Data mining applied to the cognitive rehabilitation of patients with acquired brain injury

Acquired brain injury (ABI) is one of the leading causes of death and disability in the world and is associated with high health care costs as a result of the acute treatment and long term rehabilitation involved. Different algorithms and methods have been proposed to predict the effectiveness of rehabilitation programs. In general, research has focused on predicting the overall improvement of patients with ABI. The purpose of this study is the novel application of data mining (DM) techniques to predict the outcomes of cognitive rehabilitation in patients with ABI. We generate three predictive models that allow us to obtain new knowledge to evaluate and improve the effectiveness of the cognitive rehabilitation process. Decision tree (DT), multilayer perceptron (MLP) and general regression neural network (GRNN) have been used to construct the prediction models. 10-fold cross validation was carried out in order to test the algorithms, using the Institut Guttmann Neurorehabilitation Hospital (IG) patients database. Performance of the models was tested through specificity, sensitivity and accuracy analysis and confusion matrix analysis. The experimental results obtained by DT are clearly superior with a prediction average accuracy of 90.38%, while MLP and GRRN obtained a 78.7% and 75.96%, respectively. This study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients.     

A Web-based tool for data analysis and presentation

In a distributed development environment, the display and analysis of project data are complicated by heterogeneous environments. The authors discuss WebME visualization tool that gathers disparate development data collected from distributed environments and displays them using Web technology     

A Semantic Web-based authoring tool to facilitate the planning of collaborative learning scenarios compliant with learning theories

When the goal of group activities is to support long-term learning, the task of designing well-thought-out collaborative learning (CL) scenarios is an important key to success. To help students adequately acquire and develop their knowledge and skills, a teacher can plan a scenario that increases the probability for learning to occur. Such a scenario defines pedagogically sound structures that prevent off-task behavior and engage students in more meaningful interactions. The main difficulty in designing effective CL scenarios is transforming the teacher's intentions into elements that constitute the learning scenario. This problem is frequently observed when novice teachers attempt to improve the quality of learning and instruction by blending collaborative activities with individual activities without careful planning. With the goal of helping teachers in planning collaborative scenarios, we have developed an intelligent authoring tool referred to as CHOCOLATO using Semantic Web technologies (e.g. ontologies) in order to represent knowledge about different pedagogies and practices related to collaboration. Through the use of this knowledge, CHOCOLATO can provide intelligent guidance that helps teachers to create theory-based CL scenarios which has proven to be effective in a variety of situations. We evaluated it by conducting two experiments. We were interested in verifying whether the recommendations given by CHOCOLATO help novice teachers to design pedagogically sound CL activities, and if these activities help students to learn collaboratively in real classroom settings. The first experiment had the participation of 58 pre-service teachers that created CL scenarios with and without our authoring tool and the second experiment was carried out in a Brazilian public school together with 218 students. The results suggest that the guidance provided by CHOCOLATO do help novice teachers plan, understand and share CL scenarios more easily. They also suggest that the continuous utilization of well-designed theory-based CL activities create favorable conditions for students (particularly less knowledgeable ones) to improve their overall performance throughout the school year.     

A cost-effective interactive 3D virtual reality system applied to military live firing training

The goal of the present study was to develop a cost-effective, man–machine digital interface, to improve students’ real-world firing range training, results, and achievement scores. A serious game-based learning environment was developed, integrating invisible laser infrared technology, 1:1 real-scale rifle guns with recoil effects, as well as 3D interactive virtual reality (VR) military training digital information content, to train students in military live firing. To evaluate the effectiveness of the proposed design, students’ performance, in terms of their learning achievement and learning motivation, was examined. One hundred and sixty high school students from Taiwan were divided into four individual groups of 40 students each, with one control group and three experimental groups (EG1, EG2, and EG3). The data were analyzed by one-way analysis of variance. The results of this cost-effective 3D VR showed significantly better learning motivation, learning outcomes, and positive impacts on users’ actual live firing achievement scores.     

