Chakib Tadj

A BULK QUORUM QUEUEING SYSTEM WITH A RANDOM SETUP TIME UNDER N-POLICY AND WITH BERNOULLI VACATION SCHEDULE

Abstract We aim in this paper at designing an optimal management policy for a bulk service queueing system with random set-up time under Bernoulli vacation schedule and N-policy. We first study the discrete time parameter and continuous time parameter stochastic processes and derive all the quantities required to build a linear cost structure. Then an algorithm is suggested to determine the optimal management policy. An illustrative example is presented to show how to implement this policy and a sensitivity analysis is conducted to determine the effect of the system parameters.

Generic multimedia multimodal agents paradigms and their dynamic reconfiguration at the architectural level

The multimodal fusion for natural human-computer interaction involves complex intelligent architectures which are subject to the unexpected errors and mistakes of users. These architectures should react to events occurring simultaneously, and possibly redundantly, from different input media. In this paper, intelligent agent-based generic architectures for multimedia multimodal dialog protocols are proposed. Global agents are decomposed into their relevant components. Each element is modeled separately. The elementary models are then linked together to obtain the full architecture. The generic components of the application are then monitored by an agent-based expert system which can then perform dynamic changes in reconfiguration, adaptation, and evolution at the architectural level. For validation purposes, the proposed multiagent architectures and their dynamic reconfiguration are applied to practical examples, including a W3C application.

Context handling in a pervasive computing system framework

Pervasive computing poses a great number of issues to the research community. Different research teams have focused their activities on subsets of challenges, all pursuing the objective of deeply integrate computers in almost everybody everyday activities. Unfortunately, there is no coordination in this race through THE pervasive computing system. There may be good solutions to different pervasive computing issues out there, but there is no framework to integrate them in unique software architecture. There is also a lack of a generic global framework to start building a pervasive computing system. In this paper, we describe the LATIS Pervasive Framework (LAPERF), and its core module, the Context Awareness Manager. The LAPERFs objective is to provide a base framework, and automating tools to developers intending to implement a pervasive computing application. A scenario is provided to illustrate how LAPERF can be used in healthcare application.

A bulk quorum queueing system with a random setup time under N-policy and with Bernoulli vacation schedule

This paper is concerned with the optimal management of a batch arrival, bulk service queueing system with random set-up time under Bernoulli vacation schedule and N-policy. If the number of customers in the system at a service completion is larger than some integer r, then the server starts processing a group of r customers. If, on the other hand, it is smaller than r, then the server idles and waits for the line to grow up to some other integer N, (N ≥ r). Using the embedded Markov chain and semi-regenerative techniques, we obtain all the system characteristics required to build a linear cost structure. Then, we implement a simple search procedure to determine the optimal thresholds levels r and N. An illustrative example is presented.

A cry-based babies identification system

Human biological signals convey precious information about the physiological and neurological state of the body. Crying is a vocal signal through which babies communicate their needs to their parents who should then satisfy them properly. Most of the researches dealing with infants cry intend mainly to establish a relationship between the acoustic properties of a cry and the state of the baby such as hunger, pain, illness and discomfort. In this work, we are interested in recognizing babies only by analyzing their cries through the use of an automatic analysis and recognition system using a real cry database.

Dynamic Context-Aware Service Adaptation in a Pervasive Computing System

Pervasive computing requires that devices must be context-aware in order to provide proactively adapted services to both user and applications according to the current context. Most of the proposed approaches of service adaptation in a pervasive computing system are either not context-aware or based on an inappropriate definition of context. In this paper we propose a context-aware dynamic service adaptation approach for a device operating in a pervasive computing system. The approach is then modeled using colored Petri Nets and simulated using the CPN-Tools in order to validate it.

