A what-and-where fusion neural network for recognition and tracking of multiple radar emitters A neural network recognition and tracking system is proposed for classification of radar pulses in autonomous Electronic Support Measure systems. Radar type information is combined with position-specific information from active emitters in a scene. Type-specific parameters of the input pulse stream are fed to a neural network classifier trained on samples of data collected in the field. Meanwhile, a clustering algorithm is used to separate pulses from different emitters according to position-specific parameters of the input pulse stream. Classifier responses corresponding to different emitters are separated into tracks, or trajectories, one per active emitter, allowing for more accurate identification of radar types based on multiple views of emitter data along each emitter trajectory. Such a What-and-Where fusion strategy is motivated by a similar subdivision of labor in the brain.The fuzzy ARTMAP neural network is used to classify streams of pulses according to radar type using their functional parameters. Simulation results obtained with a radar pulse data set indicate that fuzzy ARTMAP compares favorably to several other approaches when performance is measured in terms of accuracy and computational complexity. Incorporation into fuzzy ARTMAP of negative match tracking (from ARTMAP-IC) facilitated convergence during training with this data set. Other modifications improved classification of data that include missing input pattern components and missing training classes. Fuzzy ARTMAP was combined with a bank of Kalman filters to group pulses transmitted from different emitters based on their position-specific parameters, and with a module to accumulate evidence from fuzzy ARTMAP responses corresponding to the track defined for each emitter. Simulation results demonstrate that the system provides a high level of performance on complex, incomplete and overlapping radar data. Radar Electronic Support Measures (ESM) involve the search for, interception, location, analysis and identification of radiated electromagnetic energy for military purposes. ESM hereby provide valuable information for real-time situation awareness, threat detection, threat avoidance, and for timely deployment of counter-measures (Browns, 1998; Davies and Hollands, 1982; Grant and Collins, 1982; Schleher, 1986; Schleher, 1999; Sciortino, 1997; Tsui, 1986; Wiley, 1993) .. A critical function of radar ESM is the real-time identification of the radar type associated with each pulse train that is intercepted. Current approaches typically involve sorting incoming radar pulses into individual pulse trains, then comparing the pulse train characterizations with a library of parametric descriptions, which yields a list of likely radar types. This task is challenging owing to increases in environment density (e.g., pulse Doppler radars that transmit hundreds of thousands of pulses per second); dynamically changing environments; multiplication and dispersion of the modes for military radars; agility in parameters like pulse repetition interval, radio frequency and scan; unknown and reserve modes for which no ESM library entry exists; overlaps between the parameters of different radar types in the ESM library; and noise and propagation effects that lead to erroneous or incomplete signal characterization. These aspects of the problem place severe stress on current ESM systems.. It this paper, an alternative approach is examined. A new recognition system combines diverse sources of information in order to predict the most likely radar type for each intercepted pulse. Type-specific parameters of the input pulse stream are used to classify pulses according to radar type, while environment-specific parameters are used to separate pulses corresponding to active emitters. Such separation allows the system to accumulate the classifier's responses for each emitter, and therefore to predict an emitter's identity based on one or multiple responses.. A key component of the new recognition system is a neural network classifier that is trained to determine the types of radar emitters present in the environment. The system learns autonomously, directly from data collected in the field, to identify pulse parametric ranges corresponding to specific radar types. Aside from avoiding some of the pulse sorting, training on data from the actual environment to approximate an unknown mapping function may deliver greater predictive accuracy. Furthermore, the need for by-hand construction of an emitter library is obviated.. From an ESM standpoint, training a system directly on radar data is a radical departure from current practice. At present, data are collected, analyzed, combined with prior information, and distilled into ESM libraries off-line by skilled analysts. New libraries, containing explicit radar type descriptions, are disseminated to the field as needed. One inconvenience of the conventional approach is that it is very complex, time-consuming, and does not allow for rapid modifications of ESM libraries upon discovery of new radar modes in the field. Using a neural network able to learn incrementally offers a framework for refining familiar, or adding unfamiliar, radar type descriptions on the fly.. In a particular realization of the recognition system, fuzzy ARTMAP (Carpenter et al., 1991a (Carpenter et al., , 1992 is considered for neural network classification of pulses from their type-specific parameters, whereas nearest-neighbor matching with a bank of Kalman filters (Bar-Shalom and Li, 1993; Blackman, 1986 ) is considered for separation of pulses from their environmentspecific parameters. The features of the system include: (1) By virtue of the fast-learning capabilities of ARTMAP neural networks (Carpenter et al., 1991a (Carpenter et al., , 1991b (Carpenter et al., , 1992 , new information from familiar or unfamiliar radar type classes can be learned incrementally without retraining on the whole data set. (2) Classification decisions can be made on the basis of single pulses or, for greater accuracy, on the basis of streams of pulses that have been determined to come from a given emitter. This determination is performed either by a time-of-arrival (TOA) deinterleaver or, when TOA deinterleaving is not practical, by a Kalman filter that tracks the bearing and amplitude of the pulses. The system is thus an example of a neural system combining temporal -When -and positional -Where -information with featural -What -information to arrive at its decision. It is well-known that the mammalian brain also divides What and Where computations into separate, but mutually interacting, cortical processing streams. Our Whatand-Where model shows how this strategy can generate higher accuracy in identifying radar emitters. (3) The "familiarity discrimination" extension of fuzzy ARTMAP, called ARTMAP-FD (Carpenter et al., 1997a (Carpenter et al., , 1997b , allows the system not only to detect pulses from unfamiliar radar type classes (not presented during training), but to determine the threshold for rejection based on all of the training data, without the need for holding back a portion for a validation set. This ability to determine the reject threshold on-line makes possible on-line learning of pulses from unfamiliar classes (LUC) (Granger et al., 2000) . (4) New extensions to fuzzy ARTMAP permit both training and testing on data with missing components, and the use of unlabeled training data (Granger et al., 2000) .. Conventional approaches to, and challenges of, radar type identification in radar ESM systems are reviewed