Gender difference in web search perceptions and behavior: Does it vary by task performance?

This study investigates Chinese students' gender differences in their actual use of the web for online information seeking. One hundred and seven Chinese university students responded to questionnaires regarding their perceptions about the use of the web for learning purposes. Afterwards, all the participants were asked to search online to answer two questions about bees' decision for hive location. As they searched, the online system logged participants' search activities during the search, including the type of activities during search, the frequency of each activity and the time spent on each activity. Participants were compared by gender in terms of their web search efficacy, web search anxiety, frequency counts of different web search activities, time spent on each search activity and search task performance. Web search efficacy levels varied by gender but not by performance levels. Anxiety did not vary by gender or performance levels. The interaction effect between gender and performance level was found in several search process variables: significant gender differences were only found in medium-performing students wherein males were engaged in more search activities than females, as seen in the larger number of searches, search queries, and times male students updated the search queries. One factor that could explain the significant gender differences in the medium-level group was their web search efficacy. The more confident medium-performing male students were in web search, the less need they perceived to access information to solve the task. This pattern was reversed for medium-performing females. The high- and low-performing males did not differ much from females in their search activities. It appeared that students' perceptions of their web search ability did not contribute much to their search activities in these two groups. Implications of the findings were also discussed.     

NLP-based faceted search: Experience in the development of a science and technology search engine

An appropriate promotion, distribution and dissemination of scientific, artistic and technology developments can foster the collaboration between a country’s productive and academic sectors. The purpose of this paper is to present a novel search engine aiming at helping people to access science and technology advances, researchers and institutions working in specific areas of research. Our search engine first collects information disseminated on the Web in academic institution sites and in researchers personal homepages. Then, after intensive text processing, it summarizes the information in an enriched and user-friendly presentation oriented to non-expert users. Stable performance and an acceptable level of effectiveness for automatic named entities recognition indicate the potential of our approach for bridging the gap between the heterogeneous and unstructured information available on the Web about the research and development advances in a country and the innovation required by the productive sectors.     

Optimizing frequent time-window selection for association rules mining in a temporal database using a variable neighbourhood search

In this study, we investigate the problem of maximum frequent time-window selection (MFTWS) that appears in the process of discovering association rules time-windows (ARTW). We formulate the problem as a mathematical model using integer programming that is a typical combination problem with a solution space exponentially related to the problem size. A variable neighbourhood search (VNS) algorithm is developed to solve the problem with near-optimal solutions. Computational experiments are performed to test the VNS algorithm against a benchmark problem set. The results show that the VNS algorithm is an effective approach for solving the MTFWS problem, capable of discovering many large-one frequent itemset with time-windows (FITW) with a larger time-coverage rate than the lower bounds, thus laying a good foundation for mining ARTW.     

A hybrid variable neighborhood tabu search heuristic for the vehicle routing problem with multiple time windows

This paper presents a new hybrid variable neighborhood-tabu search heuristic for the Vehicle Routing Problem with Multiple Time windows. It also proposes a minimum backward time slack algorithm applicable to a multiple time windows environment. This algorithm records the minimum waiting time and the minimum delay during route generation and adjusts the arrival and departure times backward. The implementation of the proposed heuristic is compared to an ant colony heuristic on benchmark instances involving multiple time windows. Computational results on newly generated instances are provided.     

k-Nearest neighbor searching in hybrid spaces

Little work has been reported in the literature to support k-nearest neighbor (k-NN) searches/queries in hybrid data spaces (HDS). An HDS is composed of a combination of continuous and non-ordered discrete dimensions. This combination presents new challenges in data organization and search ordering. In this paper, we present an algorithm for k-NN searches using a multidimensional index structure in hybrid data spaces. We examine the concept of search stages and use the properties of an HDS to derive a new search heuristic that greatly reduces the number of disk accesses in the initial stage of searching. Further, we present a performance model for our algorithm that estimates the cost of performing such searches. Our experimental results demonstrate the effectiveness of our algorithm and the accuracy of our performance estimation model.     

Two-sided assembly line balancing problem of type I: Improvements,a simple algorithm and a comprehensive study

Many meta-heuristic methods have been applied to solve the two-sided assembly line balancing problem of type I with the objective of minimizing the number of stations, but some of them are very complex or intricate to be extended. In addition, different decoding schemes and different objectives have been proposed, leading to the different performances of these algorithms and unfair comparison. In this paper, two new decoding schemes with reduced search space are developed to balance the workload within a mated-station and reduce sequence-depended idle time. Then, graded objectives are employed to preserve the minor improvements on the solutions. Finally, a simple iterated greedy algorithm is extended for the two-sided assembly line balancing problem and modified NEH-based heuristic is introduced to obtain a high quality initial solution. And an improved local search with referenced permutation and reduced insert operators is developed to accelerate the search process. Computational results on benchmark problems prove the efficiency of the proposed decoding schemes and the new graded objectives. A comprehensive computational comparison among 14 meta-heuristics is carried out to demonstrate the efficiency of the improved iterated greedy algorithm.     

