title: A scalable Cloud-based system for data-intensive spatial analysis

abstract: Advances in Cloud computing technology and the availability of affordable and easy to use Cloud services are enabling a multitude of scientific applications to use these resources as primary or secondary computing infrastructure. The urban and built environment research domain is one area that can benefit greatly from Cloud computing. The global population growth and increase in the size and population of cities raise many challenges for governments, planners and researchers alike. The Australian Urban Research Infrastructure Network (AURIN--http://www.aurin.org.au) project has been tasked with developing an advanced platform (e-Infrastructure) across Australia to tackle these challenges. The platform leverages large-scale Cloud resources to provide federated data access to, at present over 1100 data sets from major and often definitive government and industry data-rich organisations, and for scalable data processing and visualisation. The original AURIN tools were developed using the object modelling system (OMS) and supported integrated workflows to define and enact/re-enact scientific processes. More recently the work has evolved to focus more on delivery of a workbench offering a rich range of tools delivered through an extensible workflow environment. In this paper, we provide the background to AURIN including the scientific drivers that are shaping the work and the realisation of the Cloud-based AURIN environment. We focus in particular on the workflow environment and show how it seamlessly utilizes the Cloud for urban research processes focused especially on data-intensive spatial analysis. We illustrate the utilisation of this workflow environment across a range of case studies reflecting urban research activities.


title: Modeling Li-ion Battery Capacity Depletion in a Particle Filtering Framework

abstract: This paper presents an empirical model to describe battery behavior during individual discharge cycles as well as over its cycle life. The basis for the form of the model has been linked to the internal processes of the battery and validated using experimental data. Subsequently, the model has been used in a Particle Filtering framework to make predictions of remaining useful life for individual discharge cycles as well as for cycle life. The prediction performance was found to be satisfactory as measured by performance metrics customized for prognostics. The work presented here provides initial steps towards a comprehensive health management solution for energy storage devices. *


Risks and opportunities for systems using blockchain and smart contracts


title: Improving Low Quality Stack Overflow Post Detection

abstract: Stack Overflow is a popular questions and answers (Q&A) website among software developers. It counts more than two millions of users who actively contribute by asking and answering thousands of questions daily. Identifying and reviewing low quality posts preserves the quality of site's contents and it is crucial to maintain a good user experience. In Stack Overflow the identification of poor quality posts is performed by selected users manually. The system also uses an automated identification system based on textual features. Low quality posts automatically enter a review queue maintained by experienced users. We present an approach to improve the automated system in use at Stack Overflow. It analyzes both the content of a post (e.g., simple textual features and complex readability metrics) and community-related aspects (e.g., popularity of a user in the community). Our approach reduces the size of the review queue effectively and removes misclassified good quality posts.


title: User Profiling - A Short Review

abstract: In Today’s technology driven world user profiles are the virtual representation of each user and they include a variety of user information such as personal, interest and preference data. These profiles are the outcome of the user profiling process and they are essential to service personalization. Different methods, techniques and algorithms have been proposed in the literature for the user profiling process. This paper aims to give an overview on the user profiling and its related concepts, and discuss the pros and cons of current methods for the future service personalization. Furthermore, it also give details about the simulations which have been carried out with well known classification and clustering algorithms with real world user profile dataset. This work is based on the doctoral thesis of the author.


title: An overview of data warehousing and OLAP technology

abstract: Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, which has increasingly become a focus of the database industry. Many commercial products and services are now available, and all of the principal database management system vendors now have offerings in these areas. Decision support places some rather different requirements on database technology compared to traditional on-line transaction processing applications. This paper provides an overview of data warehousing and OLAP technologies, with an emphasis on their new requirements. We describe back end tools for extracting, cleaning and loading data into a data warehouse; multidimensional data models typical of OLAP; front end client tools for querying and data analysis; server extensions for efficient query processing; and tools for metadata management and for managing the warehouse. In addition to surveying the state of the art, this paper also identifies some promising research issues, some of which are related to problems that the database research community has worked on for years, but others are only just beginning to be addressed. This overview is based on a tutorial that the authors presented at the VLDB Conference, 1996.


title: Design by optimization of an axial-flux permanent-magnet synchronous motor using genetic algorithms

abstract: In this paper, a genetic algorithm (GA) is used to optimize a motor for an automotive application. This motor is a double stator axial permanent-magnet (PM) machine, and the optimization uses a simplified analytical model. The solution obtained with the tool is compared to three-dimensional (3-D) finite-elements method (FEM) to confirm the validity of the model.


title: Learning Fair Classifiers: A Regularization-Inspired Approach

abstract: We present a regularization-inspired approach for reducing bias in learned classifiers. In particular, we focus on binary classification tasks over individuals from two populations, where, as our criterion for fairness, we wish to achieve similar false positive rates in both populations, and similar false negative rates in both populations. As a proof of concept, we implement our approach and empirically evaluate its ability to achieve both fairness and accuracy, using the COMPAS scores data for prediction of recidivism.


title: Adaptive Implicit Friends Identification over Heterogeneous Network for Social Recommendation

abstract: The explicitly observed social relations from online social platforms have been widely incorporated into recommender systems to mitigate the data sparsity issue. However, the direct usage of explicit social relations may lead to an inferior performance due to the unreliability (e.g., noises) of observed links. To this end, the discovery of reliable relations among users plays a central role in advancing social recommendation. In this paper, we propose a novel approach to adaptively identify implicit friends toward discovering more credible user relations. Particularly, implicit friends are those who share similar tastes but could be distant from each other on the network topology of social relations. Methodologically, to find the implicit friends for each user, we first model the whole system as a heterogeneous information network, and then capture the similarity of users through the meta-path based embedding representation learning. Finally, based on the intuition that social relations have varying degrees of impact on different users, our approach adaptively incorporates different numbers of similar users as implicit friends for each user to alleviate the adverse impact of unreliable social relations for a more effective recommendation. Experimental analysis on three real-world datasets demonstrates the superiority of our method and explain why implicit friends are helpful in improving social recommendation.


title: Recent Catastrophic Accidents: Investigating How Software was Responsible

abstract: Areas crucial to life such as medicine, transportation, nuclear-energy research and industry, aeronautics, and others, all make use of software in one way or another. However, the application of software to such domains means that the software may now become safety-critical such that an error in the software or an error in its use could have devastating consequences. This paper reviews 14 recent accidents, several of which resulted in the loss of life in addition to time and money, and identifies the role(s) that software played as an important causative factor. The useful lessons which can be learned from the accidents are also presented, which can then act as principles and guidelines to avoid the recurrence of similar accidents in the future.


title: A layered software architecture for quantum computing design tools

abstract: Compilers and computer-aided design tools are essential for fine-grained control of nanoscale quantum-mechanical systems. A proposed four-phase design flow assists with computations by transforming a quantum algorithm from a high-level language program into precisely scheduled physical actions.


title: Is a picture really worth a thousand words? Exploring the feasibility of graphical authentication systems

abstract: The weakness of knowledge-based authentication systems, such as passwords and Personal Identification Numbers (PINs), is well known, and reflects an uneasy compromise between security and human memory constraints. Research has been undertaken for some years now into the feasibility of graphical authentication mechanisms in the hope that these will provide a more secure and memorable alternative. The graphical approach substitutes the exact recall of alphanumeric codes with the recognition of previously learnt pictures, a skill at which humans are remarkably proficient. So far, little attention has been devoted to usability, and initial research has failed to conclusively establish significant memory improvement. This paper reports two user studies comparing several implementations of the graphical approach with PINs. Results demonstrate that pictures can be a solution to some problems relating to traditional knowledge-based authentication but that they are not a simple panacea, since a poor design can eliminate the picture superiority effect in memory. The paper concludes by discussing the potential of the graphical approach and providing guidelines for developers contemplating using these mechanisms.