in the next section. A system-level overview of our novel neural network recognition system is provided in Section 3. A radar pulse data set used for proof-of-concept simulations is presented in Section 4. The three main components that form a specific implementation of the recognition system are described in Sections 5 through 7. In Section 5, the fuzzy ARTMAP neural network is applied to the classification of pulses according to radar type from functional, type-specific parameters. Then, aspects of this network for dealing with incomplete radar data are proposed and tested. In Section 6, a module for clustering incoming pulses by emitter based on environment-specific parameters is described. In Section 7, a module that accumulates evidence from fuzzy ARTMAP responses corresponding to the tracked emitters is proposed. Finally, these three components are connected, and global simulation results using this particular realization of the entire recognition system are presented and discussed. paper is given in Table 1 . The stream of successive PDWs is fed to a grouping module, which performs either TOA deinterleaving, or sorting, or both. In short, this module seeks to recover pulse trains and their inter-pulse structure prior to further analysis. This involves progressively grouping pulses that appear to have been transmitted from the same emitter. An emitter is an instance of a radar type, and it is not uncommon to observe several emitters of a same type all being active in a theater of operation. A single type of radar can also operate under several different modes to perform various functions. To each group of pulses is associated a track. A track consists of statistical PDW parameters, plus other parameters that are derived from the sequence of grouped PDWs, like the pulse repetition interval (PRI).. Pulse grouping techniques either exploit the difference in TOA between pulses, or the actual parameters in the PDWs. Parametric ranges are associated with tracks, and updated to reflect changes in the emitter's characteristics over time. TOA deinterleaving attempts to discover consistent patterns in the TOA of pulses using techniques such as TOA difference histogramming (Davies and Hollands, 1982; Mardia, 1989; Wiley, 1993) . If TOA consistencies are found, and these correlate with radar definitions compiled in an ESM library, then the corresponding pulses are grouped based on PRI, and stripped away from the input stream of PDWs. Sorting attempts to group pulses based on the likeliness of their PDW parameters such as RF, PW and Brg. Gating (Chandra et al., 1988; Davies and Hollands, 1982; Rogers, 1985) or clustering (Anderberg, 1973; Dubes and Jain, 1988; Wilkinson and Watson, 1985) techniques are commonly used to this end.. Identification makes use of an ESM library where are stored the parametric descriptions of known radar types, and attempts to assign a single radar type to each track. Incidentally, the parametric ranges of various types can overlap in the library, and multiple candidates can appear plausible for the same track, a situation known as an "ambiguity." Therefore, a list of likely radar types is often displayed and monitored over time for every track, along with a confidence rating, threat level, latest bearings, and so on. Further analysis can assist an ESM operator in revealing mode changes in emitters, links between emitters, and inferred platforms. A novel What-and-Where architecture has been proposed for recognition and tracking of radar emitters for Electronic Support Measures (ESM). This architecture combines a neural network classifier, an on-line clustering algorithm, and an evidence accumulation module. Once trained on samples of data gathered in the field of operation, the neural network classifier can predict the radar type of intercepted pulses based on their What parameters. Meanwhile, the clustering algorithm separates these pulses according to emitter based on their Where parameters. The evidence accumulation module permits fusion of the classifier's What responses with the clustering algorithm's Where estimates, and thus allows prediction of the radar type from classifications along an entire emitter trajectory. For proof-of-concept computer simulations using a radar data set, a particular realization of the recognition system has been considered. It consists in using a variant of fuzzy ARTMAP for classification, and nearest-neighbor matching with a bank of Kalman filters for on-line clustering.. Simulations results show that fuzzy ARTMAP with negative match tracking (MT-), a core concept of the ARTMAP-IC algorithm (Carpenter and Markuzon, 1998 ), consistently delivers a high level of accuracy and compression on the radar pulse data set, even when the amount of training data is limited. Compared to several other ARTMAP variants, as well as the reference RBF and kNN classifiers, it yields one of the best classification rates, yet requires among the least resources (shortest convergence time and least storage for prototypes) and computational complexity for on-line predictions. The MT-feature allows fuzzy ARTMAP to converge naturally, by circumventing a node proliferation problem that can arise when identical or nearlyidentical input patterns in the training data correspond to different classes. Modifications of fuzzy ARTMAP with MT-have also been introduced for dealing with missing components of the input pattern, and missing classes during training. The indicator vector strategy (IVS) provides an effective means of processing partial input patterns, whenever components of the data set are missing during training or testing. Familiarity discrimination (FD) allows fuzzy ARTMAP to detect patterns belonging to unfamiliar classes during training and testing, and enables learning of unfamiliar classes (LUC) to take place during testing.. Computer simulations that test the entire What-and-Where system improve accuracy significantly over those obtained with fuzzy ARTMAP with MT-and IVS, and with kNN, while also detecting and learning patterns from unfamiliar classes. These results support the general approach of integrating What and Where information via evidence accumulation, and offer promise for application in autonomous ESM systems which may be subjected to complex, incomplete, and overlapping radar data.. A Appendix A A.1 ARTMAP Neural Network Classifiers. ART-EMAP (Stage 1) and ARTMAP-IC extend fuzzy ARTMAP to produce a distributed activation of coded F 2 nodes during testing. Furthermore, ARTMAP-IC biases distributed test set predictions according to the number of times F 2 nodes are assigned to training set patterns. It also uses negative match tracking (i.e., negative ε values), to address the problem of inconsistent cases, whereby identical training set patterns correspond to different classes labels. After an incorrect prediction during training, the vigilance parameter is raised just enough to induce a search for another internal category node J, and then lowered by a small amount ε 0.. Gaussian ARTMAP represents each category j as a separable Gaussian density function, defined by its mean µ j μ j1 µ j2 µ jM µ and its standard deviation σ j σ j1 σ j2 σ jM µ vectors. During training, the number of committed F 2 nodes, N c grows. All committed F 2 nodes that pass the vigilance test for pattern a activate, and distribute a pattern of activity y ý 1 y 2 y N c µ. Match tracking and learning are performed according to the relative activation over the "ensemble" E K of F 2 nodes linked to the predicted F ab node K. The relative acti- Table 5 highlights the main algorithmic differences between fuzzy ARTMAP and Gaussian ARTMAP.

