Fast image retrieval using color-spatial information

In this paper, we present an image retrieval system that employs both the color and spatial information of images to facilitate the retrieval process. The basic unit used in our technique is a single-colored cluster, which bounds a homogeneous region of that color in an image. Two clusters from two images are similar if they are of the same color and overlap in the image space. The number of clusters that can be extracted from an image can be very large, and it affects the accuracy of retrieval. We study the effect of the number of clusters on retrieval effectiveness to determine an appropriate value for “optimal' performance. To facilitate efficient retrieval, we also propose a multi-tier indexing mechanism called the Sequenced Multi-Attribute Tree (SMAT). We implemented a two-tier SMAT, where the first layer is used to prune away clusters that are of different colors, while the second layer discriminates clusters of different spatial locality. We conducted an experimental study on an image database consisting of 12,000 images. Our results show the effectiveness of the proposed color-spatial approach, and the efficiency of the proposed indexing mechanism. Received August 1, 1997 / Accepted December 9, 1997     

Automatic generation and refinement of hypertext links

Abstract

Current generation of hypertext systems suffer from the limitations that they are static in nature, and they do not support the automated process of link creation very well. Because of the efforts involved in manually creating links, the hyperbases created using these systems are seldom modified even when they were found not to fully support the requirements of the intended users. This paper studies the development of automated tools to aid in the process of link creation, browsing, and link refinement. Only relation links are considered in this study. The automated tools are developed to help in three of the major stages of developing and using hypertext applications: (a) during authoring to generate a set of relation links between pairs of nodes; (b) during browsing to recommend an optimal set of starting nodes for the users to begin browsing, and to guide the users at each stage of browsing by suggesting a set of “next” nodes to traverse; and, (c) during training to modify, remove and add links based on users' feedback data collected. The training will result in long-term changes in the hypertext structure.

In order to test the effectiveness of the training process objectively, a navigator is built to simulate the browsing activities of the users. The effects of training have been evaluated on two text collections using a variety of objective measures. The results indicate that the training process has improved the effectiveness of the hyperbase to support browsing.     

Examining the Effectiveness of Gamification in Human Computation

Within the human computation paradigm, gamification is increasingly gaining interest. This is because an enjoyable experience generated by game features can be a powerful approach to attract participants. Although potentially useful, little research has been conducted into understanding the effectiveness of gamification in human computation. In this experimental study, we operationalized effectiveness as perceived engagement and user acceptance and examined it by comparing the performance of a gamified human computation system against a non-gamified version. We also investigate the determinants of acceptance and how their effects differ between these two systems. Analysis of our data found that participants experienced more engagement and showed higher behavioral intentions toward the gamified system. Moreover, perceived output quality and perceived engagement were significant determinants of acceptance of the gamified system. In contrast, determinants for acceptance of the non-gamified system were perceived output quality and perceived usability.     

An intelligent multi-constraint finite capacity-based lot release system for semiconductor backend assembly environment

This paper presents an intelligent multi-constraint finite capacity-based lot release system which has been designed, developed and implemented to solve the lot release problems in a discrete manufacturing environment with huge product mix and multitude of capacity constraints. This system releases lots by firstly prioritising them according to a multi-attribute lot prioritization criteria, and then by applying predefined multiple capacity constraints to the sorted queue of lots. Finally the finite resource capacity is allocated to them sequentially. This system is designed to be adaptable and re-configurable in response to the dynamic changes in the manufacturing environment.The system can be configured to operate in both an automatic and manual lot release modes. When the system is triggered under the automatic lot release mode, it will perform lot releases automatically based on the latest lot orders information and the predefined configuration, parameters and capacity constraints. It facilitates the auto generation of lot release schedule during night shifts when the production control personnel is off duty, as the system can be triggered through scheduled task provided by the computer's operating system. The manual release mode is designed to cater for fine adaptation of the lot release schedule generated by the system through potential human intervention, through which, a user is allowed to adjust the lot release schedule, in response to some last minute urgent requests from the customers and unexpected events from the shop floor. Detailed reporting tools that provide a snapshot of the lot release results controlled by the various capacity constraints are available for analysis and further refinement of the lot release schedule.This system has been successfully implemented in a few Semiconductor Backend Assembly companies, and in most of these implementations, with direct integration to their existing manufacturing systems such as the Enterprise Resource Planning (ERP) and Manufacturing Execution System (MES).     

Multiparty sensemaking: A technology‐vendor selection case study

Information system (IS) procurement history is replete with poorly executed, multimillion dollar procurement decisions. Yet we barely understand what effective IS procurement should look like. IS procurement is highly challenging, as it requires the client to simultaneously select a technology and vendor. This paper explores the technology‐vendor selection process through the sensemaking perspective. Our study develops a sensemaking model for technology‐vendor selection that connects the multiple rounds of client‐vendor communicative actions with the client's sensemaking process. We show how the client reconciles fragmented and sometimes conflicting cues and information through three intertwined cycles: immediate, retrospective, and decision. Sensebreaking occurs as a separate process (and not a communicative action) when disruptive cues occur persistently and from different vendors over multiple rounds of sensemaking. We derive a set of critical factors, on the basis of the sensemaking perspective, for selecting an appropriate vendor and technical solution. These insights in turn help explain many poorly executed IS procurement decisions.     

On burst rescheduling in OBS networks with partial wavelength conversion capability

The core nodes in an optical burst switching (OBS) network are normally equipped with wavelength converters (WCs) to reduce the burst loss probability. Since WCs are expensive and still immature technologically, it is desirable to reduce the number of WCs in the network and to have partial wavelength conversion capability at the core nodes. Nevertheless, a majority of algorithms in the literature are proposed under the full wavelength conversion assumption. As a result, they do not consider the burst loss caused by insufficient WCs, i.e., bursts dropped due to the unavailability of free WCs to convert them to unused wavelengths. In this paper, we demonstrate how to use burst rescheduling to decrease the burst loss due to insufficient WCs and hence cut down on the overall burst loss probability in OBS networks. Two burst rescheduling algorithms are proposed. Their effectiveness in reducing the overall burst loss probability is verified through simulation experiments.     

On The Drying of Food Products in a Tunnel Dryer

We present a dryer model for simulating the drying of hygroscopic-porous food products in a tunnel dryer. The model employs an improved receding-front formulation by taking into consideration the material volumetric shrinkage and the variation of the heat and mass transfer coefficient during drying. Predicted results show close agreement when compared with experimental data. We report a parametric analysis using the dryer model to study the drying transient and the need to cascade the drying process so as to maximise the drying potential of the air stream.