FlexRadio : Fully Flexible Radios


title: Exploring connections among nature, biodiversity, ecosystem services, and human health and well-being: Opportunities to enhance health and biodiversity conservation

abstract: We are at a key juncture in history where biodiversity loss is occurring daily and accelerating in the face of population growth, climate change, and rampant development. Simultaneously, we are just beginning to appreciate the wealth of human health benefits that stem from experiencing nature and biodiversity. Here we assessed the state of knowledge on relationships between human health and nature and biodiversity, and prepared a comprehensive listing of reported health effects. We found strong evidence linking biodiversity with production of ecosystem services and between nature exposure and human health, but many of these studies were limited in rigor and often only correlative. Much less information is available to link biodiversity and health. However, some robust studies indicate that exposure to microbial biodiversity can improve health, specifically in reducing certain allergic and respiratory diseases. Overall, much more research is needed on mechanisms of causation. Also needed are a re-envisioning of land-use planning that places human well-being at the center and a new coalition of ecologists, health and social scientists and planners to conduct research and develop policies that promote human interaction with nature and biodiversity. Improvements in these areas should enhance human health and ecosystem, community, as well as human resilience.


title: Examining the relationship of payment system characteristics and behavioural intention in e-payment adoption: a case of Indonesia

abstract: This study is conducted to analyse factors that influence the behavioural intention to use e-payment system on e-commerce in Indonesia. The proposed model in this research combines behavioural theory of technology acceptance in terms of behavioural beliefs, social influences, and personal innovativeness with the characteristics of an ideal payment system. The model was tested by the structural equation modelling SEM using LISREL 8.8 software. The analysis result of 203 respondents showed that Indonesian customers tend to only consider the advantages they gain when adopting e-payment and ignore the risks associated with the technology. Concerning e-payment system characteristics, it was also proven that ease of use, privacy, trust, security, and convertibility influence directly and indirectly the intention to adopt e-payment.


title: Data-Driven Techniques in Disaster Information Management

abstract: Improving disaster management and recovery techniques is one of national priorities given the huge toll caused by man-made and nature calamities. Data-driven disaster management aims at applying advanced data collection and analysis technologies to achieve more effective and responsive disaster management, and has undergone considerable progress in the last decade. However, to the best of our knowledge, there is currently no work that both summarizes recent progress and suggests future directions for this emerging research area. To remedy this situation, we provide a systematic treatment of the recent developments in data-driven disaster management. Specifically, we first present a general overview of the requirements and system architectures of disaster management systems and then summarize state-of-the-art data-driven techniques that have been applied on improving situation awareness as well as in addressing users’ information needs in disaster management. We also discuss and categorize general data-mining and machine-learning techniques in disaster management. Finally, we recommend several research directions for further investigations.


title: Node centrality in weighted networks: Generalizing degree and shortest paths

abstract: Abstract Ties often have a strength naturally associated with them that differentiate them from each other. Tie strength has been operationalized as weights. A few network measures have been proposed for weighted networks, including three common measures of node centrality: degree, closeness, and betweenness. However, these generalizations have solely focused on tie weights, and not on the number of ties, which was the central component of the original measures. This paper proposes generalizations that combine both these aspects. We illustrate the benefits of this approach by applying one of them to Freeman’s EIES dataset.


title: Adaptive transmission based on multi-relay selection and rate-compatible LDPC codes

abstract: In order to adapt to the dynamical changeable channel condition and improve the transmissive reliability of the system, a cooperation system of rate-compatible low density parity check (RC-LDPC) codes combining with multi-relay selection protocol is proposed. In traditional relay selection protocol, only the channel state information (CSI) of source-relay and the CSI of relay-destination has been considered. The multi-relay selection protocol proposed by this paper takes the CSI between relays into extra account in order to obtain more chances of collabration. Additionally, the idea of hybrid automatic request retransmission (HARQ) and rate-compatible are introduced. Simulation results show that the transmissive reliability of the system can be significantly improved by the proposed protocol.In order to adapt to the dynamical changeable channel condition and improve the transmissive reliability of the system, a cooperation system of rate-compatible low density parity check (RC-LDPC) codes combining with multi-relay selection protocol is proposed. In traditional relay selection protocol, only the channel state information (CSI) of source-relay and the CSI of relay-destination has been considered. The multi-relay selection protocol proposed by this paper takes the CSI between relays into extra account in order to obtain more chances of collabration. Additionally, the idea of hybrid automatic request retransmission (HARQ) and rate-compatible are introduced. Simulation results show that the transmissive reliability of the system can be significantly improved by the proposed protocol.


title: BURRITO: wrapping your lab notebook in computational infrastructure

abstract: Researchers in fields such as bioinformatics, CS, finance, and applied math have trouble managing the numerous code and data files generated by their computational experiments, comparing the results of trials executed with different parameters, and keeping up-to-date notes on what they learned from past successes and failures. ::: ::: We created a Linux-based system called BURRITO that automates aspects of this tedious experiment organization and notetaking process, thus freeing researchers to focus on more substantive work. BURRITO automatically captures a researcher's computational activities and provides user interfaces to annotate the captured provenance with notes and then make queries such as, "Which script versions and command-line parameters generated the output graph that this note refers to?"


title: A Big Data Architecture for Large Scale Security Monitoring

abstract: Network traffic is a rich source of information for security monitoring. However the increasing volume of data to treat raises issues, rendering holistic analysis of network traffic difficult. In this paper we propose a solution to cope with the tremendous amount of data to analyse for security monitoring perspectives. We introduce an architecture dedicated to security monitoring of local enterprise networks. The application domain of such a system is mainly network intrusion detection and prevention, but can be used as well for forensic analysis. This architecture integrates two systems, one dedicated to scalable distributed data storage and management and the other dedicated to data exploitation. DNS data, NetFlow records, HTTP traffic and honeypot data are mined and correlated in a distributed system that leverages state of the art big data solution. Data correlation schemes are proposed and their performance are evaluated against several well-known big data framework including Hadoop and Spark.


title: Word Spotting in Silent Lip Videos

abstract: Our goal is to spot words in silent speech videos without explicitly recognizing the spoken words, where the lip motion of the speaker is clearly visible and audio is absent. Existing work in this domain has mainly focused on recognizing a fixed set of words in word-segmented lip videos, which limits the applicability of the learned model due to limited vocabulary and high dependency on the model's recognition performance. Our contribution is two-fold: 1) we develop a pipeline for recognition-free retrieval, and show its performance against recognition-based retrieval on a large-scale dataset and another set of out-of-vocabulary words. 2) We introduce a query expansion technique using pseudo-relevant feedback and propose a novel re-ranking method based on maximizing the correlation between spatio-temporal landmarks of the query and the top retrieval candidates. Our word spotting method achieves 35% higher mean average precision over recognition-based method on large-scale LRWdataset. Finally, we demonstrate the application of the method by word spotting in a popular speech video ("The great dictator" by Charlie Chaplin) where we show that the word retrieval can be used to understand what was spoken perhaps in the silent movies.


title: SimDoc: Topic Sequence Alignment based Document Similarity Framework

abstract: Document similarity is the problem of formally representing textual documents and then proposing a similarity measure that can be used to compute the linguistic similarity between two documents. Accurate document similarity computation improves many enterprise relevant tasks such as document clustering, text mining, and question-answering. Most contemporary techniques employ bag-of-words (BoW) based document representation models. In this paper, we show that a document's thematic flow, which is often disregarded by bag-of-word techniques, is pivotal in estimating their semantic similarity. In this direction, we propose a novel semantic document similarity framework, called SimDoc. We model documents as topic-sequences, where topics represent latent generative clusters of relative words. We then use a sequence alignment algorithm, that has been adapted from the Smith-Waterman gene-sequencing algorithm, to estimate their semantic similarity. For similarity computation at a finer granularity, we tune the alignment algorithm by integrating it with a word embedding matrix based topic-to-topic similarity measure. A document level similarity score is then computed by again using the sequence alignment algorithm over all sentence pairs. In our experiments, we see that SimDoc outperforms many contemporary bag-of-words techniques in accurately computing document similarity, and on practical applications such as document clustering.


title: Regular-expression derivatives reexamined

abstract: Regular-expression derivatives are an old, but elegant, technique for compiling regular expressions to deterministic finite-state machines. It easily supports extending the regular-expression operators with boolean operations, such as intersection and complement. Unfortunately, this technique has been lost in the sands of time and few computer scientists are aware of it. In this paper, we reexamine regular-expression derivatives and report on our experiences in the context of two different functional-language implementations. The basic implementation is simple and we show how to extend it to handle large character sets (e.g., Unicode). We also show that the derivatives approach leads to smaller state machines than the traditional algorithm given by McNaughton and Yamada.


title: Combining Biomarkers with EMR Data to Identify Patients in Different Phases of Sepsis

abstract: Sepsis is a leading cause of death and is the most expensive condition to treat in U.S. hospitals. Despite targeted efforts to automate earlier detection of sepsis, current techniques rely exclusively on using either standard clinical data or novel biomarker measurements. In this study, we apply machine learning techniques to assess the predictive power of combining multiple biomarker measurements from a single blood sample with electronic medical record data (EMR) for the identification of patients in the early to peak phase of sepsis in a large community hospital setting. Combining biomarkers and EMR data achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.81, while EMR data alone achieved an AUC of 0.75. Furthermore, a single measurement of six biomarkers (IL-6, nCD64, IL-1ra, PCT, MCP1, and G-CSF) yielded the same predictive power as collecting an additional 16 hours of EMR data(AUC of 0.80), suggesting that the biomarkers may be useful for identifying these patients earlier. Ultimately, supervised learning using a subset of biomarker and EMR data as features may be capable of identifying patients in the early to peak phase of sepsis in a diverse population and may provide a tool for more timely identification and intervention.


