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Empirical research involving projects is an important and common way to advance knowledge in the energy sector, and there are well-established approaches for qualitative analysis of single or few cases (1–10 cases) as well as quantitative analysis of large databases (from 50+ cases). However, the “middle-ground” of analysing 10–50 cases is an unknown territory, and very few approaches exist to deal with numbers of cases that lie in the range of 10–50. This paper shows how this “middle-ground” can be explored through Qualitative Comparative Analysis (QCA). This is a method that can be applied to energy infrastructure projects (such as construction, operations, and decommissioning of power plants) in order to study causal inference (e.g. factors associated with outcomes). This paper demonstrates the potential of QCA by showing its application on an energy infrastructure phenomenon with an intermediate number of cases, that of nuclear decommissioning projects. These projects are becoming increasingly important to society and have multibillion US dollar budgets. Moreover, their characteristics need to urgently be matched with their project performance in order to avoid even further cost overruns. The application of QCA to 24 European nuclear decommissioning projects shows that a combination of characteristics (such as a streamlined governance structure and the presence of a storage facility for radioactive material on site) might be contributing to lower cost overruns. This paper concludes by showing how QCA can be applied to other energy infrastructure phenomena with a similar intermediate number of cases.

Agile software development methods are mostly built as a set of managerial guidelines and development concepts on how to handle a software development but are not bounded to software development paradigms like object or agent orientation. Some methods, like eXtreme Programming and SCRUM are driven by operational requirements representation models called User Stories. These User Stories can be used as an anchoring point to agile methods; this means that we could take a User Stories set to drive a software transformation approach embedded in a particular development paradigm. This paper presents a process fragment for Multi-Agent Systems development with agile methods based on User Stories sets. The process fragment indeed takes advantage of an initial set of User Stories to build a reasoning model (called the Rationale Tree; typically several of these are built for a single project) that documents decompositions and means-end alternatives in scenarios for requirements realization. A Rationale Tree can then be aligned with a Multi-Agent design and implemented in an agent-oriented development language. In this paper the transformation is targeted to the JAVA Agent DEvelopment (JADE) framework. The process fragment (at least partially) covers the Requirements Analysis, Multi-Agent System Design and Multi-Agent System Implementation phases. Transformation from one phase to the other is overseen and illustrated on an example.

Scheduling is a complex problem. Heuristics are often employed to simplify this problem because of the difficultly of solving this problem directly. One of the most common classes of heuristics is dispatching rules. These rules are often dedicated to the satisfaction of a single performance criterion, though scheduling is a multi-criteria problem. The multi-criteria can be meet by using the so-called aggregated rules. A classical aggregated rule is a linear combination of decision factors, each of which is initially assigned a relative weighting. But it is always a difficult task to decide the relative weighting, especially when the requirements are changed with the evolution of the production environment. In this paper the fuzzy aggregated rules are employed to solve the scheduling problem. The ability to deal with multi-variables makes fuzzy approach a good alternative for the scheduling problem because it can easily make compromises among multi-criteria by properly combining elementary dispatching rules. These compromises can easily be adjusted in accordance with the objectives of the system and the characteristics of the jobs. A comparison study of the fuzzy aggregated rules and their classical counterparts will be presented in this paper.

The scientific literature lacks comprehensive and updated information regarding the airtightness of building components, this hindering the development and improvement of models for a robust prediction of their performance. To fill this gap, this paper presents a meta-analysis conducted on literature studies reporting laboratory and in-situ measurements of airtightness for various building components. Meta-analysis is a powerful methodological tool to compare and combine data from previous research, even when they have inherent differences. The results of the meta-analysis allowed to attain three outcomes: a classification of building components related to their airtightness performance; the update and improvement of existing databases of building component airtightness; and, the identification of factors specifically influencing airtightness performances of windows. In addition, the critical appraisal of the findings led to the definition of the requirements for future studies reporting measurements of airtightness of building components. This study offers a step ahead from existing knowledge by improving existing databases and by proposing a methodological framework of analysis that can be extended to several other domains of the built environment.

