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Distracted driving is among the primary causes for serious car accidents. Among the leading cause of death among teenagers today are traffic accidents and major part of them are related to distracted driving. We propose here an end-to-end Convolutional Neural Network-based driver distraction recognition (DDR) system that can generalize to diverse driving conditions. The proposed method consists of two steps: developing generative models to produce images of different driving scenarios and developing a discriminative model for image classification. Unlike traditional methods based on image data-sets collected by simulation experiments, we collect a diverse data-set of drivers in different driving conditions and activity patterns from the Internet and train generative models for multiple driving scenarios. By sampling from these generative models, we augment the collected data-set with new training samples and train a Convolutional Neural Network for distraction recognition. We demonstrate that the generative models are able to generate images of drivers in different driving scenarios. With augmentative images, the DDR system achieves an improvement of 11.45% on image classification performance in a driving simulation environment. Moreover, we demonstrate how the trained DDR systems can be integrated within a driver monitoring system.

Convolutional Recurrent architectures are currently preferred for spatio-temporal learning tasks in videos to the 3D convolutional networks which accompany a huge computational burden and it is imperative to understand the working of different architectural configurations. But most of the current works on visual learning, especially for video anomaly detection, predominantly employ ConvLSTM networks and focus less on other possible variants of Convolutional Recurrent configurations for temporal learning which warrants a need to study the different possible variants to make informed, optimal design choices according to the nature of the application at hand. We explore a variety of Convolutional Recurrent architectures and the influence of hyper-parameters on their performance for the task of anomaly detection. Through this work, we also intend to quantify the efficiency of the architectures based on the trade-off between their performance and computational complexity. With comprehensive quantitative and visual evidence, we establish that the ConvGRU based configurations are the most effective and perform better than the popular ConvLSTM configurations on video anomaly detection tasks, in contrast to what is seen from the literature.

Since their inception, AutoEncoders have been very important in representational learning. They have achieved ground-breaking results in the realm of automated unsupervised anomaly detection for various critical applications. However, anomaly detection through AutoEncoders suffers from lack of transparency when it comes to decision making based on the outputs of the AutoEncoder network, especially for image-based models. Though the residual reconstruction error map from the AutoEncoder helps explaining anomalies to a certain extent, it is not a good indicator of the implicitly learnt attributes by the model. A human interpretable explanation of why an instance is anomalous not only enables the experts to fine-tune the model but also establishes and increases trust by non-expert users of the model. Convolutional AutoEncoders in particular suffer the most as there are only limited studies that focus on transparency and explainability. In this paper, aiming to bridge this gap, we explore the feasibility and compare the performances of several State-of-the-Art Explainable Artificial Intelligence (XAI) frameworks on Convolutional AutoEncoders. The paper also aims at providing the basis for future developments of reliable and trustworthy AutoEncoders for visual anomaly detection.

Understanding the notion of normality in visual data is a complex issue in computer vision with plenty of potential applications in several sectors. The immense effort required for optimal design for real-world application of existing methods warrants the need for a generic framework that is efficient, automated and can be momentarily deployed for the operation, reducing the effort expended on model design and hyper-parameter tuning. Hence, we propose a novel, modular and model-agnostic improvement to the conventional AutoEncoder architecture, based on visual soft-attention for the inputs to make them robust and readily improve their performance in automated semi-supervised visual anomaly detection tasks, without any extra effort in terms of hyperparameter tuning. Besides, we discuss the role of attention in AutoEncoders (AE) that can significantly improve learning and the efficacy of the models with detailed experimental results on diverse visual anomaly detection datasets.

It is common for a time series dataset to have missing values, and it is necessary to fill these missing elements before using the dataset for training forecasting models. Usually this problem is tackled using non-machine learning methods that introduce bias into the system which results in unreliable forecasting results. Moreover, most of the work found in the literature tackles imputation of missing values when they are randomly scattered in the dataset while very little work is found tackling the case of consecutive occurrence of missing data; i.e. missing data chunks in the dataset. Therefore, in this work, comprehensive imputation models are developed to impute both random as well as chunks of missing values. Alongside, a framework is found enabling the user to impute any time series data with the optimal models. In order to carry out the task, one non-deep machine learning model (Bidirectional Imputation model) and three deep learning (DL) imputation models (Ensemble model, Transfer Learning model and Hybrid model), are tested using complete time series. The results show that the hybrid model yields a maximum of 38% improvement in the Aggregate Error (AGE) when compared with other models.

There has been a tremendous increase in the costs of caring for older adults owing to the fact that societies are aging around around the world. This has led to a decrease in the number of caregivers who are able to assist. Investigative studies indicate that older adults require social as well as physical support for their well-being which prompted researchers to use social and cognitive robots and advanced human machine interaction devices. However, most of these studies have shortcomings when it comes to providing means of a natural interaction with the machine. With speech being the most natural way for human communication and the huge developments in the Internet of Things and smart homes, equipping a robotic system with powerful natural speech interaction capabilities to maintain a conversation with an elderly while being linked to other smart home devices shows a promising direction. This paper describes a scalable and expandable system with main goal of designing a natural speech-enabled system for older adults that is capable of linking to multiple active agents with minimal integration efforts. The system makes use of the power of commercially available digital assistant systems, integrated with an intelligent conversational agent, robotics, and smart wearables. The main advantage of the system is that it could provide a portion of the population, namely older adults and the disabled, the flexibility of interacting naturally with powerful social robots in smart home environments, hence providing them with much needed independence.