title: Efficient Selection of Geospatial Data on Maps for Interactive and Visualized Exploration

abstract: With the proliferation of mobile devices, large collections of geospatial data are becoming available, such as geo-tagged photos. Map rendering systems play an important role in presenting such large geospatial datasets to end users. We propose that such systems should support the following desirable features: representativeness, visibility constraint, zooming consistency, and panning consistency. The first two constraints are fundamental challenges to a map exploration system, which aims to efficiently select a small set of representative objects from the current region of user's interest, and any two selected objects should not be too close to each other for users to distinguish in the limited space of a screen. We formalize it as the Spatial Object Selection (SOS) problem, prove that it is an NP-hard problem, and develop a novel approximation algorithm with performance guarantees. % To further support interactive exploration of geospatial data on maps, we propose the Interactive SOS (ISOS) problem, in which we enrich the SOS problem with the zooming consistency and panning consistency constraints. The objective of ISOS is to provide seamless experience for end-users to interactively explore the data by navigating the map. We extend our algorithm for the SOS problem to solve the ISOS problem, and propose a new strategy based on pre-fetching to significantly enhance the efficiency. Finally we have conducted extensive experiments to show the efficiency and scalability of our approach.


title: Clustering by pattern similarity in large data sets

abstract: Clustering is the process of grouping a set of objects into classes of similar objects. Although definitions of similarity vary from one clustering model to another, in most of these models the concept of similarity is based on distances, e.g., Euclidean distance or cosine distance. In other words, similar objects are required to have close values on at least a set of dimensions. In this paper, we explore a more general type of similarity. Under the pCluster model we proposed, two objects are similar if they exhibit a coherent pattern on a subset of dimensions. For instance, in DNA microarray analysis, the expression levels of two genes may rise and fall synchronously in response to a set of environmental stimuli. Although the magnitude of their expression levels may not be close, the patterns they exhibit can be very much alike. Discovery of such clusters of genes is essential in revealing significant connections in gene regulatory networks. E-commerce applications, such as collaborative filtering, can also benefit from the new model, which captures not only the closeness of values of certain leading indicators but also the closeness of (purchasing, browsing, etc.) patterns exhibited by the customers. Our paper introduces an effective algorithm to detect such clusters, and we perform tests on several real and synthetic data sets to show its effectiveness.


title: A wide-band slot antenna design employing a fictitious short circuit concept

abstract: A wide-band slot antenna element is proposed as a building block for designing single- or multi-element wide-band or dual-band slot antennas. It is shown that a properly designed, off-centered, microstrip-fed, moderately wide slot antenna shows dual resonant behavior with similar radiation characteristics at both resonant frequencies and therefore can be used as a wide-band or dual-band element. This element shows bandwidth values up to 37%, if used in the wide-band mode. When used in the dual-band mode, frequency ratios up to 1.6 with bandwidths larger than 10% at both frequency bands can be achieved without putting any constraints on the impedance matching, cross polarization levels, or radiation patterns of the antenna. The proposed wide-band slot element can also be incorporated in a multi-element antenna topology resulting in a very wide-band antenna with a minimum number of elements. It is also shown that, by using only two of these elements in a parallel feed topology, an antenna with good radiation parameters over a 2.5:1 bandwidth ratio can be obtained.


title: Exponential decay sine wave learning rate for fast deep neural network training

abstract: Most state-of-the-art results on image classification tasks were obtained by residual neural networks, which use stochastic gradient descent (SGD) with momentum for training. In most cases, the learning rate drops by a constant factor every pre-defined number of epochs. However, it is difficult and time-consuming to estimate how many epochs to drop the learning rate. To tackle this problem, cyclical learning rate is gaining popularity in gradient-based optimization to improve the convergence speed in accelerated gradient schemes. But cyclical learning rate scheme scans a broad range of learning rate, some of which are not suitable for deep neural network training. In this paper, we propose a simple yet effective exponential decay sine wave like learning rate technique for SGD to improve its convergence speed. In the training process, the learning rate would vary in sine wave way. While the maximum value of sine wave would decay exponentially along with training epochs. An ensemble of wide residual nets with our proposed learning scheme achieves 3.01% and 16.03% errors on CIFAR-10 and CIFAR-100 respectively. Furthermore, our proposed method uses far less number of epochs than most recent learning rate strategies, accelerating neural network training tremendously.


title: Cascaded Regressor based 3D Face Reconstruction from a Single Arbitrary View Image.

abstract: State-of-the-art methods reconstruct three-dimensional (3D) face shapes from a single image by fitting 3D face models to input images or by directly learning mapping functions between two-dimensional (2D) images and 3D faces. However, they are often difficult to use in real-world applications due to expensive online optimization or to the requirement of frontal face images. This paper approaches the 3D face reconstruction problem as a regression problem rather than a model fitting problem. Given an input face image along with some pre-defined facial landmarks on it, a series of shape adjustments to the initial 3D face shape are computed through cascaded regressors based on the deviations between the input landmarks and the landmarks obtained from the reconstructed 3D faces. The cascaded regressors are offline learned from a set of 3D faces and their corresponding 2D face images in various views. By treating the landmarks that are invisible in large view angles as missing data, the proposed method can handle arbitrary view face images in a unified way with the same regressors. Experiments on the BFM and Bosphorus databases demonstrate that the proposed method can reconstruct 3D faces from arbitrary view images more efficiently and more accurately than existing methods.


title: Conversation Extraction in Dynamic Text Message Stream

abstract: Text message stream which is produced by Instant Messager and Internet Relay Chat poses interesting and challenging problems for information technologies. It is beneficial to extract the conversations in this kind of chatting message stream for information management and knowledge finding. However, the data in text message stream are usually very short and incomplete, and it requires efficiency to monitor thousands of continuous chat sessions. Many existing text mining methods encounter challenges. This paper focuses on the conversation extraction in dynamic text message stream. We design the dynamic representation for messages to combine the text content information and linguistic feature in message stream. A memory structure of reversed maximal similar relationship is developed for renewable assignments when grouping messages into conversations. We finally propose a double time window algorithm based on above methods to extract conversations in dynamic text message stream. Experiments on a real dataset shows that our method outperforms two baseline methods introduced in a recent related paper about 47% and 15% in terms of F measure respectively.


title: Motor Patterns in Human Walking and Running

abstract: Despite distinct differences between walking and running, the two types of human locomotion are likely to be controlled by shared pattern-generating networks. However, the differences between their kinematics and kinetics imply that corresponding muscle activations may also be quite different. We examined the differences between walking and running by recording kinematics and electromyographic (EMG) activity in 32 ipsilateral limb and trunk muscles during human locomotion, and compared the effects of speed (3-12 km/h) and gait. We found that the timing of muscle activation was accounted for by five basic temporal activation components during running as we previously found for walking. Each component was loaded on similar sets of leg muscles in both gaits but generally on different sets of upper trunk and shoulder muscles. The major difference between walking and running was that one temporal component, occurring during stance, was shifted to an earlier phase in the step cycle during running. These muscle activation differences between gaits did not simply depend on locomotion speed as shown by recordings during each gait over the same range of speeds (5-9 km/h). The results are consistent with an organization of locomotion motor programs having two parts, one that organizes muscle activation during swing and another during stance and the transition to swing. The timing shift between walking and running reflects therefore the difference in the relative duration of the stance phase in the two gaits.


title: Acquisition of the algorithms of social life: a domain-based approach.

abstract: Proposing that the algorithms of social life are acquired as a domain-based process, the author offers distinctions between social domains preparing the individual for proximity-maintenance within a protective relationship (attachment domain), use and recognition of social dominance (hierarchical power domain), identification and maintenance of the lines dividing "us" and "them" (coalitional group domain), negotiation of matched benefits with functional equals (reciprocity domain), and selection and protection of access to sexual partners (mating domain). Flexibility in the implementation of domains occurs at 3 different levels: versatility at a bioecological level, variations in the cognitive representation of individual experience, and cultural and individual variations in the explicit management of social life. Empirical evidence for domain specificity was strongest for the attachment domain; supportive evidence was also found for the distinctiveness of the 4 other domains. Implications are considered at theoretical and applied levels.


title: Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach

abstract: Knowledge base completion (KBC) aims to predict missing information in a knowledge base.In this paper, we address the out-of-knowledge-base (OOKB) entity problem in KBC:how to answer queries concerning test entities not observed at training time. Existing embedding-based KBC models assume that all test entities are available at training time, making it unclear how to obtain embeddings for new entities without costly retraining. To solve the OOKB entity problem without retraining, we use graph neural networks (Graph-NNs) to compute the embeddings of OOKB entities, exploiting the limited auxiliary knowledge provided at test time.The experimental results show the effectiveness of our proposed model in the OOKB setting.Additionally, in the standard KBC setting in which OOKB entities are not involved, our model achieves state-of-the-art performance on the WordNet dataset. The code and dataset are available at this https URL. This paper has been accepted by IJCAI17.