Batteries have rapidly evolved and are widely applied in both stationary and transport applications. The safe and reliable operation is of vital importance to all types of batteries, herein an effective battery sensing system with high performance and easy implementation is critically needed. This also requires the sensing system to monitor the states of batteries in real time. Among the available methods, optical fibre sensors have shown a significant advantage due to their advanced capabilities of which include the fast measurement of multiple parameters with high sensitivity, working without interfering the battery performance, being able to be composited in multiplexed configurations and being robust to various harsh environment conditions. This paper mainly discusses the current optical fibre sensing methods for batteries in terms of the working principles and critical reviews the sensing performance corresponding to different sensing parameters. Moreover, the challenges and outlooks for future research on battery sensing are derived.

In this paper we present a spherical soft robotic arm made from fabric. The inflatable arm has a small mass and is pneumatically actuated. A configuration is employed with only three actuators controlling the two rotational degrees of freedom of a spherical joint. This differs from the commonly employed antagonistic pairs, including four actuators for two degrees of freedom. The fabrication procedure of the lightweight and compliant system is discussed in detail and uses commonly available materials and tools. The capability of the robotic arm to adjust the joint stiffness as a function of the actuator pressures is investigated and characterized for different deflection directions. The static mapping from the actuator pressures to the orientation of the robotic arm is identified from data and the inverse mapping is employed in a position controller. The modeling and controller derivation are performed for three different stiffness levels demonstrating the ability of the spherical robotic arm to change the joint stiffness independently of controlling the position. The position tracking performance is experimentally evaluated by tracking a square trajectory. A comparison of the tracking performance for the different stiffness levels shows that accurate tracking is more challenging for the smallest joint stiffness. A gray-box model capturing the interactions of the two degrees of freedom is used in a learning scheme that is applied for the smallest stiffness level. The learning approach reduces interactions between the two degrees of freedom and demonstrates the control performance achievable with the system developed., Mechatronics, 68, ISSN:0957-4158, ISSN:1873-4006

Hardware trends over the last decade show increasing complexity and heterogeneity in high performance computing architectures, which presents developers of CFD applications with three key challenges; the need for achieving good performance, being able to utilise current and future hardware by being portable, and doing so in a productive manner. These three appear to contradict each other when using traditional programming approaches, but in recent years, several strategies such as template libraries and Domain Specific Languages have emerged as a potential solution; by giving up generality and focusing on a narrower domain of problems, all three can be achieved. This paper gives an overview of the state-of-the-art for delivering performance, portability, and productivity to CFD applications, ranging from high-level libraries that allow the symbolic description of PDEs to low-level techniques that target individual algorithmic patterns. We discuss advantages and challenges in using each approach, and review the performance benchmarking literature that compares implementations for hardware architectures and their programming methods, giving an overview of key applications and their comparative performance.

Hydraulic hybrid technologies improve the fuel economy of city-use medium duty vehicles by recovering braking energy and reusing it for driving, most notably vehicle launching. In this paper, a parametric design of hydraulic driving system for a medium duty truck is conducted based on its power demand in typical urban driving cycles. The braking control strategy is designed considering both the safety regulations and braking energy recovery rate. The proportional relationship of hydraulic pump/motor output torque and its working pressure is considered when designing the braking control strategy. The influence of the high pressure accumulator minimum working pressure and the corresponding gas volume at the minimum pressure on the braking energy recovery rate is analyzed. The simulation results show that the proposed hydraulic driving system and braking control strategy helps greatly increase the braking energy recovery rate. Besides, different vehicle mass and load distributions are investigated to analyze the regenerative braking effect on various loading conditions.

