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In chance-constrained OPF models, joint chance constraints (JCCs) offer a stronger guarantee on security compared to single chance constraints (SCCs). Using Boole's inequality or its improved versions to decompose JCCs into SCCs is popular, yet the conservativeness introduced is still significant. In this letter, a non-parametric iterative framework is proposed to achieve the decomposition of JCCs with negligible conservativeness. An adaptive risk allocation strategy is also proposed and embedded in the framework. Results on an IEEE test case show that the conservativeness using the framework is nearly eliminated, thereby reducing the generation cost considerably.

This paper is focused on planning fast, accurate, and optimal trajectories for autonomous parking. Nominally, this task should be described as an optimal control problem (OCP), wherein the collision-avoidance constraints guarantee travel safety and the kinematic constraints guarantee tracking accuracy. The dimension of the nominal OCP is high because it requires the vehicle to avoid collision with each obstacle at every moment throughout the entire parking process. With a coarse trajectory guiding a homotopic route, the intractably scaled collision-avoidance constraints are replaced by within-corridor constraints, whose scale is small and independent from the environment complexity. Constructing such a corridor sacrifices partial free spaces, which may cause loss of optimality or even feasibility. To address this issue, our proposed method reconstructs the corridor in an iterative framework, where a lightweight OCP with only box constraints is quickly solved in each iteration. The proposed planner, together with several prevalent optimization-based planners are tested under 115 simulation cases w.r.t. the success rate and computational time. Real-world indoor experiments are conducted as well.

Stereo superpixel segmentation aims to obtain the superpixel segmentation results of the left and right views more cooperatively and consistently, rather than simply performing independent segmentation directly. Thus, the correspondence between two views should be reasonably modeled and fully considered. In this paper, we propose a left-right interactive optimization framework for stereo superpixel segmentation. Considering the disparity in stereo image pairs, we first divide the images into paired region and non-paired region, and propose a collaborative optimization scheme to coordinately refine the matched superpixels of the left and right views in an interactive manner. This is, to the best of our knowledge, the first attempt to generate stereo superpixels considering the parallax consistency. Quantitative and qualitative experiments demonstrate that the proposed framework achieves superior performance in terms of consistency and accuracy compared with single-image superpixel segmentation.

This paper presents two novel solution approaches for addressing large-scale multi-floor process plant layout problems. Based on the mixed integer linear programming (MILP) model, the first solution approach employs a multi-directional search strategy while the second improves solution efficiency by reducing model size through an iterative framework. Both approaches determine the spatial arrangement of the plant equipment considering equipment-floor allocation, non-overlapping constraints, tall equipment penetrating multiple floors, etc. The computational results indicate that the proposed approaches achieved potential cost savings for four illustrative examples when compared to the previous studies. Finally, engineering experience constraints were included to represent a more complex industrial situation, and their applicability was tested with the last example.

There is an increasing recognition of the critical importance of charging for the safety and life of lithium-ion batteries. This paper proposes a computationally efficient optimal control approach for the problem of real-time charging control. By incorporating specific constraints that must be satisfied during charging, a health-aware operation is promoted. To determine the optimal charging current in the given setup, a recently proposed iterative framework for solving constrained optimal control problems is leveraged. It is found that the resulting optimal charging currents can be expressed in terms of a piecewise-affine time-invariant state feedback law, which results in a high computational efficiency for the optimal control solution.

An iterative framework is introduced in this present study to detect seismic isolation precursors of the shortcut calculation method for the isolated benchmark high-speed railway RC bridge....