A cost-effective approach to testing

A method for assessing the differential risk of failure among a system's modules is proposed. The procedure has three components: external-risk assessment, module exposure, and module-failure likelihood. External-risk assessment is a consideration of the system's environment, almost independent of the software's details. To estimate module exposure, the model relates individual modules and their potential faults to the external-failure modes and their economic consequences by reverse-engineering the specifications and analyzing each module's expected use. To estimate failure likelihood, the method uses a reliability model. The method constitutes theoretical foundation for the cost-effective development of software that attempts to reduce the risk of failure. Managers can use the failure-risk estimates to better determine how much testing effort can be economically justified     

A sensor-driven approach to Web-based machining

The objective of this research is to develop a framework named Wise-ShopFloor and the enabling technologies for collaborative manufacturing in a decentralized environment. Particularly, this paper presents our latest development on Web-based and sensor-driven remote machining. Once a product design is given, its process plan and NC codes are generated by using a distributed process planning (DPP) system. The NC codes are then used for remote machining via a standard Web browser and a Java GUI interface running inside the browser. In this paper, the focus is given to the concept, architecture and a prototype implementation of the enabling technology. A case study of a test part machining on a 5-axis milling machine is also completed for testing and validation. It is expected that the developed technology can be applied to design verification via remote machining as well as real part production in a distributed manufacturing environment.     

基于Web的数据挖掘技术研究及其在电子商务中的应用

基于Web的数据挖掘是一种结合了数据挖掘和互联网系统的热门研究课题.本文首先综述了基于Web的几类数据挖掘技术,包括Web内容挖掘、Web的访问挖掘、Web页面聚类以及用户频繁访问路径发现等技术.在此基础上又着重介绍了Web数据挖掘技术在电子商务中的具体应用.     

A novel approach to signal classification with an application to identifying the alcoholic brain

We introduce a novel approach to signal classification based on evolving temporal pattern detectors (TPDs) that can find the occurrences of embedded temporal structures in discrete time signals and illustrate its application to characterizing the alcoholic brain using visually evoked response potentials. In contrast to conventional techniques used for most signal classification tasks, this approach unifies the feature extraction and classification steps. It makes no prior assumptions regarding the spectral characteristics of the data; it merely assumes that some temporal patterns exist that distinguish two classes of signals and therefore could be applied to new signal classification tasks where a body of prior work identifying important features does not exist. Evolutionary computation (EC) discovers a classifier by simply learning from the time series samples.The alcoholic classification (AC) problem consists of 2 sub-tasks, one spatial and one temporal: choosing a subset of electroencephalogram leads used to create a composite signal (the spatial task), and detecting temporal patterns in this signal that are more prevalent in the alcoholics than the controls (the temporal task). To accomplish this, a novel representation and crossover operator were devised that enable multiple feature subset tasks to be solved concurrently. Three TPD techniques are presented that differ in the mechanism by which partial credit is assigned to temporal patterns that deviate from the specified pattern. An EC approach is used for evolving a subset of sensors and the TPD specifications. We found evidence that partial credit does help evolutionary discovery. Regions on the skull of an alcoholic subject that produced abnormal electrical activity compared to the controls were located. These regions were consistent with prior findings in the literature. The classification accuracy was measured as the area under the receiver operator characteristic curve (ROC); the ROC area for the training set varied from 90.32% to 98.83% and for the testing set it varied from 87.17% to 95.9%.     

