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Feature models are commonly used to specify the valid configurations of product lines. As industrial feature models are typically complex, researchers and practitioners employ various automated analyses to study the configuration spaces. Many of these automated analyses require that numerous complex computations are executed on the same feature model, for example by querying a SAT or #SATsolver. With knowledge compilation, feature models can be compiled in a one-time effort to a target language that enables polynomial-time queries for otherwise more complex problems. In this work, we elaborate on the potential of employing knowledge compilation on feature models. First, we gather various feature-model analyses and study their computational complexity with regard to the underlying computational problem and the number of solver queries required for the respective analysis. Second, we collect knowledge-compilation target languages and map feature-model analyses to the languages that make the analysis tractable. Third, we empirically evaluate publicly available knowledge compilers to further inspect the potential benefits of knowledge-compilation target languages. [ABSTRACT FROM AUTHOR]

This paper provides a review on representation learning for videos. We classify recent spatio-temporal feature learning methods for sequential visual data and compare their pros and cons for general video analysis. Building effective features for videos is a fundamental problem in computer vision tasks involving video analysis and understanding. Existing features can be generally categorized into spatial and temporal features. Their effectiveness under variations of illumination, occlusion, view and background are discussed. Finally, we discuss the remaining challenges in existing deep video representation learning studies. [ABSTRACT FROM AUTHOR]

To enable the people involved in a software development process to communicate and reason close to their area of knowledge, we are investigating and engineering a method that formalizes and integrates knowledge of multiple domains into domain models and into specifications in terms of those domain models. We follow an action research approach, starting with a diagnosis phase, in which we have previously defined a set of method objectives, and performed a systematic literature review. During action planning, we defined how we are going to develop the method—called Multi‐Domain Formalization Method (MuDForM). This paper reports on the methodical support for using a domain model as the terminology for feature specifications. During action taking, we defined an initial version of the method and set up case studies. During the evaluation phase, we performed a case study to validate how well the method helps in the specification of processes and to realize the case‐specific objectives of the customer. The case study pertains to the formalization of the ISO26262 standard for functional safety in the automotive domain. The created models are explained to the involved experts to ensure their consistency with the original text. We found that MuDForM is suitable to systematically formalize processes described in natural language, such that the resulting process models are fully expressed in terms of domain concepts and concepts from outside the domains and processes of interest. Further, during the specifying learning phase, we have extended our method with concepts, steps, and guidelines for grammatical analysis, for formalization of constraints, and for the specification of processes. [ABSTRACT FROM AUTHOR]

To ensure the microwave scatterometer is accurately calibrated, natural targets with stability, homogeneity, and isotropy are selected as references. The broad and continuous spatial distribution of the tropical rainforest, along with its relatively consistent vegetation cover, makes it an ideal choice.A tropical rainforest optimal stable area selection algorithm, combining mean, standard deviation, and relative standard deviation, is proposed using measurements from the advanced scatterometer (ASCAT) onboard the second Meteorological Operational satellite (MetOp-B) from 2019 to 2021. It is used to identify stable areas within the Amazon, Congo, and Southeast Asian rainforests. Results show that the Amazon rainforest has a larger stable area compared to the Congo and Southeast Asian rainforests, indicating more consistent backscatter across space. However, the Southeast Asian rainforest exhibits scattered stable areas and unstable backscatter properties.To accurately model the intrinsic characteristics of targets within stable areas, influences of seasonal variations, incidence angles and azimuth angles are comprehensively considered. The scatterometer, as an independently measured remote sensing instrument, is not affected by seasonal variations on the earth and experiences minimal temperature-related fluctuations. Therefore, seasonal characteristics of backscatter coefficients in rainforests can be modeled to reduce their impact. Different incidence and azimuth angles can cause variations in the backscatter coefficient. To address this issue, responses to these aspects are also modeled. It is observed that daytime data, with lower model errors, shows greater stability in the stable areas of the Amazon and Congo rainforests. Therefore, daytime data from these areas should be selected to assess instrument stability.A stability verification of ASCAT measurements from the stable areas of the Amazon and Congo rainforest on MetOp-C, covering the period from 1 July 2019 to 31 October 2023, is carried out based on model coefficients derived from the continuous three-year data of ASCAT on MetOp-B. The calibration stability verification quantifies the magnitude of variations in ASCAT measurements over different periods. Through analysis, it's found that measurements from the ASCAT on MetOp-C shows regular fluctuations of about 0.05 dB, indicating relatively stable characteristics.