The main goal of a durable and sustainable airfield is to withstand repeated aircraft traffic loading while minimizing the environmental impact. The objective of this study is to develop a design-life cycle assessment (LCA) framework considering a balanced evaluation of structural adequacy, minimizing emission, and optimizing total energy demand. To achieve this objective, three steps are introduced: an evaluation of the structural adequacy of the design using the Federal Aviation Administration (FAA) pavement design software FAA rigid and flexible iterative elastic layered design; a preliminary performance check using field instrumentation responses; and a LCA of airfield sections using both deterministic and probabilistic approaches. In addition to presenting the design-LCA methodology, this paper offers a comparative evaluation that covers two perpetual designs (LFP1-N and LFP4-N) and one conventional section (LFC5-N). These pavement sections were built and tested at the National Airport Pavement Test Facility as part of construction cycle 7, funded by the FAA. Responses collected from instrumentation were used to compute field-based coverages to failure. Moreover, life cycle inventories from secondary sources were used to quantify the greenhouse gas emissions and energy demand associated with the construction of these sections. Results show inconsistencies between the field-predicted and theoretically predicted performance. This suggests the need for the additional calibration of the currently used performance models. Moreover, this study shows that under a specific asphalt concrete (AC) thickness limit, conventional AC may be more eco-friendly than a perpetual design.

Although there are many reciprocal recommenders based on different strategies which have found applications in different domains but in this paper we aim to design a common framework for both symmetric as well as asymmetric reciprocal recommendation systems (in Indian context), namely Job recommendation (asymmetric) and Online Indian matrimonial system (symmetric). The contributions of this paper is multifold: i) Iterative framework for Reciprocal Recommendation for symmetric as well as asymmetric systems. ii) Useful information extracted from explicit as well as implicit sources which were not explored in the existing system (Free-text mining in Indian Matchmaking System). iii) Considering job-seekers’ personal information like his marital status, kids, current location for suggesting recommendation. iv) Proposed Privacy preservation in the proposed framework for Reciprocal Recommendation system. These parameters are very important from practical viewpoint of a user and we have achieved improved efficiency through our framework as compared to the existing system.

In case of a metro disruption, the collaborative optimization for train rescheduling combined with passenger flow demand is quite essential since passenger flow is not adequately considered. To handle this gap, this study first establishes two separated optimization models, i.e. train rescheduling model and passenger flow model. On train side, short-turnings, fully cancellation or partial cancellation are taken into account to construct the disrupted timetable with the aims of minimizing train delays and timetable deviations from the original timetable. On passenger side, time-dependent OD passenger demand is incorporated into passenger’s arrival, passenger’s loading and alighting process. Then, an iterative framework is proposed to interact these two models. In each iteration loop, random short-turnings and cancellations are applied to calculate new departure and arrival for the services. Next, the corresponding departure time and arrival time is transferred to passenger flow model to evaluate the impacts on passenger’s travel time and the numbers of stranded passengers. The iterative results illustrates that less short-turning can effectively mitigate the numbers of stranded passengers at platforms but it comes at the cost of increasing train delays and passenger travel time. Cancelling trains can help to reduce passenger travel time as well but it should avoid causing heavier train delays to other non-cancelled services. Therefore, reasonable numbers of short-turnings and cancellations during disruption is trade-off between train delays and passenger delays during disruptions.

Imprecise registration between positron emission tomography (PET) and anatomical magnetic resonance (MR) images is a critical source of error in MR imaging-guided partial volume correction (MR-PVC). Here, we propose a novel framework for image registration and partial volume correction, which we term PVC-optimized registration (PoR), to address imprecise registration. The PoR framework iterates PVC and registration between uncorrected PET and smoothed PV-corrected images to obtain precise registration. We applied PoR to the [11C]PiB PET data of 92 participants obtained from the Alzheimer’s Disease Neuroimaging Initiative database and compared the registration results, PV-corrected standardized uptake value (SUV) and its ratio to the cerebellum (SUVR), and intra-region coefficient of variation (CoV) between PoR and conventional registration. Significant differences in registration of as much as 2.74 mm and 3.02° were observed between the two methods (effect size 0.8), which resulted in considerable SUVR differences throughout the brain, reaching a maximal difference of 62.3% in the sensory motor cortex. Intra-region CoV was significantly reduced using the PoR throughout the brain. These results suggest that PoR reduces error as a result of imprecise registration in PVC and is a useful method for accurately quantifying the amyloid burden in PET. Electronic supplementary material The online version of this article (10.1007/s12194-020-00591-2) contains supplementary material, which is available to authorized users.

