6995681
Quasi-optimal case-selective neural network model for software effort estimation
Abstract A number of software effort estimations have attempted using statistical models, case based reasoning, and neural networks. The research results showed that the neural network models perform at least as well as the other approaches, so we selected the neural network model as the estimator. However, since the computing environment changes so rapidly in terms of programming languages, development tools, and methodologies, it is very difficult to maintain the performance of estimation models for the new breed of projects. Therefore, we propose a search method that finds the right level of relevant cases for the neural network model. For the selected case set, the scale of the neural network model can be reduced by eliminating the qualitative input factors with the same values. Since there exist a multitude of combinations of case sets, we need to search for the optimal reduced neural network model and corresponding case set. To find the quasi-optimal model from the hierarchy of reduced neural network models, we adopted the beam search technique and devised the case-set selection algorithm. We have shown that the resulting model significantly outperforms the original full model for the software effort estimation. This approach can be also used for building any case-selective neural network.
40201784
Case-based modification for optimization agents: AGENT-OPT
For the effective implementation of an inter-organizational supply chain on the Web, many optimization model agents need to be embedded in the distributed software agents. For instance, many suppliers make requests to a delivery scheduler who manages a model warehouse at the e-hub. The scheduler deals with the scheduling of many truckers and each trucker's agent must have its own routing optimization models. Since the formulations in the model warehouse vary depending upon the requirements, it is impossible to formulate all combinations in advance. Therefore, we need a case-based model modification scheme that can generate the required formulation from the semantically specified requirement in the agent communication language.This research deals with the issues of the architecture of an optimization model agent system AGENT-OPT, modeling request language in XML, optimization model representation in semantic-level objects using UNIK-OPT, a method of selecting a base model, an optimization model modification language (OMML), and rule-based modification reasoning. The approach is applied to the delivery scheduling to study the effect of base model selection policies on the modification effort. To determine whether to start with a primitive model, full model, or the most similar model, we experimented with the sensitivity of proximity to the primitive model on 24 cases and discovered the threshold for choosing the most efficient base model.
27969393
Least modification principle for case-based reasoning: a software project planning experience
A software project plan is composed of stages of activities and detailed tasks to be performed, and precedence restrictions among them. A project network is very complex and its construction requires a vast amount of field knowledge and experience. To assist the construction of a software project network, we adopt the case-based reasoning approach. However, the software project network may be designed differently depending upon the adopted development methodology and the style of the manager, so full automation of adjusting a past case is almost impossible. Thus, reducing the modification effort to a minimum is very important for enhancing the project planner's performance. In this research, we develop the framework of the Least Modification Principle (LMP) for Case-based Reasoning to solve this kind of problem. LMP is applicable when a reliable estimation of modification effort is possible. To apply the LMP for project network planning, we have selected 17 factors and the values for each factor to specify software projects. The modification effort is estimated based on the syntactic structure of modification rules. The performance of LMP is demonstrated with each of 31 test cases based on the other 30 past cases. We found that the LMP approach can significantly outperform the Ordinary Factor Matching approach.
6969387
Case- and Constraint-Based Project Planning for Apartment Construction
To effectively generate a fast and consistent apartment construction project network, Hyundai Engineering and Construction and Korea Advanced Institute of Science and Technology developed a case- and constraint-based project-planning expert system for an apartment domain. The system, FAS-TRAK- APT, is inspired by the use of previous cases by a human expert project planner for planning a new project and the modification of these cases by the project planner using his/her knowledge of domain constraints. This large-scale, case-based, and mixed-initiative planning system, integrated with intensive constraint-based adaptation, utilizes semantic-level metaconstraints and human decisions for compensating incomplete cases imbedding specific planning knowledge. The case- and constraint-based architecture inherently supports cross-checking cases with constraints during system development and maintenance. This system has drastically reduced the time and effort required for initial project planning, improved the quality and completeness of the generated plans, and is expected to give the company the competitive advantage in contract bids for new contracts.
7110658
Automatic Rule Generation by the Transformation of Expert's Diagram: LIFT
Abstract To enhance the efficiency of revealing and refining an expert's knowledge, the Expert's Diagram approach is proposed. The Expert's Diagram proposed in this paper is specifically designed for rule-based consulting systems. Using the Expert's Diagram approach, the knowledge acquisition system LIFT is developed, which transforms the Expert's Diagram automatically to rules in the syntax of the shell SKI 2 that is developed for tax consulting-purposes. For the transformation, either the conclusion-directed approach or the condition-directed approach can be applied. The role of LIFT can be generalized to some extent to adapt to the changes in the Expert's Diagram and target shells. According to our experience in the acquisition of Korean corporate tax knowledge, experts could reveal their knowledge effectively using the Expert's Diagram after a short period of training. Thus the rules could be generated automatically by LIFT without the aid of knowledge engineers.
