Numerical implementation of the coarse step method with a varying differential group delay

The effect of having a fixed differential-group delay term in the coarse-step method results in a periodic pattern in the autocorrelation function. We solve this problem by inserting a varying DGD term at each integration step, according to a Gaussian distribution. Simulation results are given to illustrate the phenomenon and provide some evidence, about its statistical nature.

Selfstructurized Systems

The problems of constructing the selfsrtucturized systems of memory of intelligence information processing tools, allowing formation of associative links in the memory, hierarchical organization and classification, generating concepts in the process of the information input, are discussed. The principles and methods for realization of selfstructurized systems on basis of hierarchic network structures of some special class – growing pyramidal network are studied. The algorithms for building, learning and recognition on basis of such type network structures are proposed. The examples of practical application are demonstrated.

Interoperability of Cross-Border European Egovernment Services: Some Design Issues

The authors analyse some of the research outcomes achieved during the implementation of the EC GUIDE research project “Creating an European Identity Management Architecture for eGovernment”, as well as their personal experience. The project goals and achievements are however considered in a broader context. The key role of Identity in the Information Society was emphasised, that the research and development in this field is in its initial phase. The scope of research related to Identity, including the one related to Identity Management and Interoperability of Identity Management Systems, is expected to be further extended. The authors analyse the abovementioned issues in the context established by the EC European Interoperability Framework (EIF) as a reference document on interoperability for the Interoperable Delivery of European eGovernment Services to Public Administrations, Business and Citizens (IDABC) Work Programme. This programme aims at supporting the pan-European delivery of electronic government services.

The evolution of learning systems:to Bayes or not to be

Bayesian algorithms pose a limit to the performance learning algorithms can achieve. Natural selection should guide the evolution of information processing systems towards those limits. What can we learn from this evolution and what properties do the intermediate stages have? While this question is too general to permit any answer, progress can be made by restricting the class of information processing systems under study. We present analytical and numerical results for the evolution of on-line algorithms for learning from examples for neural network classifiers, which might include or not a hidden layer. The analytical results are obtained by solving a variational problem to determine the learning algorithm that leads to maximum generalization ability. Simulations using evolutionary programming, for programs that implement learning algorithms, confirm and expand the results. The principal result is not just that the evolution is towards a Bayesian limit. Indeed it is essentially reached. In addition we find that evolution is driven by the discovery of useful structures or combinations of variables and operators. In different runs the temporal order of the discovery of such combinations is unique. The main result is that combinations that signal the surprise brought by an example arise always before combinations that serve to gauge the performance of the learning algorithm. This latter structures can be used to implement annealing schedules. The temporal ordering can be understood analytically as well by doing the functional optimization in restricted functional spaces. We also show that there is data suggesting that the appearance of these traits also follows the same temporal ordering in biological systems. © 2006 American Institute of Physics.

Contextual semantics for sentiment analysis of Twitter

Sentiment analysis on Twitter has attracted much attention recently due to its wide applications in both, commercial and public sectors. In this paper we present SentiCircles, a lexicon-based approach for sentiment analysis on Twitter. Different from typical lexicon-based approaches, which offer a fixed and static prior sentiment polarities of words regardless of their context, SentiCircles takes into account the co-occurrence patterns of words in different contexts in tweets to capture their semantics and update their pre-assigned strength and polarity in sentiment lexicons accordingly. Our approach allows for the detection of sentiment at both entity-level and tweet-level. We evaluate our proposed approach on three Twitter datasets using three different sentiment lexicons to derive word prior sentiments. Results show that our approach significantly outperforms the baselines in accuracy and F-measure for entity-level subjectivity (neutral vs. polar) and polarity (positive vs. negative) detections. For tweet-level sentiment detection, our approach performs better than the state-of-the-art SentiStrength by 4-5% in accuracy in two datasets, but falls marginally behind by 1% in F-measure in the third dataset.

Learning from openness:the dynamics of breadth in external innovation linkages

We explore how openness in terms of external linkages generates learning effects, which enable firms to generate more innovation outputs from any given breadth of external linkages. Openness to external knowledge sources, whether through search activity or linkages to external partners in new product development, involves a process of interaction and information processing. Such activities are likely to be subject to a learning process, as firms learn which knowledge sources and collaborative linkages are most useful to their particular needs, and which partnerships are most effective in delivering innovation performance. Using panel data from Irish manufacturing plants, we find evidence of such learning effects: establishments with substantial experience of external collaborations in previous periods derive more innovation output from openness in the current period. © 2013 The Authors. Strategic Management Journal published by John Wiley & Sons Ltd.