title: Text Visualization for Visual Text Analytics

abstract: The termvisual text analyticsdescribes a class of information analysis techniques and processes that enable knowledge discovery via the use of interactive graphical representations of textual data. These techniques enable discovery and understanding via the recruitment of human visual pattern recognition and spatial reasoning capabilities. Visual text analytics is a subclass of visual data mining / visual analytics, which more generally encompasses analytical techniques that employ visualization of non-physically-based (or "abstract") data of all types. Text visualizationis a key component in visual text analytics. While the term "text visualization" has been used to describe a variety of methods for visualizing both structured and unstructured characteristics of text-based data, it is most closely associated with techniques for depicting the semantic characteristics of the free-text components of documents in large document collections. In contrast with text clustering techniques which serve only to partition text corpora into sets of related items, these so-called semantic mappingmethods also typically strive to depict detailed inter- and intra-set similarity structure. Text analytics software typically couples semantic mapping techniques with additional visualization techniques to enable interactive comparison of semantic structure with other characteristics of the information, such as publication date or citation information. In this way, value can be derived from the material in the form of multidimensional relationship patterns existing among the discrete items in the collection. The ultimate goal of these techniques is to enable human understanding and reasoning about the contents of large and complexly related text collections.


title: SMO-style algorithms for learning using privileged information

abstract: Recently Vapnik et al. [11, 12, 13] introduced a new learning model, called Learning Using Privileged Information (LUPI). In this model, along with standard training data, the teacher supplies the student with additional (privileged) information. In the optimistic case, the LUPI model can improve the bound for the probability of test error from O(1/ √ n) to O(1/n), where n is the number of training examples. Since semi-supervised learning model with n labeled and N unlabeled examples can only achieve the bound O(1/ √ n + N) in the optimistic case, the LUPI model can thus significantly outperform it. To implement LUPI model, Vapnik et al. [11, 12, 13] suggested to use an SVM-type algorithm called SVM+, which requires, however, to solve a more difficult optimization problem than the one that is traditionally used to solve SVM. In this paper we develop two new algorithms for solving the optimization problem of SVM+. Our algorithms have the structure similar to the empirically successful SMO algorithm for solving SVM. Our experiments show that in terms of the generalization error/running time tradeoff, one of our algorithms is superior over the widely used interior point optimizer.


title: What are real SPARQL queries like?

abstract: We present statistics on real world SPARQL queries that may be of interest for building SPARQL query processing engines and benchmarks. In particular, we analyze the syntactical structure of queries in a log of about 3 million queries, harvested from the DBPedia SPARQL endpoint. Although a sizable portion of the log is shown to consist of so-called conjunctive SPARQL queries, non-conjunctive queries that use SPARQL's union or optional operators are more than substantial. It is known, however, that query evaluation quickly becomes hard for queries including the non-conjunctive operators union or optional. We therefore drill deeper into the syntactical structure of the queries that are not conjunctive and show that in 50% of the cases, these queries satisfy certain structural restrictions that imply tractable evaluation in theory. We hope that the identification of these restrictions can aid in the future development of practical heuristics for processing non-conjunctive SPARQL queries.


title: Available Online at www.warse.ijatcse.current Impact of Technology on Teens ’ Written Language

abstract: Information technologies are having a profound effect on all aspects of life, language is no exception. Digital technology has become an increasingly popular means of communication; however, there are worries that this trend is having a detrimental effect on the writing skills of students. In this study, the relationship of new technologies to writing and its implications is reviewed; case studies and existing research on the effects of electronic communication on literacy and language development more specifically on English writing among teens are evaluated. Finally solutions are proposed with regard to future trends of technology-intensive writing.


title: On-Demand Information Extraction

abstract: At present, adapting an Information Extraction system to new topics is an expensive and slow process, requiring some knowledge engineering for each new topic. We propose a new paradigm of Information Extraction which operates 'on demand' in response to a user's query. On-demand Information Extraction (ODIE) aims to completely eliminate the customization effort. Given a user's query, the system will automatically create patterns to extract salient relations in the text of the topic, and build tables from the extracted information using paraphrase discovery technology. It relies on recent advances in pattern discovery, paraphrase discovery, and extended named entity tagging. We report on experimental results in which the system created useful tables for many topics, demonstrating the feasibility of this approach.


title: When does feedback facilitate learning of words

abstract: The question of what form of feedback best promotes associative learning and retention is of obvious practical import. However, the literature on feedback is confusing, with some researchers suggesting that although feedback may enhance performance during learning, it does so at the expense of later retention. To shed further light on this issue, subjects (N = 258) learned Luganda–English word pairs. After 2 initial exposures to the materials, subjects were tested on each item several times, with the presence and type of feedback varying between subjects. Subjects were given a final test on the same items 1 week later. Supplying the correct answer after an incorrect response not only improved performance during the initial learning session—it also increased final retention by 494%. On the other hand, feedback after correct responses made little difference either immediately or at a delay, regardless of whether the subject was confident in the response. Practical and theoretical implications are discussed.


title: Learning from Conditional Distributions via Dual Kernel Embeddings

abstract: In many machine learning problems, such as policy evaluation in reinforcement learning and learning with invariance, each data point $x$ itself is a conditional distribution $p(z|x)$, and we want to learn a function $f$ which links these conditional distributions to target values $y$. The learning problem becomes very challenging when we only have limited samples or in the extreme case only one sample from each conditional distribution $p(z|x)$. Commonly used approaches either assume that $z$ is independent of $x$, or require an overwhelmingly large sample size from each conditional distribution. ::: To address these challenges, we propose a novel approach which reformulates the original problem into a min-max optimization problem. In the new view, we only need to deal with the kernel embedding of the joint distribution $p(z,x)$ which is easy to estimate. Furthermore, we design an efficient learning algorithm based on mirror descent stochastic approximation, and establish the sample complexity for learning from conditional distributions. Finally, numerical experiments in both synthetic and real data show that our method can significantly improve over the previous state-of-the-arts.


title: OPTICAL CHARACTER RECOGNITION: AN ENCOMPASSING REVIEW

abstract: Optical character recognition (OCR) is becoming a powerful tool in the field of Character Recognition, now a days. In the existing globalized environment, OCR can play a vital role in different application fields. Basically, OCR technique converts images into editable format. This technique converts images in the form of documents such as we can edit, modify and store data more safely for longtime. This paper presents basic of OCR technique with its components such as pre-processing, Feature Extraction, Classification, post-processing etc. There are various techniques have been implemented for the recognition of character. This Review also discusses different ideas implemented earlier for recognition of a character. This paper may act as a supportive material for those who wish to know about OCR.


title: On the construction of a RoboCup small size league team

abstract: The Robot Soccer domain has become an important artificial intelligence test bench and a widely studied research area. It is a domain with real, dynamic, and uncertain environment, where teams of robots cooperate and face adversarial competition. To build a RoboCup Small Size League (SSL) team able to compete in the world championship requires multidisciplinary research in fields like robotic hardware development, machine learning, multi-robot systems, computer vision, control theory, and mechanics, among others. This paper intends to provide insights about the aspects involved on the development of the RoboFEI RoboCup SSL robot soccer team and to present the contributions produced over its course. Among these contributions, a computer vision system employing an artificial neural network (ANN) to recognize colors, a heuristic algorithm to recognize partially detected objects, an implementation of the known rapidly-exploring random trees (RRT) path planning algorithm with additional rules, enabling the angle of approach of the robot to be controlled, and a layered strategy software system. Experimental results on real robots demonstrate the high performance of the vision system and the efficiency of the RRT algorithm implementation. Some strategy functions are also experimented, with empirical results showing their effectiveness.


title: - 1-A Stochastic Framework for Evaluating Seizure Prediction Algorithms Using Hidden Markov Models

abstract: Responsive, implantable stimulation devices to treat epilepsy are now in clinical trials. New evidence suggests that these devices may be more effective when they deliver therapy before seizure onset. Despite years of effort, prospective seizure prediction, which could improve device performance, remains elusive. In large part, this is explained by lack of agreement on a statistical framework for modeling seizure generation and a method for validating algorithm performance. We present a novel stochastic framework based on a three-state hidden Markov model (HMM) (representing interictal, preictal, and seizure states) with the feature that periods of increased seizure probability can transition back to the interictal state. This notion reflects clinical experience and may enhance interpretation of published seizure prediction studies. Our model accommodates clipped EEG segments and formalizes intuitive notions regarding statistical validation. We derive equations for type I and type II errors as a function of the number of seizures, duration of interictal data, and prediction horizon length and we demonstrate the model's utility with a novel seizure detection algorithm that appeared to predicted seizure onset. We propose this framework as a vital tool for designing and validating prediction algorithms and for facilitating collaborative research in this area.


title: Can Invisible Watermarks Resolve Rightful Ownerships?

abstract: Digital watermarks have been proposed in recent literature as the means for copyright protection of multimedia data. In this paper we address the capability of invisible watermarking schemes to resolve copyright ownerships. We will show that rightful ownerships cannot be resolved by current watermarking schemes alone. In addition, in the absence of standardization of watermarking procedures, anyone can claim ownership of any watermarked image. Specifically, we provide counterfeit watermarking schemes that can be performed on a watermarked image to allow multiple claims of rightful ownerships. We also proposed non-invertible watermarking schemes in this paper and discuss in general the usefulness of digital watermarks in identifying the rightful copyright owners. The results, coupled with the recent attacks on some image watermarks, further imply that we have to carefully re-think our approaches to invisible watermarking of images, and re- evaluate the promises, applications and limitations of such digital means of copyright protection.


