches. Initially, the proposed antenna operates as a top-loaded monopole antenna fed by a co-planar waveguide (CPW). When the slot from the CPW is disturbed by switching on PIN diodes, it operates as a slot antenna. Therefore, when the switch turns on, the maximum radiation intensity occurs at the normal to the aperture surface defined as the boresight direction, which is similar to a slot antenna. When the switch turns off, the maximum radiation intensity occurs at right angles to the boresight direction, which is similar to a monopole antenna. The pattern reconfigurable capability is successfully demonstrated at 2.3 GHz by full-wave simulation and measurement.


title: Function representation of solids reconstructed from scattered surface points and contours

abstract: This paper presents a novel approach to the reconstruction of geometric models and surfaces from given sets of points using volume splines. It results in the representation of a solid by the inequality The volume spline is based on use of the Green’s function for interpolation of scalar function values of a chosen “carrier” solid. Our algorithm is capable of generating highly concave and branching objects automatically. The particular case where the surface is reconstructed from cross-sections is discussed too. Potential applications of this algorithm are in tomography, image processing, animation and CAD for bodies with complex surfaces.


title: The makers' movement and FabLabs in education: experiences, technologies, and research

abstract: In this paper, we introduce the origins and applications of digital fabrication and "making" in education, and discuss how they can be implemented, researched, and developed in schools. Our discussion is based on several papers and posters that we summarize into three categories: research, technology development, and experiences in formal and informal education.


title: Gaussian process modulated renewal processes

abstract: Renewal processes are generalizations of the Poisson process on the real line whose intervals are drawn i.i.d. from some distribution. Modulated renewal processes allow these interevent distributions to vary with time, allowing the introduction of nonstationarity. In this work, we take a nonparametric Bayesian approach, modelling this nonstationarity with a Gaussian process. Our approach is based on the idea of uniformization, which allows us to draw exact samples from an otherwise intractable distribution. We develop a novel and efficient MCMC sampler for posterior inference. In our experiments, we test these on a number of synthetic and real datasets.


title: An empirical investigation of the factors affecting data warehousing success

abstract: The IT implementation literature suggests that various implementation factors play critical roles in the success of an information system; however, there is little empirical research about the implementation of data warehousing projects. Data warehousing has unique characteristics that may impact the importance of factors that apply to it. In this study, a cross-sectional survey investigated a model of data warehousing success. Data warehousing managers and data suppliers from 111 organizations completed paired mail questionnaires on implementation factors and the success of the warehouse. The results from a Partial Least Squares analysis of the data identified significant relationships between the system quality and data quality factors and perceived net benefits. It was found that management support and resources help to address organizational issues that arise during warehouse implementations; resources, user participation, and highly-skilled project team members increase the likelihood that warehousing projects will finish on-time, on-budget, with the right functionality; and diverse, unstandardized source systems and poor development technology will increase the technical issues that project teams must overcome. The implementation's success with organizational and project issues, in turn, influence the system quality of the data warehouse; however, data quality is best explained by factors not included in the research model.


title: Utilizing passage-based language models for ad hoc document retrieval

abstract: To cope with the fact that, in the ad hoc retrieval setting, documents relevant to a query could contain very few (short) parts (passages) with query-related information, researchers proposed passage-based document ranking approaches. We show that several of these retrieval methods can be understood, and new ones can be derived, using the same probabilistic model. We use language-model estimates to instantiate specific retrieval algorithms, and in doing so present a novel passage language model that integrates information from the containing document to an extent controlled by the estimated document homogeneity. Several document-homogeneity measures that we present yield passage language models that are more effective than the standard passage model for basic document retrieval and for constructing and utilizing passage-based relevance models; these relevance models also outperform a document-based relevance model. Finally, we demonstrate the merits in using the document-homogeneity measures for integrating document-query and passage-query similarity information for document retrieval.


title: Arduino: A low-cost multipurpose lab equipment

abstract: Typical experiments in psychological and neurophysiological settings often require the accurate control of multiple input and output signals. These signals are often generated or recorded via computer software and/or external dedicated hardware. Dedicated hardware is usually very expensive and requires additional software to control its behavior. In the present article, I present some accuracy tests on a low-cost and open-source I/O board (Arduino family) that may be useful in many lab environments. One of the strengths of Arduinos is the possibility they afford to load the experimental script on the board’s memory and let it run without interfacing with computers or external software, thus granting complete independence, portability, and accuracy. Furthermore, a large community has arisen around the Arduino idea and offers many hardware add-ons and hundreds of free scripts for different projects. Accuracy tests show that Arduino boards may be an inexpensive tool for many psychological and neurophysiological labs.


Title Insights on Ceramics as Dental Materials . Part I : CeramicMaterial Types in Dentistry


title: Deep learning for automatic sale receipt understanding

abstract: As a general rule, data analytics are now mandatory for companies. Scanned document analysis brings additional challenges introduced by paper damages and scanning quality. In an industrial context, this work focuses on the automatic understanding of sale receipts which enable access to essential and accurate consumption statistics. Given an image acquired with a smart-phone, the proposed work mainly focuses on the first steps of the full tool chain which aims at providing essential information such as the store brand, purchased products and related prices with the highest possible confidence. To get this high confidence level, even if scanning is not perfectly controlled, we propose a double check processing tool-chain using Deep Convolutional Neural Networks (DCNNs) on one hand and more classical image and text processings on another hand. The originality of this work relates in this double check processing and in the joint use of DCNNs for different applications and text analysis.


title: Clock jitter in class-D audio power amplifiers

abstract: Many class-D audio amplifiers use a reference clock to fix the PWM carrier frequency. The jitter of the reference clock can cause significant voltage noise at the amplifier output. In this paper a simple model is presented that allows accurate prediction of the noise contribution from an integrated regenerative sawtooth oscillator to the output noise of a class-D audio amplifier. The implementation of an oscillator in a class-D amplifier is presented and the model is verified with measurements.


title: Applying Semantic Web Technologies for Tourism Information Systems

abstract: The Semantic Web as the next generation web is the vision of having background knowledge about the meaning of web sources stored in a machine-processable and –interpretable way. The area of tourism is highly dynamic area that currently already extensively uses the available Internet technologies. However, the shortcomings of the existing technology are that information finding and extraction as well as the interpretation of the information contained in the web sources is left to the human user. In this paper it is shown how the vision of the Semantic Web and already existing Semantic Web technologies can be used for next-generation tourism information systems.


title: Variables Related to Posttraumatic Growth in Turkish Rheumatoid Arthritis Patients

abstract: Rheumatoid arthritis (RA) is a chronic, deteriorative disease, which can cause great psychological distress. Although RA has negative psychological consequences, it may also lead to positive changes, which has been given relatively little attention in the research literature. The Posttraumatic Growth Inventory (PTGI) has been used to evaluate growth in survivor's thinking, feeling and/or behavior following the experiencing of traumatic or severely adverse life events. The purpose of the current study was to examine the domains of the PTGI and to identify variables related to Posttraumatic Growth (PTG) in 117 RA patients from Turkey, a secular, Islamic, non-western developing country. Results showed that the PTGI has three domains: relationship with others; philosophy of life; and self-perception. Regression analyses showed that problem-focused coping had significant contributions to the total PTG levels and PTGI domains of relationship with others and self-perception. Perceived social support appeared as a significant associate for the total PTG levels and for all three domains. In addition, age and perceived disease severity were found to be significantly related to some domains of PTG. Clinicians, aiming to increase positive experience of RA patients, may need to focus on and facilitate perceived social support and problem-focused coping to facilitate growth experiences.


title: Free Boundary Problems Arising in the Control of a Flexible Robot Arm,

abstract: Abstract : Modeling of a flexible robot arm mounted at a prismatic joint leads to a moving boundary problem. In a control-theoretic context with joint motion as control, these become free boundary problems. Some problems are discussed for transverse vibration (the beam equation) but the principal result is an exact controllability theorem for longitudinal vibration (the wave equation). Keywords: Free boundary, Robot arm, Prismatic joint, Beam equation, Feedback, Wave equation, Exact controllability.