It is common for a time series dataset to have missing values, and it is necessary to fill these missing elements before fitting any model for forecasting or prediction. Time series imputation remains a challenging task due to the existence of non-linear dependencies between current and past values. Conventional methods, such as deletion of rows containing missing values or filling them with the last observed value, add bias to the data and are therefore inefficient. There are situations where data is missing at consecutive points or random points in the dataset, and one particular method may not work well for all cases. In this paper, nine commonly used models in the field of imputation, based on tools of statistics, machine learning, and deep learning, are compared. Results show that Linear Memory Vector Gated Recurrent Unit (LIME-GRU) outperforms the other tested models by having the least Mean Square Error (MSE) and Root Mean Squared Error (RMSE). A predictive model to gauge the impact of imputation on prediction is also used to validate the findings. The results of the prediction model illustrate that with LIME-GRU, there was a 39% improvement in Average Aggregated Measure (AAGM) when compared with mode imputation on a particular test case.

Building highly precise prediction models for Fresh Produce (FP) market price is crucial to protect retailers from overpriced FP. In this paper we are comparing the price prediction models performance of deep learning (DL) models with statistical as well as standard machine learning (ML) models. Five types of FP are considered in performance testing. It is found that the conventional ML models outperform the statistical models such as ARIMA. On the other hand, the winning model among the conventional ML models (the Gradient Boosting model) proves to be less performant as compared with the simple or compound DL models. Moreover, the simple DL models, such as the Long Short-Term Memory (LSTM), are outperformed by the compound one, the Convolutional Long Short-Term Memory Recurrent Neural Network (CNN-LSTM), whose performance improves by adding attention. The model is capable of precisely predicting FP prices for up to three weeks ahead.

We analyze the effect of offline and online triplet mining for colorectal cancer (CRC) histopathology dataset containing 100,000 patches. We consider the extreme, i.e., farthest and nearest patches to a given anchor, both in online and offline mining. While many works focus solely on selecting the triplets online (batch-wise), we also study the effect of extreme distances and neighbor patches before training in an offline fashion. We analyze extreme cases’ impacts in terms of embedding distance for offline versus online mining, including easy positive, batch semi-hard, batch hard triplet mining, neighborhood component analysis loss, its proxy version, and distance weighted sampling. We also investigate online approaches based on extreme distance and comprehensively compare offline, and online mining performance based on the data patterns and explain offline mining as a tractable generalization of the online mining with large mini-batch size. As well, we discuss the relations of different colorectal tissue types in terms of extreme distances. We found that offline and online mining approaches have comparable performances for a specific architecture, such as ResNet-18 in this study. Moreover, we found the assorted case, including different extreme distances, is promising, especially in the online approach.

Detecting topics from Twitter streams has become an important task as it is used in various fields including natural disaster warning, users opinion assessment, and traffic prediction. In this article, we outline different types of topic detection techniques and evaluate their performance. We categorize the topic detection techniques into five categories which are clustering, frequent pattern mining, Exemplar-based, matrix factorization, and probabilistic models. For clustering techniques, we discuss and evaluate nine different techniques which are sequential k-means, spherical k-means, Kernel k-means, scalable Kernel k-means, incremental batch k-means, DBSCAN, spectral clustering, document pivot clustering, and Bngram. Moreover, for matrix factorization techniques, we analyze five different techniques which are sequential Latent Semantic Indexing (LSI), stochastic LSI, Alternating Least Squares (ALS), Rank-one Downdate (R1D), and Column Subset Selection (CSS). Additionally, we evaluate several other techniques in the frequent pattern mining, Exemplar-based, and probabilistic model categories. Results on three Twitter datasets show that Soft Frequent Pattern Mining (SFM) and Bngram achieve the best term precision, while CSS achieves the best term recall and topic recall in most of the cases. Moreover, Exemplar-based topic detection obtains a good balance between the term recall and term precision, while achieving a good topic recall and running time.

Although the future impact of plug-in electric vehicles (PEVs) on distribution grids is disputed, all parties agree that mass operation of PEVs will greatly affect load profiles and grid assets. The large-scale penetration of domestic energy storage, such as with photovoltaics (PVs), into the edges of low-voltage grids is increasing the amount of customer-generated electricity. Distribution grids, which are inherently unbalanced, tend to become even more so with the uneven spread of PVs and PEVs. In combination, PEVs and local generation could provide voltage support for distribution networks, and support increased penetration. This paper develops an interactive energy management system for incorporating PEVs in demand response (DR). Using this system, owners can immediately choose whether they want to discharge their PEV battery back into the grid. The system not only provides owners with a flexible scheme for contributing to DR but also ensures that, through real-time collaboration of PEVs and PVs, the three-phase grid operates within acceptable voltage unbalance. An extensive performance evaluation using MATLAB/GAMS simulation of the 123-bus test system verifies the effectiveness of the proposed approach.