Despite their variability, Digital Health Apps (DHAs) typically share functionalities (i.e. core assets) and can thus be considered as a family of similar products with unique features adapted to specific use cases. Objective: We aim to identify and model reusable core assets to facilitate the development of a number of similar, but adapted DHAs in the context of an initial product line engineering approach. Methods: To identify core assets, we apply a systematic analysis of six exemplary state-of-the-art DHAs. In an iterative process, they were modeled in a feature model. Results: We identified 14 core assets of DHAs out of which six are mandatory (i.e. required by each DHA) and eight are optional core assets (i.e. required by most DHAs, depending on the app complexity). Conclusions: We found that DHAs share common functionalities that could contribute to a more efficient development of the DHA, especially in terms of time and cost savings. [ABSTRACT FROM AUTHOR]

Visual aesthetic impact has become the primary embodiment of interactivity in graphic design with the rapid development of internet technology and the deep promotion of modern graphic design concepts. Based on the combination of visual object recognition algorithms and interactive features in graphic design in the field of artificial intelligence, this research aims to meet users' needs for diverse graphic design images through intelligent generation. First, a hierarchical model is proposed to objectively quantify the features of graphic design images. The model divides images into pixels, elements, relations, planes, and application levels. It defines the representation of features for each category. Element colors are extracted using a clustering algorithm to calculate layout and color perception features. The model maintains proper academic structure and language conventions while providing clear and concise information. Next, a prediction model is constructed for image geometric features in graphic design. By fitting the distribution of geometric features found in the data, a prediction is made that the probability distribution of position and color elements under uncertain conditions will facilitate the production of flat images. To create the predictive model, the method of sampling features was investigated. Finally, the graphic design is reconstructed according to the interactive features to generate diversified graphic design images. In 3000 random graphic design images, each feature is grouped and visualized as clustered. The results show that the images with more clusters are influenced by the background color, and the images with the same color system are easier to cluster. The test results imply that the accuracy rates of design elements, design labels and label borders are 99.7%, 89.5% and 83.2% respectively, indicating that in the existing labeling results, there are very few labeling errors of design elements case. By combining visual interactive displays to construct graphic design images with different feature distributions through feature quantification, graphic design images with different styles and features be effectively generated to meet the individual needs of different consumers. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The growing awareness of global warming and environmental pollution problems due to the massive emissions of carbon dioxide (CO2), as well as energy consumption, is pushing manufacturers to convert their production towards sustainable strategies. Despite the research efforts to date, work on addressing feature interactions for applications in green manufacturing is still very lacking, especially those based on CAD/CAM systems. In this study, important technologies for eco-design and sustainable manufacturing processes are combined, from machining operations to improvements in production techniques, while taking into account the reduction of the Environmental Impact (EI) that causes pollution. A sustainable manufacturing method is proposed. It uses feature sequence technology based on feature interaction technology to perform EI evaluation on the machining process in manufacturing engineering stages based on CAD/CAM systems to improve the environmental quality of process planning. Finally, the feasibility of the methodology and the algorithm are illustrated by a case study. [ABSTRACT FROM AUTHOR]

The product line approach enables the rapid building of software products by reusing core assets. How to manage the commonality and variability of product family is the core problem of product line engineering. The feature modeling approach is one of the main approaches to solve this problem. However, this approach does not define the internal functions of features. Some researchers use formal descriptions to define the function of features, but they do not consider the connection between feature specification and feature model. These product family modeling approaches result in product family models that suffer from inconsistent descriptions, which are difficult to resolve manually. To this end, this paper proposes a computer-aided approach to construct an internally consistent product family model. The method performs an inconsistency check during the formalization of each feature in the given initial feature model, pushing the evolution of the feature model and feature specification. The creation and evolution of feature models are achieved through a template-based feature modeling approach. The method is implemented by means of templates, each of which contains two parts of knowledge: the elements required for a class of feature model, and a way to convert the given elements into a formal description. An inconsistency check is achieved through a rule-based detection approach. Each rule contains two parts: a class of conditions that the feature model and the feature specification satisfy, and the actions to resolve inconsistencies once the conditions are satisfied. We have developed a tool. The tool guides the user to build an internally consistent product family model that contains the feature model and feature specifications. Experiments have shown that our approach can provide effective automation support for the process of formal product family modeling. [ABSTRACT FROM AUTHOR]

