Narrow Results

In many public spaces (e.g. colleges and shopping malls), people are frequently distributed discretely, and thus, single-source evacuation, which means there’s only one point of origin, is not always a feasible solution. Hence, this paper discusses a multi-source evacuation model and algorithm, which are intended to evacuate all the people that are trapped within the minimum possible time. This study presents a fast flow algorithm to prioritize the most time-consuming source point under the constraint of route and exit capacity to reduce the evacuation time. This fast flow algorithm overcomes the deficiencies in the existing global optimization fast flow algorithm and capacity constrained route planner (CCRP) algorithm. For the fast flow algorithm, the first step is to determine the optimal solution to single-source evacuation and use the evacuation time of the most time-consuming source and exit gate set as the initial solution. The second step is to determine a multi-source evacuation solution by updating the lower limit of the current evacuation time and the exit gate set continually. The final step is to verify the effectiveness and feasibility of the algorithm through comparison.

A novel framework is proposed to find efficient data intensive flow distributions on Networks on Chip (NoC). Voronoi diagram techniques are used to divide a NoC array of homogeneous processors and links into clusters. A new mathematical tool, named the flow matrix, is proposed to find the optimal flow distribution for individual clusters. Individual flow distributions on clusters are reconciled to be more evenly distributed. This leads to an efficient makespan and a significant savings in the number of cores actually used. The approach here is described in terms of a mesh interconnection but is suitable for other interconnection topologies.

Multi-source water distribution systems (WDSs) are critical to solving the increasing demand for urban water supply. Appropriate management of limited resources necessitates optimization of water scheduling in order to reduce energy consumption. However, certain complexities of applying such systems bring severe challenges to optimal scheduling methods, exemplified in mountain regions, where larger elevation gradients make distribution more complicated than in plain regions. Therefore, this study attempts to present best practices in how to reduce the energy consumption of water supply, especially in complex mountainous regions, through innovation of optimal scheduling methods. Based on the seagull optimization algorithm (SOA), a systematic optimization scheduling method for multi-source WDSs is proposed. The optimization results are compared with those obtained from the genetic algorithm. A case study of such optimization in the mountainous region of C-County, China is presented. Power consumption prior and post optimization is compared. The results show that this optimization scheduling method is both effective and feasible. Annual power consumption can be reduced by significant amounts, savings of 23.3% in this case study, and the optimal solution can be deployed with 40 iteration steps.

Bug triaging refers to the process of assigning a bug to the most appropriate fixer. As the scale and complexity of software increases, bug triaging becomes a tedious and time-consuming work. Existing bug triaging approaches typically treat it as a problem of optimizing recommendation accuracy. However, the time that different fixers may spend also varies. Thus, we take time cost as another optimizing objective aside from accuracy and use modern portfolio theory to strike a balance between them. In addition, for fixers with little fixing records, we need more data to build profiles about their expertise. To address these problems, we propose a bug triaging approach with awareness of accuracy and time cost, and we use bug reports from other projects to enrich the bug fixing history of fixers. We evaluate our approach with experiments on data collected from Bugzilla. The experiment results validate the effectiveness of our approach.

This document discusses about the notion of features in image analysis. With feature we designate here some information issued from the raw image data, related to a visual pattern, that is supposed to have some interest for a particular task from the pattern recognition pipeline. After discussing about the what for, whom for and how, we present some examples from the literature highlighting the high diversity of features and some of their properties, which can be worth considering both by the computer scientist and the expert when designing an image analysis system. We finally provide some potential take-away messages to the reader, arguing that an optimal choice of features is rarely handleable since such a choice is more often a compromise between computation and application needs.