Autonomous systems underpinned by cognitive intelligence represent advanced forms of artificial intelligence studied in intelligence science, systems science, and computational intelligence. Traditional theories and technologies of autonomous systems put emphases on humansystem interactions and humans in-the-loop. This paper explores the intelligence and system foundations of autonomous systems. It focuses on what structural and behavioral properties constitute the intelligence power of autonomous systems. It explains how system intelligence aggregates from reflexive, imperative, adaptive intelligence to autonomous and cognitive intelligence. A Hierarchical Intelligence Model (HIM) is introduced to elaborate the evolution of human and system intelligence as an inductive process. A set of properties of system autonomy is formally analyzed towards a wide range of autonomous system applications in computational intelligence and systems engineering. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Catastrophic forgetting has long been seen as the main obstacle to building continual learning models. We argue in this paper that an equally challenging characteristic of the continual learning framework is that data are never completely available at the same time, making it difficult to learn joint conditional distributions over them. This is most evident in the usually large gap between single-head and multi-head performance of continual learning models. We propose in this paper two strategies to improve performance of continual learning models, particularly in the single-head framework and for image classification tasks. First, we argue that learning multiple binary classifiers, rather than a single multi-class classifier, for each presentation of data is more consistent with the single-head framework. Moreover, we argue that auxiliary, unlabelled data can be used in tandem with this approach to slow the decay in performance of these binary classifiers over time.

This paper proposes an online intelligent demand coordination of plug-in electric vehicles (PEVs) in distribution systems. The proposed method is based on the assignment of scores to PEVs through a fuzzy expert system. As well, without violation of grid operational constraints, the PEVs are optimally charged in order to maximize the owners' satisfaction in terms of the energy delivered. The optimization problem of online PEV charging is defined as mixed-integer nonlinear programming. Simulation on a typical distribution network proves the effectiveness of the proposed methodology. Results of the analysis indicate that for more critical PEVs, which have shorter parking duration and higher required charging time, the proposed solution outperforms in more robust energy delivery to the PEV and, accordingly, more satisfaction for the owner.

Driver inattentiveness constitutes the main cause of road accidents, which makes it a major factor in road safety. In this paper, we propose a comprehensive framework to address the road safety problem by tackling it from a high-level information fusion standpoint, considering vehicular ad hoc networks (VANETs) as the deployment platform. The proposed framework relies on the multientity Bayesian networks (MEBNs), which exploit the expressiveness of first-order logic for semantic relations, and the strength of the Bayesian networks in handling uncertainty. First, the entities that influence the inattention phenomenon, as well as both their causal and semantic relationships, are identified. Next, an MEBN-based high-level information fusion framework is proposed through which entities, situations, and their relationships in specific contexts are modeled using MEBN fragments. Furthermore, MEBN inference is used to assess the situations of interest by estimating their states. To demonstrate the capabilities of the proposed framework, a collision warning system simulator has been developed, which evaluates the likelihood of a vehicle being in a near-collision situation using a wide variety of local and global information sources available in various VANET environments. If the threat of being in a near-collision situation is determined to be high, then the driver is warned accordingly. Our experimental results for two distinct single-vehicle and multivehicle categories of driving scenarios, as well as a novel hybrid MEBN inference, demonstrate the capability of the proposed framework to efficiently achieve situation and threat assessment on the road.

We have conducted a literature survey on situation awareness in connected cars.Situation awareness process is categorized into four main groups.Various taxonomies are proposed for different situation awareness technical tools.The capabilities of different situation awareness tools are compared.Major situation awareness frameworks in connected cars are introduced. Driving safety is among the most important factors in the design of next generation vehicles as an integral component of Intelligent Transportation Systems. Crash avoidance and reduction of potential subsequent fatalities require timely delivery of sensitive and pertinent safety information for the drivers. Hence, the driver can become aware of the current driving situation, and can consequently, take appropriate decisions to avoid potentially imminent hazards. In this paper, we propose a comprehensive survey on situation awareness within the context of connected vehicles and Internet of Cars (also called here as connected cars). We provide context for the Internet of Cars and highlight its major features. Furthermore, situation awareness in the Internet of Cars is explored through presenting an in-depth discussion on its different components. Various aspects of high and low level information fusions are described within this context. Besides, major methods/models in situation awareness are linked to the main aspects of each component, and an overall comparison between them is reported. Moreover, on-the-road safety frameworks incorporating situation awareness are highlighted. Finally, the challenging issues and the emerging trends that shall be faced by the research community are addressed.

This work addresses non-stationary traffic flow prediction by implementing an intelligent update scheme to deep neural networks. The intelligent update scheme works by monitoring the frequency domain features extracted from the traffic flow time series. The features at present are compared with the previous ones through a distance function. The resulting similarity is then fed to the exponentially weighted moving average to detect whether a notable change in the traffic flow is present or not. It has been shown in the experiments that the proposed method is able to handle the non-stationarity and produce acceptable traffic flow prediction. Moreover, the proposed method performance is comparable to the fully stochastic gradient descent update scheme while saving computational and time resources up to around 13%.