Air pollution is a major issue because Particulate Matter (PM) has a substantially higher effect on human health than other pollutants. Air Quality (AQ) prediction has become critical recently to take action to reduce pollution. This research introduces a unique methodology for assessing the effectiveness of PM10 and PM2.5. Enhanced spatial, temporal sequence-Improved Sparse Auto Encoder with Deep Learning (EISAE-DL) has been proposed to predict AQ affected by the prolonged dependency of air pollution congregation. However, Long Short-Term Memory (LSTM) used in EISAE-DL has suffered from the learning of a long-term dependent sequence of the training dataset. In addition, it is hard to create very reliable AQ forecasts at higher periodic frequencies, such as daily, weekly, or even monthly. This paper proposes Transfer learning (TL) in a Stacked Bidirectional and Unidirectional LSTM to solve the learning issue in LSTM for long-term dependencies. So, EISAE-DL with TL and modified LSTM model is named as EISAEDeep Transfer Learning (EISAE-DTL). TL with a modified structure can handle large-size datasets effectively. However, training time is increased more than twice for non-transfer learning way of modeling due to TL, Wasserstein Distance-based adversarial learning is proposed in EISAE-DTL to decrease the variances among AQ data collected from any two sites. The proposed work is named EISAE-Enhanced DTL (EISAE-EDTL). The developed EISAE-DTL and EISAE-EDTL models are compared and analyzed with the performance of existing algorithms EISAE-DL, ISAE-DL, TL-BLSTM, MMSL, and ST-DNN. The experimental findings demonstrate the accuracy, precision, sensitivity, specificity, Area Under Curve (AUC), andMatthew's correlation coefficient of the proposed model performs admirably and improves present condition approaches. [ABSTRACT FROM AUTHOR]

Low back pain (LBP) is a global health-problem phenomenon. Most patients are categorized as non-specific, thus requiring an individualized approach which still poses a major challenge. In this paper, sEMG recordings from two pairs of lumbar muscle sites were collected during an isometric trunk extension exercise. Ninety-one subjects were included in the study; 29 patients with non-specific chronic LBP (CLBP), 25 patients with radiculopathy (RLBP), and 37 control healthy subjects (HS). Six best-performing time-domain raw features were employed to model contextual secondary feature groups. Neuromuscular LBP characteristics were described with coordination, co-activation, trends, and fatigue measures. Altogether, a set of 327 secondary features was created where inputs into the classification models were further refined by employing neighborhood component analysis (NCA). NCA effectively reduced the number of features ( ${k}$ -nearest neighbor ( ${k}$ NN) based classifiers consistently demonstrated best classification results. Different profiles of CLBP patients were presented and discussed. The suggested method demonstrated the potential for patients’ subgrouping and subsequent more individualized rehabilitation treatments, backed by medical interpretations through feature modeling.

Context: Software product lines (SPLs) have reached a considerable level of adoption in the software industry. The most commonly used models for managing the variability of SPLs are feature models (FMs). The analysis of FMs is an error-prone, tedious task, and it is not feasible to accomplish this task manually with large-scale FMs. In recent years, much effort has been devoted to developing reasoning algorithms for FMs. Aim: To synthesize the evidence on the use of reasoning algorithms for feature modeling. Method: We conducted a systematic mapping study, including six research questions. This study included 66 papers published from 2010 to 2020. Results: We found that most algorithms were used in the domain stage (70%). The most commonly used technologies were transformations (18%). As for the origins of the proposals, they were mainly rooted in academia (76%). The FODA model continued to be the most frequently used representation for feature modeling (70%). A large majority of the papers presented some empirical validation process (90%). Conclusion: We were able to respond to the RQs. The FODA model is consolidated as a reference within SPLs to manage variability. Responses to RQ2 and RQ6 require further review. [ABSTRACT FROM AUTHOR]

An error compensation method based on assembly constraints of aircraft pipe is proposed for solving the problem of frequent failures caused by high assembly stress in the actual assembly process. Firstly, the pipe assembly process’ geometric modeling is carried out using geometric modeling method, and a new modeling method based on axis vector is proposed. On this basis, the Rodrigues formula is used to establish a pipe space pose calculation model based on actual assembly conditions. Then, the assembly constraints are analyzed, and the key constraint features are identified based on the pipe assembly requirements. Subsequently, the pipe assembly scenarios under different constraint forms are analyzed, and the pipe error compensation methods under a single constraint and associated constraint are respectively proposed. Finally, a typical flaring pipe is selected for the test; the pipe assembly error compensation calculation and the pipe installation air tightness test are carried out, respectively. The results show that the proposed method could effectively realize the theoretical space pose adjustment and the pipe parameter compensation, and the air tightness of the compensated pipe is better than that of the uncompensated one. [ABSTRACT FROM AUTHOR]