Truth discovery methods and rule-based data repairing methods are two classic lines of approaches to improve data quality in the field of database. Truth discovery methods resolve the multi-source conflicts for the same entity by estimating the reliabilities of different source, while rule-based data repairing methods resolve the inconsistencies among different entities using integrity constraints. However, both lines of methods suffer unsatisfactory performances due to the lacking of enough evidence. In this paper, we propose AutoRepair, a novel automatic multi-source data repairing approach to enrich the evidence by taking the advantages of truth discovery and data repairing. We use functional dependency, one of the most common types of constraints, to detect the violations, and use the source reliability as evidence to discover and repair the errors among these violations. At the same time, the repaired results are used to estimate the source reliability. As the source reliability is unknown in advance, we model the process as an iterative framework to ensure better performance. Extensive experiments are conducted on both simulated and real-world datasets. The results clearly demonstrate the advantages of our approach, which outperform both recent truth discovery and rule-based data repairing methods.

Estimating the trustworthiness of a set of actors when all the available information is provided by the actors themselves is a hard problem. When two actors have conflicting reports about each other, how do we establish which of the two (if any) deserves our trust? In this paper, we model this scenario as a network problem: actors are nodes in a network and their reports about each other are the edges of the network. To estimate their trustworthiness levels, we develop an iterative framework which looks at all the reports about each connected actor pair to define its trustworthiness balance. We apply this framework to a customer/supplier business network. We show that our trustworthiness score is a significant predictor of the likelihood a business will pay a fine if audited. We show that the market network is characterized by homophily: businesses tend to connect to partners with similar trustworthiness degrees. This suggests that the topology of the network influences the behavior of the actors composing it, indicating that market regulatory efforts should take into account network theory to prevent further degeneration and failures.

Superpixel decomposition methods are widely used in computer vision and image processing applications. By grouping homogeneous pixels, the accuracy can be increased and the decrease of the number of elements to process can drastically reduce the computational burden. For most superpixel methods, a trade-off is computed between 1) color homogeneity, 2) adherence to the image contours and 3) shape regularity of the decomposition. In this paper, we propose a framework that jointly enforces all these aspects and provides accurate and regular Superpixels with Contour Adherence using Linear Path (SCALP). During the decomposition, we propose to consider color features along the linear path between the pixel and the corresponding superpixel barycenter. A contour prior is also used to prevent the crossing of image boundaries when associating a pixel to a superpixel. Finally, in order to improve the decomposition accuracy and the robustness to noise, we propose to integrate the pixel neighborhood information, while preserving the same computational complexity. SCALP is extensively evaluated on standard segmentation dataset, and the obtained results outperform the ones of the state-of-the-art methods. SCALP is also extended for supervoxel decomposition on MRI images., Computer Vision and Image Understanding (CVIU), 2018

Influence maximization for opinion formation (IMOF) in social networks is an important problem, which is used to determine some initial nodes and propagate the most ideal opinions to the whole network. The existing researches focus on improving the opinion formation models to compute the opinion of each node. However, little work has been done to describe the IMOF process mathematically, and the current researches cannot provide an effective mechanism to deal with the IMOF. In this paper, the IMOF is formulated mathematically and solved by an iterative framework. At first, we describe the IMOF as a constrained optimization problem. Then, based on node influence and neighbor coordination, the weighted coordination model is proposed to compute the opinions of network nodes with the change of iterations. In particular, in order to determine top- $k$ influential nodes (i.e., seed nodes), an iterative framework for the IMOF, called IIMOF is presented. Based on the framework, the score and rank of each node by Iterative 2-hop algorithm, i.e., SRI2 is proposed to compute the influence score of each node. Based on small in-degree and high out-degree, one-hop measure is proposed to better reflect the rank of all initial nodes. We also prove that IIMOF converges to a stable order set within the finite iterations. The simulation results show that IIMOF has superior average opinions than the comparison algorithms.