Distracted driving is the main cause for car accidents. Driver inattention monitoring systems are promising solutions to mitigate this problem. In this paper, we propose a novel driver inattention monitoring system utilizing deep learning and fuzzy logic theory. A driver head pose estimation module is able to determine whether the driver is focusing on his frontal view, while a deep-learning-based distraction recognition module would detect whether the driver is performing a distraction activity. A danger level inference module based on fuzzy logic combines information from the head pose estimation and the distraction recognition modules to infer the danger level in a real-time manner. In the experimental work, a Convolutional Neural Network model is trained on data of high diversity allowing a more robust driver distraction detection compared to the model trained with only data collected by simulation experiments. In addition, we show that the proposed danger level inference strategy is an effective solution to detect dangerous driving situations by providing timely alerts depending on the vehicle speed.

Multi-step time series prediction is known to suffer from increasing performance degradation the farther in the future the predictions are made. In this paper, we introduce two approaches to address this weakness in recursive and multioutput prediction models. In particular, we present a model that allows recursive prediction approaches to take into account the time-step index when making predictions. In addition, we propose a conditional generative adversarial network-based data augmentation model to improve prediction performance in multioutput models. We show on real-world time series datasets that the two methods improve on multi-step time series prediction in recursive and multi-output models, respectively.

We propose IR-CAS ACN, a fully Automated Crash Notification safety application that enhances accuracy and efficiency with its precise notifications and increased decentralization. It can be considered as an improvement to the BMW Advanced ACN (AACN): It decentralizes the severity calculation by introducing in-vehicle severity estimation. It fully automates the solution and disseminates more informative messages with partial rather than graded relevance that is insensitive to differences in severity within grades. Different IR models are compared using binary and partial effectiveness measures; estimating severity by calculating the Manhattan distance between the crash and severest crash context vectors outperforms tried models.

Intelligent transportation systems (ITS) involve various emerging technologies and applications. This paper presents a comprehensive review of recent advances on data/information fusion and context-awareness referring to ITS. Data/Information fusion is necessary to fuse the data from different sensors and thereby extract relevant information on the target sources. On the other hand, context-aware information processing provides awareness of the driving environments by deploying intelligent query processing and smart information dissemination. The fusion and context-awareness should help in improving ITS operations with better road-awareness service, traffic monitoring, vehicle detection as well as development of new methods. This paper is centered on data fusion and context aware methodologies developed recently in the areas of ITS rather than on their ITS applications. We found that the recent progresses in ITS fusion are devoted to the potential cooperative approaches providing real-time/dynamic vehicle sensing technologies, whereas the recent context awareness techniques are deploying service concepts (e.g. location aware service) and frameworks. It is believed that the newly developed advanced fusion/context-aware techniques are becoming more effective to tackle complex traffic scenarios (e.g. traffic intersection) as well as complex urban environments.

A prospective approach to addressing carcinogen risk assessment is presented. Fuzzy reasoning is used to assess carcinogenic risk, characterize it, and control it. The approach is inspired by fuzzy control inference that deploys linguistic intelligence as input to a system described numerically through membership functions. Fuzzy-based reasoning to estimate carcinogenic risk provides several advantages as discussed here. The fuzzy reasoning approach has more capabilities than traditional models in dealing with risk agents that are probably carcinogens, possibly carcinogens, not classifiable as carcinogens, and probably not carcinogens. Input–output surfaces are presented for each hazard group to enable fast inferencing. Then, a hypothetical example is given to compare the results of traditional methods and the fuzzy-based approach to estimating the risk of a carcinogen to a human population. Results show similarity in risk characterization with less input information to the fuzzy-based approach. F...

Fuzzy logic systems have been implemented successfully for the design of a wide variety of control systems. They provide a powerful way for designing nonlinear controllers using human expert knowledge. In this article, we present an approach to design and implement a fuzzy logic proportional integral controller (Fuzzy-PI) for omnidirectional robot navigation system, using a field-programmable gate array (FPGA). First, we define the kinematic model of the robot system and then we design, simulate, and optimize the controller navigation system using MATLAB and Robotino Sim platforms. The main goal of this work is the design of the Fuzzy-PI controller and the hardware implementation using FPGA resources. The controller can be implemented on an FPGA using software or hardware approach. For the latter approach, the Fuzzy-PI algorithm is implemented in VHDL language, synthesized, optimized, placed and routed, and downloaded on an FPGA board.

This work addresses short-term traffic flow prediction by proposing a big-data-based framework. The proposed framework uses data fusion to deal with heterogeneous data generated from various sources. The data are categorized into two types: streams of data and event-based data. In this work, Deep Belief Networks (DBNs) are used to independently predict traffic flow using streams of data, i.e., historical traffic flow and weather data, and event-based data, i.e., tweets. Furthermore, Dempster's conditional rule for updating belief is used to fuse evidence coming from streams of data and event-based data modules to achieve enhanced prediction. The experimental results using real-world data show the merit of the proposed framework compared to the state-of-the-art ones.

