Recognizing the importance of privacy management as a business process and a business support process, this paper proposes the use of service level agreements (SLA’s) around privacy features, including qualitative and quantitative ones. Privacy metrics are defined by both parties with boundary values on each feature. Their distribution is relying on stress distributions used in this field .The use of service level agreements also casts privacy management into a business perspective with benefits and costs to either party in a process. This approach is especially relevant for communications operators as brokers and communication channels between content owners (individuals, businesses) and enterprise applications ; in this context, the privacy SLA management would be carried out by the operator, while the terms and conditions of the SLA negotiation reside with the individual and information gatherers .This work was carried out as part of the large EU project PRIME www.prime.project.eu.org on privacy enhancing technologies.

In this paper we consider an M/G/1 queue-based analytical model. The end-to-end performance of a tandem wireless router network with batch arrivals is optimized. The mean of the transmission delay (or 'response time') is minimized subject to an upper limit on the rate of losses and finite capacity queueing and recovery buffers. The optimal ratio of arrival-buffer size to recovery-buffer size is determined, which is a critical quantity, affecting both loss rate and transmission time. The impact of the retransmission probability is investigated: too high a value leads to congestion and so higher response times, too low and packets are lost forever, yielding a different penalty

The feasibility of storing large amount of digital images and the pictorial-oriented nature of many applications have attracted much interest in pictorial information systems. An important issue in such systems is the efficient retrieval of image based on its contents. Supporting content-based retrieval of image data is a difficult problem and embraces different technologies including image processing, user interface design, and database management. To provide efficient content-based retrieval, efficient access methods based on image features are required. This paper presents a survey of indexing techniques that support content-based retrieval of images. In particular, we examine access methods for three types of image features, namely, object shape, semantic objects, and spatial relationships among semantic objects. For each image feature, we look at how various approaches represent and organize the image features and how they support similarity retrieval.

Much research is currently going on about the processing of one or two-camera imagery, possibly combined with other sensors and actuators, in view of achieving attentive vision, i.e. processing selectively some parts of a scene possibly with another resolution. Attentive vision in turn is an element of active vision where the outcome of the image processing triggers changes in the image acquisition geometry and/or of the environment. Almost all this research is assuming classical imaging, scanning and conversion geometries, such as raster based scanning and processing of several digitized outputs on separate image processing units. A consortium of industrial companies comprising Digital Equipment Europe, Thomson CSF, and a few others, have taken a more radical view of this. To meet active vision requirements in industry, an intelligent camera is being designed and built, comprised of three basic elements: – a unique Thomson CSF CCD sensor architecture with random addressing – the DEC Alpha 21064 275MHz processor chip, sharing the same internal data bus as the digital sensor output – a generic library of basic image manipulation, control and image processing functions, executed right in the sensor-internal bus-processor unit, so that only higher level results or commands get exchanged with the processing environment. Extensions to color imaging (with lower spatial resolution), and to stereo imaging, are relatively straightforward. The basic sensor is 1024*1024 pixels with 2*10 bits addresses, and a 2.5 ms (400 frames/second) image data rate compatible with the Alpha bus and 64 bits addressing. For attentive vision, several connex fields of max 40 000 pixels, min 5*3 pixels, can be read and addressed within each 2.5 ms image frame. There is nondestructive readout, and the image processing addressing over 64 bits shall allow for 8 full pixel readouts in one single word. The main difficulties have been identified as the access and reading delays, the signal levels, and dimensioning of some buffer arrays in the processor. The commercial applications targeted initially will be in industrial inspection, traffic control and document imaging. In all of these fields, selective position dependent processing shall take place, followed by feature dependent processing. Very large savings are expected both in terms of solutions costs to the end users, development time, as well as major performance gains for the ultimate processes. The reader will appreciate that at this stage no further implementation details can be given.