title: Neural and neuronal discoordination in schizophrenia: From ensembles through networks to symptoms

abstract: Despite the substantial knowledge accumulated by past research, the exact mechanisms of the pathogenesis of schizophrenia and causal treatments still remain unclear. Deficits of cognition and information processing in schizophrenia are today often viewed as the primary and core symptoms of this devastating disorder. These deficits likely result from disruptions in the coordination of neuronal and neural activity. The aim of this review is to bring together convergent evidence of discoordinated brain circuits in schizophrenia at multiple levels of resolution, ranging from principal cells and interneurons, neuronal ensembles and local circuits, to large-scale brain networks. We show how these aberrations could underlie deficits in cognitive control and other higher order cognitive-behavioural functions. Converging evidence from both animal models and patients with schizophrenia is presented in an effort to gain insight into common features of deficits in the brain information processing in this disorder, marked by disruption of several neurotransmitter and signalling systems and severe behavioural outcomes.


title: Reflection Phase Characterization of Curved High Impedance Surfaces

abstract: Reflection phase characteristics of cylindrically curved high impedance surfaces (HISs) are examined. Due to the non-periodicity of the problem, full wave solutions can be time consuming. To overcome this problem, an approximate semi-analytical method, which assumes a homogenized model for the curved HIS, is developed. The model parameters can be extracted from the reflection properties of the flat HIS. For the cases where only Floquet currents are excited, the reflection phase diagram of a curved HIS is independent of the curvature. However, the surface waves generated on HISs, due to their periodic geometry, distorts their reflection phase characteristics within specific frequency intervals. In those intervals, the reflection phase changes as a function of radius of curvature and size of the HIS. These effects are not observed for the flat cases because of the lower radiation resistance of the surface waves. In this paper, the normal incidence case is considered for TEz and TMz polarizations.


title: A Framework for Evaluating Privacy Preserving Data Mining Algorithms*

abstract: Recently, a new class of data mining methods, known as privacy preserving data mining (PPDM) algorithms, has been developed by the research community working on security and knowledge discovery. The aim of these algorithms is the extraction of relevant knowledge from large amount of data, while protecting at the same time sensitive information. Several data mining techniques, incorporating privacy protection mechanisms, have been developed that allow one to hide sensitive itemsets or patterns, before the data mining process is executed. Privacy preserving classification methods, instead, prevent a miner from building a classifier which is able to predict sensitive data. Additionally, privacy preserving clustering techniques have been recently proposed, which distort sensitive numerical attributes, while preserving general features for clustering analysis. A crucial issue is to determine which ones among these privacy-preserving techniques better protect sensitive information. However, this is not the only criteria with respect to which these algorithms can be evaluated. It is also important to assess the quality of the data resulting from the modifications applied by each algorithm, as well as the performance of the algorithms. There is thus the need of identifying a comprehensive set of criteria with respect to which to assess the existing PPDM algorithms and determine which algorithm meets specific requirements. ::: ::: In this paper, we present a first evaluation framework for estimating and comparing different kinds of PPDM algorithms. Then, we apply our criteria to a specific set of algorithms and discuss the evaluation results we obtain. Finally, some considerations about future work and promising directions in the context of privacy preservation in data mining are discussed.


title: LSTM Deep Neural Networks Postfiltering for Enhancing Synthetic Voices

abstract: Recent developments in speech synthesis have produced systems capable of producing speech which closely resembles natural speech, and researchers now strive to create models that more accurately mimic human voices. One such development is the incorporation of multiple linguistic styles in various languages and accents. Speech synthesis based on Hidden Markov Models (HMM) is of great interest to researchers, due to its ability to produce sophisticated features with a small footprint. Despite some progress, its quality has not yet reached the level of the current predominant unit-selection approaches, which select and concatenate recordings of real speech, and work has been conducted to try to improve HMM-based systems. In this paper, we present an application of long short-term memory (LSTM) deep neural networks as a postfiltering step in HMM-based speech synthesis. Our motivation stems from a similar desire to obtain characteristics which are closer to those of natural speech. The paper analyzes four type...


title: First Principles Optimization of Opto-Electronic Communication Links

abstract: We introduce a first principles, end-to-end analysis of opto-electronic communication links which incorporates a thorough model of the receiver circuitry, in addition to the more familiar laser transmitter optimization. In particular, we optimize receiver sensitivity and power by studying their dependence on front-end design as well as follow-on digital sampler requirements. We find that the photo-receiver sensitivity is the most important factor in controlling the overall link power consumption. Our physical model and circuit optimization principles are applied to a heterogenous-integrated photonic+CMOS platform, where we show state-of-the-art performance through this physics-based rapid-design protocol. Incidentally this greatly simplifies the design process. Lastly, we apply this approach to extrapolate future performance trends, platform bottlenecks, and fundamental limits in optical link design while showcasing the potential for sub-1fJ/bit system efficiency at high speeds.


title: An Ontology-Based Knowledge Sharing Portal for Software Testing

abstract: Software testing is a sub area of software engineering which is also a knowledge intensive and collaborative activity. Our previous study results revealed that knowledge in the repositories were outdated, internal documents are unstructured and varied formats, less accessing facilities and lack of targeted delivery methods, such that software testers from software companies are highly affected by not being able to get vital information required to carryout their software testing activities. Ontologies emerge as one of the more appropriate knowledge management tools for supporting knowledge representation, processing, storage and retrieval. A Software testing ontology is designed to represent the necessary software testing knowledge within the software testers' context. The ontology-based Knowledge Sharing Portal is introduced into the semantic representation of software testing knowledge. SPARQL is used as the query language to retrieve software testing knowledge from the semantic storage. Both Ontology experts and non-experts evaluated the developed ontology.


title: Discriminative Deep Metric Learning for Face and Kinship Verification

abstract: This paper presents a new discriminative deep metric learning (DDML) method for face and kinship verification in wild conditions. While metric learning has achieved reasonably good performance in face and kinship verification, most existing metric learning methods aim to learn a single Mahalanobis distance metric to maximize the inter-class variations and minimize the intra-class variations, which cannot capture the nonlinear manifold where face images usually lie on. To address this, we propose a DDML method to train a deep neural network to learn a set of hierarchical nonlinear transformations to project face pairs into the same latent feature space, under which the distance of each positive pair is reduced and that of each negative pair is enlarged. To better use the commonality of multiple feature descriptors to make all the features more robust for face and kinship verification, we develop a discriminative deep multi-metric learning method to jointly learn multiple neural networks, under which the correlation of different features of each sample is maximized, and the distance of each positive pair is reduced and that of each negative pair is enlarged. Extensive experimental results show that our proposed methods achieve the acceptable results in both face and kinship verification.


title: User Vitality Ranking and Prediction in Social Networking Services: A Dynamic Network Perspective

abstract: Social networking services have been prevalent at many online communities such as Twitter.com and Weibo.com, where millions of users keep interacting with each other every day. One interesting and important problem in the social networking services is to rank users based on their vitality in a timely fashion. An accurate ranking list of user vitality could benefit many parties in social network services such as the ads providers and site operators. Although it is very promising to obtain a vitality-based ranking list of users, there are many technical challenges due to the large scale and dynamics of social networking data. In this paper, we propose a unique perspective to achieve this goal, which is quantifying user vitality by analyzing the dynamic interactions among users on social networks. Examples of social network include but are not limited to social networks in microblog sites and academical collaboration networks. Intuitively, if a user has many interactions with his friends within a time period and most of his friends do not have many interactions with their friends simultaneously, it is very likely that this user has high vitality. Based on this idea, we develop quantitative measurements for user vitality and propose our first algorithm for ranking users based vitality. Also, we further consider the mutual influence between users while computing the vitality measurements and propose the second ranking algorithm, which computes user vitality in an iterative way. Other than user vitality ranking, we also introduce a vitality prediction problem, which is also of great importance for many applications in social networking services. Along this line, we develop a customized prediction model to solve the vitality prediction problem. To evaluate the performance of our algorithms, we collect two dynamic social network data sets. The experimental results with both data sets clearly demonstrate the advantage of our ranking and prediction methods.