Electrochemical micromachining (micro-ECM) is an unconventional micromachining technology that has capability to fabricate high aspect ratio micro-holes, micro-cavities, micro-channels and grooves on conductive and difficult-to-cut materials. Both academia and industry have the consensus that it offers promising machining performance especially in terms of high surface finish, no tool wear and absence of thermally induced defects. Furthermore in order to machine novel materials with extreme properties, novel hybrid electrochemical micromachining technologies are under development. With these hybrid micro-ECM technologies, capabilities of micro-ECM can be expanded by combining it with other processes. To fully exploit the potential as well as improve micro-ECM technology and related hybrid processes, a wide spectrum of multidisciplinary knowledge is needed. The present review systematically discusses process capabilities and research developments of electrochemical micromachining and its hybrid variants considering knowledge of both basic and applied research fields. After few introductory review articles in prior state of the art, this review fills an important gap in research literature by presenting first time an extended literature source with a wide coverage of recent research developments in electrochemical micromachining technology and its hybrid variants. This paper outlines the research and engineering developments in electrochemical micromachining technology and its hybrid variants, review of the related concepts, aspects of tooling, advanced process capabilities and process energy sources. It also provides new sights into technological understanding of micro-ECM technology which will be helpful in future engineering developments of this technology. ispartof: International Journal of Machine Tools & Manufacture vol:127 pages:28-56 status: published

Most of the caregivers working in hospitals are highly skilled and educated work force. Even if they are supported by administrative staff, part of their time still consists of administrative procedures or trying to empirically fill or retrieve agendas on the basis of real-time constraints. Similarly, patients waste time waiting for adequate treatments so they occupy slots (beds) in hospitals while their hospitalization is not a necessity. This leads to huge wastes of resources for the hospital that has to hospitalize and prioritize patients in more effective need of care and monitoring. In order to ensure real-time patient support during its entire stay in the hospital (in terms of bed occupancy, appointments with doctors and/or for medico-technical exams) as well as to furnish a central solution open for future IT integration we propose, in this paper, to build a Multi-Agent System (MAS) based software solution. The latter has been built following a socio-technical approach; it maps, at runtime, the working processes of a Belgian hospital. Processes have been modeled in a patient-centric way in order to identify most relevant stages and bottlenecks in bed, cares and medico-technical exams management; real-time data transmission allows to update and re-optimize slots statuses as well as schedules. Also, data analysis (i.e., business intelligence) functions are supported by the MAS implementation for decision making/taking. Further than the proof of concept that shows the managerial support of the software solution, the MAS modules’ benefits to the overall hospital strategy is studied. The latter shows the relevance of the solution to the hospital’s governance. ispartof: Telematics and Informatics vol:35 issue:4 pages:854-882 status: published

Data analytics have become increasingly complicated as the amount of data has increased. One technique that is used to enable data analytics in large datasets is data sampling, in which a portion of the data is selected to preserve the data characteristics for use in data analytics. In this paper, we introduce a novel data sampling technique that is rooted in formal concept analysis theory. This technique is used to create samples reliant on the data distribution across a set of binary patterns. The proposed sampling technique is applied in classifying the regions of breast cancer histology images as malignant or benign. The performance of our method is compared to other classical sampling methods. The results indicate that our method is efficient and generates an illustrative sample of small size. It is also competing with other sampling methods in terms of sample size and sample quality represented in classification accuracy and F1 measure. 1 2017 Elsevier Ltd This contribution was made possible by NPRP grant #07- 794-1-145 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

This paper proposes an arbitrary view gait recognition method where the gait recognition is performed in 3-dimensional (3D) to be robust to variation in speed, inclined plane and clothing, and in the presence of a carried item. 3D parametric gait models in a gait period are reconstructed by an optimized 3D human pose, shape and simulated clothes estimation method using multi-view gait silhouettes. The gait estimation involves morphing a new subject with constant semantic constraints using silhouette cost function as observations. Using a clothes-independent 3D parametric gait model reconstruction method, gait models of different subjects with various postures in a cycle are obtained and used as galleries to construct 3D gait dictionary. Using a carrying-items posture synthesized model, virtual gait models with different carrying-items postures are synthesized to further construct an over-complete 3D gait dictionary. A self-occlusion optimized simultaneous sparse representation model is also introduced to achieve high robustness in limited gait frames. Experimental analyses on CASIA B dataset and CMU MoBo dataset show a significant performance gain in terms of accuracy and robustness. Introduces a clothes-independent 3D human pose and shape estimation method.Introduces a carried-item posture synthesized model for 3D gait recognition.Discusses a self-occlusion optimized gait simultaneous sparse representation model.

Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from each binary vessel map to produce two separate 4-D feature vectors. Independent classification is performed for each feature vector using a support vector machine (SVM) classifier. The system then combines these individual outcomes to produce a final decision. This is followed by the creation of additional features to generate 21-D feature vectors, which feed into a genetic algorithm based feature selection approach with the objective of finding feature subsets that improve the performance of the classification. Sensitivity and specificity results using a dataset of 60 images are 0.9138 and 0.9600, respectively, on a per patch basis and 1.000 and 0.975, respectively, on a per image basis.

Assigning scheduled tasks to a multi-skilled workforce is a known NP-complete problem with many applications in health care, services, logistics and manufacturing. Optimising the use and composition of costly and scarce resources such as staff has major implications on any organisation's health. The present paper introduces a new, versatile two-phase matheuristic approach to the shift minimisation personnel task scheduling problem, which considers assigning tasks to a set of multi-skilled employees, whose working times have been determined beforehand. Computational results show that the new hybrid method is capable of finding, for the first time, optimal solutions for all benchmark instances from the literature, in very limited computation time. The influence of a set of problem instance features on the performance of different algorithms is investigated in order to discover what makes particular problem instances harder than others. These insights are useful when deciding on organisational policies to better manage various operational aspects related to workforce. The empirical hardness results enable to generate hard problem instances. A set of new challenging instances is now available to the academic community. publisher: Elsevier articletitle: The shift minimisation personnel task scheduling problem: A new hybrid approach and computational insights journaltitle: Omega articlelink: http://dx.doi.org/10.1016/j.omega.2014.02.003 content_type: article copyright: Copyright © 2014 Elsevier Ltd. All rights reserved. ispartof: Omega vol:46 pages:64-73 status: published

In this paper, we present a validation of public transport origin-destination (OD) matrices obtained from smartcard and GPS data. These matrices are very valuable for management and planning but have not been validated until now. In this work, we verify the assumptions and results of the method using three sources of information: the same database used to make the estimations, a Metro OD survey in which the card numbers are registered for a group of users, and a sample of volunteers. The results are very positive, as the percentages of correct estimation are approximately 90% in all cases. (C) 2014 Elsevier Ltd. All rights reserved.

At the beginning of the 1980s, the Artificial Intelligence (AI) community showed little interest in research on methodologies for the construction of knowledge-based systems (KBS) and for knowledge acquisition (KA). The main idea was the rapid construction of prototypes with LISP machines, expert system shells, and so on. Over time, the community saw the need for a structured development of KBS projects, and KA was recognized as the critical stage and the bottleneck for the construction of KBS. Concerning KA, many publications have appeared since then. However, very few have focused on formal plans to manage knowledge acquisition and from multiple knowledge sources. This paper addresses this important problem. KAMET is a formal plan based on models designed to manage knowledge acquisition from multiple knowledge sources. The objective of KAMET is to improve, in some sense, the phase of knowledge acquisition and knowledge modeling process, making them more efficient.

Recently, IT technologies are becoming focused on different traditional industries. The large scaled intelligent video surveillance system is one of them and it integrates a large number of digitalized CCTV [1] devices through the Web. However, existing video devices have been deployed for many years by different vendors as different models with different specifications. To integrate these heterogeneous devices, the centralized management server (CMS) and its clients need a specialized architecture to deal with different types of media encodings and connection protocols etc. In particular, well-defined access control mechanism is required for a large scaled surveillance video network. In this paper, we designed and implemented a server that incorporates the architecture integrating and delivering multiple video streams from different types of video devices to multiple clients and securing the access to the video streams.