Prediction of RNA secondary structure is an important part of bioinformatics genomics research. Mastering RNA secondary structure can help us to better analyze protein synthesis, cell differentiation, metabolism, and genetic processes and thus reveal the genetic laws of organisms. Comparative sequence analysis, support vector machine, centroid method, and other algorithms in RNA secondary structure prediction algorithms often use dynamic programming algorithm to predict RNA secondary structure because of their huge time and space consumption and complex data structure. In this article, the domain of RNA secondary structure prediction algorithm based on dynamic programming (DP-SSP) is analyzed in depth, and the domain features are modeled. According to the generative programming method, the DP-SSP algorithm components are interactively designed. With the support of PAR platform, the DP-SSP algorithm component library is formally realized. Finally, the concrete algorithm is generated through component assembly, which improves the efficiency and reliability of algorithm development. [ABSTRACT FROM AUTHOR]

Prediction of RNA secondary structure is an important part of bioinformatics genomics research. Mastering RNA secondary structure can help us to better analyze protein synthesis, cell differentiation, metabolism, and genetic processes and thus reveal the genetic laws of organisms. Comparative sequence analysis, support vector machine, centroid method, and other algorithms in RNA secondary structure prediction algorithms often use dynamic programming algorithm to predict RNA secondary structure because of their huge time and space consumption and complex data structure. In this article, the domain of RNA secondary structure prediction algorithm based on dynamic programming (DP-SSP) is analyzed in depth, and the domain features are modeled. According to the generative programming method, the DP-SSP algorithm components are interactively designed. With the support of PAR platform, the DP-SSP algorithm component library is formally realized. Finally, the concrete algorithm is generated through component assembly, which improves the efficiency and reliability of algorithm development.

The product line approach enables the rapid building of software products by reusing core assets. How to manage the commonality and variability of product family is the core problem of product line engineering. The feature modeling approach is one of the main approaches to solve this problem. However, this approach does not define the internal functions of features. Some researchers use formal descriptions to define the function of features, but they do not consider the connection between feature specification and feature model. These product family modeling approaches result in product family models that suffer from inconsistent descriptions, which are difficult to resolve manually. To this end, this paper proposes a computer-aided approach to construct an internally consistent product family model. The method performs an inconsistency check during the formalization of each feature in the given initial feature model, pushing the evolution of the feature model and feature specification. The creation and evolution of feature models are achieved through a template-based feature modeling approach. The method is implemented by means of templates, each of which contains two parts of knowledge: the elements required for a class of feature model, and a way to convert the given elements into a formal description. An inconsistency check is achieved through a rule-based detection approach. Each rule contains two parts: a class of conditions that the feature model and the feature specification satisfy, and the actions to resolve inconsistencies once the conditions are satisfied. We have developed a tool. The tool guides the user to build an internally consistent product family model that contains the feature model and feature specifications. Experiments have shown that our approach can provide effective automation support for the process of formal product family modeling.

Fruit grading is an essential post-harvest task in the olive industry, where size-and-mass based fruit classification is especially important when processing high-quality table olives. Within this context, this research presents a new methodology aimed at supporting accurate automatic olive-fruit grading by using computer vision techniques and feature modeling. For its development, a total of 3600 olive-fruits from nine varieties were photographed, stochastically distributing the individuals on the scene, using an ad-hoc designed an imaging chamber. Then, an image analysis algorithm, based on mathematical morphology, was designed to individually segment olives and extract descriptive features to estimate their major and minor axes and their mass. Regarding its accuracy for the individual segmentation of olive-fruits, the algorithm was proven through 117 captures containing 11 606 fruits, producing only six fruit-segmentation mistakes. Furthermore, by linearly correlating the data obtained by image analysis and the corresponding reference measurements, models for estimating the three features were computed. Then, the models were tested on 2700 external validation samples, giving relative errors below 0.80% and 1.05% for the estimation of the major and minor axis length for all varieties, respectively. In the case of estimating olive-fruit mass, the models provided relative errors never exceeding 1.16%. The ability of the developed algorithm to individually segment olives stochastically positioned, along with the low error rates of around 1% reported by the estimation models for the three features, makes the methodology a promising alternative to be integrated into a new generation of improved and non-invasive olive classification machines. The new developed system has been registered in the Spanish Patent and Trademark Office with the number P201930297.

