Strategies for Expressing Concise, Helpful Answers

An intelligent help system needs to take into account the user's knowledgewhen formulating answers. This allows the system to provide more conciseanswers, because it can avoid telling users things that they already know.Since these concise answers concentrate exclusively on pertinent newinformation, they are also easier to understand. Information about theuser's knowledge also allows the system to take advantage of the user'sprior knowledge in formulating explanations. The system can provide betteranswers by referring to the user's prior knowledge in the explanation(e.g., through use of similes). This process of refining answers is calledanswer expression.The process of answer expression has been implemented in the UCExpresscomponent of UC (UNIX Consultant), a natural language system that helps theuser solve problems in using the UNIX operating system. UCExpress separatesanswer expression into two phases: pruning and formatting.In the pruning phase, subconcepts of the answer are pruned by being markedas already known by the user (and hence do not need to be generated), ormarked as candidates for generating anaphora or ellipsis (since they arepart of the conversational context). In the formatting phase, UCExpressuses information about the user's prior domain knowledge to select amongspecialized expository formats,such as similes and examples, for expressing information to the user. Theseformats allow UCExpress to present different types of information to theuser in a clear, concise manner. The result of UCExpress' answer expressionprocess is an internal form that a tactical level generator can easily useto produce good English.     

Planning Intelligent Responses in a Natural Language System

Intelligent help systems cannot merely respond passively to the user'scommands and queries. They need to be able to volunteer information,correct user misconceptions, and reject unethical requests when appropriate.In order to do these things, a system must be designed as an intelligentagent. That is, a system needs to have its own goals and then plan forthese goals. A system which did not have its own goals would never refuseto help users perform unethical actions.Such an intelligent agent has been implemented in the UCEgo component of UC(Wilensky et al. 1984; Wilensky et al. 1988) (UNIX Consultant), a natural languagesystem that helps the user solve problems in using the UNIX operatingsystem. UCEgo provides UC with its own goals and plans. By adoptingdifferent goals in different situations, UCEgo creates and executesdifferent plans, enabling it to interact appropriately with the user.UCEgo adopts goals when it notices that the user either lacks necessaryknowledge, or has incorrect beliefs. In these cases, UCEgo plans tovolunteer information or correct the user's misconception as appropriate.These plans are pre-stored skeletal plans that are indexed under the types ofsituations in which they are typically useful. Plan suggestion situationsinclude the goal which the plan is used to achieve, the preconditions of theplan, and appropriateness conditions for the plan. Indexing plans bysituations improves efficiency and allows UC to respond appropriately to theuser in real time.Detecting situations in which a plan should be suggested or a goal adoptedis implemented using if-detected daemons. These daemons provide asingle mechanism which can be used both for detecting goals and suggestingplans. Different methodologies for the efficient implementation ofif-detected daemons are discussed.     

Medical displays by using plasma‐beam‐alignment technology

Abstract— Novel anode layer plasma within minimum chamber space was developed for non‐contact alignment process. The plasma‐treated polyimide (PI) surface showed no particle contamination and no micro‐scratches. Surface morphology was investigated by using scanning electron microscope (SEM), an atomic force microscope (AFM), and X‐ray photoemission spectroscopy. The different oxygen‐to‐carbon ratio ([O]/[C] ratio) for XPS spectra indicated a composition change after plasma treatment. Surface pretilt angles were varied from 0 to 2.1° under different plasma exposure times. Finally, a prototype 20.8‐in. QXGA IPS‐mode gray‐scale medical liquid‐crystal display was successfully demonstrated with high contrast ratio, excellent uniformity, and wide viewing angle using this new plasma‐beam‐alignment technique.     

Empirical Evaluation of User Models and User-Adapted Systems

Empirical evaluations are needed to determine which users are helped or hindered by user-adapted interaction in user modeling systems. A review of past UMUAI articles reveals insufficient empirical evaluations, but an encouraging upward trend. Rules of thumb for experimental design, useful tests for covariates, and common threats to experimental validity are presented. Reporting standards including effect size and power are proposed.     

