Learning styles and performance in the introductory programming sequence

Thomas, L., Ratcliffe, M., Woodbury, J., and Jarman, E. 2002. Learning styles and performance in the introductory programming sequence. SIGCSE Bull. 34, 1 (Mar. 2002), 33-37.

The effect of a life development intervention on sports career transition adjustment

Lavallee, D. (2005). The effect of a life development intervention on sports career transition adjustment. The Sport Psychologist. 19(2), pp.193-202 RAE2008

Constructing extensions of CP-maps via tensor dilations with rhe help of von Neumann modules

Gohm, Rolf; Skeide, M., (2005) 'Constructing extensions of CP-maps via tensor dilations with rhe help of von Neumann modules', Infinite Dimensional Analysis, Quantum Probability and Related Topics 8(2) pp.291-305 RAE2008

The rpf gene of Micrococcus luteus encodes an essential secreted growth factor

Galina V. Mukamolova, Obolbek A. Turapov, Konstantin Kazarian, Miroslav Telkov, Arseny S. Kaprelyants, Douglas B. Kell and Michael Young (2002). The rpf gene of Micrococcus luteus encodes an essential secreted growth factor. Molecular Microbiology, 46 (3), 611-621. Sponsorship: BBSRC / Russian Foundation for Basic Research (grant 00-04-48691)/ WHO Global Programme for Vaccines and Immunization / Wellcome Trust RAE2008

Modelowanie i przetwarzanie wiedzy czaso-przestrzennej. Model XCDC – aspekty teoretyczne oraz rozwiązania aplikacyjne

Wydział Matematyki i Informatyki: Zakład Lingwistyki Informatycznej i Sztucznej Inteligencji

DSpace Manual: Software version 1.5

DSpace is an open source software platform that enables organizations to: - Capture and describe digital material using a submission workflow module, or a variety of programmatic ingest options - Distribute an organization's digital assets over the web through a search and retrieval system - Preserve digital assets over the long term This system documentation includes a functional overview of the system, which is a good introduction to the capabilities of the system, and should be readable by nontechnical personnel. Everyone should read this section first because it introduces some terminology used throughout the rest of the documentation. For people actually running a DSpace service, there is an installation guide, and sections on configuration and the directory structure. Note that as of DSpace 1.2, the administration user interface guide is now on-line help available from within the DSpace system. Finally, for those interested in the details of how DSpace works, and those potentially interested in modifying the code for their own purposes, there is a detailed architecture and design section.

The DSpace Course - Item Submission Workflows

This module will introduce the item submission workflows available in DSpace. Workflows allow submissions to be checked before entering the repository. Submissions may be checked for accuracy, in order to improve the metadata, or simply to decide if they are OK to be archived. The module will show the three workflow steps available in DSpace, along with details about adding, changing and removing them from the submission process of collections.

itmBench: Generalized API for Internet Traffic Managers

Internet Traffic Managers (ITMs) are special machines placed at strategic places in the Internet. itmBench is an interface that allows users (e.g. network managers, service providers, or experimental researchers) to register different traffic control functionalities to run on one ITM or an overlay of ITMs. Thus itmBench offers a tool that is extensible and powerful yet easy to maintain. ITM traffic control applications could be developed either using a kernel API so they run in kernel space, or using a user-space API so they run in user space. We demonstrate the flexibility of itmBench by showing the implementation of both a kernel module that provides a differentiated network service, and a user-space module that provides an overlay routing service. Our itmBench Linux-based prototype is free software and can be obtained from http://www.cs.bu.edu/groups/itm/.

On trip planning queries in spatial databases

In this paper we discuss a new type of query in Spatial Databases, called Trip Planning Query (TPQ). Given a set of points P in space, where each point belongs to a category, and given two points s and e, TPQ asks for the best trip that starts at s, passes through exactly one point from each category, and ends at e. An example of a TPQ is when a user wants to visit a set of different places and at the same time minimize the total travelling cost, e.g. what is the shortest travelling plan for me to visit an automobile shop, a CVS pharmacy outlet, and a Best Buy shop along my trip from A to B? The trip planning query is an extension of the well-known TSP problem and therefore is NP-hard. The difficulty of this query lies in the existence of multiple choices for each category. In this paper, we first study fast approximation algorithms for the trip planning query in a metric space, assuming that the data set fits in main memory, and give the theory analysis of their approximation bounds. Then, the trip planning query is examined for data sets that do not fit in main memory and must be stored on disk. For the disk-resident data, we consider two cases. In one case, we assume that the points are located in Euclidean space and indexed with an Rtree. In the other case, we consider the problem of points that lie on the edges of a spatial network (e.g. road network) and the distance between two points is defined using the shortest distance over the network. Finally, we give an experimental evaluation of the proposed algorithms using synthetic data sets generated on real road networks.

3D Hand Pose Reconstruction Using Specialized Mappings

A system for recovering 3D hand pose from monocular color sequences is proposed. The system employs a non-linear supervised learning framework, the specialized mappings architecture (SMA), to map image features to likely 3D hand poses. The SMA's fundamental components are a set of specialized forward mapping functions, and a single feedback matching function. The forward functions are estimated directly from training data, which in our case are examples of hand joint configurations and their corresponding visual features. The joint angle data in the training set is obtained via a CyberGlove, a glove with 22 sensors that monitor the angular motions of the palm and fingers. In training, the visual features are generated using a computer graphics module that renders the hand from arbitrary viewpoints given the 22 joint angles. We test our system both on synthetic sequences and on sequences taken with a color camera. The system automatically detects and tracks both hands of the user, calculates the appropriate features, and estimates the 3D hand joint angles from those features. Results are encouraging given the complexity of the task.

SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-Oriented Navigation System

Both animals and mobile robots, or animats, need adaptive control systems to guide their movements through a novel environment. Such control systems need reactive mechanisms for exploration, and learned plans to efficiently reach goal objects once the environment is familiar. How reactive and planned behaviors interact together in real time, and arc released at the appropriate times, during autonomous navigation remains a major unsolved problern. This work presents an end-to-end model to address this problem, named SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-oriented Navigation system. The model comprises several interacting subsystems, governed by systems of nonlinear differential equations. As the animat explores the environment, a vision module processes visual inputs using networks that arc sensitive to visual form and motion. Targets processed within the visual form system arc categorized by real-time incremental learning. Simultaneously, visual target position is computed with respect to the animat's body. Estimates of target position activate a motor system to initiate approach movements toward the target. Motion cues from animat locomotion can elicit orienting head or camera movements to bring a never target into view. Approach and orienting movements arc alternately performed during animat navigation. Cumulative estimates of each movement, based on both visual and proprioceptive cues, arc stored within a motor working memory. Sensory cues are stored in a parallel sensory working memory. These working memories trigger learning of sensory and motor sequence chunks, which together control planned movements. Effective chunk combinations arc selectively enhanced via reinforcement learning when the animat is rewarded. The planning chunks effect a gradual transition from reactive to planned behavior. The model can read-out different motor sequences under different motivational states and learns more efficient paths to rewarded goals as exploration proceeds. Several volitional signals automatically gate the interactions between model subsystems at appropriate times. A 3-D visual simulation environment reproduces the animat's sensory experiences as it moves through a simplified spatial environment. The SOVEREIGN model exhibits robust goal-oriented learning of sequential motor behaviors. Its biomimctic structure explicates a number of brain processes which are involved in spatial navigation.

An Active Pattern Recognition Architecture for Mobile Robots

An active, attentionally-modulated recognition architecture is proposed for object recognition and scene analysis. The proposed architecture forms part of navigation and trajectory planning modules for mobile robots. Key characteristics of the system include movement planning and execution based on environmental factors and internal goal definitions. Real-time implementation of the system is based on space-variant representation of the visual field, as well as an optimal visual processing scheme utilizing separate and parallel channels for the extraction of boundaries and stimulus qualities. A spatial and temporal grouping module (VWM) allows for scene scanning, multi-object segmentation, and featural/object priming. VWM is used to modulate a tn~ectory formation module capable of redirecting the focus of spatial attention. Finally, an object recognition module based on adaptive resonance theory is interfaced through VWM to the visual processing module. The system is capable of using information from different modalities to disambiguate sensory input.

Fusion Artmap: A Neural Network Architecture for Multi-Channel Data Fusion and Classification

Fusion ARTMAP is a self-organizing neural network architecture for multi-channel, or multi-sensor, data fusion. Single-channel Fusion ARTMAP is functionally equivalent to Fuzzy ART during unsupervised learning and to Fuzzy ARTMAP during supervised learning. The network has a symmetric organization such that each channel can be dynamically configured to serve as either a data input or a teaching input to the system. An ART module forms a compressed recognition code within each channel. These codes, in turn, become inputs to a single ART system that organizes the global recognition code. When a predictive error occurs, a process called paraellel match tracking simultaneously raises vigilances in multiple ART modules until reset is triggered in one of them. Parallel match tracking hereby resets only that portion of the recognition code with the poorest match, or minimum predictive confidence. This internally controlled selective reset process is a type of credit assignment that creates a parsimoniously connected learned network. Fusion ARTMAP's multi-channel coding is illustrated by simulations of the Quadruped Mammal database.

Orientational Harmonic Model for Rotation, Translation, and Scale Invariant Pattern Representation in Biological Vision

The proposed model, called the combinatorial and competitive spatio-temporal memory or CCSTM, provides an elegant solution to the general problem of having to store and recall spatio-temporal patterns in which states or sequences of states can recur in various contexts. For example, fig. 1 shows two state sequences that have a common subsequence, C and D. The CCSTM assumes that any state has a distributed representation as a collection of features. Each feature has an associated competitive module (CM) containing K cells. On any given occurrence of a particular feature, A, exactly one of the cells in CMA will be chosen to represent it. It is the particular set of cells active on the previous time step that determines which cells are chosen to represent instances of their associated features on the current time step. If we assume that typically S features are active in any state then any state has K^S different neural representations. This huge space of possible neural representations of any state is what underlies the model's ability to store and recall numerous context-sensitive state sequences. The purpose of this paper is simply to describe this mechanism.

Neural Pattern Recognition With Multi-Scale Pyramidal Coding and Selective Attention

A new neural network architecture for spatial patttern recognition using multi-scale pyramida1 coding is here described. The network has an ARTMAP structure with a new class of ART-module, called Hybrid ART-module, as its front-end processor. Hybrid ART-module, which has processing modules corresponding to each scale channel of multi-scale pyramid, employs channels of finer scales only if it is necesssary to discriminate a pattern from others. This process is effected by serial match tracking. Also the parallel match tracking is used to select the spatial location having most salient feature and limit its attention to that part.