Collective intelligence (CI) is an emerging research field which aims at combining human and machine intelligence, to improve community processes usually performed by large groups. CI systems may be collaborative, like Wikipedia, or competitive, like a number of recently established problem-solving companies that attempt to find solutions to difficult R&D or marketing problems drawing on the competition among web users. The benefits that CI systems earn user communities, combined with the fact that they share a number of basic common characteristics, open up the prospect for the design of a general methodology that will allow the efficient development and evaluation of CI. In the present work, an attempt is made to establish the analytical foundations and main challenges for the design and construction of a generic collective intelligence system. First, collective intelligence systems are categorized into active and passive and specific examples of each category are provided. Then, the basic modeling framework of CI systems is described. This includes concepts such as the set of possible user actions, the CI system state and the individual and community objectives. Additional functions, which estimate the expected user actions, the future state of the system, as well as the level of objective fulfillment, are also established. In addition, certain key issues that need to be considered prior to system launch are also described. The proposed framework is expected to promote efficient CI design, so that the benefit gained by the community and the individuals through the use of CI systems, will be maximized. Copyright 2009 ACM.

Abstract The increasing popularity of e‐learning has created a need for accurate student achievement prediction mechanisms, allowing instructors to improve the efficiency of their courses by addressing specific needs of their students at an early stage. In this paper, a student achievement prediction method applied to a 10‐week introductory level e‐learning course is presented. The proposed method uses multiple feed‐forward neural networks to dynamically predict students' final achievement and to cluster them in two virtual groups, according to their performance. Multiple‐choice test grades were used as the input data set of the networks. This form of test was preferred for its objectivity. Results showed that accurate prediction is possible at an early stage, more specifically at the third week of the 10‐week course. In addition, when students were clustered, low misplacement rates demonstrated the adequacy of the approach. The results of the proposed method were compared against those of linear regression and the neural‐network approach was found to be more effective in all prediction stages. The proposed methodology is expected to support instructors in providing better educational services as well as customized assistance according to students' predicted level of performance.

License plate recognition (LPR) algorithms in images or videos are generally composed of the following three processing steps: 1) extraction of a license plate region; 2) segmentation of the plate characters; and 3) recognition of each character. This task is quite challenging due to the diversity of plate formats and the nonuniform outdoor illumination conditions during image acquisition. Therefore, most approaches work only under restricted conditions such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. Numerous techniques have been developed for LPR in still images or video sequences, and the purpose of this paper is to categorize and assess them. Issues such as processing time, computational power, and recognition rate are also addressed, when available. Finally, this paper offers to researchers a link to a public image database to define a common reference point for LPR algorithmic assessment.

TDMA has increasingly been used as the MAC layer for ad-hoc networks. However, the use of TDMA in multi-hop networks leads to the problem of distributing the available slots among the wireless nodes in an effective manner. The exact way the slots are distributed among the transmitting nodes has an impact on various performance parameters of the network, such as the end-to-end delay and the capacity. In general, an increase of the end-to-end delay may lead to an increased network capacity. In this paper, we study two new end-to-end TDMA scheduling algorithms, and compare them in terms of end-to-end delay and network capacity.

The DiffServ architecture provides a scalable mechanism for QoS introduction in a TCP/IP network. DiffServ model is based on the aggregation of traffic flows at an ingress (or egress) point of a network and the IP packet marking for different priority flows, according to several classification criteria. Two approaches exist in the DiffServ architecture: the absolute and the relative. In absolute DiffServ, an admission control scheme is used to provide QoS guarantees as absolute bounds of specific QoS parameters. The relative DiffServ model provides QoS guarantees per service class expressed with reference to guarantees given to other classes defined. Our study presents a QoS absolute and relative differentiation provisioning & management system aiming to provide QoS differentiation (absolute and relative) in IP DiffServ based Networks. The proposed system has been applied and performed well on a real network testbed