7307952
Optimal variable-density k-space sampling in MRI
Magnetic resonance imaging (MRI) is often times limited by scan time. To reduce scan time, there have been various efforts to reduce the number of sampling points. In most cases, this is done by utilizing a priori knowledge of the signal of interest. In this paper, we propose an algorithm that will guide the determination of an optimal sampling pattern based on prior knowledge of the signal. Applications of this method include optimal variable-density k-space trajectory design or reduced phase encoding in 3DFT and spectroscopy. Preliminary results are shown as a proof of concept.
9094168
Microscopic examination of TCP flows over transatlantic links
Much of the recent research and development in the area of high-speed TCP is focused on the steady state behavior of TCP flows. However, our experience with the first research only transatlantic 2.5 Gbps Lambda link clearly demonstrates the need to focus on the initial stages of TCP. The work we present here examines the behavior of TCP flows at microscopic level over high-bandwidth long delay networks. This examination has led us to study the influence of the minute properties of the underlying network on bursty protocols such as TCP at these very high speeds combined with high latency. In this paper we briefly describe the requirements for such an extreme network environment to support high-speed TCP flows. We also present results collected using transatlantic links at iGrid2002 where we tuned various host parameters and used modified TCP stacks.
6800173
Automatic Implementation of Programming Language Consistency Models
Concurrent threads executing on a shared memory system can access the same memory locations. A consistency model defines constraints on the order of these shared memory accesses. For good run-time performance, these constraints must be as few as possible. Programmers who write explicitly parallel programs must take into account the consistency model when reasoning about the behavior of their programs. Also, the consistency model constrains compiler transformations that reorder code. It is not known what consistency models best suit the needs of the programmer, the compiler, and the hardware simultaneously. We are building a compiler infrastructure to study the effect of consistency models on code optimization and run-time performance. The consistency model presented to the user will be a programmable feature independent of the hardware consistency model. The compiler will be used to mask the hardware consistency model from the user by mapping the software consistency model onto the hardware consistency model. When completed, our compiler will be used to prototype consistency models and to measure the relative performance of different consistency models. We present preliminary experimental data for performance of a software implementation of sequential consistency using manual inter-thread analysis.
14431473
Two-Phase Barrier: A Synchronization Primitive for Improving the Processor Utilization
Barrier is widely used for synchronization in parallel programs. Since the process arrived earlier than others should wait at the barrier, the total processor utilization decreases. In this paper, to find the sources of the barrier waiting time, parallel programs are executed on the various grain sizes through execution-driven simulations. In simulation studies, we found that even if approximately equal amounts of work are distributed to each processor, all processes may not arrive at a barrier at the same time. The reasons are that the different numbers of cache misses and instructions within in partitioned grains result in the difference in arrival time of processors at the barrier. In this paper, the two-phased barrier is considered to reduce the blind waiting time in the traditional barrier scheme, which can be simply constructed by dividing one specific stage for the synchronization into two stages. On each stage, processes decide their stall or not, which is dependent on the current execution state of grains running on any given processors. Simulation results show that the reduced barrier waiting times attributed to the two-phased barrier contribute to the performance improvement of parallel programs.
223678
Automatic classification of digestive organs in wireless capsule endoscopy videos
Wireless Capsule Endoscopy (WCE) allows a physician to examine the entire small intestine without any surgical operation. With the miniaturization of wireless and camera technologies the ability comes to view the entire gestational track with little effort. Although WCE is a technical break-through that allows us to access the entire intestine without surgery, it is reported that a medical clinician spends one or two hours to assess a WCE video, It limits the number of examinations possible, and incur considerable amount of costs. To reduce the assessment time, it is critical to develop a technique to automatically discriminate digestive organs such as esophagus, stomach, small intestinal (i.e., duodenum, jejunum, and ileum) and colon. In this paper, we propose a novel technique to segment a WCE video into these anatomic parts based on color change pattern analysis. The basic idea is that the each digestive organ has different patterns of intestinal contractions that are quantified as the features. We present the experimental results that demonstrate the effectiveness of the proposed method.