Analysis of factors contributing to success in a program of micromanipulation-assisted fertilization

OBJECTIVE: To determine factors important to clinical success in micromanipulation-assisted in vitro fertilization (IVF). DESIGN: Procedures invoked in two separate series of micromanipulation-assisted IVF cycles, one unsuccessful (series I) and the other successful (series II), were compared in an effort to identify changes that led to clinical success. SETTING: University-based IVF clinic. PATIENTS: In both IVF series involving micromanipulation, patients consisted of infertile couples who fit any of five categories of male-factor related infertility. The female patients underwent controlled hyperstimulation for oocyte retrieval and the oocytes were inseminated normally or were subjected either to partial zona dissection or subzonal sperm insertion to assist fertilization. Results in all groups were compared between the two patient series. RESULTS: In the successful series II, a noticeable improvement in fertilization rate and embryo quality was observed compared with series I. A significant increase in the percentage of patients reaching embryo transfer, the pregnancy rate per transfer, and the pregnancy rate per retrieval were noted in series II; a 25% ongoing pregnancy rate per retrieval was observed overall in this successful group, with "ongoing" defined as manifestation of at least a fetal sac on ultrasound with no detectable problems. Patients with a mixed transfer of embryos derived from manipulated and normally inseminated oocytes had a 75% rate of pregnancy per transfer in series II. Differences between the two series could not be attributed to patient selection or biases in selection of oocytes relegated to micromanipulation. However, oocyte handling, micromanipulation, and culture protocols differed significantly between the two series in that temperature and pH of oocytes was better controlled, and micromanipulation time was minimized in series II. CONCLUSION: Success in micromanipulation depends on maintenance of the oocyte in a stable and supportive environment throughout the micromanipulation procedure. It is also important to minimize trauma to the eggs by performing micromanipulation rapidly and with minimal distortion of the egg. Patients with a poor fertilization rate in standard IVF may experience a substantial increase in the likelihood of pregnancy when micromanipulation-assisted fertilization is performed on some eggs.     

Exploiting spatial indexes for semijoin-based join processing indistributed spatial databases

In a distributed spatial database system, a user may issue a query that relates two spatial relations not stored at the same site. Because of the sheer volume and complexity of spatial data, spatial joins between two spatial relations at different sites are expensive in terms of computational and transmission costs. In this paper, we address the problems of processing spatial joins in a distributed environment. We propose a semijoin-like operator, called the spatial semijoin, to prune away objects that do not contribute to the join result. This operator also reduces both the transmission and local processing costs for a later join operation. However, the cost of the elimination process must be taken into account, and we consider approaches to minimize these overheads. We also study and compare two families of distributed join algorithms that are based on the spatial semijoin operator. The first is based on multi-dimensional approximations obtained from an index such as the R-tree, and the second is based on single-dimensional approximations obtained from object mapping. We have conducted experiments on real data sets and report the results in this paper     

Simultaneous nonlinear absorption and index effects in the propagation of intense TEA CO2 laser pulses through CDF3

Changes in the beam profile of the CO2 laser 10R(26) line, caused by transmission through, and absorption by, CDF3 were studied using an array of pyroelectric detectors. During the propagation of the laser beam through CDF3, nonlinear absorption and self-defocusing of the beam have both been determined from measurements of the effect on the exit beam of fluence, radiant energy, CDF3 pressure, transmission cell length, and distance from the exit of the cell to the detector array.     