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Traffic jam driving with NMV avoidance In recent years, the development of advanced driver assistance systems (ADAS) -mainly based on lidar and cameras -has considerably improved the safety of driving in urban environments. These systems provide warning signals for the driver in the case that any unexpected traffic circumstance is detected. The next step is to develop systems capable not only of warning the driver but also of taking over control of the car to avoid a potential collision. In the present communication, a system capable of autonomously avoiding collisions in traffic jam situations is presented. First, a perception system was developed for urban situations-in which not only vehicles have to be considered, but also pedestrians and other non-motor-vehicles (NMV). It comprises a differential global positioning system (DGPS) and wireless communication for vehicle detection, and an ultrasound sensor for NMV detection. Then, the vehicle's actuatorsbrake and throttle pedals -were modified to permit autonomous control. Finally, a fuzzy logic controller was implemented capable of analyzing the information provided by the perception system and of sending control commands to the vehicle's actuators so as to avoid accidents. The feasibility of the integrated system was tested by mounting it in a commercial vehicle, with the results being encouraging. Safety plays a key role in the automotive sector [1 ] . For this reason, a considerable number of driver assistance systems have been installed in commercial cars in this last decade. The aim of these systems is to control the car's actuators through sensor systems to prevent or mitigate collisions. For example, radar [2] , vision-based [3] , and differential global positioning system (DGPS) [4] technologies have been used to detect any leading vehicle so as to perform adaptive cruise control (ACC)-a now well-proven technique consisting of speed control to maintain a pre-defined time gap with a leading vehicle. Likewise; these technologies have been used for pre-crash systems development. These systems are able to detect imminent collisions, warning the driver and preparing both vehicle and passengers for crash [5] . These commercial systems are extensively used in motorway driving, and are designed for speeds greater than 30 km/h. The next step is to obtain systems capable of working at lower speeds. Indeed, 90% of urban crashes occur at speeds less than 25 km/h due to the driver's attention being distracted [6] . At these low speeds, most road accident victims are not the motor vehicle's occupants, but pedestrians, motorcyclists, cyclists, and non-motor-vehicle (NMV) occupants [7] . The main The third and last stage is electro-mechanical, responsible for converting the output generated by the fuzzy controllers into voltages that can then be sent to the actuators-the throttle and brake pedals. The actuator automation will be described in Section 6.. In the actual implementation, an on-board control unit (OCU) is located in the car's boot. It is in charge of collecting the information from the sensor systems. Specifically, an RS-232 connection is used for both the DGPS and the IMU positioning systems. A peripheral component interconnect (PCI) card is used to connect the PCMCIA Proxim Wireless ComboCard so as to acquire the information coming from other cars. With respect to the CAN bus, another PCMCIA CAN card is installed in the OCU. Finally, the information coming from the ultrasound sensor via the PCMCIA is received on a laptop computer which is responsible for processing the data received and sending the calculated distance to the OCU via an Ethernet connection. In this way, the integration of additional sensorial systems -cameras or lidars -can be easily carried out using Ethernet connections to send the relevant information to the OCU. Fig. lisa schematic diagram of the complete system.

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Enabling Broadcast of User-Generated Live Video without Servers We are witnessing the unprecedented popularity of User-GeneratedContent (UGC) on the Internet. While YouTube hosts pre-recorded video clips, in near future, we expect to see the emergence of UserGenerated Live Video, for which any user can create its own temporary live video channel from a webcam or a hand-held wireless device. Hosting a large number of UG live channels on commercial servers can be very expensive. Server-based solutions also involve various economic, copyright and content control issues between users and the companies hosting their content. In this paper, leveraging on the recent successes of P2P video streaming, we study the strategies for end users to directly broadcast their own live channels to a large number of audiences without resorting to any server support. The key challenge is that end users are normally bandwidth constrained and can barely send out one complete video stream to the rest of the world. Existing P2P streaming solutions cannot maintain a high level of user Quality-of-Experience (QoE) with such a highly constrained video source. We propose two strategies to address this challenge. We first propose a proactive-push based source-side scheduling algorithm to increase the scale of P2P broadcasts that can be driven by end users. We then propose a novel layered P2P streaming architecture that introduces peer playback delay differentiations to further boost end users' capability of driving large-scale video streaming. Through detailed packet-level simulations and PlanetLab experiments, we show that the proposed strategies enable a source with upload bandwidth slightly higher than the video streaming rate to stream video to tens of thousands of peers with premium quality of experience. User-Generated-Content (UGC) has become tremendously popular on the Internet in recently years. The global connectivity provided by the Internet makes it extremely easy for users to share a wide variety of UGC, including blogs, photos and video clips. YouTube [1] , the popular UGC video streaming site, serves 100 million distinct videos and attracts 65, 000 uploads daily. While Youtube offers pre-recorded video, in the upcoming years, we expect to see the emergence of User-Generated Live Video, for which Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Copyright 200X ACM X-XXXXX-XX-X/XX/XX ...$5.00.. any user can create its own temporary live video channel from a webcam or a hand-held wireless device. The live channel could be a professor's lecture, a little-league baseball game, a wedding, an artistic performance, or a political demonstration. Unlike prerecorded video, live video streaming has to meet the stringent video playback deadlines. The dissemination of user-generated live video to a large number of audience is a challenging problem, and is the focus of this paper.. One may naturally resort to a server-based solution and build a live version of YouTube. Server farms and CDN networks can be employed to host UG live channels and stream videos directly to viewers. However, the server and network infrastructure cost grows proportionally to the number of viewers. YouTube is estimated to pay over 1 million dollars per month to content distribution network (CDN) for distributing videos [2] . Similarly, it will be very expensive to host a large number of UG live channels. Peer-to-Peer (P2P) technology has recently been adopted to offload servers by efficiently utilizing the upload bandwidth of end users. Dozens of commercial P2P video streaming systems, such as PPLive [3] and PPStream [4] , have been deployed on the Internet to deliver live and on-demand video services. Although existing P2P solutions can offload server efficiently, a reasonably large server bandwidth is still assumed to achieve good streaming performance, such as low startup delay and chunk loss ratio, in large scale P2P streaming. Current commercial P2P IPTV systems still invest