Why People Search for Images using Web Search Engines

What are the intents or goals behind human interactions with image search engines? Knowing why people search for images is of major concern to Web image search engines because user satisfaction may vary as intent varies. Previous analyses of image search behavior have mostly been query-based, focusing on what images people search for, rather than intent-based, that is, why people search for images. To date, there is no thorough investigation of how different image search intents affect users' search behavior. In this paper, we address the following questions: (1)Why do people search for images in text-based Web image search systems? (2)How does image search behavior change with user intent? (3)Can we predict user intent effectively from interactions during the early stages of a search session? To this end, we conduct both a lab-based user study and a commercial search log analysis. We show that user intents in image search can be grouped into three classes: Explore/Learn, Entertain, and Locate/Acquire. Our lab-based user study reveals different user behavior patterns under these three intents, such as first click time, query reformulation, dwell time and mouse movement on the result page. Based on user interaction features during the early stages of an image search session, that is, before mouse scroll, we develop an intent classifier that is able to achieve promising results for classifying intents into our three intent classes. Given that all features can be obtained online and unobtrusively, the predicted intents can provide guidance for choosing ranking methods immediately after scrolling

Least squares support vector machine with self-organizing multiple kernel learning and sparsity

© 2018 In recent years, least squares support vector machines (LSSVMs) with various kernel functions have been widely used in the field of machine learning. However, the selection of kernel functions is often ignored in practice. In this paper, an improved LSSVM method based on self-organizing multiple kernel learning is proposed for black-box problems. To strengthen the generalization ability of the LSSVM, some appropriate kernel functions are selected and the corresponding model parameters are optimized using a differential evolution algorithm based on an improved mutation strategy. Due to the large computation cost, a sparse selection strategy is developed to extract useful data and remove redundant data without loss of accuracy. To demonstrate the effectiveness of the proposed method, some benchmark problems from the UCI machine learning repository are tested. The results show that the proposed method performs better than other state-of-the-art methods. In addition, to verify the practicability of the proposed method, it is applied to a real-world converter steelmaking process. The results illustrate that the proposed model can precisely predict the molten steel quality and satisfy the actual production demand

Information Support Technology of Ship Survey Based on Case-based Reasoning

Recently, the significance of ship inspections hasbeen increasingly recognized because sea pollution andsafety problems are occurring more and more frequently. However, current ship inspections rely on the experience ofthe workers. Therefore, it is difficult to understand, and hence to improve, the state of ship inspections. The present problemsare that the ship inspection technical support level in China is not balanced, especially as to the current situation with low level, poor technologyin under-developed areas. In this paper, the case technology about the case-based reasoning to the ship inspection is proposed. The methods of normative inspection case representation are discussed.This is considered to be the basis of case-based reasoning. Then the tertiary case structure with the index is defined, in which the K-nearest neighbor method to calculate the similarity between caseswas used so that the ship’s inspection information can be searched effectively. In addition, animproved retrievalstrategy of the nearest neighbor method is introduced. Therefore, in the inspection site,the inspectors can acquire support information by the case bases, and then the new cases are calculated automatically. Further, a ship inspection case managementwereintroduced to improve the stability of the system. By carrying the case-based reasoning into an inspection, an inspector can generate fault information and inspection information simply and easily. Some examples of the organization and retrieval are shown at the end of the paper

Robust assignment of airport gates with operational safety constraints

This paper reviews existing approaches to the airport gate assignment problem (AGAP) and presents an optimization model for the problem considering operational safety constraints. The main objective is to minimize the dispersion of gate idle time periods (to get robust optimization) while ensuring appropriate matching between the size of each aircraft and its assigned gate type and avoiding the potential hazard caused by gate apron operational conflict. Genetic algorithm is adopted to solve the problem. An illustrative example is given to show the effectiveness and efficiency of the algorithm. The algorithm performance is further demonstrated using data of a terminal from Beijing Capital International Airport (PEK)

An MILP model and a hybrid evolutionary algorithm for integrated operation optimisation of multi-head surface mounting machines in PCB assembly