Assessing the impact of management concerns in e-business requirements planning in manufacturing organizations

This paper describes the application of a model, initially developed for determining the e-business requirements of a manufacturing organization, to assess the impact of management concerns on the functions generated. The model has been tested on 13 case studies in small, medium and large organizations. This research shows that the incorporation of concerns for generating the requirements for e-business functions improves the results, because they expose issues that are of relevance to the decision making process relating to e-business. Running the model with both and without concerns, and then presenting the reasons for major variances, can expose the issues and enable them to be studied in detail at the individual function/ reason level. © IFIP International Federation for Information Processing 2013.

Understanding innovation in outsourcing services

Recent years have witnessed an expansion in service industries such as finance, travel and retail. Firms in the services have shifted their traditional occupation with products to consider how value can be created and appropriated in the service industry [1]. In particular, information technology (IT) and IT-enabled business services have become central to a firm's ability to deliver value to its customers, driving firms to seek ways to improve their services and maintain their competitive position. In this regard, the last ten years have witnessed significant growth in the outsourcing industry which shifted from focus on low cost simple tasks such as coding to end-to-end delivery of services that range from IT services and customer services to more complex business services such as Finance and Accounting, Human Resources, Procurement, and knowledge-intensive services such as customer analysis and research services [2]. © 2012 Springer-Verlag.

Effective decision making and problem solving in projects

Processing information and forming opinions pose special challenges when attempting to effectively manage the new or complex tasks that typically arise in projects. Based on research in organizational and social psychology, we introduce mechanisms and strategies for collective information processing which are important for forming opinions and handling information in projects.

A new unsupervised convolutional neural network model for Chinese scene text detection

As one of the most popular deep learning models, convolution neural network (CNN) has achieved huge success in image information extraction. Traditionally CNN is trained by supervised learning method with labeled data and used as a classifier by adding a classification layer in the end. Its capability of extracting image features is largely limited due to the difficulty of setting up a large training dataset. In this paper, we propose a new unsupervised learning CNN model, which uses a so-called convolutional sparse auto-encoder (CSAE) algorithm pre-Train the CNN. Instead of using labeled natural images for CNN training, the CSAE algorithm can be used to train the CNN with unlabeled artificial images, which enables easy expansion of training data and unsupervised learning. The CSAE algorithm is especially designed for extracting complex features from specific objects such as Chinese characters. After the features of articficial images are extracted by the CSAE algorithm, the learned parameters are used to initialize the first CNN convolutional layer, and then the CNN model is fine-Trained by scene image patches with a linear classifier. The new CNN model is applied to Chinese scene text detection and is evaluated with a multilingual image dataset, which labels Chinese, English and numerals texts separately. More than 10% detection precision gain is observed over two CNN models.

Sub-cortical sources of the somatosensory pathway are hypoactive in migraine interictally:a Functional Source Separation analysis

Background: Recent morpho-functional evidence pointed out that abnormalities in the thalamus could play a major role in the expression of migraine neurophysiological and clinical correlates. Whether this phenomenon is primary or secondary to its functional disconnection from the brainstem remains to be determined. We used a Functional Source Separation algorithm of EEG signal to extract the activity of the different neuronal pools recruited at different latencies along the somatosensory pathway in interictal migraine without aura (MO) patients. Methods: Twenty MO patients and 20 healthy volunteers (HV) underwent EEG recording. Four ad-hoc functional constraints, two sub-cortical (FS14 at brainstem and FS16 at thalamic level) and two cortical (FS20 radial and FS22 tangential parietal sources), were used to extract the activity of successive stages of somatosensory information processing in response to the separate left and right median nerve electric stimulation. A band-pass digital filter (450-750 Hz) was applied offline in order to extract high-frequency oscillatory (HFO) activity from the broadband EEG signal. Results: In both stimulated sides, significant reduced sub-cortical brainstem (FS14) and thalamic (FS16) HFO activations characterized MO patients when compared with HV. No difference emerged in the two cortical HFO activations between the two groups. Conclusions: Present results are the first neurophysiological evidence supporting the hypothesis that a functional disconnection of the thalamus from the subcortical monoaminergic system may underline the interictal cortical abnormal information processing in migraine. Further studies are needed to investigate the precise directional connectivity across the entire primary subcortical and cortical somatosensory pathway in interictal MO. Written informed consent to publication was obtained from the patient(s).