considerably on servers that track peers, host video and bootstrap P2P video sharing. Technically, it is possible for those companies to provide a service to host UG live channels in the near future. However users and companies will have to reach agreement on various economical, legal, copyright and content control issues to make it happen.. In this paper, we investigate a "pure" P2P streaming solution that enables a user to broadcast his/her UG live video to a large number of audiences without any server support. In our solution, any user generating a live video can act as a video source and directly drive a P2P live broadcast to users interested in his/her video. Unlike a commercial video streaming server, the user generating the video only has an upload bandwidth slightly higher than the encoded video rate and can barely send out one complete video stream to the rest of the world. The challenge is how to drive a small, medium or even large scale P2P live streaming using such a constrained source. We propose two strategies to address this challenge. First we investigate the impact of source-side chunk scheduling on the system streaming performance. We show that, by simply switching the source-side chunk scheduling from passive to proactive mode, a source with upload bandwidth slightly higher than the streaming rate can drive a small or medium scale P2P live broadcast. In P2P streaming, a certain amount of video playback delay on peers is necessary to facilitate video sharing. The playback delays tolerated by peers in current commercial P2P IPTV systems are on the order of tens of seconds [5] . Leveraging on peer's playback tolerance, we propose a Layered P2P Streaming (LPS) architecture that introduces peer playback delay differentiations and constructs virtual servers out of peers to drive large-scale streaming. In LPS, a small fraction (e.g., 5%) of peers are assigned to an amplifier layer, and the rest of peers are assigned to a base layer. Peers at the amplifier layer have shorter target playback delays than peers at the base layer. The source only serves a small number of amplifier layer peers, which will then forward downloaded video to peers at the base layer. Effectively, there are multiple virtual "servers" from the amplifier layer that collaboratively deliver a high level of video Quality-of-Experience to a large number of peers at the base layer.. The contribution of this paper is three-fold:. 1. We study the impact of source-side chunk scheduling and propose a proactive push-based algorithm to increase of the scale of P2P broadcast that can be driven by a bandwidthconstrained source.. 2. We dissect various delay components in P2P streaming. By differentiating peer playback delays, we propose a layered P2P streaming architecture to further boost source's capacity in driving large-scale P2P streaming. We develop a robust mesh-based implementation for the proposed LPS architecture. A set of peer assignment and management algorithms are also developed. To the best of our knowledge, our work is the first one to explicitly differentiate peer playback delays to improve the performance of P2P streaming.. 3. To evaluate the performance the proposed solutions, we build a detailed packet-level P2P simulator and conduct extensive simulations driven by traces from real systems. Our simulation results demonstrate that the proposed strategies enable bandwidth-constrained source to stream video to a large number of peers with excellent video quality and low startup delay. We also develop a prototype and conduct experiments on PlanetLab to examine the feasibility and performance of LPS in real Internet environment.. The remainder of this paper is organized as follows. We briefly discuss the related work in Section 2. The strategy of source-side chunk scheduling is discussed in Section 3. The LPS architecture and detailed system implementation is presented in Section 4. The simulation setting and numerical results are presented in Section 5. The paper is concluded in Section 6. In this paper, we studied the strategies for users to directly broadcast User-Generated live video to a large number of audiences without any server support. First we discussed the impact of source chunk scheduling on the system performance. We proposed a proactive source scheme to push rarest-first contents. Then we proposed a novel layered P2P streaming architecture (LPS) to amplify the source bandwidth by introducing peer playback delay differentiations deliberately. We developed a detailed mesh-pull based LPS implementation, which consists of tracker-based peer assignment, dynamic peer management and cross-layer push-pull data scheduling. Through extensive trace-driven simulations and experiments, we demonstrated that the proposed strategies enable a "weak" source with upload bandwidth slightly higher than the encoded video rate to drive a P2P streaming session with tens of thousands of peers. By sacrificing a little bit the playback delays for peers at the base layer, the proposed LPS architecture can achieve short start-up delays and low video chunk loss ratios for all peers at both the amplifier layer and the base layer. P2P technology has made great progress since its debut, and been widely applied to various content distribution applications, such as file sharing and video streaming.Traditionally, P2P live streaming systems can be broadly classified into two categories, namely treebased and mesh-based. In tree-based approach (such as ESM [6] ), peers form multiple multicast trees in application level and relay traffic as interior nodes. The multiple trees are optimized further in terms of peer bandwidth, peer load and latency in SplitStream [7] and Chunkyspread [8] . Compared with tree-based approach, the mesh-based ones have been widely adopted by current commercial systems due to their lightweight management requirement and robustness to peer churn. Peers form a overlay network and dynamically exchange data with their neighbors. Many mesh-based P2P streaming systems have been proposed, such as Chainsaw [9] , DONet/CoolStreaming [10] and PRIME [11] . Hybrid streaming solutions combine tree-push and mesh-pull schemes. The work in [12] tries to improve video chunk scheduling efficiency with sub-stream based push method. Peers establish the parent-child relationships and sub-streams are pushed from parents to their children. mTreebone [13] constructs a single tree with stable nodes rooted at the server and delivers video via a combination of pushing through the tree and pulling through the underlying auxiliary mesh. To assess the stability of nodes, it needs to know the lifetime of the channel beforehand ??. Frequent tree adaptation is also needed to optimize the tree performance. Besides the overlay topology construction and scheduling, other aspects of P2P streaming have also been investigated in a rich literature [14, 15, 16, 17, 18] .. However, the previous live streaming systems generally endeavor to optimize the performance under the assumption that the server has reasonably large bandwidth to drive the system. From the perspective of fluid theory, the authors in [19] have analyzed the supportable streaming rate based on server capacity and peer upload capabilities. The online server capacity provisioning algorithm proposed in Ration [20] , dynamically adjusts the server bandwidth distribution among concurrent channels according to forecasted demands. The strategies with very constrained server bandwidth have not been explored so far. To support UG live channels, our work focuses on the scheduling and system architecture design to enable source with constrained bandwidth to stream video to a large number of peers. The proposed LPS architecture is the first one to deliberately differentiate the playback delays of peers in the same channel. We show that peer playback delay differentiation can "amplify" source upload capacity and lead to largely improved QoE in large-scale video streaming.