This paper focuses on an operation optimisation problem for a class of multi-head surface mounting machines in printed circuit board assembly lines. The problem involves five interrelated sub-problems: assigning nozzle types as well as components to heads, assigning feeders to slots and determining component pickup and placement sequences. According to the depth of making decisions, the sub-problems are first classified into two layers. Based on the classification, a two-stage mixed-integer linear programming (MILP) is developed to describe it and a two-stage problem-solving frame with a hybrid evolutionary algorithm (HEA) is proposed. In the first stage, a constructive heuristic is developed to determine the set of nozzle types assigned to each head and the total number of assembly cycles; in the second stage, constructive heuristics, an evolutionary algorithm with two evolutionary operators and a tabu search (TS) with multiple neighbourhoods are combined to solve all the sub-problems simultaneously, where the results obtained in the first stage are taken as constraints. Computational experiments show that the HEA can obtain good near-optimal solutions for small size instances when compared with an optimal solver, Cplex, and can provide better results when compared with a TS and an EA for actual instances

Simulation Combined Approach to Police Patrol Services Staffing

Motivated by the squeeze on public service expenditure, staffing is an important issue for service systems, which are required to maintain or even improve their service levels in order to meet general public demand. This paper considers Police Patrol Service Systems (PPSSs) where staffing issues are extremely serious and important because they have an impact on service costs, quality and public-safety. Police patrol service systems are of particularly interest because the demand for service exhibits large time-varying characteristics. In this case, incidents with different urgent grades have different targets of patrol officers’ immediate attendances. A new method is proposed which aims to determine appropriate staffing levels. This method starts at a refinement of the Square Root Staffing (SRS) algorithm which introduces the possibility of a delay in responding to a priority incident. Simulation of queueing systems will then be implemented to indicate modifications in shift schedules. The proposed method is proved to be effective on a test instance generated from real patrol activity records in a local police force

Optimizing the selection of product recovery options

This paper investigates the problem of optimizing product recovery options within the reverse logistic context.A linear programming model is developed to find optimal allocation of returned products in different quality classes to certain recovery options.The objective is to maximize the profit. Qualities and quantities of returned products, demands, prices of the recovered products and costs for recovery are all considered in the model.The model is used to examine the effects of flexibility in product recovery allocation.Computation results show that flexible allocation between the returned products in different quality classes and the recovery options are beneficial

Differential evolution with an individual-dependent mechanism

Differential evolution (DE) is a well-known optimization algorithm that utilizes the difference of positions between individuals to perturb base vectors and thus generate new mutant individuals. However, the difference between the fitness values of individuals, which may be helpful to improve the performance of the algorithm, has not been used to tune parameters and choose mutation strategies. In this paper, we propose a novel variant of DE with an individual-dependent mechanism that includes an individual-dependent parameter (IDP) setting and an individual-dependent mutation (IDM) strategy. In the IDP setting, control parameters are set for individuals according to the differences in their fitness values. In the IDM strategy, four mutation operators with different searching characteristics are assigned to the superior and inferior individuals, respectively, at different stages of the evolution process. The performance of the proposed algorithm is then extensively evaluated on a suite of the 28 latest benchmark functions developed for the 2013 Congress on Evolutionary Computation special session. Experimental results demonstrate the algorithm's outstanding performance

Scheduling and pricing of services to minimise CO2 emissions of delivery vehicles

Previous research found that minimising emissions often conflicts with maximising profit in service delivery. In this study, we consider a service scheduling problem and propose a new approach to the problem which applies low-emission vehicle scheduling techniques with dynamic pricing to reduce CO2 emissions and maximise profit. Incentives are included in the service prices to influence the customer’s choice in order to reduce CO2 emissions. To help the company determining the incentives, our approach solves the problem in two phases. The first phase solves vehicle scheduling models with the objective of minimising CO2 emissions and the second phase solves a dynamic pricing model to maximise profit. This approach is tested through numerical experiments

Resource location for relief distribution and victim evacuation after a sudden-onset disaster

Quick responses to sudden-onset disasters and the effective allocation of rescue and relief resources are vital for saving lives and reducing the suffering of the victims. This paper deals with the problem of positioning medical and relief distribution facilities after a sudden-onset disaster event. The background of this study is the situation in Padang Pariaman District after the West Sumatra earthquake. Three models are built for the resource location and deployment decisions. The first model reflects current practice where relief distribution and victim evacuation are performed separately and relief is distributed by distribution centers within administrative boundaries. The second model allows relief to be distributed across boundaries by any distribution center. The third model further breaks down functional barriers to allow the evacuation and relief distribution operations share vehicles. These models are solved directly for small problems and by using a direct approach as well as heuristics for large problems. Test results on small problems show that resource sharing measures, both across boundaries and across different functions, improve on current practice. For large problems, the results give similar conclusions to those for small problems when each model is solved using its own best approach