This paper presents a feature modeling approach to address the 3D structural topology design optimization with feature constraints. In the proposed algorithm, various features are formed into searchable shape features by the feature modeling technology, and the models of feature elements are established. The feature elements that meet the design requirements are found by employing a feature matching technology, and the constraint factors combined with the pseudo density of elements are initialized according to the optimized feature elements. Then, through controlling the constraint factors and utilizing the optimization criterion method along with the filtering technology of independentmesh, the structural design optimization is implemented. The present feature modeling approach is applied to the feature-based structural topology optimization using empirical data. Meanwhile, the improved mathematical model based on the density method with the constraint factors and the corresponding solution processes are also presented. Compared with the traditional methodwhich requires complicated constraint processing, the present approach is flexibly applied to the 3D structural design optimization with added holes by changing the constraint factors, thus it can design a structure with predetermined features more directly and easily. Numerical examples show effectiveness of the proposed feature modeling approach, which is suitable for the practical engineering design. [ABSTRACT FROM AUTHOR]

Feature models are a helpful means to document, manage, maintain, and configure the variability of a software system, and thus are a core artifact in software product-line engineering. Due to the various purposes of feature models, they can be a cross-cutting concern in an organization, integrating technical and business aspects. For this reason, various stakeholders (e.g., developers and consultants) may get involved into modeling the features of a software product line. Currently, collaboration in such a scenario can only be done with face-to-face meetings or by combining single-user feature-model editors with additional communication and version-control systems. While face-to-face meetings are often costly and impractical, using version-control systems can cause merge conflicts and inconsistency within a model, due to the different intentions of the involved stakeholders. Advanced tools that solve these problems by enabling collaborative, real-time feature modeling, analogous to Google Docs or Overleaf for text editing, are missing. In this article, we build on a previous paper and describe (1) the extended formal foundations of collaborative, real-time feature modeling, (2) our conflict resolution algorithm in more detail, (3) proofs that our formalization converges and preserves causality as well as user intentions, (4) the implementation of our prototype, and (5) the results of an empirical evaluation to assess the prototype’s usability. Our contributions provide the basis for advancing existing feature-modeling tools and practices to support collaborative feature modeling. The results of our evaluation show that our prototype is considered helpful and valuable by 17 users, also indicating potential for extending our tool and opportunities for new research directions. [ABSTRACT FROM AUTHOR]

Context: Software product lines (SPLs) have reached a considerable level of adoption in the software industry. The most commonly used models for managing the variability of SPLs are feature models (FMs). The analysis of FMs is an error-prone, tedious task, and it is not feasible to accomplish this task manually with large-scale FMs. In recent years, much effort has been devoted to developing reasoning algorithms for FMs. Aim: To synthesize the evidence on the use of reasoning algorithms for feature modeling. Method: We conducted a systematic mapping study, including six research questions. This study included 66 papers published from 2010 to 2020. Results: We found that most algorithms were used in the domain stage (70%). The most commonly used technologies were transformations (18%). As for the origins of the proposals, they were mainly rooted in academia (76%). The FODA model continued to be the most frequently used representation for feature modeling (70%). A large majority of the papers presented some empirical validation process (90%). Conclusion: We were able to respond to the RQs. The FODA model is consolidated as a reference within SPLs to manage variability. Responses to RQ2 and RQ6 require further review.

A software product line is used for the development of a family of products utilizing the reusability of existing resources with low costs and time to market. Feature Model (FM) is used extensively to manage the common and variable features of a family of products, such as Internet of Things (IoT) applications. In the literature, the binary pattern for nested cardinality constraints (BPNCC) approach has been proposed to compute all possible combinations of development features for IoT applications without violating any relationship constraints. Relationship constraints are a predefined set of rules for the selection of features from an FM. Due to high probability of relationship constraints violations, obtaining optimum features combinations from large IoT-based FMs are a challenging task. Therefore, in order to obtain optimum solutions, in this paper, we have proposed multi-objective optimum-BPNCC that consists of three independent paths (first, second, and third). Furthermore, we applied heuristics on these paths and found that the first path is infeasible due to space and execution time complexity. The second path reduces the space complexity; however, time complexity increases due to the increasing group of features. Among these paths, the performance of the third path is best as it removes optional features that are not required for optimization. In experiments, we calculated the outcomes of all three paths that show the significant improvement of optimum solution without constraint violation occurrence. We theoretically prove that this paper is better than previously proposed optimization algorithms, such as a non-dominated sorting genetic algorithm and an indicator-based evolutionary algorithm.