title: Automatic Protocol Format Reverse Engineering through Context-Aware Monitored Execution

abstract: Protocol reverse engineering has often been a manual process that is considered time-consuming, tedious and error-prone. To address this limitation, a number of solutions have recently been proposed to allow for automatic protocol reverse engineering. Unfortunately, they are either limited in extracting protocol fields due to lack of program semantics in network traces or primitive in only revealing the flat structure of protocol format. In this paper, we present a system called AutoFormat that aims at not only extracting protocol fields with high accuracy, but also revealing the inherently “non-flat”, hierarchical structures of protocol messages. AutoFormat is based on the key insight that different protocol fields in the same message are typically handled in different execution contexts (e.g., the runtime call stack). As such, by monitoring the program execution, we can collect the execution context information for every message byte (annotated with its offset in the entire message) and cluster them to derive the protocol format. We have evaluated our system with more than 30 protocol messages from seven protocols, including two text-based protocols (HTTP and SIP), three binary-based protocols (DHCP, RIP, and OSPF), one hybrid protocol (CIFS/SMB), as well as one unknown protocol used by a real-world malware. Our results show that AutoFormat can not only identify individual message fields automatically and with high accuracy (an average 93.4% match ratio compared with Wireshark), but also unveil the structure of the protocol format by revealing possible relations (e.g., sequential, parallel, and hierarchical) among the message fields. ∗Part of this research has been supported by the National Science Foundation under grants CNS-0716376 and CNS-0716444. The bulk of this work was performed when the first author was visiting George Mason University in Summer 2007.


title: Cloud Computing: Overview & Current Research Challenges

abstract: Cloud computing is a set of IT services that are provided to a customer over a network on a leased basis and with the ability to scale up or down their service requirements. Usually Cloud Computing services are delivered by a third party provider who owns the infrastructure.Cloud Computing holds the potential to eliminate the requirements for setting up of high-cost computing infrastructure for IT-based solutions and services that theindustry uses. It promises to provide a flexible IT architecture, accessible through internet from lightweight portable devices.This would allow multi-fold increase in the capacity and capabilities of the existing and new software.This new economic model for computing has found fertile ground and is attracting massive global investment. Many industries, such as banking, healthcare and education are moving towards the cloud due to the efficiency of services provided by the pay-per-use pattern based on the resources such as processing power used, transactions carried out, bandwidth consumed, data transferred, or storage space occupied etc.In a cloud computing environment, the entire data resides over a set of networked resources, enabling the data to be accessed through virtual machines. Despite the potential gains achieved from the cloud computing, the organizations are slow in accepting it due to security issues and challenges associated with it. Security is one of the major issues which hamper the growth of cloud. There are various research challenges also there for adopting cloud computing such as well managed service level agreement (SLA), privacy, interoperability and reliability.This research paper presents what cloud computing is, the various cloud models and the overview of the cloud computing architecture. This research paper also analyzes the key research challenges present in cloud computing and offers best practices to service providers as well as enterprises hoping to leverage cloud service to improve their bottom line in this severe economic climate.


title: Learning the Structural Vocabulary of a Network

abstract: Networks have become instrumental in deciphering how information is processed and transferred within systems in almost every scientific field today. Nearly all network analyses, however, have relied on humans to devise structural features of networks believed to be most discriminative for an application. We present a framework for comparing and classifying networks without human-crafted features using deep learning. After training, autoencoders contain hidden units that encode a robust structural vocabulary for succinctly describing graphs. We use this feature vocabulary to tackle several network mining problems and find improved predictive performance versus many popular features used today. These problems include uncovering growth mechanisms driving the evolution of networks, predicting protein network fragility, and identifying environmental niches for metabolic networks. Deep learning offers a principled approach for mining complex networks and tackling graph-theoretic problems.


title: Towards Seamless Tracking-Free Web: Improved Detection of Trackers via One-class Learning

abstract: Numerous tools have been developed to aggressively block the execution of popular JavaScript programs (JS) in Web browsers. Such blocking also affects functionality of webpages and impairs user experience. As a consequence, many privacy preserving tools (PP-Tools) that have been developed to limit online tracking, often executed via JS, may suffer from poor performance and limited uptake. A mechanism that can isolate JS necessary for proper functioning of the website from tracking JS would thus be useful. Through the use of a manually labelled dataset composed of 2,612 JS, we show how current PP-Tools are ineffective in finding the right balance between blocking tracking JS and allowing functional JS. To the best of our knowledge, this is the first study to assess the performance of current web PP-Tools. ::: To improve this balance, we examine the two classes of JS and hypothesize that tracking JS share structural similarities that can be used to differentiate them from functional JS. The rationale of our approach is that web developers often borrow and customize existing pieces of code in order to embed tracking (resp. functional) JS into their webpages. We then propose one-class machine learning classifiers using syntactic and semantic features extracted from JS. When trained only on samples of tracking JS, our classifiers achieve an accuracy of 99%, where the best of the PP-Tools achieved an accuracy of 78%. ::: We further test our classifiers and several popular PP-Tools on a corpus of 4K websites with 135K JS. The output of our best classifier on this data is between 20 to 64% different from the PP-Tools. We manually analyse a sample of the JS for which our classifier is in disagreement with all other PP-Tools, and show that our approach is not only able to enhance user web experience by correctly classifying more functional JS, but also discovers previously unknown tracking services.


title: TiNb2O7 nanoparticles assembled into hierarchical microspheres as high-rate capability and long-cycle-life anode materials for lithium ion batteries

abstract: As a competitor for Li4Ti5O12 with a higher capacity and extreme safety, monoclinic TiNb2O7 has been considered as a promising anode material for next-generation high power lithium ion batteries. However, TiNb2O7 suffers from low electronic conductivity and ionic conductivity, which restricts the electrochemical kinetics. Herein, a facile and advanced architecture design of hierarchical TiNb2O7 microspheres is successfully developed for large-scale preparation without any surfactant assistance. To the best of our knowledge, this is the first report on the one step solvothermal synthesis of TiNb2O7 microspheres with micro- and nano-scale composite structures. When evaluated as an anode material for lithium ion batteries, the electrode exhibits excellent high rate capacities and ultra-long cyclability, such as 258 mA h g−1 at 1 C, 175 mA h g−1 at 5 C, and 138 mA h g−1 at 10 C, extending to more than 500 cycles.


title: Comparative Evaluation of Flow for Pharmaceutical Powders and Granules

abstract: The objective of the present work was to carry out a systematic evaluation of flow of pharmaceutical powders and granules using compendial and non-compendial methods. Angle of repose, bulk density, tapped density, Carr’s compressibility index, and Hausner ratios were evaluated. Additionally, flow was characterized using a powder rheometer in which a sensitive force transducer monitors the forces generated as a result of the sample displacement. The critical attributes such as cohesivity index, caking strength, and flow stability were determined for samples. The samples consisted of different grades of magnesium stearate powder including bovine, vegetable, and food grade, physical mixture powder blend consisting of a model formulation, granules prepared by various methods including slugging, high shear granulator, and fluid bed dryer. Lubricant efficiency was also determined for granules lubricated with various concentrations of magnesium stearate. It was observed that the compendial methods were often non-discriminating for minor variations in powder flow. The additional characterization such as cohesivity, and caking strength were helpful in understanding the flow characteristics of pharmaceutical systems. The flow stability test determined that the powders were not affected by the test conditions on the rheometer. The non-compendial tests were discriminating to even minor variations in powder flow.


title: Formation Control of Leader–Follower Mobile Robots’ Systems Using Model Predictive Control Based on Neural-Dynamic Optimization

abstract: In this paper, a neural-dynamic optimization-based nonlinear model predictive control (NMPC) is developed for controlling leader–follower mobile robots formation. Consider obstacles in the environments, a control strategy is proposed for the formations which includes separation-bearing-orientation scheme (SBOS) for regular leader–follower formation and separation-distance scheme (SDS) for obstacle avoidance. During the formation motion, the leader robot shall track a desired trajectory and the desire leader–follower relationship can be maintained through SBOS method; meanwhile, the followers can avoid the collision by applying the SDS. The formation-error kinematics of both SBOS and SDS are derived and a constrained quadratic programming (QP) can be obtained by transforming the MPC method. Then, over a finite-receding horizon, the QP problem can be solved by utilizing the primal-dual neural network (PDNN) with parallel capability. The computation complexity can be greatly reduced by the implemented neural-dynamic optimization. Compared with other existing formation control approaches, the developed solution in this paper is rooted in NMPC techniques with input constraints and the novel QP problem formulation. Finally, experimental studies of the proposed formation control approach have been performed on several mobile robots to verify the effectiveness.