title: Classification of papillomaviruses.

abstract: One hundred eighteen papillomavirus (PV) types have been completely described, and a yet higher number of presumed new types have been detected by preliminary data such as subgenomic amplicons. The classification of this diverse group of viruses, which include important human pathogens, has been debated for three decades. This article describes the higher-order PV taxonomy following the general criteria established by the International Committee on the Taxonomy of Viruses (ICTV), reviews the literature of the lower order taxa, lists all known "PV types", and interprets their phylogenetic relationship. PVs are a taxonomic family of their own, Papillomaviridae, unrelated to the polyomaviruses. Higher-order phylogenetic assemblages of PV types, such as the "genital human PVs", are considered a genus, the latter group, for example, the genus "Alpha-Papillomavirus". Lower-order assemblages of PV types within each genus are treated as species because they are phylogenetically closely related, but while they have distinct genomic sequences, they have identical or very similar biological or pathological properties. The taxonomic status of PV types, subtypes, and variants remains unchanged and is based on the traditional criteria that the sequence of their L1 genes should be at least 10%, 2-10%, and maximally 2% dissimilar from one another.


title: Language, Emotions, and Cultures: Emotional Sapir-Whorf Hypothesis

abstract: An emotional version of Sapir-Whorf hypothesis suggests that differences in language emotionalities influence differences among cultures no less than conceptual differences. Conceptual contents of languages and cultures to significant extent are determined by words and their semantic differences; these could be borrowed among languages and exchanged among cultures. Emotional differences, as suggested in the paper, are related to grammar and mostly cannot be borrowed. Conceptual and emotional mechanisms of languages are considered here along with their functions in the mind and cultural evolution. A fundamental contradiction in human mind is considered: language evolution requires reduced emotionality, but "too low" emotionality makes language "irrelevant to life," disconnected from sensory-motor experience. Neural mechanisms of these processes are suggested as well as their mathematical models: the knowledge instinct, the language instinct, the dual model connecting language and cognition, dynamic logic, neural modeling fields. Mathematical results are related to cognitive science, linguistics, and psychology. Experimental evidence and theoretical arguments are discussed. Approximate equations for evolution of human minds and cultures are obtained. Their solutions identify three types of cultures: "conceptual"-pragmatic cultures, in which emotionality of language is reduced and differentiation overtakes synthesis resulting in fast evolution at the price of uncertainty of values, self doubts, and internal crises; "traditional-emotional" cultures where differentiation lags behind synthesis, resulting in cultural stability at the price of stagnation; and "multi-cultural" societies combining fast cultural evolution and stability. Unsolved problems and future theoretical and experimental directions are discussed.


title: Inferring human intent from video by sampling hierarchical plans

abstract: This paper presents a method which allows robots to infer a human's hierarchical intent from partially observed RGBD videos by imagining how the human will behave in the future. This capability is critical for creating robots which can interact socially or collaboratively with humans. We represent intent as a novel hierarchical, compositional, and probabilistic And-Or graph structure which describes a relationship between actions and plans. We infer human intent by reverse-engineering a human's decision-making and action planning processes under a Bayesian probabilistic programming framework. We present experiments from a 3D environment which demonstrate that the inferred human intent (1) matches well with human judgment, and (2) provides useful contextual cues for object tracking and action recognition.


title: Bringing CUPID Indoor Positioning System to Practice

abstract: WiFi based indoor positioning has recently gained more attention due to the advent of the IEEE 802.11v standard, requirements by the FCC for E911 calls, and increased interest in location-based services. While there exist several indoor localization techniques, we find that these techniques tradeoff either accuracy, scalability, pervasiveness or cost -- all of which are important requirements for a truly deployable positioning solution. Wireless signal-strength based approaches suffer from location errors, whereas time-of-flight (ToF) based solutions provide good accuracy but are not scalable. Recent solutions address these issues by augmenting WiFi with either smartphone sensing or mobile crowdsourcing. However, they require tight coupling between WiFi infrastructure and a client device, or they can determine the client's location only if it is mobile. In this paper, we present CUPID2.0 which improved our previously proposed CUPID indoor positioning system to overcome these limitations. We achieve this by addressing the fundamental limitations in Time-of-Flight based localization and combining ToF with signal strength to address scalability. Experiments from $6$ cities using $40$ different mobile devices, comprising of more than $2.5$ million location fixes demonstrate feasibility. CUPID2.0 is currently under production, and we expect CUPID2.0 to ignite the wide adoption of WLAN-based positioning systems and their services.


title: Cross-lingual Word Clusters for Direct Transfer of Linguistic Structure

abstract: It has been established that incorporating word cluster features derived from large unlabeled corpora can significantly improve prediction of linguistic structure. While previous work has focused primarily on English, we extend these results to other languages along two dimensions. First, we show that these results hold true for a number of languages across families. Second, and more interestingly, we provide an algorithm for inducing cross-lingual clusters and we show that features derived from these clusters significantly improve the accuracy of cross-lingual structure prediction. Specifically, we show that by augmenting direct-transfer systems with cross-lingual cluster features, the relative error of delexicalized dependency parsers, trained on English treebanks and transferred to foreign languages, can be reduced by up to 13%. When applying the same method to direct transfer of named-entity recognizers, we observe relative improvements of up to 26%.


title: Four-Arm Spiral Antenna Fed by Tapered Transmission Line

abstract: This letter presents a four-arm Archimedean spiral antenna with a novel integrated tapered transmission line feed structure. The center-fed spiral antenna consists of a radiation region with an Archimedean spiral configuration and a transmission region that comprises four spiral-shaped transmission lines with exponentially decreasing widths. The tapered transmission line, acting as an impedance transformer, provides a broadband matching network between the 50-Ω input impedance and the higher impedance of the radiation region. Using this design, a mode-1 spiral antenna attains a return loss of greater than 15 dB and a 3-dB broadside axial ratio throughout the 0.8–3-GHz band. The fabricated antenna demonstrates good agreement between the simulated and measured results.


title: Dataset for a Neural Natural Language Interface for Databases (NNLIDB)

abstract: Progress in natural language interfaces to databases (NLIDB) has been slow mainly due to linguistic issues (such as language ambiguity) and domain portability. Moreover, the lack of a large corpus to be used as a standard benchmark has made data-driven approaches difficult to develop and compare. In this paper, we revisit the problem of NLIDBs and recast it as a sequence translation problem. To this end, we introduce a large dataset extracted from the Stack Exchange Data Explorer website, which can be used for training neural natural language interfaces for databases. We also report encouraging baseline results on a smaller manually annotated test corpus, obtained using an attention-based sequence-to-sequence neural network.


title: Ontology-based context modeling for emotion recognition in an intelligent web

abstract: We describe an ontological model for representation and integration of electroencephalographic (EEG) data and apply it to detect human emotional states. The model (BIO_EMOTION) is an ontology-based context model for emotion recognition and acts as a basis for: (1) the modeling of users' contexts, including user profiles, EEG data, the situation and environment factors, and (2) supporting reasoning on the users' emotional states. Because certain ontological concepts in the EEG domain are ill-defined, we formally represent and store these concepts, their taxonomies and high-level representation (i.e., rules) in the model. To evaluate the effectiveness for inferring emotional states, DEAP dataset is used for model reasoning. Result shows that our model reaches an average recognition ratio of 75.19 % on Valence and 81.74 % on Arousal for eight participants. As mentioned above, the BIO-EMOTION model acts like a bridge between users' emotional states and low-level bio-signal features. It can be integrated in user modeling techniques, and be used to model web users' emotional states in human-centric web aiming to provide active, transparent, safe and reliable services to users. This work aims at, in other words, creating an ontology-based context model for emotion recognition using EEG. Particularly, this model completely implements the loop body of the W2T data cycle once: from low-level EEG feature acquisition to emotion recognition. A long-term goal for the study is to complete this model to implement the whole W2T data cycle.


title: Fraudulent ID using face morphs: Experiments on human and automatic recognition

abstract: Matching unfamiliar faces is known to be difficult, and this can give an opportunity to those engaged in identity fraud. Here we examine a relatively new form of fraud, the use of photo-ID containing a graphical morph between two faces. Such a document may look sufficiently like two people to serve as ID for both. We present two experiments with human viewers, and a third with a smartphone face recognition system. In Experiment 1, viewers were asked to match pairs of faces, without being warned that one of the pair could be a morph. They very commonly accepted a morphed face as a match. However, in Experiment 2, following very short training on morph detection, their acceptance rate fell considerably. Nevertheless, there remained large individual differences in people’s ability to detect a morph. In Experiment 3 we show that a smartphone makes errors at a similar rate to ‘trained’ human viewers—i.e. accepting a small number of morphs as genuine ID. We discuss these results in reference to the use of face photos for security.