Situation prediction is a crucial part of active Advanced Driver Assistance Systems (ADAS) to prevent rear, lateral and other collisions. Majority of road crashes can be prevented if the ADAS issue a warning about a potential threat at least one-half second prior to the prominent accident. To take the suitable maneuver, active safety systems should assess succinctly the danger caused by other errant drivers and analyse surrounding drivers intent. This study presents a game theory impact assessment and decision making model that allows drivers to assess threat level caused during different road scenarios. Our model is based on Fuzzy Multi-Entity Bayesian Network (Fuzzy-MEBN) enriched by a game theory component. Illustrative scenarios are provided to show the merit of our model.

Clustering has raised as an important problem in many different domains like biology, computer vision, text analysis and robotics. Thus, many different clustering techniques were developed to address this essential problem and propose astonishing solutions to conquer it. However, traditional clustering techniques suffer either from their limitations to detect specific shapes like K-means and PAM or from their limitations to detect clusters with specific densities as in DBSCAN and SNN. Moreover, exploiting the data relations and similarities has been proven to provide better insights to enhance the clustering quality as shown in spectral clustering and affinity propagation. Our observations have shown that using variance of similarities between each data point and its neighbors can well distinguish between within-cluster points, points connecting two clusters and outlier points. Therefore, we have utilized this variance measure to calculate each data point density and developed a Local Variance-based Clustering (LVC) technique that employs this measure to cluster the data. Experimental results show that LVC outperforms spectral clustering and affinity propagation in clustering quality using control charts, ecoli and images datasets, while maintaining a good running time. In addition, results show that LVC can detect topics from Twitter with higher topic recall by 15% and higher term precision by 3% over DBSCAN.

For VANET safety services a context aware system for the Automatic Crash Notification (ACN) is developed while the context aware Congested Road Notification system (CRN) is developed for the convenience services. A simple fuzzy logic model is proposed and compared to different severity estimation models deployed for both systems. The performance of the ACN models is compared using a test collection that is based on nineteen years of real life crash records associated with their severity levels while the performance of the CRN models is tested using nearly 500,000 different urban and rural freeways flow situations associated with their congestion severity levels. The non-binary Spearman correlation coefficient and the Average Distance Measure (ADM) are used to evaluate the performance of the tested models. Results show that the simple fuzzy severity estimation model has a comparable performance to more complicated systems such as the CoTEC (CoOperative Traffic congestion detECtion) fuzzy system and the URGENCY algorithm, and outperforms the binary severity estimation models for the ACN and CRN systems.

In real applications, data continuously evolve over time and change from one setting to another. This inspires the development of adaptive learning algorithms to deal with this data dynamics. Adaptation mechanisms for unsupervised learning have received an increasing amount of attention from researchers. This research activity has produced a lot of results in tackling some of the challenging problems of the adaptation process that are still open. This paper is a brief review of adaptation mechanisms in unsupervised learning focusing on approaches recently reported in the literature for adaptive clustering and novelty detection and discussing some future directions. Although these approaches have able to cope with different levels of data non-stationarity, there is a crucial need to extend these approaches to be able to handle large amount of data in distributed resource-limited environments.

In this paper, a novel semi-supervised dictionary learning and sparse representation (SS-DLSR) is proposed. The proposed method benefits from the supervisory information by learning the dictionary in a space where the dependency between the data and class labels is maximized. This maximization is performed using Hilbert-Schmidt independence criterion (HSIC). On the other hand, the global distribution of the underlying manifolds were learned from the unlabeled data by minimizing the distances between the unlabeled data and the corresponding nearest labeled data in the space of the dictionary learned. The proposed SS-DLSR algorithm has closed-form solutions for both the dictionary and sparse coefficients, and therefore does not have to learn the two iteratively and alternately as is common in the literature of the DLSR. This makes the solution for the proposed algorithm very fast. The experiments confirm the improvement in classification performance on benchmark datasets by including the information from both labeled and unlabeled data, particularly when there are many unlabeled data.

The optic disc (OD) is an important anatomical feature in retinal images, and its detection is vital for developing automated screening programs. Currently, there is no algorithm designed to automatically detect the OD in fundus images captured from Asians which are larger and have thicker vessels compared to Caucasians. In this paper, we propose such a method to complement current algorithms using two steps: OD vessel candidate detection and OD vessel candidate matching. The first step is achieved with multiscale Gaussian filtering, scale production, and double thresholding to initially extract the vessels' directional map of various thicknesses. The map is then thinned before another threshold is applied to remove pixels with low intensities. This result forms the OD vessel candidates. In the second step, a Vessels' Directional Matched Filter (VDMF) of various dimensions is applied to the candidates to be matched, and the pixel with the smallest difference designated the OD center. We tested the proposed method on a new database consisting of 402 images from a diabetic retinopathy (DR) screening programme consisting of Asians. The OD center was successfully detected with an accuracy of 99.25% (399/402).

Recently, increasing attention has been directed to the study of the emotional content of speech signals, and hence, many systems have been proposed to identify the emotional content of a spoken utterance. This paper is a survey of speech emotion classification addressing three important aspects of the design of a speech emotion recognition system. The first one is the choice of suitable features for speech representation. The second issue is the design of an appropriate classification scheme and the third issue is the proper preparation of an emotional speech database for evaluating system performance. Conclusions about the performance and limitations of current speech emotion recognition systems are discussed in the last section of this survey. This section also suggests possible ways of improving speech emotion recognition systems.