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Predicting sentencing outcomes with centrality measures Despite their importance for stakeholders in the criminal justice system, few methods have been developed for determining which criminal behavior variables will produce accurate sentence predictions. Some approaches found in the literature resort to techniques based on indirect variables, but not on the social network behavior with exception of the work of Baker and Faulkner [ASR 58: 837-860, 1993]. Using information on the Caviar Network narcotics trafficking group as a real-world case, we attempt to explain sentencing outcomes employing the social network indicators. Specifically, we report the ability of centrality measures to predict a) the verdict (innocent or guilty) and b) the sentence length in years. We show that while the set of indicators described by Baker and Faulkner yields good predictions, introduction of the additional centrality measures generates better predictions. Some ideas for orienting future research on further improvements to sentencing outcome prediction are discussed. This study examines the prediction of criminal trial sentencing outcomes on the basis of social network measures. Though it has received relatively little attention in criminology, sentencing predictions are extremely important to various stakeholders in the criminal justice system [1, 2] . In general terms, law enforcement entities are responsible for three central tasks: a) monitoring, b) making arrests, and c) charging one or more persons [2] . In their pursuit of these activities, however, they normally do not have the necessary data and methods at their disposal to identify which individual and group characteristics influence the fate of those they lay charges against. Furthermore, attempts at prediction are complicated by the fact that judicial processes are not free of bias due to discrimination or errors stemming from the lack of standard sentencing guidelines [3] [4] [5] [6] [7] .. While studies aimed at specifying the factors influencing criminal conduct may be found in past research [8] , few focus on explaining sentencing outcome based on the networking features of offenders. Research has concentrated rather on explaining outcomes using sociodemographic and socioeconomic variables. For example, sentences have been shown to fluctuate in accordance with political environment indicators [9] , an individual's race and age [10, 11] , and an individual's criminal history and the presence of a police confession [12] .. Our concern is to access sentencing outcomes as a function of behavior and positioning in criminal networks in order to determine whether the judicial processes that define sentences capture and take this behavior into account. From a social network perspective, networks of nodes represent individuals (or actors) and the direct and indirect relationships between them [13] . As Sarnecki has noted: "One of the most important tasks of network analysis is to attempt to explain, at least in part, the behavior of the elements in a network by studying specific properties of the relations between these elements" [14] p. 5. This method is already established as a powerful tool in many fields such as marketing, political science, organizational behavior, epidemiology, sociology, and software development [15] [16] [17] . Other theoretical and empirical initiatives have extended its use to the analysis of the social behavior of criminal groups and organizations [2, [18] [19] [20] and terrorists operations [21, 22] .. A key aspect of the social network approach was pointed to by McGloin and Kirk: "[n]etwork analysis requires different data than most criminologists typically employ. It may be clear by now that the unit of analysis in network studies is not the node or individual, but the tie between entities (i.e., links among the nodes)" [23] , p. 212. More specifically, this approach allows us to identify a) the central individuals in a criminal network, b) its subgroups, c) interactions between the subgroups, d) the network's structure, e) the impact of removing an individual from the network, and f) the network's information flows [20, 23] . It also affords a broad array of measures and technologies from various generations that facilitate the study of organized criminal behavior [24] . In short, network analysis provides both a theoretical basis and practical methods criminologists can employ to study the variables that explain criminal behavior and the interpersonal relationships underlying it.. Most existing studies of criminal networks focus on characterizing the roles of individual members, especially those whose roles are central [1, 20, 23, 25] or who serve as brokers [18, [26] [27] [28] [29] [30] [31] . Yet despite the growing popularity among criminologists of these social network applications, their practical usefulness is still limited [32] . The state of the art suggests there is little information regarding which social network variables can predict who will or will not be arrested or convicted and how long a sentence will be. One of the few works that explores the explanatory ability of certain social network and sentencing outcome measures is Baker and Faulkner [1] . The authors use various centrality indicators defined in Freeman [33] to explore the capacity of social network metrics to explain verdicts and prison sentence length. Thus, they explore whether a predictive model of sentencing outcome can be constructed from measures of degree centrality, betweenness centrality, and closeness centrality together with graph density and centralization metrics. Their results show that degree centrality explains the likelihood of a guilty or innocent verdict but is less successful in predicting sentence length [1] . What is certain, however, is that these efforts have played a pioneering role in the search for explanatory relationships between sentencing outcome and social network indicators.. Our aim in this study is to pursue Baker and Faulkner's initial findings by assessing variations in criminal justice outcomes in one specific case study. Our analysis expands the repertoire of network measures by integrating a variety of centrality measures that were not assessed by Baker and Faulkner or other researchers since. These variables and the main data source for this case study will be presented in the subsequent section. This data and method description is followed by a series of analyses that demonstrate how positioning in a criminal network is key to understanding how an individual will be reacted to by criminal justice agents. This study has shown that social network centrality indicators have good predictive abilities that can strengthen the efforts of criminologists to explain criminal trial sentencing outcomes using data on the communication behavior of a criminal network. In what follows we offer a number of observations on our findings.. The first point to note is that using a single network analysis centrality measure leads to good verdict predictions (see Table 4 ). The results were in fact better than the ones generated by the binary regression model reported in [1] , although it must be kept in mind that the cases analyzed were different. Our model posits that the out-degree variable explains the finding of innocence or guilt in that it shows that nodes making calls to other nodes are more likely to be classified as guilty, while those found innocent are those who communicate with relatively fewer individuals. It is also true, however, that the model is better at classifying the innocent than the guilty. This result may be due to the fact that the values of the social network indicators are close to 0, which creates numerical stability problems (on numerical stability in matrices, (see [58, 59] ). Furthermore, the number of individuals convicted is low compared to the total number observed (14 out of 110, or 12.7%) and a value in the classification is missing, suggesting a separation problem [60] . We therefore conclude that the model predicts guilt or innocence well with a single social network indicator but classifies those found innocent better than those found guilty.. Second, the use of four social network centrality measures generates good predictions of the length of imposed sentences (see Table 5 ). Certain variables were found to increase or decrease the length of the sentence. Thus, individuals who mediate communication more (betweenness centrality) and those who communicate more with individuals having high centrality levels (eigenvector centrality) receive longer sentences. By contrast, high values for authority or hub centrality decrease sentence length while low values increase it. Individual N 1 , for example, had the highest hub centrality value whereas individual N 3 displayed the highest authority centrality value, and both were given relatively short sentences.. This last finding might seem to contradict our working hypothesis that higher values for hub and authority centrality should increase sentence length. But it must be kept in mind that in order to be a good hub or authority an individual's node must point to good hubs or good authorities. We suspect that the judicial process imposed light sentences to some nodes that had high hub or authority centralities because the investigative process did not recognize the Caviar Network's complex communication structure. The complexity of the algorithm for computing the hub and authority variables makes it virtually impossible to interpret these variables visually in a graph or sociogram. It appears that the complexity of the network's behavior and authority distribution structures protected certain members while making others vulnerable. In this sense we conclude that our approach explains sentence length based on social network variables.. Although the model generates good predictions, it is noteworthy that the negative weights indicate reductions in sentence length as hub and authority centrality increase, just the opposite of what we would have expected. We believe this occurred because some individuals with high values for these indicators were not punished. This is not to say that the investigation was poorly handled, but nor do we feel obliged to conclude these measures are not good sentence length predictors. A more likely