title: Toughening wood/polypropylene composites with polyethylene octene elastomer (POE)

abstract: Polyethylene octene elastomer (POE) as impact modifier was incorporated into wood/polypropylene composites (WPC) to enhance the impact strength of the composite. Two extruding routes, i.e. direct extruding route and two-stage extruding route, were adopted to produce Wood Powder/PP/POE ternary composites. The mechanical and dynamic mechanical analysis (DMA) properties of the composites were investigated. The results showed that the addition of POE can increase the impact strength of the composites, and the composites produced via two-stage extruding route showed superior mechanical properties. The results of the DMA confirmed the mechanical tests.


title: The Ontology Design of Intangible Cultural Heritage Based on CIDOC CRM

abstract: Intangible Cultural Heritage is of comparatively high value in culture, economy, science and tourism, which is at its peak of development and utilization. Intangible cultural heritage needs preservation, so the development and utilization of the digital storage of intangible cultural heritage are a subject of theoretical and practical values. Among the various complicated types of intangible cultural heritage, the authors choose Pang Wang Festival of Yao nationality of Guangdong Province of China as an example and apply the CIDOC Conceptual Reference Model to the construction of the entities and properties of the knowledge ontology of the Pang Wang Festival, and the well-constructed knowledge ontology has been stored in the database. This study has provided the solution to the construction of the knowledge ontology of the intangible cultural heritage and benefits the digital storage, preservation and management of intangible cultural heritage, which offers a new way of thinking and is of great significance to the preservation and inheritance of intangible cultural heritage.


title: Sequential injection spectrophotometric determination of orthophosphate in beverages, wastewaters and urine samples by electrogeneration of molybdenum blue using tubular flow-through electrodes

abstract: A novel and automated sequential injection procedure is proposed for the spectrophotometric determination of orthophosphate without requiring unstable chemical reducing species used in the classical molybdenum blue method. The flowing methodology is based on the on-line generation of the detectable species by electrochemical reduction of the 12-molybdophosphoric acid complex using a stainless steel tubular flow-through working electrode. The established method is linear up to 20 mg/l P, with coefficients of variation (n = 10) of 2.4 and 1.8% for 2.0 and 10 mg/l P, respectively. The versatility of the sequential injection method to analyse samples containing high orthophosphate levels has been demonstrated by the implementation of a dilution chamber as well as flow-reversal techniques, yielding relative standard deviations (n = 17) better than 2.0% for standards containing 200 and 800 mg/l P. The proposed analyser features an extremely wide dynamic range (viz., 0.3–800 mg/l) as well as improved tolerance to silicate interference, so that Si/P ratios higher than 50 are tolerated at the 5% level. Electrochemical conditions, reagent concentrations and physical variables have been thoroughly investigated. The method has been applied to the determination of orthophosphate in wastewaters as well as beverages and biological samples containing high concentrations of the target analyte. The t-test comparison of the means for the developed sequential injection system with electroreduction and both the molybdenum blue classical spectrophotometric batch procedure and inductively coupled plasma-optical emission spectrometric detection selected as external reference methods revealed that there is no evidence of significant differences between the obtained results at the 95% confidence level. © 2004 Elsevier B.V. All rights reserved.


title: Use Cases for Linked Data Visualization Model

abstract: There is a vast amount of Linked Data on the web spread across a large number of datasets. One of the visions behind Linked Data is that the published data is conveniently reusable by others. This, however, depends on many details such as conformance of the data with commonly used vocabularies and adherence to best practices for data modeling. Therefore, when an expert wants to reuse existing datasets, he still needs to analyze them to discover how the data is modeled and what it actually contains. This may include analysis of what entities are there, how are they linked to other entities, which properties from which vocabularies are used, etc. What is missing is a convenient and fast way of seeing what could be usable in the chosen unknown dataset without reading through its RDF serialization. In this paper we describe use cases based on this problem and their realization using our Linked Data Visualization Model (LDVM) and its new implementation. LDVM is a formal base that exploits the Linked Data principles to ensure interoperability and compatibility of compliant analytic and visualization components. We demonstrate the use cases on examples from the Czech Linked Open Data cloud.


title: The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery

abstract: Abstract Once limited to the military domain, unmanned aerial vehicles are now poised to gain widespread adoption in the commercial sector. One such application is to deploy these aircraft, also known as drones, for last-mile delivery in logistics operations. While significant research efforts are underway to improve the technology required to enable delivery by drone, less attention has been focused on the operational challenges associated with leveraging this technology. This paper provides two mathematical programming models aimed at optimal routing and scheduling of unmanned aircraft, and delivery trucks, in this new paradigm of parcel delivery. In particular, a unique variant of the classical vehicle routing problem is introduced, motivated by a scenario in which an unmanned aerial vehicle works in collaboration with a traditional delivery truck to distribute parcels. We present mixed integer linear programming formulations for two delivery-by-drone problems, along with two simple, yet effective, heuristic solution approaches to solve problems of practical size. Solutions to these problems will facilitate the adoption of unmanned aircraft for last-mile delivery. Such a delivery system is expected to provide faster receipt of customer orders at less cost to the distributor and with reduced environmental impacts. A numerical analysis demonstrates the effectiveness of the heuristics and investigates the tradeoffs between using drones with faster flight speeds versus longer endurance.


title: Making Bertha Drive—An Autonomous Journey on a Historic Route

abstract: 125 years after Bertha Benz completed the first overland journey in automotive history, the Mercedes Benz S-Class S 500 INTELLIGENT DRIVE followed the same route from Mannheim to Pforzheim, Germany, in fully autonomous manner. The autonomous vehicle was equipped with close-to-production sensor hardware and relied solely on vision and radar sensors in combination with accurate digital maps to obtain a comprehensive understanding of complex traffic situations. The historic Bertha Benz Memorial Route is particularly challenging for autonomous driving. The course taken by the autonomous vehicle had a length of 103 km and covered rural roads, 23 small villages and major cities (e.g. downtown Mannheim and Heidelberg). The route posed a large variety of difficult traffic scenarios including intersections with and without traffic lights, roundabouts, and narrow passages with oncoming traffic. This paper gives an overview of the autonomous vehicle and presents details on vision and radar-based perception, digital road maps and video-based self-localization, as well as motion planning in complex urban scenarios.


title: Depth Images-based Human Detection, Tracking and Activity Recognition Using Spatiotemporal Features and Modified HMM

abstract: Human activity recognition using depth information is an emerging and challenging technology in computer vision due to its considerable attention by many practical applications such as smart home/office system, personal health care and 3D video games. This paper presents a novel framework of 3D human body detection, tracking and recognition from depth video sequences using spatiotemporal features and modified HMM. To detect human silhouette, raw depth data is examined to extract human silhouette by considering spatial continuity and constraints of human motion information. While, frame differentiation is used to track human movements. Features extraction mechanism consists of spatial depth shape features and temporal joints features are used to improve classification performance. Both of these features are fused together to recognize different activities using the modified hidden Markov model (M-HMM). The proposed approach is evaluated on two challenging depth video datasets. Moreover, our system has significant abilities to handle subject"s body parts rotation and body parts missing which provide major contributions in human activity recognition.


title: J.F.: Software Project Management with GAs

abstract: A Project Scheduling Problem consists in deciding who does what during the software project lifetime. This is a capital issue in the practice of software engineering, since the total budget and human resources involved must be managed optimally in order to end in a successful project. In short, companies are principally concerned with reducing the duration and cost of projects, and these two goals are in conflict with each other. In this work we tackle the problem by using genetic algorithms (GAs) to solve many different software project scenarios. Thanks to our newly developed instance generator we can perform structured studies on the influence the most important problem attributes have on the solutions. Our conclusions show that GAs are quite flexible and accurate for this application, and an important tool for automatic project management.