Information Privacy Awareness (IPA): A Review of the Use, Definition and Measurement of IPA


title: Empirical analysis of programming language adoption

abstract: Some programming languages become widely popular while others fail to grow beyond their niche or disappear altogether. This paper uses survey methodology to identify the factors that lead to language adoption. We analyze large datasets, including over 200,000 SourceForge projects, 590,000 projects tracked by Ohloh, and multiple surveys of 1,000-13,000 programmers. We report several prominent findings. First, language adoption follows a power law; a small number of languages account for most language use, but the programming market supports many languages with niche user bases. Second, intrinsic features have only secondary importance in adoption. Open source libraries, existing code, and experience strongly influence developers when selecting a language for a project. Language features such as performance, reliability, and simple semantics do not. Third, developers will steadily learn and forget languages. The overall number of languages developers are familiar with is independent of age. Finally, when considering intrinsic aspects of languages, developers prioritize expressivity over correctness. They perceive static types as primarily helping with the latter, hence partly explaining the popularity of dynamic languages.


title: MobileNetV2: Inverted Residuals and Linear Bottlenecks

abstract: In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite. Additionally, we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3. The MobileNetV2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input an MobileNetV2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer. Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain representational power. We demonstrate that this improves performance and provide an intuition that led to this design. Finally, our approach allows decoupling of the input/output domains from the expressiveness of the transformation, which provides a convenient framework for further analysis. We measure our performance on Imagenet classification, COCO object detection, VOC image segmentation. We evaluate the trade-offs between accuracy, and number of operations measured by multiply-adds (MAdd), as well as the number of parameters


title: Real-Time Action Recognition with Enhanced Motion Vector CNNs

abstract: The deep two-stream architecture [23] exhibited excellent performance on video based action recognition. The most computationally expensive step in this approach comes from the calculation of optical flow which prevents it to be real-time. This paper accelerates this architecture by replacing optical flow with motion vector which can be obtained directly from compressed videos without extra calculation. However, motion vector lacks fine structures, and contains noisy and inaccurate motion patterns, leading to the evident degradation of recognition performance. Our key insight for relieving this problem is that optical flow and motion vector are inherent correlated. Transferring the knowledge learned with optical flow CNN to motion vector CNN can significantly boost the performance of the latter. Specifically, we introduce three strategies for this, initialization transfer, supervision transfer and their combination. Experimental results show that our method achieves comparable recognition performance to the state-of-the-art, while our method can process 390.7 frames per second, which is 27 times faster than the original two-stream method.


title: Acute Abdominal Pain in Children.

abstract: Acute abdominal pain accounts for approximately 9% of childhood primary care office visits. Symptoms and signs that increase the likelihood of a surgical cause for pain include fever, bilious vomiting, bloody diarrhea, absent bowel sounds, voluntary guarding, rigidity, and rebound tenderness. The age of the child can help focus the differential diagnosis. In infants and toddlers, clinicians should consider congenital anomalies and other causes, including malrotation, hernias, Meckel diverticulum, or intussusception. In school-aged children, constipation and infectious causes of pain, such as gastroenteritis, colitis, respiratory infections, and urinary tract infections, are more common. In female adolescents, clinicians should consider pelvic inflammatory disease, pregnancy, ruptured ovarian cysts, or ovarian torsion. Initial laboratory tests include complete blood count, erythrocyte sedimentation rate or C-reactive protein, urinalysis, and a pregnancy test. Abdominal radiography can be used to diagnose constipation or obstruction. Ultrasonography is the initial choice in children for the diagnosis of cholecystitis, pancreatitis, ovarian cyst, ovarian or testicular torsion, pelvic inflammatory disease, pregnancy-related pathology, and appendicitis. Appendicitis is the most common cause of acute abdominal pain requiring surgery, with a peak incidence during adolescence. When the appendix is not clearly visible on ultrasonography, computed tomography or magnetic resonance imaging can be used to confirm the diagnosis.


title: Engaging the Faith Community for Public Health Advocacy: An Agenda for the Surgeon General

abstract: This article proposes an agenda for the Surgeon General of the United States that is consonant with the traditional public health approach of “upstream” and “midstream” intervention addressing social and institutional determinants of health. Accordingly, this features a prominent role for expanded partnerships between the faith-based and public health sectors. Such an agenda would revise the current status quo for the Surgeon General, whose celebrated bully pulpit is currently focused more on encouraging “downstream” compliance with federal guidelines related to lifestyle behavior modification. A new faith-based agenda, by contrast, could more effectively advocate for core features of the traditional public health ethic, including primary prevention, the multiple determinants of population health, communitarianism and social justice, and a global perspective, supported by the historic prophetic role of the faith traditions.


title: Neural respiratory drive measurement for COPD assessment and monitoring.

abstract: Currently there is an unmet need for more objective assessments that could determine COPD severity. Ideally such objective assessments could also anticipate COPD exacerbations in order to decrease the need for repeated hospital admissions. In this review we outline how patients' neural respiratory drive (NRD) may be determined using the electromyography of the diaphragm as an objective measurement of COPD severity. Respiratory muscle NRD is indeed less influenced by patients' voluntary effort limitation than for example when testing for exercise tolerance in which case the patients themselves decide when to stop. Exercise tolerance tests are better correlated with muscle weakness rather than COPD severity per se. NRD would also be less dependent upon patients' subjective perception of the severity of their breathlessness. A key further advantage is that recent studies showed that the diaphragm electromyography measurements using electrodes placed on the skin are correlated with those obtained using specific electrodes, therefore this method is non-invasive and more acceptable for routine clinical practice. Thus, NRD measurements could be used in COPD in a similar way as electrocardiography is used to evaluate and monitor ischemic heart disease. NRD measurements could therefore complement more established instruments such as lung function tests, FEV1, exercise tolerance tests, the BODE index etc. in COPD. This could lead to better COPD management and reduce the acute exacerbations which are amongst the most common causes of repeated hospital admissions and consume significant resources.


title: Unravelling the evolution of neural stem cells in arthropods: notch signalling in neural stem cell development in the crustacean Daphnia magna.

abstract: The genetic regulatory networks controlling major developmental processes seem to be conserved in bilaterians regardless of an independent or a common origin of the structures. This has been explained by the employment of a genetic toolkit that was repeatedly used during bilaterian evolution to build the various forms and body plans. However, it is not clear how genetic networks were incorporated into the formation of novel structures and how homologous genes can regulate the disparate morphological processes. Here we address this question by analysing the role of Notch signalling, which is part of the bilaterian toolkit, in neural stem cell evolution in arthropods. Within arthropods neural stem cells have evolved in the last common ancestor of insects and crustaceans (Tetraconata). We analyse here for the first time the role of Notch signalling in a crustacean, the branchiopod Daphnia magna, and show that it is required in neural stem cells for regulating the time of neural precursor production and for binary cell fate decisions in the ventral neuroectoderm. The function of Notch signalling has diverged in the ventral neuroectoderm of insects and crustaceans accompanied by changes in the morphogenetic processes. In the crustacean, Notch controlled mechanisms of neuroblast regulation have evolved that are surprisingly similar to vertebrates and thus present a remarkable case of parallel evolution. These new data on a representative of crustaceans complete the arthropod data set on Notch signalling in the nervous system and allow for reconstructing how the Notch signalling pathway has been co-opted from pre-existing structures to the development of the evolving neural stem cells in the Tetraconata ancestor.


title: Ensemble Deep Learning for Speech Recognition

abstract: Deep learning systems have dramatically improved the accuracy of speech recognition, and various deep architectures and learning methods have been developed with distinct strengths and weaknesses in recent years. How can ensemble learning be applied to these varying deep learning systems to achieve greater recognition accuracy is the focus of this paper. We develop and report linear and log-linear stacking methods for ensemble learning with applications specifically to speechclass posterior probabilities as computed by the convolutional, recurrent, and fully-connected deep neural networks. Convex optimization problems are formulated and solved, with analytical formulas derived for training the ensemble-learning parameters. Experimental results demonstrate a significant increase in phone recognition accuracy after stacking the deep learning subsystems that use different mechanisms for computing high-level, hierarchical features from the raw acoustic signals in speech.


title: Computer vision algorithms for intersection monitoring

abstract: The goal of this project is to monitor activities at traffic intersections for detecting/predicting situations that may lead to accidents. Some of the key elements for robust intersection monitoring are camera calibration, motion tracking, incident detection, etc. In this paper, we consider the motion-tracking problem. A multilevel tracking approach using Kalman filter is presented for tracking vehicles and pedestrians at intersections. The approach combines low-level image-based blob tracking with high-level Kalman filtering for position and shape estimation. An intermediate occlusion-reasoning module serves the purpose of detecting occlusions and filtering relevant measurements. Motion segmentation is performed by using a mixture of Gaussian models which helps us achieve fairly reliable tracking in a variety of complex outdoor scenes. A visualization module is also presented. This module is very useful for visualizing the results of the tracker and serves as a platform for the incident detection module.


