Frode Eika Sandnes

Toward a realistic task scheduling model

Task scheduling is an important aspect of parallel programming. Most of the heuristics for this NP-hard problem are based on a very simple system model of the target parallel system. Experiments revealed the inappropriateness of this classic model to obtain accurate and efficient schedules for real-systems. In order to overcome this shortcoming, a new scheduling model was proposed that considers the contention for communication resources. Even though the accuracy and efficiency improved with the consideration of contention, the new contention model is still not good enough. The crucial aspect is the involvement of the processor in communication. This paper investigates the involvement of the processor in communication and its impact on task scheduling. A new system model is proposed based on the contention model that is aware of the processor involvement. The challenges for the scheduling techniques are analyzed and two scheduling algorithms are proposed. Experiments on real parallel systems show the significantly improved accuracy and efficiency of the new model and algorithms.

Future Generation Information Technology

Keynotes.- Computer Science: Where Is the Next Frontier?.- Video Forgery.- Data Analysis, Data Processing, Advanced Computation Models.- Intelligent Data Granulation on Load: Improving Infobrights Knowledge Grid.- Data Analysis Methods for Library Marketing.- HMM Approach for Classifying Protein Structures.- Investigation of Average Mutual Information for Species Separation Using GSOM.- Speech Recognition System and Formant Based Analysis of Spoken Arabic Vowels.- A Study on Mental Tasks Discriminative Power.- A Straight Line-Based Distance Measure to Compute Photographic Compositional Dissimilarity.- Data Gathering for Gesture Recognition Systems Based on Mono Color-, Stereo Color- and Thermal Cameras.- Object Surface Reconstruction from One Camera System.- The Study of Development Strategy for Bank Distribution Network through the Analysis of Inter-regional Financial Transaction Network.- Global Synchronization Properties for Different Classes of Underlying Interconnection Graphs for Kuramoto Coupled Oscillators.- Predicting the Performance of a GRID Environment: An Initial Effort to Increase Scheduling Efficiency.- Towards an Integrated Vision across Inter-cooperative Grid Virtual Organizations.- Effective GIS Mobile Query System.- Modeling and Simulation of Tandem Tollbooth Operations with Max-Algebra Approach.- Security, Software Engineering, Communication and Networking.- Intrusion Detection Based on Back-Propagation Neural Network and Feature Selection Mechanism.- Automatic Detection for JavaScript Obfuscation Attacks in Web Pages through String Pattern Analysis.- Fragmentation Point Detection of JPEG Images at DHT Using Validator.- Secure and Energy Efficient Key Management Scheme Using Authentication in Cluster Based Routing Protocol.- Automatic Detection of Infinite Recursion in AspectJ Programs.- A Hierarchical Test Model and Automated Test Framework for RTC.- A Bi-objective Model Inspired Greedy Algorithm for Test Suite Minimization.- Analysing Object Type Hierarchies to Identify Crosscutting Concerns.- A Bayesian Inference Tool for NHPP-Based Software Reliability Assessment.- AGILE Rate Control for IEEE 802.11 Networks.- Low Density Parity Check Code for the Single Carrier Frequency Division Multiple Access.- Dual Optimization of Dynamic Sensor Function Allocation and Effective Sensed Data Aggregation in Wireless Sensor Networks.- Depth-Spatio-Temporal Joint Region-of-Interest Extraction and Tracking for 3D Video.- Dynamic Routing Algorithm for Reliability and Energy Efficiency in Wireless Sensor Networks.- QoS Multicast Routing Algorithms Based on Tabu Search with Hybrid Candidate List.- A Lifetime Enhancing Node Deployment Strategy in WSN.- Data Analysis, Data Processing, Advanced Computation Models.- Experimental Investigation of Three Machine Learning Algorithms for ITS Dataset.

An intelligent strategy for checking the annual inspection status of motorcycles based on license plate recognition

License plate recognition techniques have been successfully applied to the management of stolen cars, management of parking lots and traffic flow control. This study proposes a license plate based strategy for checking the annual inspection status of motorcycles from images taken along the roadside and at designated inspection stations. Both a UMPC (Ultra Mobile Personal Computer) with a web camera and a desktop PC are used as hardware platforms. The license plate locations in images are identified by means of integrated horizontal and vertical projections that are scanned using a search window. Moreover, a character recovery method is exploited to enhance the success rate. Character recognition is achieved using both a back propagation artificial neural network and feature matching. The identified license plate can then be compared with entries in a database to check the inspection status of the motorcycle. Experiments yield a recognition rate of 95.7% and 93.9% based on roadside and inspection station test images, respectively. It takes less than 1s on a UMPC (Celeron 900MHz with 256MB memory) and about 293ms on a PC (Intel Pentium 4 3.0GHz with 1GB memory) to correctly recognize a license plate. Challenges associated with recognizing license plates from roadside and designated inspection stations images are also discussed.

Ubiquitous information transfer across different platforms by QR codes

In this paper we present an event driven surveillance system that uses multiple cameras. The purpose of this system is to enable thorough exploration of surveillance events. The system uses a client-server web architecture as this provides scalability for further development of the system infrastructure. The system is designed to be accessed by surveillance operators who can review and comment on events generated by our event detection processing modules. We do not just focus on event detection, but are working towards the optimization of event detection. A multiple camera network system that tracks a moving object (or person) and decides if this is an event of interest is also examined. Dynamic switching of the cameras is implemented to aid in human monitoring of the network. The camera displayed in the main view should be the camera with the most interesting activity occurring. Unusual activity is defined as activity occurring that is not of the norm. Normal activity is considered to be everyday repeated activity. Further thought will be given to the extension of this system into a distributed system that would effectively create an event web system. Our contributions are to the development of automated real-time switching of camera views to aid camera operators in the effort of effective video surveillance, and also the detection of events of interest within a surveillance environment, with appropriate alerts and storage of these events. To the best of our knowledge this system provides a novel approach to the technological surveillance paradigm.