title: Labeled Phrase Latent Dirichlet Allocation and its online learning algorithm

abstract: There is a mass of user-marked text data on the Internet, such as web pages with categories, papers with corresponding keywords, and tweets with hashtags. In recent years, supervised topic models, such as Labeled Latent Dirichlet Allocation, have been widely used to discover the abstract topics in labeled text corpora. However, none of these topic models have taken into consideration word order under the bag-of-words assumption, which will obviously lose a lot of semantic information. In this paper, in order to synchronously model semantical label information and word order, we propose a novel topic model, called Labeled Phrase Latent Dirichlet Allocation (LPLDA), which regards each document as a mixture of phrases and partly considers the word order. In order to obtain the parameter estimation for the proposed LPLDA model, we develop a batch inference algorithm based on Gibbs sampling technique. Moreover, to accelerate the LPLDA’s processing speed for large-scale stream data, we further propose an online inference algorithm for LPLDA. Extensive experiments were conducted among LPLDA and four state-of-the-art baselines. The results show (1) batch LPLDA significantly outperforms baselines in terms of case study, perplexity and scalability, and the third party task in most cases; (2) the online algorithm for LPLDA is obviously more efficient than batch method under the premise of good results.


title: IM2HEIGHT: Height Estimation from Single Monocular Imagery via Fully Residual Convolutional-Deconvolutional Network

abstract: In this paper we tackle a very novel problem, namely height estimation from a single monocular remote sensing image, which is inherently ambiguous, and a technically ill-posed problem, with a large source of uncertainty coming from the overall scale. We propose a fully convolutional-deconvolutional network architecture being trained end-to-end, encompassing residual learning, to model the ambiguous mapping between monocular remote sensing images and height maps. Specifically, it is composed of two parts, i.e., convolutional sub-network and deconvolutional sub-network. The former corresponds to feature extractor that transforms the input remote sensing image to high-level multidimensional feature representation, whereas the latter plays the role of a height generator that produces height map from the feature extracted from the convolutional sub-network. Moreover, to preserve fine edge details of estimated height maps, we introduce a skip connection to the network, which is able to shuttle low-level visual information, e.g., object boundaries and edges, directly across the network. To demonstrate the usefulness of single-view height prediction, we show a practical example of instance segmentation of buildings using estimated height map. This paper, for the first time in the remote sensing community, attempts to estimate height from monocular vision. The proposed network is validated using a large-scale high resolution aerial image data set covered an area of Berlin. Both visual and quantitative analysis of the experimental results demonstrate the effectiveness of our approach.


title: Design of a Wireless Sensor Network-Based IoT Platform for Wide Area and Heterogeneous Applications

abstract: Internet of Things (IoT) is not only a promising research topic but also a blooming industrial trend. Although the basic idea is to bring things or objects into the Internet, there are various approaches, because an IoT system is highly application oriented. This paper presents a wireless sensor network(WSN)-based IoT platform for wide area and heterogeneous sensing applications. The platform, consisting of one or multiple WSNs, gateways, a Web server, and a database, provides a reliable connection between sensors at fields and the database on the Internet. The WSN is built based on the IEEE 802.15.4e time slotted channel hopping protocol, because it has the benefits such as multi-hop transmission, collision-free transmission, and high energy efficiency. In addition to the design of a customized hardware for range extension, a new synchronization scheme and a burst transmission feature are also presented to boost the network capacity and reduce the energy waste. As a result, the proposed platform can fulfill the high throughput requirement for high-rate applications and the requirement of long battery life for low-rate applications at the same time. We have developed a testbed in our campus to validate the proposed system.


title: A brief survey of Cryptocurrency systems

abstract: Cryptocurrencies have emerged as important financial software systems. They rely on a secure distributed ledger data structure; mining is an integral part of such systems. Mining adds records of past transactions to the distributed ledger known as Blockchain, allowing users to reach secure, robust consensus for each transaction. Mining also introduces wealth in the form of new units of currency. Cryptocurrencies lack a central authority to mediate transactions because they were designed as peer-to-peer systems. They rely on miners to validate transactions. Cryptocurrencies require strong, secure mining algorithms. In this paper we survey and compare and contrast current mining techniques as used by major Cryptocurrencies. We evaluate the strengths, weaknesses, and possible threats to each mining strategy. Overall, a perspective on how Cryptocurrencies mine, where they have comparable performance and assurance, and where they have unique threats and strengths are outlined.


title: Physical activity, obesity, and cardiovascular risk factors in children. The Belgian Luxembourg Child Study II.

abstract: Physical activity was measured in relation to cardiovascular (CV) risk factors in a randomly selected population of 1028 children from Province de Luxembourg in Belgium, a mainly rural area with a high prevalence of such risk factors among adults and children. Physical activity was estimated as participation in sport activities, a major indicator of leisure-time physical activity in schoolchildren, and physical inactivity was estimated as frequency and duration of television (TV) watching. Boys participated more frequently in sport activities than girls did (p = 0.001). A majority of the children watched TV daily. After age adjustment, bodyweight (girls, p < 0.012; boys, p < 0.027) and, in boys, body mass index (BMI) (p < 0.039) were related to days per week of TV watching. No significant relationships with other CV risk factors remained after adjustments for BMI. In analyses of independent contributions of age, TV watching, and sports activity on CV risk factors, age showed highly significant relationships. In boys, TV showed relationships with BMI (p < 0.04) and (borderline) with systolic blood pressure, independent of age and sports activity, whereas the latter was significantly related to subscapular skinfold (p < 0.04) and (borderline) with triceps skinfold and cholesterol. In girls, no significant independent contributions to risk factor associations were found. The father's education was directly associated with sports activities, whereas the mother being a housewife showed negative relationships to physical activity and positive to TV watching in their children, suggesting socioeconomic influence on the activity patterns of children. Furthermore, registrations suggested less physical activity in the most rural part of the area. It is concluded that children in this mainly rural area watch TV frequently. In boys, physical inactivity, measured both as TV watching and as registrations of sports activities, contributes independently to body fat mass. In girls, no contribution or weaker contributions of physical inactivity were found. This suggests that contributory factors leading to obesity might be different in girls and boys.


title: A Survey of Algorithmic Shapes

abstract: In the context of computer-aided design, computer graphics and geometry processing, the idea of generative modeling is to allow the generation of highly complex objects based on a set of formal construction rules. Using these construction rules, a shape is described by a sequence of processing steps, rather than just by the result of all applied operations: shape design becomes rule design. Due to its very general nature, this approach can be applied to any domain and to any shape representation that provides a set of generating functions. The aim of this survey is to give an overview of the concepts and techniques of procedural and generative modeling, as well as their applications with a special focus on archeology and architecture.


title: Multi-View Clustering and Semi-Supervised Classification with Adaptive Neighbours

abstract: Due to the efficiency of learning relationships and complex structures hidden in data, graph-oriented methods have been widely investigated and achieve promising performance in multi-view learning. Generally, these learning algorithms construct informative graph for each view or fuse different views to one graph, on which the following procedure are based. However, in many real world dataset, original data always contain noise and outlying entries that result in unreliable and inaccurate graphs, which cannot be ameliorated in the previous methods. In this paper, we propose a novel multi-view learning model which performs clustering/semi-supervised classification and local structure learning simultaneously. The obtained optimal graph can be partitioned into specific clusters directly. Moreover, our model can allocate ideal weight for each view automatically without additional weight and penalty parameters. An efficient algorithm is proposed to optimize this model. Extensive experimental results on different real-world datasets show that the proposed model outperforms other state-of-the-art multi-view algorithms.


title: Machine Reading Tea Leaves: Automatically Evaluating Topic Coherence and Topic Model Quality

abstract: Topic models based on latent Dirichlet allocation and related methods are used in a range of user-focused tasks including document navigation and trend analysis, but evaluation of the intrinsic quality of the topic model and topics remains an open research area. In this work, we explore the two tasks of automatic evaluation of single topics and automatic evaluation of whole topic models, and provide recommendations on the best strategy for performing the two tasks, in addition to providing an open-source toolkit for topic and topic model evaluation.


title: Ontologies for aviation data management

abstract: Managing complex aviation data can be a significant challenge for any enterprise — whether a government agency, airline, airframe manufacturer, or aviation service provider. To handle this challenge, data models are typically developed to characterize and manage the data generated, used, and stored by a given enterprise. Unfortunately, different data providers employ qualitatively different data models, and this gives rise to problems exchanging data across organizational boundaries. Over the past decade, these problems have motivated data producers and consumers to look toward standardized data exchange models to address data interoperability. In this paper we examine some of these standardized data exchange models and compare them with a new type of data model based on ontologies. Ontology models have emerged in recent years from a confluence of research in the artificial intelligence, semantic web, and information science communities. This paper introduces ontology models, provides several use cases for ontologies relevant to aviation data management, and summarizes state-of-the-art aviation prototype applications that utilize ontologies.