Most of the common techniques in text mining are based on the statistical analysis of a term, either word or phrase. Statistical analysis of a term frequency captures the importance of the term within a document only. However, two terms can have the same frequency in their documents, but one term contributes more to the meaning of its sentences than the other term. Thus, the underlying text mining model should indicate terms that capture the semantics of text. In this case, the mining model can capture terms that present the concepts of the sentence, which leads to discovery of the topic of the document. A new concept-based mining model that analyzes terms on the sentence, document, and corpus levels is introduced. The concept-based mining model can effectively discriminate between nonimportant terms with respect to sentence semantics and terms which hold the concepts that represent the sentence meaning. The proposed mining model consists of sentence-based concept analysis, document-based concept analysis, corpus-based concept-analysis, and concept-based similarity measure. The term which contributes to the sentence semantics is analyzed on the sentence, document, and corpus levels rather than the traditional analysis of the document only. The proposed model can efficiently find significant matching concepts between documents, according to the semantics of their sentences. The similarity between documents is calculated based on a new concept-based similarity measure. The proposed similarity measure takes full advantage of using the concept analysis measures on the sentence, document, and corpus levels in calculating the similarity between documents. Large sets of experiments using the proposed concept-based mining model on different data sets in text clustering are conducted. The experiments demonstrate extensive comparison between the concept-based analysis and the traditional analysis. Experimental results demonstrate the substantial enhancement of the clustering quality using the sentence-based, document-based, corpus-based, and combined approach concept analysis.

Most of text categorization techniques are based on word and/or phrase analysis of the text. Statistical analysis of a term frequency captures the importance of the term within a document only. However, two terms can have the same frequency in there documents, but one term contributes more to the meaning of its sentences than the other term. Thus, the underlying model should identify terms that capture the semantics of text. In this case, the model can capture terms that present the concepts of the sentence, which leads to discovering the topic of the document. A new concept-based model that analyzes terms on the sentence, document, and corpus levels rather than the traditional analysis of document only is introduced. The concept-based model can effectively discriminate between nonimportant terms with respect to sentence semantics and terms which hold the concepts that represent the sentence meaning. A set of experiments using the proposed concept-based model on different datasets in text categorization is conducted in comparison with the traditional models. The results demonstrate the substantial enhancement of the categorization quality using the sentence-based, document-based and corpus-based concept analysis.

Data clustering is a process of extracting similar groups of the underlying data whose labels are hidden. This paper describes different approaches for solving data clustering problem. Particle swarm optimization (PSO) has been recently used to address clustering task. An overview of PSO-based clustering approaches is presented in this paper. These approaches mimic the behavior of biological swarms seeking food located in different places. Best locations for finding food are in dense areas and in regions far enough from others. PSO-based clustering approaches are evaluated using different data sets. Experimental results indicate that these approaches outperform K-means, K-harmonic means, and fuzzy c-means clustering algorithms.

We consider here the design aspect of a real-time scheduler for a class of embedded systems. For this purpose, we design a feedback controller for a reservation-based CPU scheduler for soft real-time systems. The execution time of soft real-time systems, such as multimedia systems, portable MP3 players, personal digital assistants, cellular phones, and embedded Web servers is highly variable. Hence, it is crucial to assign an adequate amount of CPU resources for the running tasks to guarantee the quality of service. On the other hand, it is also important not to allocate the large amount of resources to avoid waste. The purpose of this paper is to attain the aforementioned crucial objectives for a class of embedded systems under real-time computing constraints. Specifically, we provide an analytical model for a real-time scheduler in terms of a switched system with time-varying uncertainty. Moreover, by using Lyapunov stability in a linear matrix inequalities (LMIs) framework, we design a state feedback controller that stabilizes the switched system. This, in fact, achieves the regulation of scheduling errors caused by time-varying uncertainty to a desired level. We extend an LMI-framework-based control scheme to a relatively new control application domain, i.e., a soft realtime scheduling domain. We provide performance analysis under scheduler simulation environments and implement a feedback bandwidth server scheduler under a real-time kernel simulator. In the simulation studies, the advantages of the controller design scheme are clearly highlighted in comparison with some conventional existing open-loop systems.

Novel network technology combined with advances in hardware development have permitted the enabling of distributed real-time systems and have shortened the time-to-market period. Distributed frameworks, also known as middleware, are often used to integrate enterprise systems, shorten the development time, and reduce complexity. However, to deploy standard middleware in robotics and control applications, we have to deal with the challenge of producing predictable outputs. Most real-time applications in these areas are developed in ad hoc manner, and as such, it is hard to migrate them to new platforms. To overcome this issue while minimizing development effort and increasing reusability for distributed real-time systems, we propose a control framework for distributed real-time systems based on standard middleware specifications. The control framework is composed of asynchronously running task modules, which can be located on either the local or the remote machines. The task modules are connected by an event channel, which uses the publish/subscribe communication method. We also have developed an adaptive event channel in order to meet real-time system requirements and to produce predictable outputs. Detailed development of the control framework along with the adaptive event channel are assessed through a set of experimental results.