A context-sensitive incremental learning paradigm of an ubiquitous multimodal multimedia computing system

In this paper, we proposed a context-sensitive incremental learning paradigm of an ubiquitous multimodal multimedia computing system. An ubiquitous computing environment supports a busy and mobile users need of being able to work on his task anytime and anywhere he wants. Along with users data (his profile, task, and application registry) the machine-acquired intelligence needs to be transported as well in order that the user could continue working on an intelligent environment. Machine intelligence is acquired through incremental learning. In a context-sensitive environment that has a rich selection of modalities and media for data input and output, an intelligent computing system could determine the I/O devices appropriate for the users setting after considering the users location, the noise level in the environment, and the presence or absence of other people in the vicinity. Every new setting (pre-condition scenario) produces a new I/O devices configuration (post-condition scenario) suited for the setting; each new scenario knowledge gets stored onto knowledge database. Overtime, the machine would have enough knowledge to deal with whatever context scenario that comes up.

Cry-based classification of healthy and sick infants using adapted boosting mixture learning method for gaussian mixture models

We make use of information inside infants cry signal in order to identify the infants psychological condition. Gaussian mixture models (GMMs) are applied to distinguish between healthy full-term and premature infants, and those with specific medical problems available in our cry database. Cry pattern for each pathological condition is created by using adapted boosting mixture learning (BML)method to estimatemixture model parameters. In the first experiment, test results demonstrate that the introduced adapted BML method for learning of GMMs has a better performance than conventional EM-based reestimation algorithm as a reference system in multipathological classification task. This newborn cry-based diagnostic system (NCDS) extracted Melfrequency cepstral coefficients (MFCCs) as a feature vector for cry patterns of newborn infants. In binary classification experiment, the system discriminated a test infants cry signal into one of two groups, namely, healthy and pathological based on MFCCs. The binary classifier achieved a true positive rate of 80.77% and a true negative rate of 86.96% which show the ability of the system to correctly identify healthy and diseased infants, respectively.

A Multi-Agent based Multimodal System Adaptive to the User’s Interaction Context

Communication is an important aspect of human life; it is with communication that helps human beings connect with each other as individuals and as independent groups. In informatics, the very purpose of the existence of computer is information dissemination – to be able to send and receive information. Humans are quite successful in conveying ideas with one another and reacting appropriately because we share the richness of our language, have a common understanding of how things work and have an implicit understanding of everyday situations. When human communicate with human, they comprehend the information that is apparent to the current situation, or context, hence increasing the conversational bandwidth. This ability to convey ideas, however, does not transfer when human interacts with computer. On its own, computers do not understand our language, do not understand how the world works and cannot sense information about the current situation. In a typical impoverished computing set-up where providing computer with information is through the use of mouse, keyboard and screen, the result is we explicitly provide information to computers, producing an effect that is contrary to the promise of transparency and calm technology in Marc Weiser’s vision of ubiquitous computing (Weiser 1991; Weiser 1993; Weiser and Brown 1996). To reverse this, it is imperative that methodologies are developed that will enable computers to have access to context. It is through context-awareness that we can increase the richness of communication in humancomputer interaction, through which we can reap the most likely benefit of more useful computational services. Context (Dey and Abowd 1999; Gwizdka 2000; Dey 2001; Coutaz, Crowley et al. 2005) is a subjective idea and its interpretation is personal. Context evolves and the acquisition of contextual information is essential. However, we believe that the one with the final word on whether the envisioned context is correctly captured/acquired or not is the end user. Current research works indicate that some contextual information are already predefined by their systems from the very beginning – this is correct if the application domain is fixed but is incorrect if we infer that a typical user does different computing tasks in different occasions. With the aim of coming up with more conclusive and inclusive design, we conjure that the contextual information that is important to the user should be left to the judgment of the end

A ubiquitous context-sensitive multimodal multimedia computing system and its machine learning-based reconfiguration at the architectural level

In this paper, we present our work on a ubiquitous context-sensitive multimodal multimedia computing system that progressively acquires machine knowledge. This ubiquitous computing system supports an automatic selection of media and modalities deemed appropriate for the users context and users profile. The ability of the system to do so constitutes its acquired knowledge. The decision making for media/modality selection takes into account if the user has some special needs due to disability. The architecture of the system is designed to be pervasive and is conceived to resist failure. In case of one or more components being missing or found defective, the machine would resist failure by reconfiguring itself dynamically in the architectural level. It finds alternative replacement to the failed component using its acquired knowledge.