An Ontology for Clinical Laboratory Standard Operating Procedures


title: Swarm intelligence systems for transportation engineering: Principles and applications

abstract: Abstract Agent-based modeling is an approach based on the idea that a system is composed of decentralized individual “agents” and that each agent interacts with other agents according to localized knowledge. Special kinds of artificial agents are the agents created by analogy with social insects. Social insects (bees, wasps, ants, and termites) have lived on Earth for millions of years. Their behavior is primarily characterized by autonomy, distributed functioning, and self-organizing capacities. Social insect colonies teach us that very simple organisms can form systems capable of performing highly complex tasks by dynamically interacting with each other. Swarm intelligence is the branch of artificial intelligence based on study of behavior of individuals in various decentralized systems. The paper presents a classification and analysis of the results achieved using swarm intelligence (SI) to model complex traffic and transportation processes. The primary goal of this paper is to acquaint readers with the basic principles of Swarm Intelligence, as well as to indicate potential swarm intelligence applications in traffic and transportation.


title: Customer experience modeling: designing interactions for service systems

abstract: Designers aspire to create engaging and desirable experiences. To that end they study users, aiming to better understand their preferences, ways of thinking and desired outcomes. In the service sector this task is more intricate as experiences encompass the whole customer journey, or the sequence of moments of interaction between customer and company. In services, one poorly designed interaction can severely compromise the overall experience. Despite experience holistic nature, current methods address its components separately, failing to provide an overall systematized picture. This paper presents Customer Experience Modeling, a novel multidisciplinary approach to systematize, represent and evaluate customer experiences to guide service and interaction design efforts. We illustrate this method with an application to a multimedia service provider built upon 17 interviews with service users.


title: Implications of loyalty program membership and service experiences for customer retention and value

abstract: Companies that offer loyalty reward programs believe that their programs have a long-run positive effect on customer evaluations and behavior. However, if loyalty rewards programs increase relationship durations and usage levels, customers will be increasingly exposed to the complete spectrum of service experiences, including experiences that may cause customers to switch to another service provider. Using cross-sectional, time-series data from a worldwide financial services company that offers a loyalty reward program, this article investigates the conditions under which a loyalty rewards program will have a positive effect on customer evaluations, behavior, and repeat purchase intentions. The results show that members in the loyalty reward program overlook or discount negative evaluations of the company vis-a-vis competion. One possible reason could be that members of the loyalty rewards program perceive that they are getting better quality and service for their price or, in other words, “good value.”


title: Deep level transient spectroscopy characterization of 1 keV He, Ne, and Ar ion bombarded, epitaxially grown n-Si

abstract: Deep level transient spectroscopy has been used to investigate the electronic properties and isochronal annealing behavior of defects formed in epitaxially grown n-Si by 1 keV He-, Ne-, and Ar-ion bombardment. Similarities between peaks found for the different bombardment gases suggested that they were from structurally related defects. Two families of such related defects were observed in the unannealed samples. Annealing data revealed additional peaks and enabled another defect family formed above 400 °C to be identified. The energy levels and capture cross sections have been determined for three new families of related defects. The defect families were presumed to be either complex vacancy clusters or hydrogen related.


title: Osgood-Schlatter disease in ultrasound diagnostics - a pictorial essay

abstract: This paper tries to describe reasons, pathophysiological sequence of events, including the sequelae, and location of the traumatic tears of the ossification center of the tibial tuberosity in adolescents. Ultrasonographic features characteristically seen in the traction type of Osgood-Schlatter disease (OSD) are presented in a pictorial. The classification and definitions used is based on the three types of OSD described by Czyrny&Greenspan in 2009.


title: Voting in social networks

abstract: A voting system is a set of rules that a community adopts to take collective decisions. In this paper we study voting systems for a particular kind of community: electronically mediated social networks. In particular, we focus on delegative democracy (a.k.a. proxy voting) that has recently received increased interest for its ability to combine the benefits of direct and representative systems, and that seems also perfectly suited for electronically mediated social networks. In such a context, we consider a voting system in which users can only express their preference for one among the people they are explicitly connected with, and this preference can be propagated transitively, using an attenuation factor. We present this system and we study its properties. We also take into consideration the problem of missing votes, which is particularly relevant in online networks, as some recent case shows. Our experiments on real-world networks provide interesting insight into the significance and stability of the results obtained with the suggested voting system.


title: Review Does the chimpanzee have a theory of mind? 30 years later

abstract: On the 30th anniversary of Premack and Woodruff's seminal paper asking whether chimpanzees have a theory of mind, we review recent evidence that suggests in many respects they do, whereas in other respects they might not. Specifically, there is solid evidence from several different experimental paradigms that chimpanzees understand the goals and intentions of others, as well as the perception and knowledge of others. Nevertheless, despite several seemingly valid attempts, there is currently no evidence that chimpanzees understand false beliefs. Our conclusion for the moment is, thus, that chimpanzees understand others in terms of a perception–goal psychology, as opposed to a full-fledged, human-like belief–desire psychology.


title: Motion artifact removal from photoplethysmographic signals by combining temporally constrained independent component analysis and adaptive filter

abstract: BackgroundThe calculation of arterial oxygen saturation (SpO2) relies heavily on the amplitude information of the high-quality photoplethysmographic (PPG) signals, which could be contaminated by motion artifacts (MA) during monitoring.MethodsA new method combining temporally constrained independent component analysis (cICA) and adaptive filters is presented here to extract the clean PPG signals from the MA corrupted PPG signals with the amplitude information reserved. The underlying PPG signal could be extracted from the MA contaminated PPG signals automatically by using cICA algorithm. Then the amplitude information of the PPG signals could be recovered by using adaptive filters.ResultsCompared with conventional ICA algorithms, the proposed approach is permutation and scale ambiguity-free. Numerical examples with both synthetic datasets and real-world MA corrupted PPG signals demonstrate that the proposed method could remove the MA from MA contaminated PPG signals more effectively than the two existing FFT-LMS and moving average filter (MAF) methods.ConclusionsThis paper presents a new method which combines the cICA algorithm and adaptive filter to extract the underlying PPG signals from the MA contaminated PPG signals with the amplitude information reserved. The new method could be used in the situations where one wants to extract the interested source automatically from the mixed observed signals with the amplitude information reserved. The results of study demonstrated the efficacy of this proposed method.


title: Positive allosteric modulation of the GABAB receptor by GS39783 attenuates the locomotor stimulant actions of ethanol and potentiates the induction of locomotor sensitization

abstract: Abstract Acute ethanol-induced locomotor stimulation and ethanol-induced locomotor sensitization are two behavioral assays thought to model the rewarding effects of ethanol. Recent evidence suggests that GS39783, a GABA B positive allosteric modulator, may be effective at reducing both the rewarding and reinforcing effects of several drugs of abuse, including ethanol. The goal of this study was to determine if GS39783 was capable of altering acute ethanol-induced stimulation, and the induction and expression of ethanol-induced locomotor sensitization, without effecting basal locomotion levels. Several doses of GS39783 (ranging from 0 to 100 mg/kg, depending on experiment) were tested on adult male DBA/2J mice in four experiments using 3-day basal locomotion and acute ethanol stimulation paradigms, and 14-day induction and expression of ethanol sensitization paradigms. The results of experiment 1 are in agreement with current literature, suggesting that 30 mg/kg doses of GS39783 and lower do not alter basal locomotor activity. In experiment 2, we found that GS39783 significantly decreased acute ethanol stimulation, but only at the 30 mg/kg dose, supporting our hypothesis and other publications suggesting that GABA B receptors modulate acute ethanol stimulation. Contrary to our hypothesis, GS39783 did not alter the expression of locomotor sensitization. Additionally, repeated administration of GS39783 in conjunction with ethanol unexpectedly potentiated ethanol-induced locomotor sensitization. Further study of GS39783 is warranted as it may be a more tolerable treatment for alcoholism than full agonists, due to its behavioral efficacy at doses that lack sedative side effects. Our results add to current literature suggesting that the GABA B receptor system is indeed involved in the modulation of ethanol-induced locomotor stimulation and sensitization.