Hand gesture recognition is one of the most popular Human Computer Interface. The first step in most vision-based gesture recognition system is the hand detection and segmentation. Since hands are involved in a variety of daily tasks, the detection work suffers from both extreme illumination changes and the intrinsic variability of hand appearance. To overcome these problems, we propose a new method for 2D hand detection which can be considered as a combination of Multi-Feature based hand proposal generation and cascaded convolutional neural network (CCNN) classification. Considered various luminance, we choose color, Gabor, HOG and SIFT feature to discriminate skin region and generate hand proposal. Also, we propose a cascaded CNN that keeps the deep context information to detect hand among the proposals. The proposed Multi-Feature Supervised Cascaded CNN (MFS-CCNN) method is tested on a combination of several datasets including Oxford Hands Dataset, VIVA hand detection and Egohands Dataset as positive sample and ImageNet 2012, FDDB dataset as negative sample. The proposed method achieves competitive results. [ABSTRACT FROM AUTHOR]

Highlights • A group of algorithms for the feature model optimization problem is proposed. • Algorithms rely on classic search techniques and admissible heuristics. • Simplifications and limitations of related approaches are addressed. • Evaluation is based on a large set of real models. Abstract Feature models are a well-known formalism for capturing variability, commonalities and configuration rules of software systems. These models are a compact representation of the set of products in a software product line or configurations of a system at runtime, in terms of features and logical relationships among them. The feature model optimization problem consists of selecting a valid product from the model that satisfies a set of resource or business restrictions and optimizes an objective function commonly related to user preferences. This problem, although computationally intractable, has been addressed in several works with different algorithms. However, these approaches appeal to simplifications of the problem or present drawbacks that limit their application. For example, several approaches do not contemplate feature interactions, and some of them do not guarantee exact solutions or even valid solutions satisfying complex constraints. In this article, we propose a novel algorithm called CSA that overcomes the performance and common weaknesses of existing approaches. CSA can be parameterized with a set of classic search strategies (Backtracking, Branch & Bound, and Best-First Search) and heuristics that allow us to leverage solution optimality and search efficiency. This makes CSA appropriate for automating decisions both at design-time, where exact solutions are generally required, and at run-time, where selection must be done efficiently but sub-optimal solutions are acceptable. The algorithm supports different formats of objective functions, including multi-linear polynomial functions that are capable of representing feature interactions. We present an analysis to validate algorithm properties, and then a series of experiments with synthetic and real models to empirically compare CSA with existing alternatives to show the benefits of our approach. In our analysis, CSA showed to be complete, exact, and scalable for searching approximate solutions. The empirical results showed that the approximate variant of CSA can reach an optimality degree of 99%, against a 84% and 93% reached by other approximate alternatives based on genetic and greedy algorithms respectively. In terms of response time, CSA performed a 72% better than other approximate algorithms. Compared to other exact approaches, CSA improves response time on specific problem types. Furthermore, CSA was evaluated with problem instances involving feature interactions, showing that performance properties scale properly when the number of feature interactions increases. [ABSTRACT FROM AUTHOR]

In an industrial project, we addressed the challenge of developing a software-based video generator such that consumers and providers of video processing algorithms can benchmark them on a wide range of video variants. This article aims to report on our positive experience in modeling, controlling, and implementing software variability in the video domain. We describe how we have designed and developed a variability modeling language, called VM, resulting from the close collaboration with industrial partners during 2 years. We expose the specific requirements and advanced variability constructs; we developed and used to characterize and derive variations of video sequences. The results of our experiments and industrial experience show that our solution is effective to model complex variability information and supports the synthesis of hundreds of realistic video variants. From the software language perspective, we learned that basic variability mechanisms are useful but not enough; attributes and multi-features are of prior importance; meta-information and specific constructs are relevant for scalable and purposeful reasoning over variability models. From the video domain and software perspective, we report on the practical benefits of a variability approach. With more automation and control, practitioners can now envision benchmarking video algorithms over large, diverse, controlled, yet realistic datasets (videos that mimic real recorded videos)—something impossible at the beginning of the project. [ABSTRACT FROM AUTHOR]