The application of various clustering techniques for large-scale gene-expression measurement experiments is a well-established method in bioinformatics. Clustering is also usually accompanied by functional characterization of gene sets by assessing statistical enrichments of structured vocabularies, such as the gene ontology (GO) [Gene Ontology Consortium. The gene ontology (GO) project in 2006. Nucleic Acids Research (Database issue), vol. 34; 2006. p. D322-6]. If different clusters are generated for correlated experiments, a machine learning step termed cluster meta-analysis may be performed, in order to discover relations among the components of such sets. Several approaches have been proposed: in particular, kernel methods may be used to exploit the graphical structure of typical ontologies such as GO. Following up the formulation of such approach [Merico D, Zoppis I, Antoniotti M, Mauri G. Evaluating graph kernel methods for relation discovery in GO-annotated clusters. In: KES-2007/WIRN-2007, Part IV, Lecture notes in artificial intelligence, vol. 4694. Berlin: Springer; 2007. p. 892-900; Zoppis I, Merico D, Antoniotti M, Mishra B, Mauri G. Discovering relations among GO-annotated clusters by graph kernel methods. In: Proceedings of the 2007 international symposium on bioinformatics research and applications. Lecture notes in computer science, vol. 4463. Berlin: Springer; 2007], in this paper we discuss, from an information-theoretic point of view, further results about its applicability and its performance.

The exponential growth of information available on the World Wide Web, and retrievable by search engines, has implied the necessity to develop efficient and effective methods for organizing relevant contents. In this field document clustering plays an important role and remains an interesting and challenging problem in the field of web computing. In this paper we present a document clustering method, which takes into account both contents information and hyperlink structure of web page collection, where a document is viewed as a set of semantic units. We exploit this representation to determine the strength of a relation between two linked pages and to define a relational clustering algorithm based on a probabilistic graph representation. The experimental results show that the proposed approach, called RED-clustering, outperforms two of the most well known clustering algorithm as k-Means and Expectation Maximization.

In this paper, an intelligent agent-based communication support platform for multimodal transport is developed. The rationale for doing so is found in the potential of such a system to increase cost efficiency, service and safety for different transport-related actors. Although, at present several comparable systems exist, their current implementation is far from successful because of technological and economic obstacles. The new expert communication platform put forward here (called MamMoeT) addresses these two issues by using a software agent-based approach. Software agents are pieces of software representing a single user. They are autonomous, communicative and intelligent. The MamMoeT system developed can be described as a real-time decision support system in which intelligent software agents handle communicative tasks, exchange desired amounts of information among different users using common exchange protocols which act as translators between different systems. ispartof: Expert Systems with Applications vol:36 issue:7 pages:10280-10287 status: published

This paper presents an approach to predict the operating conditions of machine based on\ud classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS)\ud in association with direct prediction strategy for multi-step ahead prediction of time series\ud techniques. In this study, the number of available observations and the number of predicted\ud steps are initially determined by using false nearest neighbor method and auto mutual\ud information technique, respectively. These values are subsequently utilized as inputs for\ud prediction models to forecast the future values of the machines’ operating conditions. The performance of the proposed approach is then evaluated by using real trending data of low methane compressor. A comparative study of the predicted results obtained from CART and ANFIS models is also carried out to appraise the prediction capability of these models. The results show that the ANFIS prediction model can track the change in machine conditions and has the potential for using as a tool to machine fault prognosis.

This paper aims to predict some possible futures for the World Wide Web based on several key network parameters: size, complexity, cost and increasing connection speed through the uptake of broadband technology. This is done through the production of a taxonomy specifically evaluating the stability properties of the fully connected star and complete networks, based on the Jackson and Wolinsky [(1996). A strategic model of social and economic networks. Journal of Economic Theory, 71, 44-74] connections model, modified to incorporate complexity concerns. The taxonomy demonstrates that when connection speeds are low neither the star nor complete networks are stable, and when connection speeds are high the star network is usually stable, while the complete network is never stable. For intermediate speed levels much depends upon the other parameters. Under plausible assumptions about the future, the taxonomy suggests that the Web may be increasingly dominated by a single intermediate site, perhaps best described as a search engine.

In this paper a semi-online algorithm for scheduling multiprocessor tasks with partial information is proposed. We consider the case in which it is possible to exploit probabilistic information and use this information to obtain better solutions in comparison with standard non clairvoyant on-line algorithms. A wide computational analysis shows the effectiveness of our algorithm. Moreover, we also consider a test framework with a continuous generation of tasks in order to study the behavior of the proposed approach in real applications, which confirms the efficiency of our approach.