Bootstrapping a Romanian Corpus for Medical Named Entity Recognition


title: Context-aware mobile crowdsourcing

abstract: Ubiquity of internet-connected media-and sensor-equipped portable devices has emerged a range of opportunities for direct involvement of citizens into public decision making, leading to a new participatory format of public administration functioning. Intersecting the power of the crowdsourcing problem-solving paradigm by directly relying on human intelligence, with instantaneity and situation-awareness of mobile technologies, one gets a context-aware crowdsourcing approach for problem-solving in the right circumstances with the right people. In this paper, we present a prototype implementation of a context-aware mobile crowdsourcing system that enables the deployment and execution of crowdsourcing campaigns with users carrying mobile devices. The system is designed to maximize conditions for user participation, while minimizing the usage of energy. The paper describes the system architecture, defines an optimized sampling algorithm, and outlines a preliminary experimentation study carried out.


title: Control and Performance of a Transformerless Cascade PWM STATCOM With Star Configuration

abstract: This paper presents a three-phase transformerless cascade pulsewidth-modulation (PWM) static synchronous compensator (STATCOM) intended for installation on industrial and utility power distribution systems. It proposes a control algorithm that devotes itself not only to meeting the demand of reactive power but also to voltage balancing of multiple galvanically isolated and floating dc capacitors. The control algorithm based on a phase-shifted carrier modulation strategy is prominent in having no restriction on the cascade number. Experimental waveforms verify that a 200-V 10-kVA cascade PWM STATCOM with star configuration has the capability of inductive to capacitive (or capacitive to inductive) operation at the rated reactive power of 10 kVA within 20 ms while keeping the nine dc mean voltages controlled and balanced even during the transient state.


title: Multi-objective design of a close-coupled inductor for a three-phase interleaved 140kW DC-DC converter

abstract: A multi-objective design procedure is applied to the design of a close-coupled inductor for a three-phase interleaved 140kW DC-DC converter. For the multi-objective optimization, a genetic algorithm is used in combination with a detailed physical model of the inductive component. From the solution of the optimization, important conclusions about the advantages and disadvantages of using close-coupled inductors compared to separate inductors can be drawn.


title: IARPA Janus Benchmark-B Face Dataset

abstract: Despite the importance of rigorous testing data for evaluating face recognition algorithms, all major publicly available faces-in-the-wild datasets are constrained by the use of a commodity face detector, which limits, among other conditions, pose, occlusion, expression, and illumination variations. In 2015, the NIST IJB-A dataset, which consists of 500 subjects, was released to mitigate these constraints. However, the relatively low number of impostor and genuine matches per split in the IJB-A protocol limits the evaluation of an algorithm at operationally relevant assessment points. This paper builds upon IJB-A and introduces the IARPA Janus Benchmark-B (NIST IJB-B) dataset, a superset of IJB-A. IJB-B consists of 1,845 subjects with human-labeled ground truth face bounding boxes, eye/nose locations, and covariate metadata such as occlusion, facial hair, and skintone for 21,798 still images and 55,026 frames from 7,011 videos. IJB-B was also designed to have a more uniform geographic distribution of subjects across the globe than that of IJB-A. Test protocols for IJB-B represent operational use cases including access point identification, forensic quality media searches, surveillance video searches, and clustering. Finally, all images and videos in IJB-B are published under a Creative Commons distribution license and, therefore, can be freely distributed among the research community.


title: Difficulties in Simulating the Internet

abstract: Simulating how the global Internet behaves is an immensely challenging undertaking because of the network's great heterogeneity and rapid change. The heterogeneity ranges from the individual links that carry the network's traffic, to the protocols that interoperate over the links, the "mix" of different applications used at a site, and the levels of congestion seen on different links. We discuss two key strategies for developing meaningful simulations in the face of these difficulties: searching for invariants and judiciously exploring the simulation parameter space. We finish with a brief look at a collaborative effort within the research community to develop a common network simulator.


title: Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions

abstract: Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges -- from investment timing to drug discovery, and fraud detection to recommendation systems -- where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization -- today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods -- bagging, random forests, and boosting -- to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which is essential to applications such as credit scoring and fault diagnosis. Lastly, the authors explain the paradox of how ensembles achieve greater accuracy on new data despite their (apparently much greater) complexity.This book is aimed at novice and advanced analytic researchers and practitioners -- especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques. (edited by author)


title: Self-compassion, psychological resilience and social media use in Thai students

abstract: Social media is widely used in Thai students, but relatively few studies have explored the effects of social media use on psychological wellbeing. In this study we explored the effects of social media use on self-compassion and psychological resilience. A total of 484 Thai undergraduate students completed a questionnaire and 21 Thai students were interviewed addressing these three constructs. The findings revealed that those Thai students who sacrificed the friends and family time to spend more time on social media use were less self-compassionate and less resilient.


title: Monocular Pedestrian Detection: Survey and Experiments

abstract: Pedestrian detection is a rapidly evolving area in computer vision with key applications in intelligent vehicles, surveillance, and advanced robotics. The objective of this paper is to provide an overview of the current state of the art from both methodological and experimental perspectives. The first part of the paper consists of a survey. We cover the main components of a pedestrian detection system and the underlying models. The second (and larger) part of the paper contains a corresponding experimental study. We consider a diverse set of state-of-the-art systems: wavelet-based AdaBoost cascade, HOG/linSVM, NN/LRF, and combined shape-texture detection. Experiments are performed on an extensive data set captured onboard a vehicle driving through urban environment. The data set includes many thousands of training samples as well as a 27-minute test sequence involving more than 20,000 images with annotated pedestrian locations. We consider a generic evaluation setting and one specific to pedestrian detection onboard a vehicle. Results indicate a clear advantage of HOG/linSVM at higher image resolutions and lower processing speeds, and a superiority of the wavelet-based AdaBoost cascade approach at lower image resolutions and (near) real-time processing speeds. The data set (8.5 GB) is made public for benchmarking purposes.


title: D2D Big Data: Content Deliveries over Wireless Device-to-Device Sharing in Large Scale Mobile Networks

abstract: Recently the topic of how to effectively offload cellular traffic onto device-to-device (D2D) sharing among users in proximity has been gaining more and more attention of global researchers and engineers. Users utilize wireless short-range D2D communications for sharing contents locally, due to not only the rapid sharing experience and free cost, but also high accuracy on deliveries of interesting and popular contents, as well as strong social impacts among friends. Nevertheless, the existing related studies are mostly confined to small-scale datasets, limited dimensions of user features, or unrealistic assumptions and hypotheses on user behaviors. In this article, driven by emerging Big Data techniques, we propose to design a big data platform, named D2D Big Data, in order to encourage the wireless D2D communications among users effectively, to promote contents for providers accurately, and to carry out offloading intelligence for operators efficiently. We deploy a big data platform and further utilize a large-scale dataset (3.56 TBytes) from a popular D2D sharing application (APP), which contains 866 million D2D sharing activities on 4.5 million files disseminated via nearly 850 million users in 13 weeks. By abstracting and analyzing multidimensional features, including online behaviors, content properties, location relations, structural characteristics, meeting dynamics, social arborescence, privacy preservation policies and so on, we verify and evaluate the D2D Big Data platform regarding predictive content propagating coverage. Finally, we discuss challenges and opportunities regarding D2D Big Data and propose to unveil a promising upcoming future of wireless D2D communications.


title: Real-Time GPS/GPRS Based Vehicle Tracking System

abstract: Designing a vehicle tracking system which can work effective, accurate and reliable is emerging in many areas. Such system is commonly used GPS technology to determine location of the vehicle. It can be used to track a vehicle or fleet of vehicles and get information related to current location of the vehicles. High required operating cost of the vehicle tracking systems, including hardware and software requirements, usually, prevent these systems from being widespread applicable. In this paper a vehicle tracking system based on GPS and GPRS is proposed. The location of the vehicle is retrieved using embedded GPS sensor. A modified coding method is used to encode and compress location data before it is sent to offer cost effective usage of network traffic. The privacy of the transmitted data is guaranteed using a simple security mechanism. The encoded and encrypted location data then send to tracking server using GPRS technology. The authorized user can track a vehicle using a secure web interface.


title: The Design and Implementation of the NDN Protocol Stack for RIOT-OS

abstract: The Named Data Networking (NDN) architecture has been proposed as a promising solution for supporting communications in IoT environments. An important class of IoT platform is the constrained devices that have limited computing resources and are connected by constrained networks. This paper presents the design and implementation of the NDN protocol stack for RIOT-OS, a popular operating system for constrained IoT platforms. We succeeded in integrating the core NDN packet forwarding logic into the RIOT-OS kernel together with a high-level application interface with data security support. Our results demonstrated the feasibility of using NDN protocol stack to support applications on constrained devices with only 10s of KB of RAM and flash memory.