Effects of the heterogeneity of game complexity and user population in learning performance of business simulation games

Previous studies on business simulation games (BSGs) have concluded that improved performance may not be the primary benefit of using BSGs, due to mixed results between student performance and perceptions. Two relevant and insightful issues attract our attention, namely, the impacts of the heterogeneous student population and the different complexity levels of BSG software. To address these issues, the present study aims to understand the relationship between student profile/characteristics and performance in the classroom with BSG-facilitated learning. An in-depth case study is conducted on a general college course designed to teach three different complexities of BSGs to students enrolled in different majors. Four student profile factors are individually tested for differences in performance scores as evaluated by the teacher. Additionally, the influences of 11 student characteristics are assessed with regard their self-reported perceived learning performances. Regression analysis and ANOVA are used to investigate the impacts of heterogeneous users and game complexity on student performance. Based on the regression analyses of the data collected from 43 respondents who participated in the general course, the study concludes that knowledge and skill may influence the heterogeneous student population; moreover, student participation and tacit learning preference improve performance, and students with an auditory learning preference or high learning motivation may not perform well in classroom BSG learning. However, the low value of adjusted R square implies that more dimensions or variables are needed to increase the explaining power of the performance scores in the regression analyses. In contrast, heterogeneous BSG software with different complexity levels present different results. The current research contributes practical and incremental knowledge on the complexity of heterogeneous BSG software on performance scores and the perceived learning performance of heterogeneous student populations. With the research limitations acknowledged, a series of suggestions for teachers pertaining to appropriate applications of BSGs in classes is offered as well as recommendations to BSG providers. Nevertheless, in-depth analyses are required, preferably with larger student population samples, to further explore the insignificant relationship between student perceptions and attitude under nonlinear extended complexity.     

An enhanced Support Vector Machine classification framework by using Euclidean distance function for text document categorization

This paper presents the implementation of a new text document classification framework that uses the Support Vector Machine (SVM) approach in the training phase and the Euclidean distance function in the classification phase, coined as Euclidean-SVM. The SVM constructs a classifier by generating a decision surface, namely the optimal separating hyper-plane, to partition different categories of data points in the vector space. The concept of the optimal separating hyper-plane can be generalized for the non-linearly separable cases by introducing kernel functions to map the data points from the input space into a high dimensional feature space so that they could be separated by a linear hyper-plane. This characteristic causes the implementation of different kernel functions to have a high impact on the classification accuracy of the SVM. Other than the kernel functions, the value of soft margin parameter, C is another critical component in determining the performance of the SVM classifier. Hence, one of the critical problems of the conventional SVM classification framework is the necessity of determining the appropriate kernel function and the appropriate value of parameter C for different datasets of varying characteristics, in order to guarantee high accuracy of the classifier. In this paper, we introduce a distance measurement technique, using the Euclidean distance function to replace the optimal separating hyper-plane as the classification decision making function in the SVM. In our approach, the support vectors for each category are identified from the training data points during training phase using the SVM. In the classification phase, when a new data point is mapped into the original vector space, the average distances between the new data point and the support vectors from different categories are measured using the Euclidean distance function. The classification decision is made based on the category of support vectors which has the lowest average distance with the new data point, and this makes the classification decision irrespective of the efficacy of hyper-plane formed by applying the particular kernel function and soft margin parameter. We tested our proposed framework using several text datasets. The experimental results show that this approach makes the accuracy of the Euclidean-SVM text classifier to have a low impact on the implementation of kernel functions and soft margin parameter C.     

Accelerated hypothesis generation for multistructure data via preference analysis

Random hypothesis generation is integral to many robust geometric model fitting techniques. Unfortunately, it is also computationally expensive, especially for higher order geometric models and heavily contaminated data. We propose a fundamentally new approach to accelerate hypothesis sampling by guiding it with information derived from residual sorting. We show that residual sorting innately encodes the probability of two points having arisen from the same model, and is obtained without recourse to domain knowledge (e.g., keypoint matching scores) typically used in previous sampling enhancement methods. More crucially, our approach encourages sampling within coherent structures and thus can very rapidly generate all-inlier minimal subsets that maximize the robust criterion. Sampling within coherent structures also affords a natural ability to handle multistructure data, a condition that is usually detrimental to other methods. The result is a sampling scheme that offers substantial speed-ups on common computer vision tasks such as homography and fundamental matrix estimation. We show on many computer vision data, especially those with multiple structures, that ours is the only method capable of retrieving satisfactory results within realistic time budgets.