explanation is that the investigators and judges on the case did not have the information generated for this study available to them given the impossibility of obtaining it or observing it directly and were thus not in a position to ensure sentence length was related to network communication behavior. The information they would have needed was derived from a complex algorithmic computation and a sociogram of 110 individuals that is difficult to represent visually. Indeed, at the time of the investigation into the Caviar Network the hub and authority computation algorithm [39] had not yet been formulated. Identifying the authorities in a criminal network is therefore highly important, for without this data the sentences handed down will not be proportional to the actual communication behavior of the Network's member individuals.. To conclude, the social network indicators used in previous studies already generated good sentencing outcome predictions, the inclusion of additional network centrality measures improves their accuracy. In light of this, three principal considerations should guide future research in this area.. 1. The addition of sociodemographic and socioeconomic variables used in the conventional literature to those posited in this study should be explored to determine whether they would improve sentence prediction. In our view, the criminal history variable has already demonstrated its predictive ability regarding sentencing outcome [3, 7, 12, 61] . In the Caviar Network case, for example, the node given the longest sentence was individual N 12 , who was condemned to 15 years in prison because of previous convictions. 2. Whereas [1] only included local and distance measures as predictor variables, our results show that local variables function best for predicting the verdict while distance and network feedback variables do a better job of explaining sentence length. Further investigation of sentencing outcomes should include various types of social network measures. 3. There is still no methodology for identifying criminals over time. In the present study we aggregated the 11 matrices describing the behavior of the Caviar Network over a two-year period. The next step would be to carry out a dynamic classification analysis such as the one proposed by [62] to create behavior profiles or patterns for different groups in terms of their social network indicators.. This study has attempted to respond to a number of questions and define new tasks for explaining sentencing outcomes. The ultimate goal is to provide criminologists with valuable feedback for decisions regarding future research and the allocation of resources and effort to issues of public interest. The application of the social network approach requires further study of criminal networks, particularly as regards ethical and legal questions that arise in real-world cases [63] . We agree fully with the observation of McGloin and Kirk that "the use of formal social network analysis is still quite limited in the fields of criminology and criminal justice" [23] , p. 222. Greater application of this approach together with the development of new social network measures could provide valuable information on the centrality of individuals in these networks and their network behavior. Endnotes a Morselli [2] reports that these seizures led to a loss of approximately US$32 million for participants in the network.. b Data was organized and analyzed with the Organizational Risk Analyzer (see [64] ), a program made available by the CASOS project at Carnegie Mellon University.. c This sociogram was produced using visone software [65] . For more information on graph-layout algorithms and their application in networks social analysis, see the work of [38, [66] [67] [68] [69] .. d Individuals N 82 to N 110 represented such facilitators in the network.. e A guilty verdict was correctly predicted for individual N 11 that the first logistic regression model were unable to classify.. f The correlation matrix of all variables presented in Table 1 suggests that there is no problem of multicollinearity. As can be note, there is no one variable highly correlated with all others variables. The results will be reported as follows. First, we report the summary of logistic regression for predicting the verdict (first Subsection). Second, we report the summary of multiple regressions for predicting the sentence in years (second Subsection).

O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


A multi-agent approach for provisioning of e-services in u-commerce environments Purpose -Taking into account the importance of e-commerce and the current applications of AI techniques in this area, this research aims to adequate the design of a multi-agent system for the provisioning of e-services in u-commerce environments. This proposal is centred on the methods of evaluation in a u-e-commerce environment. Design/methodology/approach -The multi-agent systems (MAS) approach is based on an MAS model developed for AmI that has been redesigned to support u-commerce. The use of a recommendation system, previously developed by the research group, is suggested for this MAS. The methodological proposal centres on the evaluation of this type of system. Findings -The evaluation of this type of system is the principal problem of current research. Therefore, this is the main contribution of the paper. Research limitations/implications -The different evaluation methods that are proposed, whether qualitative or quantitative, offer the possibility of measuring the added value that the context can give to the use of e-services in different domains of application. Qualitative evaluation should consider the customer as a central piece in the system. In addition, quantitative methods should objectively evaluate the contribution of context to the application. Practical implications -At present, there is no single method for evaluating the benefits of different u-commerce systems, so a new method needs to be found based on these techniques. Originality/value -The research proposes an MAS designed for u-commerce domains, analyzes the capacity of trust management techniques in this environment, and proposes several evaluation methods to show the benefits of context information in the use of e-services. Several real developments are described to show the different applications of MAS in u-commerce and how evaluation is carried out. Owing to advances in communication technologies, such as sensor networks and radio frequency identification (RFID), ubiquitous computing is increasingly entering all aspects of life and in all sectors, opening a world of unprecedented scenarios where customers interact with electronic devices embedded in environments that are sensitive to the presence of users (Lyytinen and Yoo, 2002) . These environments combine ubiquitous information, and communication, with enhanced personalization, natural interaction and intelligence (Russell et al., 2005) . The use of this context offers the possibility of tailoring a new type of advanced applications. This work has been partially supported by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAM CONTEXT and DPS2008-07029-C02-02. Owing to the evolution of ubiquitous computing technologies, it is possible to augment our everyday environments with abundant ways to access information. There is a great deal of background research on architectures and prototypes of ubiquitous computing. First, the pioneering work at Xerox Parc and at AT & T Labs in Cambridge (Watson et al., 2002) , both of which emphasize deploying infrastructures to facilitate ubiquitous computing applications. There is also the Guide and Cooltown Fhg (Addlesee et al., 2001) , which focuses more on creating ubiquitous computing applications and deploying them in large user communities. The MediaCup project (Cheverst et al., 2000) at Karlsruhe is another one, which explores the issue of deployment in everyday artifacts and many more. But there is an important difference between the ubiquitous computing systems mentioned previously and those in which ubiquitous environments provide a mechanism whereby commercial activity can be initiated in a seamless and transparent manner (i.e. those systems closely related to ubiquitous commerce) (Watson et al., 2002; Junglas and Watson, 2006; Galanxhi-Janaqi and Nah, 2004) . The difference lies in the fact that the latter needs to maximize the benefit to the customer due to the economic interchange generated. When using these kinds of systems, a transactional act is carried out by the customer, in order to access those services, which providers are able to offer.. The evolution of e-commerce into m-commerce is leading to a world of ubiquitous commerce (u-commerce). The trend is for ubiquitous universal access via multipurpose terminals over sensor networks or RFID technology, which have the ability to identify, track and trace objects automatically, permitting the delivery of content over any network (Asif and Mandviwalla, 2005; Ohkubo et al., 2005) . Context is a central key in ubiquitous commerce (Coutaz et al., 2005) . This content delivery can be adapted to the unique context of the person, the time, the place and the network and can act in unison in order to support smarter and more intelligent delivery (Lyytinen and Yoo, 2002; Russell et al., 2005) . This trend was named u-commerce and is facilitated by the emergence of four u-forces: ubiquity, universality, uniqueness and unity. Watson defines u-commerce as the use of ubiquitous networks to support personalized and uninterrupted communications and transactions between an organization and its various stakeholders to provide a level of value over, above and beyond traditional commerce (Watson et al., 2002) .. The trend toward u-commerce does not represent simply a change in the way customers access, and use information. In the end it will have a profound effect on the way customers use services, enabling new classes of services that only make sense by virtue of being embedded in the environment.. The twenty-first century is rapidly becoming ubiquitous and systematic to suit individuals, and intelligent agents offer another benefit when they are considered in the context of u-commerce. This system needs an organization similar to the one envisaged by artificial agent societies. If we assume that