title: Psychological Well-Being: Meaning, Measurement, and Implications for Psychotherapy Research

abstract: A model of positive psychological functioning that emerges from diverse domains of theory and philosophy is presented. Six key dimensions of wellness are defined, and empirical research summarizing th


title: Injury of the Inferior Alveolar Nerve during Implant Placement: a Literature Review

abstract: The purpose of present article was to review aetiological factors, mechanism, clinical symptoms, and diagnostic methods as well as to create treatment guidelines for the management of inferior alveolar nerve injury during dental implant placement.


title: Integrating association rule mining with relational database systems: alternatives and implications

abstract: Data mining on large data warehouses is becoming increasingly important. In support of this trend, we consider a spectrum of architectural alternatives for coupling mining with database systems. These alternatives include: loose-coupling through a SQL cursor interface; encapsulation of a mining algorithm in a stored procedure; caching the data to a file system on-the-fly and mining; tight-coupling using primarily user-defined functions; and SQL implementations for processing in the DBMS. We comprehensively study the option of expressing the mining algorithm in the form of SQL queries using Association rule mining as a case in point. We consider four options in SQL-92 and six options in SQL enhanced with object-relational extensions (SQL-OR). Our evaluation of the different architectural alternatives shows that from a performance perspective, the Cache-Mine option is superior, although the performance of the SQL-OR option is within a factor of two. Both the Cache-Mine and the SQL-OR approaches incur a higher storage penalty than the loose-coupling approach which performance-wise is a factor of 3 to 4 worse than Cache-Mine. The SQL-92 implementations were too slow to qualify as a competitive option. We also compare these alternatives on the basis of qualitative factors like automatic parallelization, development ease, portability and inter-operability.


title: Vive La Différence: Paxos vs. Viewstamped Replication vs. Zab

abstract: Paxos, Viewstamped Replication, and Zab are replication protocols that ensure high-availability in asynchronous environments with crash failures. Various claims have been made about similarities and differences between these protocols. But how does one determine whether two protocols are the same, and if not, how significant the differences are? ::: We propose to address these questions using refinement mappings, where protocols are expressed as succinct specifications that are progressively refined to executable implementations. Doing so enables a principled understanding of the correctness of the different design decisions that went into implementing the various protocols. Additionally, it allowed us to identify key differences that have a significant impact on performance.


title: Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.

abstract: Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements.


title: Extreme learning machine: Theory and applications

abstract: Abstract It is clear that the learning speed of feedforward neural networks is in general far slower than required and it has been a major bottleneck in their applications for past decades. Two key reasons behind may be: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning algorithms. Unlike these conventional implementations, this paper proposes a new learning algorithm called e xtreme l earning m achine (ELM) for s ingle-hidden l ayer f eedforward neural n etworks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide good generalization performance at extremely fast learning speed. The experimental results based on a few artificial and real benchmark function approximation and classification problems including very large complex applications show that the new algorithm can produce good generalization performance in most cases and can learn thousands of times faster than conventional popular learning algorithms for feedforward neural networks. 1


title: Cross-Modal Correlation Learning by Adaptive Hierarchical Semantic Aggregation

abstract: With the explosive growth of web data, effective and efficient technologies are in urgent needs for retrieving semantically relevant contents of heterogeneous modalities. Previous studies construct global transformations to project the heterogeneous data into a measurable subspace. However, global projections cannot appropriately adapt to diverse contents, and the naturally existing multi-level semantic relation in web data is ignored. We study the problem of semantic coherent retrieval, where documents from different modalities should be ranked by the semantic relevance to the queries. Accordingly, we propose TINA, a correlation learning method by Adaptive Hierarchical Semantic Aggregation. First, by joint modeling of content and ontology similarities, we build a semantic hierarchy to measure multi-level semantic relevance. Second, with a set of local linear projections aggregated by gating functions, we optimize the structure risk objective function that involves semantic coherence measurement, local projection consistency and the complexity penalty of local projections. Therefore, semantic coherence and a better bias-variance trade-off can be achieved by TINA. Extensive experiments on widely used NUS-WIDE and ICML-Challenge datasets demonstrate that TINA outperforms state-of-the-art, and achieves better adaptation to the multi-level semantic relation and content divergence.


title: Cognitive Learning, Cognitive Response to Persuasion, and Attitude Change

abstract: It is a common assumption that the effectiveness of a persuasive communication is, at least in part, a function of the extent to which its content is learned and retained by its audience. This assumed learning-persuasion relation is based on a reasonable analogy between the persuasive communication and an informational communication such as a classroom lecture. In the lecture, it is by definition of the educational situation that retention of content is taken as a measure of effectiveness. In the persuasion situation, however, the essential criterion of effectiveness is acceptance of content. It remains an empirical question to determine whether acceptance of a persuasive communication is related to retention of its content. The hypothesis that acceptance of a communication is, in some part, a function of learning or retention of its content has received explicit endorsement by a number of attitude researchers and theorists (e.g., Hovland, Janis, & Kelley, 1953; McGuire, this volume; Miller


title: A digitally-tuned triple-band transformer power combiner for CMOS power amplifiers

abstract: This paper presents the design and implementation of a CMOS transformer combiner that can be reconfigured to have similar efficiencies at widely separated frequency bands. Conventional transformer combiners employ a fixed tuning capacitance in the secondary network to optimize the efficiency for single frequency standard. In this work, we present a modified transformer combiner where digitally-switchable capacitors introduced at low-swing nodes within the combiner network enable frequency reconfiguration using CMOS switches. A 65 nm CMOS triple-band (2.5/3/3.5 GHz) power amplifier (PA) chip employing the reconfigurable combiner is also presented.


title: Somaclonal variation in Asparagus officinalis plants regenerated by organogenesis from long-term callus cultures*

abstract: Somaclonal variation in plants regenerated by organogenesis from long-term cultured calluses of two diploid staminate genotypes of Asparagus officinalis cv. Argenteuil was characterized by plant phenotype, ploidy, meiotic behavior, pollen viability, fruit and seed set, and AFLP profiles. Phenotypic deviations from the donors were detected in foliage color, flower size, and cladode and flower morphology. Ploidy changes were observed in 37.8% of the 37 regenerants studied. Meiotic alterations in 12 out of 21 regenerants included laggards, dicentric bridges, micronuclei, restitution nuclei and polyads. Of the 408 AFLP markers screened in 43 regenerants and the donors, 2.94% showed polymorphism. High pollen viability was observed in the 22 regenerants analyzed. All crosses between one pistillate plant and 35 regenerants, as well as the controls, produced fruits and seeds; however, no plump seeds resulted in 35.3% of the crosses with regenerants, and no seeds germinated in 12.5% of those with apparently normal seeds. Fruit and seed set was similar in crosses with diploid regenerants with normal meiosis and the controls but was lower in crosses with diploid and polyploid regenerants with abnormal meiosis. Our results show that the regenerated plants exhibited conspicuous somaclonal variation that could be eventually exploited for in vitro selection systems.


title: Deep Predictive Coding Network for Object Recognition

abstract: Based on the predictive coding theory in neuroscience, we designed a bi-directional and recurrent neural net, namely deep predictive coding networks (PCN). It has feedforward, feedback, and recurrent connections. Feedback connections from a higher layer carry the prediction of its lower-layer representation; feedforward connections carry the prediction errors to its higher-layer. Given image input, PCN runs recursive cycles of bottom-up and top-down computation to update its internal representations and reduce the difference between bottom-up input and top-down prediction at every layer. After multiple cycles of recursive updating, the representation is used for image classification. With benchmark data (CIFAR-10/100, SVHN, and MNIST), PCN was found to always outperform its feedforward-only counterpart: a model without any mechanism for recurrent dynamics. Its performance tended to improve given more cycles of computation over time. In short, PCN reuses a single architecture to recursively run bottom-up and top-down processes. As a dynamical system, PCN can be unfolded to a feedforward model that becomes deeper and deeper over time, while refining it representation towards more accurate and definitive object recognition.


title: Stability Analysis of Underconstrained Cable-Driven Parallel Robots

abstract: This paper studies cable-driven parallel robots with less than six cables, in crane configuration. A geometrico-static model is provided, and the stability of static equilibrium is assessed within the framework of a constrained optimization problem. The method relies on ordinary linear-algebra routines, and it may be very simply applied to the most general architectures. Several examples are provided, concerning robots with a number of cables that range from 2 to 4.


title: Energino: A hardware and software solution for energy consumption monitoring

abstract: Accurate measurement of energy consumption of practical wireless deployments is vital to the research community to develop pragmatic simulators and analytical models for the synthesis of new energy-aware and energy-efficient protocols and algorithms for wireless networks. However, there is considerable dearth in the availability of affordable and scalable energy consumption monitoring tools for the research community. In this paper, we introduce Energino, a scalable and affordable solution for energy consumption monitoring in wireless networks. The Energino power meter is a standalone plug-load meter based on the Arduino platform providing high resolution and sampling rate capabilities. We evaluate the capabilities and distinctive features of the Energino power meter in a real WLAN network. Results show that our solution is capable of isolating high resolution/frequency dynamics that can not be analyzed using commercially available tools. We also release both the hardware schematics and the software with a permissive license in order to encourage the research community to use and extend it.