We consider a Cournot oligopoly game, where firms produce an homogenous good and the demand and cost functions are nonlinear. These features make the classical best reply solution difficult to be obtained, even if players have full information about their environment. We propose two different kinds of repeated games based on a lower degree of rationality of the firms, on a reduced information set and reduced computational capabilities. The first adjustment mechanism is called "Local Monopolistic Approximation" (LMA). First firms get the correct local estimate of the demand function and then they use such estimate in a linear approximation of the demand function where the effects of the competitors' outputs are ignored. On the basis of this subjective demand function they solve their profit maximization problem. By using the second adjustment process, that belongs to a class of adaptive mechanisms known in the literature as "Gradient Dynamics" (GD), firms do not solve any optimization problem, but they adjust their production in the direction indicated by their (correct) estimate of the marginal profit. Both these repeated games may converge to a Cournot-Nash equilibrium, i.e. to the equilibrium of the best reply dynamics. We compare the properties of the two different dynamical systems that describe the time evolution of the oligopoly games under the two adjustment mechanisms, and we analyze the conditions that lead to non-convergence and complex dynamic behaviors. The paper extends the results of other authors that consider similar adjustment processes assuming Linear cost functions or linear demand functions. (c) 2005 Elsevier Ltd. All rights reserved

Bank failure prediction is an important issue for the regulators of the banking industries. The collapse and failure of a bank could trigger an adverse financial repercussion and generate negative impacts such as a massive bail out cost for the failing bank and loss of confidence from the investors and depositors. Very often, bank failures are due to financial distress. Hence, it is desirable to have an early warning system (EWS) that identifies potential bank failure or high-risk banks through the traits of financial distress. Various traditional statistical models have been employed to study bank failures [J Finance 1 (1975) 21: J Banking Finance 1 (1977) 249; J Banking Finance 10 (1986) 511; J Banking Finance 19 (1995) 1073]. However, these models do not have the capabilily to identity the characteristics of financial distress and thus function as black boxes. This paper proposes the use of a new neural fuzzy system [Foundations of neuro-fuzzy systems, 1997], namely the Generic Self-organising Fuzzy Neural Network (GenSoFNN) [IEEE Trans Neural Networks 13 (2002c) 1075] based on the compositional rule of inference (CRI) [Commun ACM 37 (1975) 77], as an alternative to predict banking failure. The CRI based GenSoFNN neural fuzzy network, henceforth denoted as GenSoFNN-CRI(S), functions as an EWS and is able to identify the inherent traits of financial distress based on financial covariates (features) derived from publicly available financial statements. The interaction between the selected features is captured in the form of highly intuitive IF-THEN fuzzy rules. Such easily comprehensible rules provide insights into the possible characteristics of financial distress and form the knowledge base for a highly desired EWS that aids bank regulation. The performance of the GenSoFNN-CRI(S) network is subsequently benchmarked against that of the Cox's proportional hazards model [J Banking Finance 10 (1986) 511: J Banking Finance 19 (1995) 1073], the multi-layered perceptron (MLP) and the modified cerebellar model articulation controller (MCMAC) [IEEE Trans Syst Man Cybern: Part B 30 (2000) 491] in predicting bank failures based on a population of 3635 US banks observed over a 21 years period. Three sets of experiments are performed-bank failure classification based on the last available financial record and prediction using financial records one and two years prior to the last available financial statements. The performance of the GenSoFNN-CRI(S) network as a bank failure classification and EWS is encouraging.

This paper proposes an integrated system for unconstrained face recognition in complex scenes. The scale and orientation tolerant system comprises a face detector followed by a recognizer. Given a color input image of a person, the face detector encloses the face from the complex scene within a circular boundary, and locates the position of the nose. A radial grid mapping centered on the nose is then performed to extract a feature vector within the boundary. The feature vector is input to a radial basis function neural network classifier for face identification. The proposed face detector achieved an average detection rate of 95.8% while the face recognizer achieved an average recognition rate of 97.5% on a database of 21 persons with variations in scale, orientation, natural illumination and background. The two modules were combined to form an automatic face recognition system that was evaluated in the context of a security system using a video database of 21 users and 10 intruders, acquired in an unconstrained environment. A recognition rate of 93.5% with 0% false acceptance rate was achieved.