The Role of Electronic Pocket Dictionaries as an English Learning Tool among Chinese Students

This study addressed the role of electronic pocket dictionaries as a language learning tool among university students in Hong Kong and Taiwan. The target groups included engineering and humanities students at both undergraduate and graduate level. Speed of reference was found to be the main motivator for using an electronic pocket dictionary. Next, the functionality used was found to be connected to the language proficiency of the learner. Finally, multimedia content was ranked least important. The results of this study have implications for the design of electronic dictionaries and for the teaching of second languages with electronic dictionaries. In particular, device developers should focus on improving the accessing speed and pay less attention to multimedia functionality. Educators should ensure that the device functionality matches the language proficiency level of the students.

Toward accessible self-service kiosks through intelligent user interfaces

Public self-service kiosks provide key services such as ticket sales, airport check-in and general information. Such kiosks must be universally designed to be used by society at large, irrespective of the individual users’ physical and cognitive abilities, level of education and familiarity with the system. The noble goal of universal accessibility is hard to achieve. This study reports experiences with a universally designed kiosk prototype based on a multimodal intelligent user interface that adapts to the user’s physical characteristics. The user interacts with the system via a tall rectangular touch-sensitive display where the interaction area is adjusted to fit the user’s height. A digital camera is used to measure the user’s approximate reading distance from the display such that the text size can be adjusted accordingly. The user’s touch target accuracy is measured, and the target sizes are increased for users with motor difficulties. A Byzantine visualization technique is employed to exploit unused and unreachable screen real estate to provide the user with additional visual cues. The techniques explored in this study have potential for most public self-service kiosks.

A BACK PROPAGATION BASED REAL-TIME LICENSE PLATE RECOGNITION SYSTEM

License plate recognition systems have been used extensively for many applications including parking lot management, tollgate monitoring, and for the investigation of stolen vehicles. Most researches focus on static systems, which require a clear and level image to be taken of the license plate. However, the acquisition of images that can be successfully analyzed relies on both the location and movement of the target vehicle and the clarity of the environment. Moreover, only few studies have addressed the problems associated with instant car image processing. In view of these problems, a real-time license plate recognition system is proposed that recognizes the video frames taken from existing surveillance cameras. The proposed system finds the location of the license plate using projection analysis, and the characters are identified using a back propagation neural network. The strategy achieves a recognition rate of 85.8% and almost 100% after the neural network has been retrained using the erroneously recognized characters, respectively.

Bimanual text entry using game controllers: Relying on users' spatial familiarity with QWERTY

A strategy for entering text using two-handed game controllers with two analogue joysticks is proposed where the QWERTY keyboard layout is used as a spatial mnemonic. The technique is inspired by two-finger QWERTY typing where the fingers are represented by the two joysticks. Characters are organized into a QWERTY layout with the joystick resting position conceptually located where the index fingers are in touch position. The user moves the relevant joystick in the direction of the desired character. The technique is easy to learn for users already familiar with QWERTY two-finger typing or touch-typing. Furthermore, text can potentially be entered with limited visual feedback, and the bimanual nature of the approach implies a potential for high input speed as the operation of each hand can be overlapped. The technique can be realized with commonly available off-the-shelf hardware and it is especially applicable to online gamers communicating textually. Experimental evaluations show that text can be entered at a mean rate of 6.75 words per minute with less than one hour of practice.

Where was that photo taken? Deriving geographical information from image collections based on temporal exposure attributes

This paper demonstrates a novel strategy for inferring approximate geographical information from the exposure information and temporal patterns of outdoor images in image collections. Image exposure is reliant on light and most photographs are therefore taken in daylight which again depends on the position of the sun. Clearly, the sun results in different lighting conditions at different geographical location and at different times of the day, and hence the observed intensity patterns can be used to deduce the approximate location of the photographer at the time the photographs were taken. Images taken inside or at night are temporally connected to the daylight images and the geographical information can therefore be transferred to related “dark” photographs. The strategy is efficient as it only considers meta information and not image contents. Large databases can therefore be indexed efficiently. Experimental results demonstrate that the current approach yields a longitudinal error of 15.7° and a latitudinal error of 30.5° for authentic image collections comprising a mixture of outdoor and indoor images. The strategy determined the correct hemisphere in all the tests. Although not as accurate as GPS receiver, the geographical information is sufficiently detailed to be useful. Applications include improved image retrieval, image browsing and automatic image tagging. The strategy does not require a GPS receiver and can be applied to the existing digital image collections.

An Intelligent Subtitle Detection Model for Locating Television Commercials

A strategy for locating television (TV) commercials in TV programs is proposed. Based on the observation that most TV commercials do not have subtitles, the first stage exploits six subtitle constraints and an adaptive neurofuzzy inference system model to determine whether a frame contains a subtitle or not. The second stage involves locating the mark-in/mark-out points using a genetic algorithm. An interactive user interface allows users to efficiently identify and fine-tune the exact boundaries separating the commercials from the program content. Furthermore, erroneous boundaries are manually corrected. Experimental results show that the precision rate and recall rates exceed 90%