This paper presents driver simulation results for multi-sensory platform aimed at providing data that are used for driver states classification. This work explores PCA and S-PCA, for dimensionality reduction, and Random Forest, for classification. Finally, PSO-based multi-view classification is used for final fusion of the individual classifiers. The results suggest S-PCA, Random Forest and PSO-based multi-view classification as the best combination reaching an accuracy of 91.46% for the limited available input data.

In this paper, a soft+hard data fusion model is proposed that is capable of combining the data generated from human-based sources with those generated by physical sensors. The basis of this model is our previously introduced Fuzzy extension to the Mutli-Entity Bayesian Network (MEBN) language, which is a High-Level Information Fusion (HLIF) framework capable of expressing the semantic and causal relationships between the entities constituting a world model, as well as managing their ambiguity and uncertainty. In our proposed model, the unstructured soft data is presented by undergoing a novel soft-data-association process, through which the data is semantically analyzed, and accurately structured in a fuzzy random variable. Moreover, the clique tree inference algorithm for Bayesian Networks is modified to handle fuzzy evidence in Fuzzy-MEBN. The simulation results, in transportation domain, show that our improved HLIF model is capable of handling both soft and hard data, and consequently, provide the user with more precise situation assessment.

Dempster-Shafer evidence theory (DSET) is a popular paradigm for dealing with uncertainty and imprecision. Its corresponding evidential reasoning framework is theoretically attractive. However, there are outstanding issues that hinder its use in real-life applications. Two prominent issues in this regard are 1) the issue of basic probability assignments (masses) and 2) the issue of dependence among information sources. This paper attempts to deal with these issues by utilizing neural networks in the context of pattern classification application. First, a multilayer perceptron neural network with the mean squared error as a cost function is implemented to calculate, for each information source, posteriori probabilities for all classes. Second, an evidence structure construction scheme is developed for transferring the estimated posteriori probabilities to a set of masses along with the corresponding focal elements, from a Bayesian decision point of view. Third, a network realization of the Dempster-Shafer evidential reasoning is designed and analyzed, and it is further extended to a DSET-based neural network, referred to as DSETNN, to manipulate the evidence structures. In order to tackle the issue of dependence between sources, DSETNN is tuned for optimal performance through a supervised learning process. To demonstrate the effectiveness of the proposed approach, we apply it to three benchmark pattern classification problems. Experiments reveal that the DSETNN out-performs DSET and provide encouraging results in terms of classification accuracy and the speed of learning convergence.

Creating a distributed and remote operated robotic system is a very challenging and time-consuming task. This paper contains the development issues taking into account when multiple robotic components are integrated to create a distributed robotic application using the standard middleware, Common Object Request Broker Architecture (CORBA) specification. The main idea is to define a set of generic interfaces using the Interface Definition Language (IDL) that can be used with common components in order to facilitate the integration of new components or the modification of them. The generic IDL interfaces are based on wrapper functions, which provide an abstract encapsulated behaviors of the low level components. The approach is shown using two types of arm manipulators and two different pan-tilt model units. Because of the modularity and the abstraction of this approach, this development can be seen as the first stage in constructing more autonomous and complex system.

Soft computing based tools including Fuzzy Inference Systems (FIS), Artificial Neural Networks (ANN), and Genetic Algorithms (GA) are used here to tackle the optimization problem of large-scale reservoir operations. At first, a nonlinear programming optimization method develops the optimal release policy of the system. The policy is then simulated to provide the trajectory of optimal releases and storages of the reservoir for simulated stochastic inflows. These trajectories are then used as input-output data to train an Adaptive Neuro Fuzzy Inference System (ANFIS) to obtain updated fuzzy operating rules. A subtractive clustering algorithm is used to estimate the number of clusters and cluster centers in optimal data obtained from the optimization step to build an initial FIS. This initial FIS is then optimized using the ANFIS model. The ANFIS based fuzzy rules are simulated and compared with policies developed using a multiple regression analysis. In another test, a parameterized T-norm operator is appli...

Information Retrieval (IR) techniques are utilized in developing context aware systems for VANET safety and convenience services. For the safety services a context aware system for the Automatic Crash Notification called IR-CAS ACN is developed while the context aware Congested Road Notification system IR-CAS CRN is developed for the convenience services. Different IR models like the vector space, fuzzy logic and binary models are proposed for each of these systems. The performance of the proposed models for IR-CAS ACN is compared using test collections that are based on nineteen years of real life crash records associated with their severity levels while the performance of the IR-CAS CRN is tested using nearly 500,000 different urban and rural freeways flow situations associated with their congestion severity levels. The highway capacity manual (HCM) speed-flow curves along with the Greenshield model are utilized in generating these freeway flow cases and their levels of service. The average distance measure (ADM) is used to evaluate the tested IR models. Results show that using the vector space model for severity estimation by calculating the Manhattan distance between the crash/congestion current context vectors and the severest crash/congestion context vectors outperforms the fuzzy and binary severity estimation models.