Summary Early quantitative structure-activity relationship (QSAR) technologies have unsatisfactory versatility and accuracy in fields such as drug discovery because they are based on traditional machine learning and interpretive expert features. The development of Big Data and deep learning technologies significantly improve the processing of unstructured data and unleash the great potential of QSAR. Here we discuss the integration of wet experiments (which provide experimental data and reliable verification), molecular dynamics simulation (which provides mechanistic interpretation at the atomic/molecular levels), and machine learning (including deep learning) techniques to improve QSAR models. We first review the history of traditional QSAR and point out its problems. We then propose a better QSAR model characterized by a new iterative framework to integrate machine learning with disparate data input. Finally, we discuss the application of QSAR and machine learning to many practical research fields, including drug development and clinical trials., Graphical abstract, Data analysis in structural biology; Machine learning; Structural biology

A recently emerging seismic acquisition technology called simultaneous source shooting has attracted much attention from both academia and industry. The key topic in the newly developed technique is the removal of intense blending interferences caused by the simultaneous ignition of multiple airgun sources. In this paper, I propose a novel inversion strategy with multiple convex constraints to improve the deblending performance based on the projection onto convex sets (POCS) iterative framework. In the POCS iterative framework, as long as the multiple constraints are convex, the iterations are guaranteed to converge. In addition to the sparse constraint, I seek another important constraint from the untainted data. I create a blending mask in order to fully utilize the useful information hidden behind the noisy blended data. The blending mask is constructed by numerically blending a matrix with all its entries set to be one and then setting the non-one entries of the blended matrix zero. I use both synthetic and field data examples to demonstrate the successful performance of the proposed method.

Since disruptions in railway networks are inevitable, railway operators and infrastructure managers need reliable measures and tools for disruption management. Current literature on railway disruption management focuses most of the time on rescheduling one resource (timetable, rolling stock or crew) at the time. In this research, we describe the application of an iterative framework in which all these three resources are considered. The framework applies existing models and algorithms for rescheduling the individual resources. We extensively test our framework on instances from Netherlands Railways and show that schedules which are feasible for all three resources can be obtained within short computation times. This case study shows that the framework and the existing rescheduling approaches can be of great value in practice. HighlightsWe present an iterative framework for real-time railway disruption management.Our framework reschedules the timetable, the rolling stock, and the crew.Our approach is tested on a huge set of real-world instances from the Netherlands.The framework converges within few iterations for all test instances.

We propose a model reduction procedure for approximating large-scale linear systems with quadratic output by means of lower dimensional systems with the same structure. The framework is based on an iteration that, at each step, computes left and right projections matrices (hence two-sided). This is done by means of solving two linear Sylvester equations.

We address the issue of estimating the topology and dynamics of sparse linear dynamic networks in a hyperparameter-free setting. We propose a method to estimate the network dynamics in a computationally efficient and parameter tuning-free iterative framework known as SPICE (Sparse Iterative Covariance Estimation). The estimated dynamics directly reveal the underlying topology. Our approach does not assume that the network is undirected and is applicable even with varying noise levels across the modules of the network. We also do not assume any explicit prior knowledge on the network dynamics. Numerical experiments with realistic dynamic networks illustrate the usefulness of our method.

The recently adopted global Sustainable Development Goals (SDGs) are intended to catalyse implementation of sustainable development. Their success or failure will rely heavily upon national implementation. However, the SDGs represent a broad, integrated and complex agenda that will be very challenging for countries to implement. Over the past decade, scenario analysis has emerged as a method that is particularly well suited to sustainable development and has seen a rapid expansion in national development planning practice. It will be an invaluable tool for governments in formulating their national SDG strategies. However, despite its increased application, there is limited guidance available on the use of scenario modelling in national development planning. By undertaking a review of the recent best practice literature as well as 22 contemporary scenario modelling case studies, this paper draws out lessons learned and proposes an iterative framework for ex ante scenario modelling to support national SDG planning. Copyright © 2017 John Wiley & Sons, Ltd and ERP Environment