agents are abstractions for the interaction within a ubiquitous, intelligent environment, one aspect that we need to ensure is that their behavior is regulated and coordinated. Nevertheless, society is there not only to regulate behavior but also to distribute responsibility and contextual information among the member agents (O'Hare et al., 2004) advocate the use of agents as key enablers in the delivery of ambient intelligence and ubiquitous environments.. There are several approaches to intelligent agents in the field of e-commerce; they are distinguished by their inherent characteristics: autonomy, reactivity, proactivity and mobility. Thus, intelligent agents represent a logical choice for using intelligence in the u-commerce applications. The challenge, therefore, comes from the idea of offering an intuitive and conceptually simple model in which customers can achieve the objective of receiving the adequate service as they are paying for it.. As the next generation business model, u-commerce immediately triggered a lot of attention among enterprises. To facilitate the correct provisioning of u-commerce services, it is important to have a proper evaluation method that can take into account the user's feedback.. In this research we propose MAS designed for u-commerce domains, analyze the capacity of trust management techniques in this environment and propose several evaluation methods to show the benefits of context information in the use of e-services. There are several evaluation techniques proposed in literature for ubiquitous systems (Schmidt, 2002) , but there is no established evaluation framework. That is why the principal aim of this research is to provide a proper evaluation system that would take customer feedback into account.. The main contributions of this work are:. .. Effectively adapting the design and architecture of an agent-based system developed in previous works (Beigl et al., 2001; Sanchez-Pi et al., 2008a) , in order to provide context-awareness and situation sensitivity so that e-services and recommendations, which are suitable to his location, geographical context, communication context preferences and needs can be discovered, processed and provided to the customer (Young, 2002) ;. .. Modifying and instantiating the ontology designed in Sanchez-Pi et al. (2008a) in order to support e-services provisioning in u-commerce environments; and .. Developing an evaluation system for a u-commerce environment: a shopping mall, which provides user feedback into the system. We introduced an on-line customer evaluation into the system that is complemented with the off-line feedback.. Following this introduction, the remainder of the paper is organized as follows. Section 2 describes aspects related to ubiquitous computing and the agent's paradigm; a description of the state-of-the-art in these areas is provided. Section 3 describes the MAS architecture and ontology adaptation for a u-commerce environment, as well as a reputation system for providing u-services to customers. Section 4 presents a brief background on evaluation techniques and, finally, our approach to managing customer evaluations is presented. Section 5 shows a practical implementation of the evaluation system for a particular u-commerce scenario: a shopping mall. Finally, our conclusions are presented.. 2. Ubiquitous computing and agent's paradigm Ambient Intelligence (AmI) is the paradigm used to equip environments with advanced technology in order to create an ergonomic space where customers can interact with their digital environment in the same way they interact with each other (Aarts et al., 2001) . It is also associated with a society based on unobtrusive, often invisible interactions among customers and computer based services taking place in a global computing environment (Kovács and Kopácsi, 2006) . Services in AmI will be ubiquitous in that there will be no specific bearer or provider but, instead, they will be associated with a variety of objects and devices in the environment, which will not bear any resemblance to computers. Customers will interact with these services through intelligent and intuitive interfaces embedded in these objects and devices, which, in turn, will be sensitive to what customers need. Ubiquitous computing offers many varied applications but will probably have its greatest impact on everyday activities. The essence of the definition is understood through Weiser's (1991) words "The most profound technologies are those that disappear" (Fuentes et al., 2007) . The anytime/ anyplace principle of ubiquitous computing emerges as the natural result of research and technological advances in wireless and sensor networks, embedded systems, mobile computing, distributed computing, agent technologies, autonomic computing and communication. Ubiquitous computing paradigm integrates computation into the environment. For ubiquitous computing to work efficiently and transparently, it is essential to have some knowledge of the customer. This is when the customer context needs to be considered (Dey and Abowd, 2000) define context as "any information that can be used to characterize the situation of entities (i.e. whether a person, place or object) that are considered relevant to the interaction between a customer and an application, including the customer and the application themselves".. Context and context-awareness are also central issues to ambient intelligence. The main goal of context-aware computing is to acquire and utilize information about the context to provide appropriate services to the customers at the right time and at the right place. Context-aware applications are a large and important subset of the overall set of ubiquitous computing applications, and have already demonstrated the advantages gained from the ability to perceive the surrounding environment (Hess et al., 2002; Yau and Karim, 2004; Biegel and Cahill, 2004) . There are numerous approaches in context-aware applications but most of the available applications are designed for working on specific domains.. A good point seen on the AmI vision is that the electronic or digital part of the ambience (devices) will often need to act intelligently on behalf of customers (Youll et al., 2000) . These components will need to be both reactive and proactive, behaving as if they were agents that act on behalf of customers. AmI meets the requirements for the utilisation of agents' technology for the delivery of ubiquitous services, continuous communications and intelligent user interfaces, where different users are empowered by interacting with an environment that is aware of their presence and context, and is able to provide them with personalized services.. We can find several approaches developing platforms, frameworks and applications for offering context-aware services in ubiquitous computing where intelligent agents technology has been applied in order to provide the right information at the right time to its customers. Tourism, healthcare, education, transportation, etc. are some of the sectors where been developed context-aware systems and applications have been developed.. Such applications include location-based services like mapping and points of interest searches, travel planning and, recently, pushing information and events to the customer. Works like the ones carried out in CRUMPET (Poslad et al., 2001) projects have addressed the state-of-the-art and even furthered it. Recently, another clear application for supporting virtual elderly assistance communities was presented in the context of the TeleCARE project (Afsarmanesh et al., 2003) . Also (Corchado et al., 2007) proposed a planning agent, AGALZ, which uses case-based reasoning (CBR) architecture, to respond to events and monitor Alzheimer's patients' health care in execution time. Research on developing multi-agent systems for context aware services is increasing. For instance: SMAUG (Nieto-Carvajal et al., 2004 ) is a multi-agent context-aware system that allows tutors and pupils in a university to fully manage their activities. SMAUG offers its customers context-aware information from their environment and also gives them a location service to physically locate every customer in the system. Another one is the BerlinTainment (Wohltorf et al., 2005) ; the project has developed a framework for providing activity recommendations based on mobile agents. There is also a case study consisting in automating -the internal mail management of a department that is physically distributed on a single floor of a building (a restricted and well-known test environment) using ARTIS agent architecture (Bajo et al., 2008) .. There are also several architectures and prototypes of u-commerce systems that have been described in the literature. Shopper's Eye (Fano, 1998 ) implements a new set of location services enabled by location sensitive information, in a PDA or smart phones in a wireless manner. A customer's personal Shopper's Eye transmits messages to the Shopper's Eye control center. These messages detail the customer's location, goals, preferences, and related purchase history. On receipt of this information, the stores create a customized offer of goods and services. Impulse (Youll et al., 2000) is another one; it is a PDA system where customers may add products to a product list, indicating preferences such as a warranty terms, reputation, availability, preferred price and time limit for the purchase. As the customer is visiting shopping zones, the user agent is in charge of negotiation and delivering efficient and effective product offerings to customers. MyGrocer (Kourouthanassis and Roussos, 2003 ) is a specification of the Impulse system. It is concerned with grocery shopping in large stores. There are some that not only follow the specifications of a u-commerce system, but they also integrate intelligent agents to their implementations. They are: AmbieSense project (Lech and Wienhofen, 2005) an agent-based infrastructure for context-based information delivery for mobile users; InterMarket (Kowalczyk et al., 2002) , which is a project aimed at the realization of an e-marketplace integrating mobile agents and intelligent decision-making agents. However, none of them combines the agents' paradigm, context-aware information with a recommendation system that can provide personalized e-services to customers.