The author surveys key requirements and specific design techniques for artificial intelligence (AI) applications in the financial services industry. After discussing some of the fundamental challenges which the financial services industry presents for decision technology, the motivations for the use of AI are related to a number of typical applications, which are broadly categorized into suitable development and delivery environments and generic domain utilities required from shells or customized systems for that industry. Markets and trades are then shown to be well described in terms of knowledge representation and data structures, by object-oriented features embedded into logic programming environments. Conflict resolution strategies, preference aggregation and revealed beliefs are studied. A formal analysis of investment decision criteria is given. An example from currency risk management exemplifies this area. Intelligent information screens are presented to stress the need for knowledge-based techniques in financial information retrieval.>

A general-purpose sensor fusion editor is presented. It allows for the customization of fusion algorithms and features for later real-time implementation, helps carry out exploratory offline fusion analysis tasks, and serves as a testbed for a fusion interpretation workstation. The sensor fusion editor essentially implements as an integrated environment a library of object-oriented methods and classes for carrying out the three-level sensor fusion knowledge representation and the standard sensor fusion tasks, such as registration, combination, clustering, explanation, etc., on data fields, images, maps, signals, and image sequences. The three knowledge representation levels are each supplemented by a set of transformation methods, helping in deriving attributes for analysis at the other levels, and of knowledge-based search methods (rule-based).>

The added functionality such as contour tracking and corner detection which logic programming lends to standard image operators is described. An environment for implementing low-level imaging operations with Prolog predicates is considered. Within this environment, higher-level image predicates (contour tracking and corner detection) are constructed. The emphasis is not on building better corner detectors, but on presenting ways of using the unification and backtracking features of logic programming for these tasks. The performance of this implementation of contour tracking and corner detection has been very good in many more complex images, as it allows for feedback both ways between sensor input and symbolic models. More important is the parameter selection capability in a dynamic version where background properties change. The authors present examples of Prolog predicates for performing the contour and corner detection operations.>

A description is given of a knowledge-based system called SOFTM, serving the following purposes: (1) assisting a software programmer or analyst in his application code maintenance tasks, (2) generating and updating automatically software correction documentation, (3) helping the end user register, and possibly interpret, observed errors on the successive application code versions. SOFTM is written in Prolog II and is largely applicable to application codes written in different programming languages, provides code descriptors can be retrieved. SOFTM does not address any of the syntactic, input-output, or procedural errors normally detected by the syntactic analyzer, compiler, or by the operating system environment. SOFTM relies on a unique ATN network-based code description, on diagnostic inference procedure based on context-based pattern classification, on maintenance log report generators, and on interfacing capabilities of Prolog II to a variety of other languages.>

This paper surveys the applications of vision to fish sorting, fish fillet sorting and detection of surface and sub-surface defects (such as worms and bones). It stresses the specific implementation context, needed performances, illumination, detection principle, as well as vision algorithms. Also analyzed are the optical properties of fish. Examples of results are given.

This paper studies in theory, and gives a solution to, the following concerns which may eventually be simultaneous: 1) obtain alternative classification decisions, ranked by some decreasing order of class membership probabilities; 2) imperfect teacher at the learning stage, or effects of labeling errors due to unsupervised learning by clustering; 3) noncooperative teacher, manipulating the a priori class probabilities; 4) unknown a priori class probabilities. These requirements are taken into account by considering a game between the recognition system and the teacher, in a game theoretical framework. Both players will ultimately select ``mixed strategies,'' which are probability distributions over the set of N alternative pattern classes, determined for each feature vector to be classified. This solution concept is interpreted in terms of the requirements 1)-4); numerical algorithms, as well as numerical examples are given with their solutions.

This paper deals with the integrated pre-cap testing of integrated circuits (IC's), defined as a simultaneous combination of electrical testing and of visual inspection using image analysis techniques. The emphasis is on image analysis models and algorithms for integrated testing of small and large defects. Two algorithms are presented for the analysis of visible and infrared imagery during electrical testing. Algorithm 1 matches bridges or subgraphs derived from the topological layout. Algorithm 2 computes a figure of merit for the IC from a fuzzy language description.