In this paper, a new algorithm for vehicle license plate identification is proposed, on the basis of a novel adaptive image segmentation technique (sliding concentric windows) and connected component analysis in conjunction with a character recognition neural network. The algorithm was tested with 1334 natural-scene gray-level vehicle images of different backgrounds and ambient illumination. The camera focused in the plate, while the angle of view and the distance from the vehicle varied according to the experimental setup. The license plates properly segmented were 1287 over 1334 input images (96.5%). The optical character recognition system is a two-layer probabilistic neural network (PNN) with topology 108-180-36, whose performance for entire plate recognition reached 89.1%. The PNN is trained to identify alphanumeric characters from car license plates based on data obtained from algorithmic image processing. Combining the above two rates, the overall rate of success for the license-plate-recognition algorithm is 86.0%. A review in the related literature presented in this paper reveals that better performance (90% up to 95%) has been reported, when limitations in distance, angle of view, illumination conditions are set, and background complexity is low.

The efficiency of event detection in video surveillance applications depends on the performance of the early stage of content change detection. However, this stage encounters difficulties deriving from the presence of noise in image acquisition devices. Furthermore, illumination variations in the surveyed scene tend to cause degradation in change detection results. This paper introduces a real-time statistic change detection technique which employs a block-based clustering procedure for the estimation of the noise model. The method is applied in conjunction with a brightness normalization stage for the suppression of ambient illumination variations. Additionally, an adaptation of the technique for real-time surveillance of subway environments is proposed.

Video-based surveillance techniques have extended their scope to the field of undervehicle inspection. However, their role is currently restricted to the visualization of the inspection results, while the evaluation of their outcome depends solely on the judgement of the operator. This paper presents an intelligent template-guided approach, which addresses the task of computer-assisted undervehicle inspection and access control. It combines a license plate identification stage which authenticates incoming vehicles and a template-guided undervehicle inspection stage which alerts the operator in case of questionable content alterations.

In this paper, an original general methodology is introduced to establish whether a handmade shape corresponds to a given geometrical prototype. Using this methodology, one can decide if an artist had the intention of drawing a specific mathematical prototype or not. This analysis is applied to the 1650 B.C. wall paintings from the prehistoric settlement on Thera, and inferences of great archaeological and historical importance are made. In particular, strong evidence is obtained suggesting that the spirals depicted on the wall paintings correspond to linear (Archimedes) spirals, certain shapes correspond to canonical 48-gon and 32-gon, while other shapes correspond to parts of ellipses. It seems that the presented wall paintings constitute the earliest archaeological findings on which these geometrical patterns appear with such remarkable accuracy.

Mobile agent technology (MAT) seems to promising approach for achieving flexible and decentralised network management. A system independent mobile agent platform is used in order to control the roaming and downloading of the required management code, in the form of mobile agents, onto the network devices. A management task such as the configuration or monitoring of a specific QoS parameter related to a particular application may require that a mobile agent (MA) or a set of MAs should visit several network nodes. We present an approach for enhancing the performance of a mobile agent based network management system by monitoring and controlling the communication paths between the network management applications and the mobile agents. In particular we show that when a mobile agent should act on a set of nodes, the time required to perform a management task is decreased if for the sequence by which the nodes will be visited we take into account the workload on them, i.e. at any time to try to visit the less loaded ones.

Abstract Textile manufacturers have to monitor the quality of their products in order to maintain the high‐quality standards established for the clothing industry. Thus, textile quality control is a key factor for the increase of competitiveness of their companies. Textile faults have traditionally been detected by human visual inspection. However, human inspection is time consuming and does not achieve a high level of accuracy. Therefore, industrial vision units are of strategic interest for the textile industry as they could form the basis of a system achieving a high degree of accuracy on textile inspection. This work describes the software core of a system designed for fabric inspection on the basis of simple image‐processing operations as well as its efficiency on detection of usual textile defects. The prerequisites of the overall system are then discussed analytically, as well as the limitations and the restrictions imposed due to the nature of the problem. The software algorithm and the evaluation of the first results are also presented in details. Copyright © 2001 John Wiley & Sons, Ltd.