In this paper, a model is proposed to define situations structures and situation evolution. This model is the fundamental basis of a Threat/Impact assessment system that is implemented using a Fuzzy extension of Multi-Entity Bayesian Network in Vehicular Ad-hoc Networks. The proposed model is built on top of our previously presented situation assessment system, and completes our novel High-Level Information Fusion framework for VANET. To show the capabilities of the proposed model, a Collision Warning System in VANET is implemented in OpenDS simulation environment coupled with a real driving simulator. Furthermore, different situation structures along with situation evolution towards temporal and lateral dimensions are discussed. Finally a threat assessment system using Fuzzy MEBN is constructed on top of the situations of interest to help in identifying the source of threat.

A real time method to manage charge scheduling of plug-in electric vehicles (PEVs) is proposed in this paper. The proposed method is based on assigning scores and prioritized PEVs using a fuzzy expert system. Further, the PEVs are optimally charged to maximize the owners' satisfaction, in terms of the delivered energy, without violating system operational constraints. The simulation on a typical distribution network is carried out, which proves the effectiveness of the proposed methodology.

This paper presents a novel comprehensive Fuzzy extension to Multi-Entity Bayesian Networks (MEBN) that is deemed a well-studied and theoretically rich language that expressively handles semantics analysis, and effectively model uncertainty management. However, MEBN lack the capability of modeling the inherent conceptual and structural ambiguity that is delivered with the knowledge gained through human language. In this paper, Fuzzy MEBN that is a new version of MEBN which is based on First-order Fuzzy Logic, and Fuzzy Bayesian Networks is introduced. Furthermore, its applicability is evaluated by implementing an application related to Vehicular Ad-hoc Networks area. The results demonstrate that Fuzzy MEBN is capable of dealing with ambiguous semantical and uncertain causal relationships between the knowledge entities very efficiently.

Game theory deals with decision-making processes involving two or more parties, also known as players, with partly or completely conflicting interests. Decision-makers in a conflict must often make their decisions under risk and under unclear or fuzzy information. In this paper, two distinct fuzzy approaches are employed to investigate an extensively studied 2/spl times/2 game model-the game of Chicken. The first approach uses a fuzzy multicriteria decision analysis method to obtain optimal strategies for the players. It incorporates subjective factors into the decision-makers' objectives and aggregates objectives using a weight vector. The second approach applies the theory of fuzzy moves (TFM) to the game of Chicken. The theory of moves (TOM) is designed to bring a dynamic dimension to the classical theory of games by allowing decision-makers to look ahead for one or several steps so that they can make a better decision. TOM is the crisp counterpart of TFM, the approach we implement here to deal with games that include fuzzy and uncertain information. The application of fuzzy approaches to the game of Chicken demonstrates their effectiveness in manipulating subjective, uncertain, and fuzzy information and provides valuable insights into the strategic aspects of Chicken.

Although the perceptron algorithm has been considered a simple supervised learning algorithm, it has the advantage of learning from the training data set one at a time. This makes it more suitable for online learning tasks and new families of kernelized perceptrons have been shown to be effective in handling streaming data. However, the amount of memory required for storing the online model which grows without any limits and the consequent excessive computation and time complexity makes this framework infeasible in real problems. A common solution to this restriction is to limit the allowed budget size and discard some of the examples in the memory when the budget size is exceeded. In this paper we present a framework for choosing a proper adaptive budget size based on underlying properties of data streams. The experimental results on several synthetic and real data sets show the efficiency of our proposed system compared to other algorithms.

The layout of temporary facilities in a construction site deals with the selection of their most efficient layout in order to operate efficiently and cost effectively. The layout design seeks the best arrangement of facilities within the available area. In the design process of the layout, many objectives must be considered to effectively utilize people resources, equipment, space, and energy. This study proposes a soft-computing-based approach to improve the layout process of facilities. The main objective is on obtaining the closeness relationship values between each pair of facilities in a construction site. To achieve this, an integrated approach, using fuzzy set theory and genetic algorithms, is used to investigate the layout of temporary facilities in relation with the planned building(s) in a construction site. An example application is presented to illustrate the proposed approach and the results are then discussed along with recommendations for further work. Depending on the importance of relationships among the various facilities in the construction site, this study is expected to provide engineers with an appropriate tool to compare and evaluate different layouts and select the most appropriate and efficient one.

Various regression models are used to predict the continuous emotional contents of social signals. The common trend to train those models is by minimizing a sense of prediction error or maximizing the likelihood of the training data. According to those optimization criteria, among two models, the one which results in a lower prediction error, or higher likelihood, should be favored. However, that might not be the case, since to compare the prediction quality of different models, the correlation coefficient of their prediction with the actual values is prevalently used. Hence, given the fact that a lower prediction error does not imply a higher correlation coefficient, we might need to reconsider the optimization criteria that we undertake in order to learn the regression coefficients, in order to synchronize it with the hypothesis testing criteria. Motivated by this reasoning, in this work we suggest to maximize a sense of correlation for learning regression coefficients. Two senses of correlation, namely Pearson's correlation coefficient and Hilbert-Schmidt independence criterion, are seen for this purpose. We have chosen the continuous audio/visual emotion challenge 2012 as the framework of our experiments. The numerical results of this study show that compared to support vector regression, the suggested learning algorithms offer higher correlation coefficient and lower prediction error.