A novel replication technique to attenuate churn effects

Dealing with the churn problem is an important requirement in distributed systems in general and more precisely in structured peer-to-peer overlay networks due to their dynamicity. This problem refers to the change in the set of participating nodes due to the frequent joins, leaves or failures. Such variations induce at worst the loss of objects and at least performance degradation. This is due to the reorganization of the set of replicas of the affected objects. Till now, this problem has been mostly addressed at the p2p routing level to ensure the reach ability of peers by maintaining the consistency of the logical neighbors. However, the main challenge for structured p2p systems is to protect the ability of the system to locate any present object. At the storage level, avoiding data losses is still an issue when a reconfiguration of the participant peers occurs. In this paper and after presenting first the existing replication techniques, we propose the zone replication technique used in both Chord and Kademlia protocols in order to attenuate churn effects. Finally, we point out some ways that can lead to better and maybe robust replication protocols.     

Enhancement of noise-suppressed speech by spectral processing implemented in a digital signal processor

This paper presents a noisy suppressed speech enhancement method by combining the basic spectral subtraction technique and spectral processing in the frequency domain to provide better noise suppression as well as better enhancement in the speech regions. In contrast to several previous approaches we do not try to achieve a complete removal of the noise, but instead our goal is to preserve a pre-defined amount of the original noise in the processed signal. This is accomplished by exploiting the masking properties of the human auditory system. The proposed algorithm is named PM “Proposed Method” which simulates properties of the human auditory system and applies it to the speech recognition system to enhance its robustness. The performance of the speech enhancement algorithm using the proposed masking model was compared with three other speech enhancement methods over 4 different noise types and five SNRs. The performances of the proposed approach are objectively and subjectively compared to the conventional approaches to highlight the aforementioned improvement. In this paper we discuss the design and development of a digital signal processor (DSP) implementation to achieve real-time performance of our filter. The target processor is a Texas Instruments TMS320C6713 floating point DSP.     

Neural solutions to interact with computers by hand gesture recognition

This paper attempts to present a vision-based interface which interacts with computers by hand gesture recognition. This work aims at creating a natural and intuitive application employing both static and dynamic hand gestures. The proposed application can be summarized in three main steps: hands detection in a video, hands tracking and converting hand shapes or trajectories into computer commands. To accomplish this application, a classification phase is paramount whether at the part of hand detection, or at the phase of “commanding computers”. For this reason, we have proposed to use a wavelet network classifier (WNC) learnt by fast wavelet transform (FWT). To emphasize the robustness of this classifier, we have used a neural network classifier (NNC) version in order to compare the two classifiers’ performances aiming at proving the strength of our proposed one. Global rates given by experimental results show the effectiveness of our proposed approaches of hand detection, hand tracking and hand gesture recognition. The comparison of the two classifier’s result helps to choose the best classifier, which can improve the performances of our application.     

A priori parallel machines scheduling

We address the probabilistic generalization of weighted flow time on parallel machines. We present some results for situations which ask for “long-term robust” schedules of n jobs (tasks) on m parallel machines (processors): on any given day, only a random subset of jobs needs to be processed. The goal is to design robust a priori schedules (before we know which jobs need to be processed) which, on a long-term horizon, are optimal (or near optimal) with respect to total weighted flow time. The originality of this work is that probabilities are explicitly associated with data such that further classical properties of a task (processing time and weight) we consider a probability of presence. After motivating this investigation we analyze the computational complexity, analytical properties, and solution procedures for these problems. Special care is also devoted to assess experimentally the performance of a priori strategies.     

Re-visiting the artificial immune recognition system: a survey and an improved version

This paper surveys the major works related to an artificial immune system based classifier that was proposed in the 2000s, namely, the artificial immune recognition system (AIRS) algorithm. This survey has revealed that most works on AIRS was dedicated to the application of the algorithm to real-world problems rather than to theoretical developments of the algorithm. Based on this finding, we propose an improved version of the AIRS algorithm which we dub AIRS3. AIRS3 takes into account an important parameter that was ignored by the original algorithm, namely, the number of training antigens represented by each memory cell at the end of learning (numRepAg). Experiments of the new AIRS3 algorithm on data sets taken from the UCI machine learning repository have shown that taking into account the numRepAg information enhances the classification accuracy of AIRS.     

Proactive and reactive e-government services recommendation

Governmental portals designed to provide electronic services are generally overloaded with information that may hinder the effectiveness of e-government services. This paper proposes a new framework to supply citizens with adapted content and personalized services that satisfy their requirements and fit with their profiles in order to guarantee universal access to governmental services. The proposed reactive and proactive solutions combine several recommendation techniques that use different data sources i.e., citizen profile, social media databases, citizen’s feedback databases and service databases. It is shown that recommender systems provide citizens with accessible personalized e-government services.     

Multiclass support vector machines for environmental sounds classification in visual domain based on log-Gabor filters

This paper presents an approach aimed at recognizing environmental sounds for surveillance and security applications. We propose a robust environmental sound classification approach, based on spectrograms features derive from log-Gabor filters. This approach includes three methods. In the first two methods, the spectrograms are passed through an appropriate log-Gabor filter banks and the outputs are averaged and underwent an optimal feature selection procedure based on a mutual information criteria. The third method uses the same steps but applied only to three patches extracted from each spectrogram. To investigate the accuracy of the proposed methods, we conduct experiments using a large database containing 10 environmental sound classes. The classification results based on Multiclass Support Vector Machines show that the second method is the most efficient with an average classification accuracy of 89.62 %.     

Using geometric spectral subtraction approach for feature extraction for DSR front-end Arabic system

Noise robustness and Arabic language are still considered as the main challenges for speech recognition over mobile environments. This paper contributed to these trends by proposing a new robust Distributed Speech Recognition (DSR) system for Arabic language. A speech enhancement algorithm was applied to the noisy speech as a robust front-end pre-processing stage to improve the recognition performance. While an isolated Arabic word engine was designed, and developed using HMM Model to perform the recognition process at the back-end. To test the engine, several conditions including clean, noisy and enhanced noisy speech were investigated together with speaker dependent and speaker independent tasks. With the experiments carried out on noisy database, multi-condition training outperforms the clean training mode in all noise types in terms of recognition rate. The results also indicate that using the enhancement method increases the DSR accuracy of our system under severe noisy conditions especially at low SNR down to 10 dB.     

A review on simulation models applied to emergency medical service operations

Emergency medical services (EMS) are public safety systems responsible for the pre-hospital stabilization and transport of seriously injured patients. The goal of such systems is to respond adequately to population calls by providing first aid services and transferring patients, when needed, to the emergency department of the appropriate hospital. In order to achieve this goal, a variety of tools (e.g. simulation, mathematical programming and queuing theory models) have been used to improve the performance of EMS. This paper focuses specifically on computer simulation models used for the analysis and improvement of EMS. In particular, we give a critical overview of the existing international literature on simulation models for EMS by pinpointing the issues considered, the associated modeling assumptions as well as the results obtained. Such a contribution is lacking in the current literature.