基于Web的数据挖掘技术研究及其在电子商务中的应用

基于Web的数据挖掘是一种结合了数据挖掘和互联网系统的热门研究课题。本文首先综述了基于Web的几类数据挖掘技术,包括Web内容挖掘、Web的访问挖掘、Web页面聚类以及用户频繁访问路径发现等技术。在此基础上又着重介绍了Web数据挖掘技术在电子商务中的具体应用。     

A Web-based tool and a heuristic method for cooperation of manufacturing supply chain decisions

Supply chain issues have been given much attention. Various technologies and concepts have been applied to improving and optimizing supply chain performance. However, few methods to explore supply chain inter-relationships, detect process key problems and co-ordinate planning processes in different supply chain partners are available. A Web-based co-ordinated planning process supported by quality function deployment (QFD) approach is proposed in this research. The planning method is focused on integrating planning processes in supply chains by optimizing each planning process and interactively adjusting key parameters in different business processes. The QFD approach is employed to inter-relate different business processes and detect key problems through Internet technique application, so that the global solution can be heuristically improved. The planning method imitates the real-world supply chain planning environment and provides a mechanism for decision-makers to communicate with quantitative information in planning processes through the Web system. An illustrative case study in packaging industry is conducted to describe the planning procedure. The result of the example shows prospect of the method in improving supply chain cooperation. The approach is expected to facilitate supply chain planning and support managers to solve targeted problems more efficiently.     

A cost-effective approach to deadlock handling in wormhole networks

Wormhole networks have traditionally used deadlock avoidance strategies. More recently, deadlock recovery strategies have begun to gain acceptance. In particular, progressive deadlock recovery techniques allocate a few dedicated resources to quickly deliver deadlocked packets. Deadlock recovery is based on the assumption that deadlocks are rare; otherwise, recovery techniques are not efficient. Measurements of deadlock occurrence frequency show that deadlocks are highly unlikely when enough routing freedom is provided. However, networks are more prone to deadlocks when the network is close to or beyond saturation, causing some network performance degradation. Similar performance degradation behavior at saturation was also observed in networks using deadlock avoidance strategies. In this paper, we take a different approach to handling deadlocks and performance degradation. We propose the use of an injection limitation mechanism that prevents performance degradation near the saturation point and, at the same time, reduces the probability of deadlock to negligible values. We also propose an improved deadlock detection mechanism that uses only local information, detects all deadlocks, and considerably reduces the probability of false deadlock detection over previous proposals. In the rare case when impending deadlock is detected, our proposal consists of using a simple recovery technique that absorbs the deadlocked message at the current node and later reinjects it for continued routing toward its destination. Performance evaluation results show that our new approach to handling deadlock is more efficient than previously proposed techniques     

IFS: a tool to build application systems

The interpretive frame system (IFS), a tool for building application systems, is presented. IFS separates high-level design and user-interface programming from domain-specific programming. It offers a language suitable for implementing systems of interconnected tasks, simplifies the construction of sophisticated but easy-to-use user interfaces, and increases tool reuse in system construction. A system built with IFs is called a frame system, and it consists of four layers-the user interface, system structure, computational functions, and data architecture-which are described. The IFS language and programming environment are also described. A programming example is given. Reuse and prototyping are discussed     

A fast neural net training algorithm and its application to speech classification

This paper describes a fast training algorithm for feedforward neural nets, as applied to a two-layer neural network to classify segments of speech as voiced, unvoiced, or silence. The speech classification method is based on five features computed for each speech segment and used as input to the network. The network weights are trained using a new fast training algorithm which minimizes the total least squares error between the actual output of the network and the corresponding desired output. The iterative training algorithm uses a quasi-Newtonian error-minimization method and employs a positive-definite approximation of the Hessian matrix to quickly converge to a locally optimal set of weights. Convergence is fast, with a local minimum typically reached within ten iterations; in terms of convergence speed, the algorithm compares favorably with other training techniques. When used for voiced-unvoiced-silence classification of speech frames, the network performance compares favorably with current approaches. Moreover, the approach used has the advantage of requiring no assumption of a particular probability distribution for the input features.     

