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Characterization of the optimization problem is a crucial task in many recent optimization research topics (e.g., explainable algorithm performance assessment, and automated algorithm selection and configuration). The state-of-the-art approaches use exploratory landscape analysis to represent the optimization problem, where for each one, a set of features is extracted using a set of candidate solutions sampled by a sampling strategy over the whole decision space. This paper proposes a novel representation of continuous optimization problems by encoding the information found in the interaction between an algorithm and an optimization problem. The new problem representation is learned using the information from the states/positions in the optimization run trajectory (i.e., the candidate solutions visited by the algorithm). With the novel representation, the problem can be characterized dynamically during the optimization run, instead of using a set of candidate solutions from the whole decision space that have never been observed by the algorithm. The novel optimization problem representation is called Opt2Vec and uses an autoencoder type of neural network to encode the information found in the interaction between an optimization algorithm and optimization problem into an embedded subspace. The Opt2Vec representation efficiency is shown by enabling different optimization problems to be successfully identified using only the information obtained from the optimization run trajectory. • Representation learning is applied on each individual population of optimization process trajectory. • The Opt2Vec representations are captured through the algorithm's behavior (its populations). • The Opt2Vec representations are suitable for dynamic problem characterization. • The Opt2Vec representations are invariant to simple transformations (shifting/scaling). • The Opt2Vec representations are scalable over different problem dimensions. [ABSTRACT FROM AUTHOR]

Currently, image segmentation is widely used in face recognition, medical imaging, traffic control systems, and many other fields. The traditional possibilistic C-means (PCM) algorithm reduces the impact of outliers and noise on the computation of clustering centers; however, the clustering results are still poor due to high noise points. In this paper, an interval possibilistic C-means (IVPCM) algorithm is proposed that expands the natural number of image pixels to an interval value. In addition, a method to calculate the sample distance is proposed. Then, secondary feature extraction of interval values is conducted to improve the clustering. Several experiments show that IVPCM has more advantages in segmentation performance. [ABSTRACT FROM AUTHOR]

A method to enhance Web service clustering is proposed in this paper. Since current service clustering methods usually face low quality of service representation vectors and lack consideration of service collaboration, we try to provide an improved topic model to generate high-quality service representation vectors and design a service clustering method to integrate function similarity and collaboration similarity. First, by introducing feature word extraction and probability distribution correction into GSDMM, we present a model called TE-GSDMM (topic enhanced Gibbs sampling algorithm for the Dirichlet Multinomial Mixture model). Then, a service collaboration graph is put forward to model cooperation relationships and generate service collaboration vectors. Collaboration similarity is assessed by the similarity of service collaboration vectors. Finally, the K-means++ algorithm is employed to cluster Web services by evaluating service function similarity and collaboration similarity. Experiments show that TE-GSDMM outperforms other topic models in generating high-quality service representation vectors for service clustering. Moreover, service clustering performance is further improved by integrating collaboration similarity. Thus, the proposed method effectively enhances Web service clustering by improving the quality of service representation vectors and integrating service collaboration similarity. [ABSTRACT FROM AUTHOR]

It is widely accepted that webpage layout is important for website aesthetics. However, knowledge is limited about which kind of webpage layout is aesthetic. In this paper, we conduct a large-scale analysis of 13,017 real webpages with different aesthetic levels. The results demonstrate that there are significant differences between aesthetic webpages and non-aesthetic webpages in several aspects of layout design, such as the position of objects and the average number of objects. In order to identify whether the web layout is aesthetic, we propose a novel Webpage Layout Aesthetic Evaluation model (WLAE) based on an improved Adaboost algorithm to automatically predict the aesthetics of a webpage layout. Experimental results show that our WLAE model is significantly better than other existing methods. Based on the feature analysis of 13,017 real-world webpages and the contribution of each feature to webpage aesthetic prediction, we have summarized 4 webpage design principles for webpage designers. Besides, a large number of real webpages collected by our team can be used for further research on webpage layout aesthetic design and/or related research. [ABSTRACT FROM AUTHOR]