Graphical abstractDisplay Omitted HighlightsAdd and delete operations are encoded as a list/string of integers (ADL).An effective hyper-heuristic approach operating with ADLs is proposed.Low level heuristics perform search over the space of feasible solutions.Proposed approach produces new best solutions to some instances.Proposed approach achieves generality across two variants of the timetabling problem. Hyper-heuristics are (meta-)heuristics that operate at a higher level to choose or generate a set of low-level (meta-)heuristics in an attempt of solve difficult optimization problems. Iterated local search (ILS) is a well-known approach for discrete optimization, combining perturbation and hill-climbing within an iterative framework. In this study, we introduce an ILS approach, strengthened by a hyper-heuristic which generates heuristics based on a fixed number of add and delete operations. The performance of the proposed hyper-heuristic is tested across two different problem domains using real world benchmark of course timetabling instances from the second International Timetabling Competition Tracks 2 and 3. The results show that mixing add and delete operations within an ILS framework yields an effective hyper-heuristic approach.

Feature selection is an important problem in machine learning and data mining. In reality, the wrapper methods are broadly used in feature selection. It treats feature selection as a search problem using a predictor as a black-box. However, most wrapper methods are time-consuming due to the large search space. In this paper, we propose a novel wrapper method, called FeatureBand, for feature selection. We use the early stopping strategy to terminate bad candidate feature subsets and avoid wasting of training time. Further, we use a genetic local search to generate new subsets based on previous ones. These two techniques are combined under an iterative framework in which we gradually allocate more resources for more promising candidate feature subsets. The experimental result shows that FeatureBand achieves a better trade-off between search time and search accuracy. It is 1.45\(\times \) to 17.6\(\times \) faster than the state-of-the-art wrapper-based methods without accuracy loss.

We demonstrated the design pattern as a practical design method utilized for designing adaptive human-robot interaction for social robots. Our research distilled a pattern library to instruct designers and practitioners how to construct compound interaction patterns with interaction blocks. We also devised an iterative framework to investigate the implementation of interaction patterns in the workflow of intelligent robot systems. The authors developed a slew of social robots applying design pattern in adaptive HRI design and prototyping to showcase the advantages and outcomes.

Nowadays, more and more people have their own accounts in different social networks, and they might use the different email addresses or phone numbers in different networks, so how to identify the same person among different social networks become a vital problem, called network alignment. Users with different accounts are called anchor users, researches showed that using some known anchor users to predict the potential anchor links for the full network is an effective way. To predict more accurate anchor links, the paper proposes a new prediction framework ISS, based on a reality of partially aligned social networks, it applies supervised learning based on social feature extraction and strict stable matching, which improve the accuracy of the prediction result, what is more, we apply an iterative framework to refine known information and maximize the prediction results. Experiments have conducted in two realworld heterogeneous social networks, Foursquare and Twitter, and it demonstrates that ISS can predict anchor links among heterogeneous social networks very well and outperform other similar prediction methods.

It has been shown that iterative reweighted strategies will often improve the performance of many sparse reconstruction algorithms. Iterative Framework for Sparse Reconstruction Algorithms (IFSRA) is a recently proposed method which iteratively enhances the performance of any given arbitrary sparse reconstruction algorithm. However, IFSRA assumes that the sparsity level is known. Forward-Backward Pursuit (FBP) algorithm is an iterative approach where each iteration consists of consecutive forward and backward stages. Based on the IFSRA, this paper proposes the Iterative Forward-Backward Pursuit (IFBP) algorithm, which applies the iterative reweighted strategies to FBP without the need for the sparsity level. By using an approximate iteration strategy, IFBP gradually iterates to approach the unknown signal. Finally, this paper demonstrates that IFBP significantly improves the reconstruction capability of the FBP algorithm, via simulations including recovery of random sparse signals with different nonzero coefficient distributions in addition to the recovery of a sparse image.