Handling high level of censoring for endovascular aortic repair risk prediction

Feature selection is important in medical field for many reasons. However, selecting important variables is a difficult task with the presence of censoring that is a unique feature in survival data analysis. This paper proposed an approach to deal with the censoring problem in endovascular aortic repair survival data through Bayesian networks. It was merged and embedded with a hybrid feature selection process that combines cox's univariate analysis with machine learning approaches such as ensemble artificial neural networks to select the most relevant predictive variables. The proposed algorithm was compared with common survival variable selection approaches such as; least absolute shrinkage and selection operator LASSO, and Akaike information criterion AIC methods. The results showed that it was capable of dealing with high censoring in the datasets. Moreover, ensemble classifiers increased the area under the roc curves of the two datasets collected from two centers located in United Kingdom separately. Furthermore, ensembles constructed with center 1 enhanced the concordance index of center 2 prediction compared to the model built with a single network. Although the size of the final reduced model using the neural networks and its ensembles is greater than other methods, the model outperformed the others in both concordance index and sensitivity for center 2 prediction. This indicates the reduced model is more powerful for cross center prediction.

Modelling and linking transformations in EPCIS governing supply chain business processes

Supply chains comprise of complex processes spanning across multiple trading partners. The various operations involved generate large number of events that need to be integrated in order to enable internal and external traceability. Further, provenance of artifacts and agents involved in the supply chain operations is now a key traceability requirement. In this paper we propose a Semantic web/Linked data powered framework for the event based representation and analysis of supply chain activities governed by the EPCIS specification. We specifically show how a new EPCIS event type called "Transformation Event" can be semantically annotated using EEM - The EPCIS Event Model to generate linked data, that can be exploited for internal event based traceability in supply chains involving transformation of products. For integrating provenance with traceability, we propose a mapping from EEM to PROV-O. We exemplify our approach on an abstraction of the production processes that are part of the wine supply chain.

Salient factors in child molestation: Moral development, social competence, and type of offender

The current study was designed to build on and extend the existing knowledge base of factors that cause, maintain, and influence child molestation. Theorized links among the type of offender and the offender's levels of moral development and social competence in the perpetration of child molestation were investigated. The conceptual framework for the study is based on the cognitive developmental stages of moral development as proposed by Kohlberg, the unified theory, or Four-Preconditions Model, of child molestation as proposed by Finkelhor, and the Information-Processing Model of Social Skills as proposed by McFall. The study sample consisted of 127 adult male child molesters participating in outpatient group therapy. All subjects completed a Self-Report Questionnaire which included questions designed to obtain relevant demographic data, questions similar to those used by the researchers for the Massachusetts Treatment Center: Child Molester Typology 3's social competency dimension, the Defining Issues Test (DIT) short form, the Social Avoidance and Distress Scale (SADS), the Rathus Assertiveness Schedule (RAS), and the Questionnaire Measure of Empathic Tendency (Empathy Scale). Data were analyzed utilizing confirmatory factor analysis, t-tests, and chi-square statistics. Partial support was found for the hypothesis that moral development is a separate but correlated construct from social competence. As predicted, although the actual mean score differences were small, a statistically significant difference was found in the current study between the mean DITP scores of the subject sample and that of the general male population, suggesting that child molesters, as a group, function at a lower level of moral development than does the general male population, and the situational offenders in the study sample demonstrated a statistically significantly higher level of moral development than the preferential offenders. The data did not support the hypothesis that situational offenders will demonstrate lower levels of social competence than preferential offenders. Relatively little significance is placed on this finding, however, because the measure for the social competency variable was likely subject to considerable measurement error in that the items used as indicators were not clearly defined. The last hypothesis, which involved the potential differences in social anxiety, assertion skills, and empathy between the situational and preferential offender types, was not supported by the data. ^

Semantic wrapping for heterogeneous database management

Query processing is a commonly performed procedure and a vital and integral part of information processing. It is therefore important and necessary for information processing applications to continuously improve the accessibility of data sources as well as the ability to perform queries on those data sources. ^ It is well known that the relational database model and the Structured Query Language (SQL) are currently the most popular tools to implement and query databases. However, a certain level of expertise is needed to use SQL and to access relational databases. This study presents a semantic modeling approach that enables the average user to access and query existing relational databases without the concern of the database's structure or technicalities. This method includes an algorithm to represent relational database schemas in a more semantically rich way. The result of which is a semantic view of the relational database. The user performs queries using an adapted version of SQL, namely Semantic SQL. This method substantially reduces the size and complexity of queries. Additionally, it shortens the database application development cycle and improves maintenance and reliability by reducing the size of application programs. Furthermore, a Semantic Wrapper tool illustrating the semantic wrapping method is presented. ^ I further extend the use of this semantic wrapping method to heterogeneous database management. Relational, object-oriented databases and the Internet data sources are considered to be part of the heterogeneous database environment. Semantic schemas resulting from the algorithm presented in the method were employed to describe the structure of these data sources in a uniform way. Semantic SQL was utilized to query various data sources. As a result, this method provides users with the ability to access and perform queries on heterogeneous database systems in a more innate way. ^