The large amount of information available and the difficulty on processing it has made knowledge management a promising area of research. Several topics are related to it, for example distributed and intelligent information retrieval, information filtering and information evaluation, which became crucial. In this paper, we focus our attention on the knowledge evaluation problem. With the aim of evaluating information coded in the standard non-proprietary format SGML (as also in XML), we propose some evaluation methods based on L-grammars which are fuzzy grammars. In particular we apply these methods to the evaluation of documents in SGML-format and to the evaluation of HTML-pages in the World Wide Web. L-grammars generate recursively enumerable L-languages, as it has been proved in Gerla ((1991), Information Sciences 53), and so they can be used to generate fuzzy languages based on extensions of the document type definitions (DTD) involved by SGML. Given a DTD, we extend its associated language by adding a judgement label. By selecting a particular label and by taking the start symbol of the grammar associated to the DTD, we can generate any DTD-compliant document with a fuzzy degree of membership derived from the judgement label. In this way we fit the computational model underlying the recursively enumerable L-languages to the process of collecting different evaluations of the same document. Finally, we outline how the generalization of these methods of evaluation can be applied in different contexts and for different roles, as for example for information filtering.

The paper deals with the invariant recognition of patterns, and aims at developing (i) their pulse-coded representation; and (ii) an algorithm for their recognition. The proposed pattern encoder utilizes the properties of complex logarithmic mapping (CLM) (computed with reference to the center of gravity, CoG, of the shape), which maps rotation and scaling in its domain to shifts in its range. The encoder, then, invokes a pulse-encoding scheme similar to the one proposed by Dodwell [1] in order to handle these shifts, thereby generating pulse-codes invariant to scaling, rotation, and shift in the input shape. These pulses are then fed to a novel multi-layered neural recognizer which (i) invokes template matching with a distinctly implemented architecture; and (ii) achieves robustness (to noise and pattern deformation) by virtue of its overlapping strategy for code classification. The proposed encoder–recognizer (E–R), which is hardware implementable by a high-speed electronic switching circuit, can add new patterns on-line to the existing ones. The E–R is illustrated with experimental results. While human visual system has been the main motivation to the proposed model, no claim, however, has been made on its direct biological plausibility.

In this paper we introduce DEGAS (Dynamic Entities Get Autonomous Status), an active temporal data model based on autonomous objects. The natural combination of active and temporal databases is discussed. The active dimension of DEGAS means that we define the behaviour of objects in terms of production rules. The temporal dimension means that the history of an object is included in the DEGAS data model. Further novel features of DEGAS are the encapsulation of the complete behaviour of an object, both potential and actual. Thus, DEGAS combines dynamic and structural specifications in one model. In addition, DEGAS allows easy evolution of object capabilities through a clear distinction between inherent types and capabilities that can be acquired and lost. This addon mechanism makes DEGAS very suitable as a formalism for role modelling. Finally, the rule model in DEGAS is both simple, through the use of finite automata, and general, because it allows different strategies for dealing with constraints and reacting to events in other objects.

The article of record as published may be found at https://doi.org/ The Battle Enhanced Analysis Methodologies (BEAM) project was designed to investigate the use of computer graphics in describing the performance of battalion-sized units in simulated combat. These descriptions were to be data-based and objective, providing useful critiques of actual performance according to standard Army doctrine. They would be natural candidates for use at the Army's Combat Training Centers. The project was conducted in two phases. In the first, objective graphic displays were derived which portray the destructive potential of direct fire weapons (the shooter must be able to see the target) in the defense. These displays allow straightforward objective comparisons of different defensive alignments and, from simulated battle runs, of defensive fire control strategies. These references also describe simple uncluttered displays that portray the movements and interactions of company (or higher) sized units throughout a battle. This paper describes results of the second phase of the BEAM project. A major result is the derivation of displays which portray the destructive potential of indirect fire weapons (the shooter normally cannot see the target) in the defense using the same units as the direct fire displays. This allows separate and joint examination of the direct and indirect fire destructive potential, providing, among other things, objective measures of the synchronization and agility of a force, as well as indicators of its intelligence function