Recently, construction industry groups in several countries have initiated performance measurement and benchmarking programmes as business strategies to improve performance through comparisons to the best practices, and have been enjoying benefits from them. To draw implications for the Singaporean construction industry, this paper reviews seven global performance measurement and benchmarking initiatives, and two company initiatives, with cross-initiative comparisons. In addition, based on the discussions, an iterative framework is proposed for the performance measurement and benchmarking initiative in the Singaporean construction industry. Although the results are interpreted in the context of Singapore, the review provides an understanding of the existing performance measurement and benchmarking initiatives for students, practitioners and academics in other countries.

Living labs and co-production are increasingly popular strategies for universities to address sustainability challenges and yet the links between them remain largely implicit. This paper discusses the potential of living labs to provide a holistic and iterative framework for the co-production of knowledge. The University Living Lab initiative was launched in 2012 to transform the University of Manchester campus into a site for applied teaching and research around sustainability. Its goal was to provide a framework for students and academics to engage with the opportunities to work with Estates staff and their environmental consultants on applied sustainability challenges. This paper discusses the generation of living lab projects, the design of the campus as a living lab, and institutional visibility, identifying the key strengths of the living lab approach and the challenges of applying it more broadly.

Spectral feature selection (SFS) is getting more and more attention in recent years. However, conventional SFS has some weaknesses that may corrupt the performance of feature selection, since (1) SFS generally preserves the either global structure or local structure, which can’t provide comprehensive information for the model; (2) graph learning and feature selection of SFS is two individual processes, which is hard to achieve the global optimization. Thus, a novel SFS is proposed via introducing a low-rank constraint for capturing inherent structure of data, and utilizing an adaptive graph learning to couple the graph learning and feature data learning in an iterative framework to output a robust and accurate learning model. A optimization algorithm is proposed to solve the proposed problem with a fast convergence. By comparing to some classical and first-class feature selection methods, our method has exhibited a competitive performance.

To deal with the rigid template matching problem in real-world scenarios, we propose a novel iterative feature-pair updating framework which is also robust to high levels of outliers, such as background changing, complex nonrigid deformation and partial occlusion. Given a pair of template image and target image, we first extract a set of corresponding feature-pairs as candidates. Then, we propose a robust objective function under the iterative framework for discriminatively updating these candidates, where the space distance, appearance distance, and the overlapping percentage of feature pairs are integrated simultaneously. Finally, a hierarchical matching strategy is provided with the parameter discussion. Experimental results compared with the-state-of-art methods on public data sets demonstrate the effectiveness of the proposed method.

In this paper, the autofocus compressed sensing (ACS) imaging method is proposed to obtain the imagery with some unknown parameters in the forward model. The proposed method updates the unknown parameters of the measurement matrix and constructs the imagery alternately within an iterative framework. The unknown parameters, denoted as hyperparameters, are estimated using the nonlinear conjugate gradient method. The proposed ACS imaging method is validated using through-the-wall imaging radar data.

The goal of an object category discovery system is to annotate a pool of unlabeled image data, where the set of labels is initially unknown to the system, and must therefore be discovered over time by querying a human annotator. The annotated data is then used to train object detectors in a standard supervised learning setting, possibly in conjunction with category discovery itself. Category discovery systems can be evaluated in terms of both accuracy of the resulting object detectors, and the efficiency with which they discover categories and annotate the training data. To improve the accuracy and efficiency of category discovery, we propose an iterative framework which alternates between optimizing nearest neighbor classification for known categories with multiple kernel metric learning, and detecting clusters of unlabeled image regions likely to belong to a novel, unknown categories. Experimental results on the MSRC and PASCAL VOC2007 data sets show that the proposed method improves clustering for category discovery, and efficiently annotates image regions belonging to the discovered classes.