A hybrid tabu search for batching and sequencing decisions in a single machine environment

This paper proposes a hybrid tabu search (HTS) to minimise the total weighted tardiness (TWT) for the batching and sequencing of jobs originating from incompatible families in which sequence dependent family setup times exist on single machine. The developed HTS includes distinguished features such as the strict arc based tabu classification along with dynamic tabu tenures, hybrid neighbourhood structures and iterative phases which consist of job and batch sequencing phases. The authors developed a testing methodology to determine the quality of the HTS solution. A mixed integer linear programing (MILP) model was developed to evaluate the optimality of the solution of the HTS for a small-size instance that consists of 640 problems. In addition, three dispatching rule heuristic combinations (EDD–EDD, EDD–BATCS and ATC–BATCS) were developed to test the HTS for large-size instances that deals with 1440 problems. The HTS provided comparable results with the MILP for small-size instances and outperformed the developed dispatching heuristics.     

Variable neighborhood search for the bi-objective post-sales network design problem: A fitness landscape analysis approach

Post-sales services are important markets in electronics industry due to their impact on marginal profit, market share, and their ability to retain customers. In this study, designing a multi-product four-layer post-sales reverse logistics network operated by a 3PL is investigated. A bi-objective MILP model is proposed to minimize network design costs as well as total weighted tardiness of returning products to customers. To solve the proposed model, a novel multi-start variable neighborhood search is suggested that incorporates nine neighborhood structures and three new encoding–decoding mechanisms. In particular, a fitness landscape measure is employed to select an effective neighborhood order for the proposed VNS. Extensive computational experiments show the effectiveness of the proposed heuristic and the three encoding–decoding mechanisms. The proposed method finds significantly better Pareto optimal sets in comparison with the original Priority method based on the number and the quality of obtained Pareto optimal solutions. In addition, it shows high efficiency by finding near-optimal solutions for the single objective versions of the problem.     

Combined heat and power economic dispatch using integrated civilized swarm optimization and Powell’s pattern search method

An integrated technique that embeds civilized swarm optimization (CSO) and Powell’s pattern search (PPS) method is proposed to search economic dispatch of combined heat and power (CHP) dispatch problem. In the proposed technique, CSO is selected as global search technique and PPS is undertaken as a local search technique. Civilized swarm optimization is having attributes of particle swarm optimization (PSO) and society civilization algorithm (SCA). In CSO, mutually interacting societies forms the civilization. The positions of society particles are updated through the guidance of own leader along with their best positions. The best performing particle of CSO is further improved by PPS method based on a certain set criterion. The PPS method is based on the conjugate search direction method and does not require the gradient or Hessian matrix of the function to be optimized. The CHP dispatch problem has a mutual dependency of demand and heat-power capacity of generating units, so it requires an effective constraint handling strategy. In this work, variable reduction strategy with exterior penalty method is applied to satisfy equality constraints. The proposed technique is tested on five CHP test systems considering valve-point loading effect, prohibited operating zones constraint, and transmission losses. The obtained results are compared to the results reported in the literature and found satisfactory. Further, for verification of statistical performance of the proposed technique, t-test and Wilcoxon signed rank test is also performed.     

Improved non-maximum suppression for object detection using harmony search algorithm

Non-maximum suppression (NMS) plays a key role in many modern object detectors. It is responsible to remove detection boxes that cover the same object. NMS greedily selects the detection box with maximum score; other detection boxes are suppressed when the degree of overlap between these detection boxes and the selected box exceeds a predefined threshold. Such a strategy easily retain some false positives, and it limits the ability of NMS to perceive nearby objects in cluttered scenes. This paper proposes an effective method combining harmony search algorithm and NMS to alleviate this problem. This method regards the task of NMS as a combination optimization problem. It seeks final detection boxes under the guidance of an objective function. NMS is applied to each harmony to remove imprecise detection boxes, and the remaining boxes are used to calculate the fitness value. The remaining detection boxes in a harmony with highest fitness value are chosen as the final detection results. The standard Pattern Analysis, Statistical Modeling and Computational Learning Visual Object Classes dataset and the Microsoft Common Objects in Context dataset are used in all of the experiments. The proposed method is applied to two popular detection networks, namely Faster Region-based Convolutional Neural Networks and Region-based Fully Convolutional Networks. The experimental results show that the proposed method improves the average precision of these two detection networks. Moreover, the location performance and average recall of these two detectors are also improved.