A streamlined, cost-effective database approach to manage requirements traceability

Requirements traceability offers many benefits to software projects, and it has been identified as critical for successful development. However, numerous challenges exist in the implementation of traceability in the software engineering industry. Some of these challenges can be overcome through organizational policy and procedure changes, but the lack of cost-effective traceability models and tools remains an open problem. A novel, cost-effective solution for the traceability tool problem is proposed, prototyped, and tested in a case study using an actual software project. Metrics from the case study are presented to demonstrate the viability of the proposed solution for the traceability tool problem. The results show that the proposed method offers significant advantages over implementing traceability manually or using existing commercial traceability approaches.     

A tool to coordinate tools

An expert-system planner is presented to automate the clerical and routine activities associated with software development using present-day environments such as Unix. The three functions of such a planner-coordination of tools, integration of tools, and representation of objects-are examined. The planner is described from both the user's and the designer's viewpoint. The functionality provided by this planner is compared with that provided by standard environments     

The crossing function and its application to zig-zag tool paths

In zig-zag paths, which are used to sweep planar areas in applications such as machining and surveillance, the number of switch-backs in the path is a major contributor to cutting time. We develop algorithms to pick the direction in which a zig-zag path on a polygon will have the minimum number of switch-backs. We introduce the concept of a crossing function of a two-dimensional contour, which is a measure of how many times a finely pitched set of parallel raster-lines at some angle intersects with the contour. We show that minimizing the crossing-function minimizes the number of switch-backs. We then show that for polygons, the crossing-function is minimized at a finite set of orientations parallel to the edges of the polygon. We show that the problem of minimizing the crossing function can be reduced to minimizing the width of an equivalent convex polygon, and develop an algorithm that takes n log(n) time for an n-sided polygon. Finally, we discuss how these algorithms are useful in machining.     

Machine learning-based assessment tool for imbalance and vestibular dysfunction with virtual reality rehabilitation system

Background and objective

Dizziness is a major consequence of imbalance and vestibular dysfunction. Compared to surgery and drug treatments, balance training is non-invasive and more desired. However, training exercises are usually tedious and the assessment tool is insufficient to diagnose patient's severity rapidly.

Methods

An interactive virtual reality (VR) game-based rehabilitation program that adopted Cawthorne–Cooksey exercises, and a sensor-based measuring system were introduced. To verify the therapeutic effect, a clinical experiment with 48 patients and 36 normal subjects was conducted. Quantified balance indices were measured and analyzed by statistical tools and a Support Vector Machine (SVM) classifier.

Results

In terms of balance indices, patients who completed the training process are progressed and the difference between normal subjects and patients is obvious.

Conclusions

Further analysis by SVM classifier show that the accuracy of recognizing the differences between patients and normal subject is feasible, and these results can be used to evaluate patients’ severity and make rapid assessment.     

METKIT: training in how to use measurement as a software management tool

A Science is as mature as its measurement tools. Louis PasteurThe main purpose of this paper is to present the results of the ESPRIT project METKIT, a three-year collaborative technology transfer project in the field of software engineering measurement. METKIT was concerned with developing a set of integrated training courses that show how to implement process improvement programmes and use measurement as a powerful management tool. METKIT can be used by an organization to enable them to provide in-house training to their managers and software developers to enable them to understand control and then improve software development.     

A software tool to acquire, synchronise and playback multimedia data: an application in kinesiology

Assessing the physical condition of the human body frequently requires evaluating different tests performed by different devices. This information has to be analysed simultaneously to derive conclusions, and so a temporal relationship must be established between the data sources. In this paper, synchronisation of the sources of information is achieved by synchronising the clocks of computers connected in a Local Area Network. A clock synchronisation protocol is used and a global time is stamped in each information data flow. During analysis, the time-stamp is used to playback the data in a synchronised way. The proposed system is valid for any medical application in which data synchronisation is needed. However, it has initially been used in a distributed medical environment for analysing electrical muscular activity and lumbar movement. The dual aim is to detect injuries and plan training sessions for athletes. It was specifically used to analyse data generated by: electromyographs; lumbar equipment; and images generated by video cameras. The simple operation of the application and the precision of the synchronisation protocol enabled several interesting medical conclusions to be made.