In this work, we propose a hybrid simulation-based optimization framework to solve the supply chain management problem. The hybrid approach combines a mathematical programming model with an agent-based simulation model and uses them in an iterative framework. The optimization model is used to guide the decisions toward an optimal allocation of resources given the realistic supply chain representation given by the simulation. Thus, the proposed approach provides a more realistic solution compared to a stand-alone optimization model, often a simplified representation of the actual system, by making use of the simulation model, which captures the detailed dynamic behavior of the system. A multiobjective problem has been formulated by taking into consideration the environmental impact of supply chain operations. The proposed framework has been applied to small-scale case studies to study the effectiveness of the approach for such problems. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4612–4626, 2013

Reassembling hand shredded content-less pages is a challenging task, with applications in forensics and fun games. This paper proposes an efficient iterative framework to solve apictorial jigsaw puzzles of hand shredded content-less pages, using only the shape information. The proposed framework consists of four phases. In the first phase, normalized shape features are extracted from fragment contours. Then, for all possible matches between pairs of fragments transformation parameters for alignment of fragments and three goodness scores are estimated. In the third phase, incorrect matches are eliminated based on the score values. The alignments are refined by pruning the set of pairwise matched fragments. Finally, a modified graph-based framework for agglomerative clustering is used to globally reassemble the page(s). Experimental evaluation of our proposed framework on an annotated dataset of shredded documents shows the efficiency in the reconstruction of multiple content-less pages from arbitrarily torn fragments.

Hand shredded content-less pages reassembly is a challenging task. This has applications in forensics and fun games. The process is even more tedious when the number of pages from which the fragments are obtained is unknown. An iterative framework to solve the jigsaw puzzles of multiple hand shredded content-less pages has been proposed in this paper. This framework makes use of the shape-based information alone to solve the puzzle. All pairs of fragments are matched using the normalized shape-based features. Then, incorrect matches between the fragments are pruned using three scores that measure the goodness of the alignment. Finally, a graph-based technique is used to densely arrange the fragments for the global reassembly of the page(s). Experimental evaluation of our proposed framework on an annotated dataset of shredded documents shows the efficiency in the reconstruction of multiple content-less pages from arbitrarily torn fragments and performance metrics have been proposed to numerically evaluate the reassembly.

In this paper, the auto-focusing compressed sensing (ACS) algorithm is proposed to retrieve behind-the-wall image with unknown wall parameters. The ACS algorithm proposed updates the measurement matrix and constructs the behind-the-wall image alternately within an iterative framework. The nonlinear conjugate gradient method is used to estimate the wall parameters, where the gradient functions in analytical form are derived based on the generalized electromagnetic wave penetrating propagation model for through-the-wall radars. Real data are used to validate the proposed method. It is shown that the proposed method can yield finer resolution with less data as compared with the conventional through-the-wall imaging method, e.g. back-projection penetrating imaging method.

Hyper-heuristics are (meta-)heuristics that operate at a higher level to choose or generate a set of low-level (meta-)heuristics in an attempt of solve difficult optimization problems. Iterated local search (ILS) is a well-known approach for discrete optimization, combining perturbation and hill-climbing within an iterative framework. In this study, we introduce an ILS approach, strengthened by a hyper-heuristic which generates heuristics based on a fixed number of add and delete operations. The performance of the proposed hyper-heuristic is tested across two different problem domains using real world benchmark of course timetabling instances from the second International Timetabling Competition Tracks 2 and 3. The results show that mixing add and delete operations within an ILS framework yields an effective hyper-heuristic approach.

Spatial co-location pattern mining discovers the subsets of features whose events are frequently located together in geographic space. The current research on this topic adopts a threshold-based approach that requires users to specify in advance the thresholds of distance and prevalence. However, in practice, it is not easy to specify suitable thresholds. In this article, we propose a novel iterative mining framework that discovers spatial co-location patterns without predefined thresholds. With the absolute and relative prevalence of spatial co-locations, our method allows users to iteratively select informative edges to construct the neighborhood relationship graph until every significant co-location has enough confidence and eventually to discover all spatial co-location patterns. The experimental results on real world data sets indicate that our framework is effective for prevalent co-locations discovery.