title: Navigating the maze of graph analytics frameworks using massive graph datasets

abstract: Graph algorithms are becoming increasingly important for analyzing large datasets in many fields. Real-world graph data follows a pattern of sparsity, that is not uniform but highly skewed towards a few items. Implementing graph traversal, statistics and machine learning algorithms on such data in a scalable manner is quite challenging. As a result, several graph analytics frameworks (GraphLab, CombBLAS, Giraph, SociaLite and Galois among others) have been developed, each offering a solution with different programming models and targeted at different users. Unfortunately, the "Ninja performance gap" between optimized code and most of these frameworks is very large (2-30X for most frameworks and up to 560X for Giraph) for common graph algorithms, and moreover varies widely with algorithms. This makes the end-users' choice of graph framework dependent not only on ease of use but also on performance. In this work, we offer a quantitative roadmap for improving the performance of all these frameworks and bridging the "ninja gap". We first present hand-optimized baselines that get performance close to hardware limits and higher than any published performance figure for these graph algorithms. We characterize the performance of both this native implementation as well as popular graph frameworks on a variety of algorithms. This study helps end-users delineate bottlenecks arising from the algorithms themselves vs. programming model abstractions vs. the framework implementations. Further, by analyzing the system-level behavior of these frameworks, we obtain bottlenecks that are agnostic to specific algorithms. We recommend changes to alleviate these bottlenecks (and implement some of them) and reduce the performance gap with respect to native code. These changes will enable end-users to choose frameworks based mostly on ease of use.


title: Show, Attend and Interact: Perceivable Human-Robot Social Interaction through Neural Attention Q-Network

abstract: For a safe, natural and effective human-robot social interaction, it is essential to develop a system that allows a robot to demonstrate the perceivable responsive behaviors to complex human behaviors. We introduce the Multimodal Deep Attention Recurrent Q-Network using which the robot exhibits human-like social interaction skills after 14 days of interacting with people in an uncontrolled real world. Each and every day during the 14 days, the system gathered robot interaction experiences with people through a hit-and-trial method and then trained the MDARQN on these experiences using end-to-end reinforcement learning approach. The results of interaction based learning indicate that the robot has learned to respond to complex human behaviors in a perceivable and socially acceptable manner.


title: Is Working Memory Still Working?

abstract: The current state of A.D. Baddeley and G.J. Hitch's (1974) multicomponent working memory model is reviewed. The phonological and visuospatial subsystems have been extensively investigated, leading both to challenges over interpretation of individual phenomena and to more detailed attempts to model the processes underlying the subsystems. Analysis of the controlling central executive has proved more challenging, leading to a proposed clarification in which the executive is assumed to be a limited capacity attentional system, aided by a newly postulated fourth system, the episodic buffer. Current interest focuses most strongly on the link between working memory and long-term memory and on the processes allowing the integration of information from the component subsystems. The model has proved valuable in accounting for data from a wide range of participant groups under a rich array of task conditions. Working memory does still appear to be working.


title: A Bluetooth Low Energy Indoor Positioning System with Channel Diversity, Weighted Trilateration and Kalman Filtering

abstract: Indoor Positioning Systems (IPS) using Bluetooth Low Energy (BLE) technology are currently becoming real and available, which has made them grow in popularity and use. However, there are still plenty of challenges related to this technology, especially in terms of Received Signal Strength Indicator (RSSI) fluctuations due to the behaviour of the channels and the multipath effect, that lead to poor precision. In order to mitigate these effects, in this paper we propose and implement a real Indoor Positioning System based on Bluetooth Low Energy, that improves accuracy while reducing power consumption and costs. The three main proposals are: frequency diversity, Kalman filtering and a trilateration method what we have denominated "weighted trilateration". The analysis of the results proves that all the proposals improve the precision of the system, which goes up to 1.82 m 90% of the time for a device moving in a middle-size room and 0.7 m for static devices. Furthermore, we have proved that the system is scalable and efficient in terms of cost and power consumption. The implemented approach allows using a very simple device (like a SensorTag) on the items to locate. The system enables a very low density of anchor points or references and with a precision better than existing solutions.


title: Sit-to-Stand Trainer: An Apparatus for Training “Normal-Like” Sit to Stand Movement

abstract: Sit-to-stand (STS) transfer training is probably the most demanding task in rehabilitation. We have developed an innovative STS trainer that offers variable levels of mechanical support and speeds of STS transfer. In a group of neurologically intact individuals we compared kinematics, kinetics and electromyography (EMG) patterns of STS transfer assessed in three experimental conditions with increasing degree of mechanical support (MIN STS-T, MED STS-T, and MAX STS-T) to natural, unassisted STS movement (NO STS-T). The resulting ankle, knee, hip joint and trunk angles in experimental conditions MED STS-T and MIN STS-T were very similar to experimental condition NO STS-T. Vertical ground reaction forces and EMG patterns in the tibialis anterior, quadriceps and hamstrings show a clear trend toward “normal” patterns as the level of mechanical support from the device is progressively reduced. We have further tested the feasibility of the STS trainer in five stroke subjects at two levels of support showing that increased voluntary effort is needed when the support is reduced. Based on these results we conclude that negligible constraints are imposed by the device on a user's STS transfer kinematics, which is an important prerequisite for considering clinical use of the device for training in neurologically impaired.


title: A Compact, Low Input Capacitance Neural Recording Amplifier

abstract: Conventional capacitively coupled neural recording amplifiers often present a large input load capacitance to the neural signal source and hence take up large circuit area. They suffer due to the unavoidable trade-off between the input capacitance and chip area versus the amplifier gain. In this work, this trade-off is relaxed by replacing the single feedback capacitor with a clamped T-capacitor network. With this simple modification, the proposed amplifier can achieve the same mid-band gain with less input capacitance, resulting in a higher input impedance and a smaller silicon area. Prototype neural recording amplifiers based on this proposal were fabricated in 0.35 μm CMOS, and their performance is reported. The amplifiers occupy smaller area and have lower input loading capacitance compared to conventional neural amplifiers. One of the proposed amplifiers occupies merely 0.056 mm2. It achieves 38.1-dB mid-band gain with 1.6 pF input capacitance, and hence has an effective feedback capacitance of 20 fF. Consuming 6 μW, it has an input referred noise of 13.3 μVrms over 8.5 kHz bandwidth and NEF of 7.87. In-vivo recordings from animal experiments are also demonstrated.


title: A Tidy Data Model for Natural Language Processing using cleanNLP

abstract: The package cleanNLP provides a set of fast tools for converting a textual corpus into a set of normalized tables. The underlying natural language processing pipeline utilizes Stanford's CoreNLP library, exposing a number of annotation tasks for text written in English, French, German, and Spanish. Annotators include tokenization, part of speech tagging, named entity recognition, entity linking, sentiment analysis, dependency parsing, coreference resolution, and information extraction.


title: OFDM Based Real Time Video Transmission Using USRP

abstract: Development in the wireless communication systems is the evolving field of research in today's world. The demand of high data rate, low latency at the minimum cost by the user requires many changes in the hardware organization. The use of digital modulation techniques like OFDM assures the reliability of communication in addition to providing flexibility and robustness. Modifications in the hardware structure can be replaced by the change in software only which gives birth to Software Define Radio (SDR): a radio which is more flexible as compared to conventional radio and can perform signal processing at the minimum cost. GNU Radio with the help of Universal Software Peripheral Radio (USRP) provides flexible and the cost effective SDR platform for the purpose of real time video transmission. The results given in this paper are taken from the experiment performed on USRP-1 along with the GNU Radio version 3.2.2.


title: Cervical joint position sense in neck pain. Immediate effects of muscle vibration versus mental training interventions: a RCT.