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Computational methods for underdetermined convolutive speech localization and separation via model-based sparse component analysis In this paper, the problem of speech source localization and separation from recordings of convolutive underdetermined mixtures is studied. The problem is cast as recovering the spatio-spectral speech information embedded in a microphone array compressed measurements of the acoustic field. A model-based sparse component analysis framework is formulated for sparse reconstruction of the speech spectra in a reverberant acoustic resulting in joint localization and separation of the individual sources. We compare and contrast the computational approaches to model-based sparse recovery exploiting spatial sparsity as well as spectral structures underlying spectrographic representation of speech signals. In this context, we explore identification of the sparsity structures at the auditory and acoustic representation spaces. The auditory structures are formulated upon the principles of structural grouping based on proximity, autoregressive correlation and harmonicity of the spectral coefficients and they are incorporated for sparse reconstruction. The acoustic structures are formulated upon the image model of multipath propagation and they are exploited to characterize the compressive measurement matrix associated with microphone array recordings.Three approaches to sparse recovery relying on combinatorial optimization, convex relaxation and Bayesian methods are studied and evaluated based on thorough experiments. The sparse Bayesian learning method is shown to yield better perceptual quality while the interference suppression is also achieved using the combinatorial approach with the advantage of offering the most efficient computational cost. Furthermore, it is demonstrated that an average autoregressive model can be learned for speech localization and exploiting the proximity structure in the form of block sparse coefficients enables accurate localization. Throughout the extensive empirical evaluation, we confirm that a large and random placement of the microphones enables significant improvement in source localization and separation performance. Source localization and separation are central problems in various microphone array applications. This work takes place at the intersection of sparse component analysis and computational auditory scene analysis (CASA). Motivated by the commonalities between these two approaches to source separation, we consider structural dependencies influencing our auditory system for structured sparse recovery to develop a modelbased sparse component analysis framework.. We consider the microphone array acquisition model as a linear convolutive mixing process stated as. h mn s n , ∀m ∈ {1, ..., M}. where the signal of each microphone x m is characterized as a superposition of the signal of individual sources s n , ∀n ∈ {1, · · · , N} convolved with the acoustic channel, h mn , between the position of source n and microphone m; N and M denote the number of sources and microphones, respectively.. This formulation is stated in time domain. To exploit the sparsity structures of sound as we shall see in this paper, the frequency domain representation is considered. Hence, the short time Fourier transform is applied on the microphone array signals. The convolution-multiplication property of the Fourier transform leads to the following mixing model. H mn S n , ∀m ∈ {1, ..., M}. where X m , H mn and S n are the frequency domain representations of x m , h mn and s n respectively.. The goal is to recover the individual source signals from M recorded mixtures. There is no prior knowledge about N, M and the acoustic channels H mn and the estimation of the signals can only be achieved under assumptions about the signal or channel characteristics. Furthermore, the linear system expressed in (2) is underdetermined if N M. Hence, additional assumptions are required to circumvent the ill-posedness of the separation problem. In the next section, we overview some of the prior works on multichannel techniques for speech separation. In this paper, the underdetermined convolutive speech localization and separation is formulated as structured sparse recovery from compressive acoustic measurements. In this context, our studies aim to address two important issues: (1) Identification of the sparsity structures underlying spatio-spectral representation of multiparty speech recordings and (2) Evaluation of the computational methods that incorporate these structures for model-based sparse recovery.. We exploit the spatial sparsity of multipath propagation for characterization of the acoustic measurements based on its unique relation to the source locations inside the enclosure. The spectrographic sparsity structures are formulated upon the auditory principles of structural grouping. It is demonstrated that localization of simultaneous speech sources is more accurate, and quality of the separated speech is better preserved, if the proximity, autoregressive correlation and harmonicity of the spectral or temporal coefficients are incorporated in the sparse reconstruction procedure.. Through the source localization evaluation, we found that grouping a few consecutive frames enables exact localization of up to 9 simultaneous sources from only 4 microphone recordings using either of the convex, combinatorial or Bayesian methods. We could also achieve highly accurate localization using a single frame of overlapping speech by taking into account the spectral structures. To exploit the autoregressive correlation model, it was shown that an average model exists at both temporal and spectral levels and it is more efficient than learning the AR coefficients online. The BSBL method yields the best localization results per frame.. Furthermore, the source separation evaluation under clean and noisy scenarios with acoustic model mismatch confirmed the effectiveness of the sparse Bayesian learning via BSBL algorithm to achieve the best results in terms of perceived speech quality and distortion whereas a high inference suppression is also obtained by IHT and OMP.. Although the performance of both localization and separation improve by incorporating the sparsity structures, learning the hyper-parameters for these structures is application-specific. For example, it is desirable to exploit a large block (e.g. size = 16) of frequencies or frames to enable accurate localization whereas the quality of the reconstructed speech is degraded as the block size goes beyond 2. In addition, the AR correlation model can be computed offline for the source localization task, but it needs to be learned online through the BSBL procedure for high quality speech separation.. The computational cost of the greedy combinatorial methods developed for IHT and OMP demonstrates the efficiency required for real time implementation. Given the accurate localization and high interference suppression achieved by this approach, in some applications that the perceptual quality of the separated speech is not a desired objective, the greedy combinatorial methods may be considered for sparse recovery.. Throughout the extensive experimental evaluation, we observe that the layout of microphone placement has a great impact on localization and separation performance. Indeed, a random placement of the microphones at a large pairwise distance enables better results compared to a uniform compact setup. The optimal design of the microphones for sparse localization and separation framework remains an open question for future research.

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