abstract: BACKGROUND ::: Impaired cervical joint position sense is a feature of chronic neck pain and is commonly argued to rely on abnormal cervical input. If true, muscle vibration, altering afferent input, but not mental interventions, should have an effect on head repositioning acuity and neck pain perception. ::: ::: ::: AIM ::: The aim of the present study was to determine the short-term effects of neck muscle vibration, motor imagery, and action observation on cervical joint position sense and pressure pain threshold in people with chronic neck pain. ::: ::: ::: DESIGN ::: Forty-five blinded participants with neck pain received concealed allocation and were randomized in three treatment groups. A blinded assessor performed pre- and post-test measurement. ::: ::: ::: SETTING ::: Patients were recruited from secondary outpatient clinics in the southwest of Germany. ::: ::: ::: POPULATION ::: Chronic, non specific neck pain patients without arm pain were recruited for this study. ::: ::: ::: METHODS ::: A single intervention session of 5 minutes was delivered to each blinded participant. Patients were either allocated to one of the following three interventions: (1) neck muscle vibration; (2) motor imagery; (3) action observation. Primary outcomes were cervical joint position sense acuity and pressure pain threshold. Repeated measures ANOVAs were used to evaluate differences between groups and subjects. ::: ::: ::: RESULTS ::: Repositioning acuity displayed significant time effects for vibration, motor imagery, and action observation (all P<0.05), but revealed no time*group effect. Pressure pain threshold demonstrated a time*group effect (P=0.042) as only vibration significantly increased pressure pain threshold (P=0.01). ::: ::: ::: CONCLUSION ::: Although motor imagery and action observation did not modulate proprioceptive, afferent input, they nevertheless improved cervical joint position sense acuity. This indicates that, against the common opinion, changes in proprioceptive input are not prerequisite to improve joint repositioning performance. However, the short-term applications of these cognitive treatments had no effect on pressure pain thresholds, whereas vibration reduced pressure pain thresholds. This implies different underlying mechanisms after vibration and mental training. ::: ::: ::: CLINICAL REHABILITATION IMPACT ::: Mental interventions were effective in improving cervical joint position sense and are easy to integrate in rehabilitation regimes. Neck muscle vibration is effective in improving cervical joint position sense and pressure pain thresholds within 5 minutes of application.


title: Genetics of autoimmune myasthenia gravis, a model for antibody-mediated autoimmunity in man.

abstract: Autoimmune myasthenia gravis is an antibody-mediated autoimmune disease of the neuromuscular junction aimed at the muscle acetylcholine receptor. Recent genetic analyses focusing on a nonclass II HLA-linked locus, MYAS1, and on one of the genes encoding the AChR self-antigen, CHRNA1, are discussed.


title: Analytical models for low-power rectenna design

abstract: The design of a low-cost rectenna for low-power applications is presented. The rectenna is designed with the use of analytical models and closed-form analytical expressions. This allows for a fast design of the rectenna system. To acquire a small-area rectenna, a layered design is proposed. Measurements indicate the validity range of the analytical models.


title: Hand-Gesture Recognition System by Using Microwave Doppler Sensors

abstract: A microwave doppler sensor is a very popular device for detecting moving objects such as human beings. This paper proposes a new appliance-control method using microwave Doppler sensors. The user moves his/her hand back and forth in front of the target appliance for the proposed system. The system recognizes 1) two types of gesture, 2) the direction of hand movement, and 3) the number of hand movements. A prototype system with two light fixtures was developed. The gesture was detected within two seconds in real-time operation. A total of 200 on/off commands were experimentally evaluated. As a result, 85% of the gestures were correctly detected and 3% were misdetected with incorrect appliance selection. The remaining 15% generated no response.


title: A 9.4-ENOB 1V 3.8μW 100kS/s SAR ADC with Time-Domain Comparator

abstract: The ADC-SAR is fabricated in a 0.18mum 2P5M CMOS process. This SAR-ADC converter achieves 56fJ/conversion-step FOM with 58dB SNDR. It uses a comparator, named time-domain comparator, that instead of operating in the voltage domain, transforms the input and the reference voltages into pulses and compares their duration.


title: Siren: A Platform for Deployment of VNFs in Distributed Infrastructures

abstract: Fog computing is conceiving an Internet where general purpose compute is ubiquitous, in turn this is providing new infrastructures for Network Functions Virtualisation (NFV). However, current NFV designs focus on the Cloud, resulting in broken and suboptimal deployments when deploying to the Fog. Through a case study with preliminary results, this paper presents the effectiveness of Siren: a new prototype platform designed as a tool to deploy and manage Virtual Network Functions in Fog environments.


title: Literature search for research planning and identification of research problem

abstract: Literature search is a key step in performing good authentic research. It helps in formulating a research question and planning the study. The available published data are enormous; therefore, choosing the appropriate articles relevant to your study in question is an art. It can be time-consuming, tiring and can lead to disinterest or even abandonment of search in between if not carried out in a step-wise manner. Various databases are available for performing literature search. This article primarily stresses on how to formulate a research question, the various types and sources for literature search, which will help make your search specific and time-saving.


title: Arduino based low cost active dual axis solar tracker

abstract: Sun is an abundant source of energy and this solar energy can be harnessed successfully using solar photovoltaic cells and photovoltaic effect to convert solar energy into electrical energy. But the conversion efficiency of a normal PV cell is low. One of the main reason for this is that the output of PV cell is dependent directly on the light intensity and with the position of the sun in the sky changing continuously from time to time; the absorption efficiency of an immobile solar panel would be significantly less at certain time of the day and year; for the solar photovoltaic cells are maximum productive when they are perpendicular to the sun and less productive otherwise. So to maximize the energy generation and improve the efficiency; solar trackers come into play. This paper presents the design and construction of an inexpensive active dual-axis solar tracking system for tracking the movement of the sun so as to get maximum power from the solar panels as they follow the sun. It uses Light Dependent Resistors to sense the position of the sun which is communicated to a Arduino Uno microcontroller which then commands a set of two servo-motors to re-orient the panel in order to stay perpendicular to the sun rays. The design was constructed successfully and tested using Lab View to determine the improvements in efficiency. Evaluation results show that the new system performs 13.44% better than the immobile solar PV system.


title: Cost-efficient workload scheduling in Cloud Assisted Mobile Edge Computing

abstract: Mobile edge computing is envisioned as a promising computing paradigm with the advantage of low latency. However, compared with conventional mobile cloud computing, mobile edge computing is constrained in computing capacity, especially under the scenario of dense population. In this paper, we propose a Cloud Assisted Mobile Edge computing (CAME) framework, in which cloud resources are leased to enhance the system computing capacity. To balance the tradeoff between system delay and cost, mobile workload scheduling and cloud outsourcing are further devised. Specifically, the system delay is analyzed by modeling the CAME system as a queuing network. In addition, an optimization problem is formulated to minimize the system delay and cost. The problem is proved to be convex, which can be solved by using the Karush-Kuhn-Tucker (KKT) conditions. Instead of directly solving the KKT conditions, which incurs exponential complexity, an algorithm with linear complexity is proposed by exploiting the linear property of constraints. Extensive simulations are conducted to evaluate the proposed algorithm. Compared with the fair ratio algorithm and the greedy algorithm, the proposed algorithm can reduce the system delay by up to 33% and 46%, respectively, at the same outsourcing cost. Furthermore, the simulation results demonstrate that the proposed algorithm can effectively deal with the challenge of heterogeneous mobile users and balance the tradeoff between computation delay and transmission overhead.


title: Learning-based view synthesis for light field cameras

abstract: With the introduction of consumer light field cameras, light field imaging has recently become widespread. However, there is an inherent trade-off between the angular and spatial resolution, and thus, these cameras often sparsely sample in either spatial or angular domain. In this paper, we use machine learning to mitigate this trade-off. Specifically, we propose a novel learning-based approach to synthesize new views from a sparse set of input views. We build upon existing view synthesis techniques and break down the process into disparity and color estimation components. We use two sequential convolutional neural networks to model these two components and train both networks simultaneously by minimizing the error between the synthesized and ground truth images. We show the performance of our approach using only four corner sub-aperture views from the light fields captured by the Lytro Illum camera. Experimental results show that our approach synthesizes high-quality images that are superior to the state-of-the-art techniques on a variety of challenging real-world scenes. We believe our method could potentially decrease the required angular resolution of consumer light field cameras, which allows their spatial resolution to increase.


title: Social Media: Changing the Paradigm for Surgical Education

abstract: The role of social media (SoMe) in surgical education is emerging as a tool that augments and complements traditional learning. As SoMe usage has steadily increased in our personal and professional lives, it is no surprise that it has permeated into surgical education. Different SoMe sites offer distinct platforms from which knowledge can be transmitted, while catering to various learning styles. The purpose of this review is to outline the various SoMe platforms and their use in surgical education. Moreover, it will discuss their effectiveness in teaching and learning surgical knowledge and skills as well as other potential roles SoMe has to offer to improve surgical education.