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Advanced organizational processes today require many process-relevant artifacts to be formally represented in process models. As these processes become more complex, process designers face the challenge of maintaining high comprehensibility while adequately visualizing process-relevant information. Modularization has been introduced to address the increasing complexity and information overload of process models, tackling this issue. While research acknowledges the importance of process model comprehensibility in order to benefit from the merits of such models, there still needs to be more clarity about the benefits of process model modularization. Recent research has focused on the human visual system and investigated visual behavior in the context of process model literacy. Continuing this branch of research, this paper analyses gaze behavior when comprehending modularized process models. A follow-up eye-tracking study was conducted to comprehend differently modularized BPMN 2.0 process models (i.e., vertical, horizontal, orthogonal). The results indicated that process model readers comprehend modularization approaches differently. Vertical modularization was perceived as the best comprehensible approach, whereas horizontal received the least acceptance. Considered eye movement performance measures confirm prior observation that attention is deployed more effectively in vertical modularization. Based upon the findings, the paper proposes a revised theoretical model from prior work that relates visual tasks to the execution of visual routines, explaining comprehension and information processing in modularized process models. The theoretical model elaborates how the visual system process and comprehends information obtained from modularized process models. Furthermore, the paper derived implications for modeling and practical applications, pointing out solid representational points and shortcomings of each modularization approach regarding how modularized process models are read and comprehended. [ABSTRACT FROM AUTHOR]

The uncertainty associated with option price predictions has largely been overlooked in the literature. This paper aims to fill this gap by quantifying such uncertainty using conformal prediction. Conformal prediction is a model-agnostic procedure that constructs prediction intervals, ensuring valid coverage in finite samples without relying on distributional assumptions. Through the simulation of synthetic option prices, we find that conformal prediction generates prediction intervals for gradient boosting machines with an empirical coverage close to the nominal level. Conversely, non-conformal prediction intervals exhibit empirical coverage levels that fall short of the nominal target. In other words, they fail to contain the actual option price more frequently than expected for a given coverage level. As anticipated, we also observe a decrease in the width of prediction intervals as the size of the training data increases. However, we uncover significant variations in the width of these intervals across different options. Specifically, out-of-the-money options and those with a short time-to-maturity exhibit relatively wider prediction intervals. Then, we perform an empirical study using American call and put options on individual stocks. We find that the empirical results replicate those obtained in the simulation experiment. • Conformal prediction quantifies well the uncertainty of option price predictions. • Conformal prediction intervals have empirical coverage near the nominal level. • Non-conformal prediction intervals have empirical coverage below the nominal level. • Large variations in prediction intervals are found for American call and put options. • Larger intervals are found for out-of-the-money and short time-to-maturity options. [ABSTRACT FROM AUTHOR]

Over recent decades, China's economy has continued to develop steadily and rapidly, contributing to a large environmental cost. Therefore, many researchers propose various methods to evaluate environmental efficiency to deal with these serious issues accurately. This paper is the first to evaluate environmental efficiency using a model featuring not only a parallel structure with three component processes (primary, secondary, and tertiary industries) in the first stage but also a sewage treatment process in the second stage. Simultaneously, we propose radial but also nonradial versions of the Environmental Multiple Variable Return to Scale (EMVRS) model. These two models both consider shared inputs as well as shared outputs, and the proportion of the shared inputs and shared outputs utilized in each component process is assumed to be unknown. Additionally, we further analyze the factors influencing environmental efficiency by utilizing the Tobit model. The results when EMVRS is applied to Chinese provinces can be divided into 3 parts: (1) Most provinces do not purify wastewater appropriately contributing to the bad environmental efficiency. (2) The efficiency values of the three industry types are similar or identical to each other in most provinces. (3) The population of people employed negatively impacts environmental efficiency. [ABSTRACT FROM AUTHOR]

Prior research lacks understanding about how collaborative arrangements and network structures influence innovation outcomes in platform-based ecosystems. This paper addresses this gap by using an NK simulation model to investigate the impacts of different collaboration patterns and network structures on innovation performance. The NK simulation approach overcomes the shortcomings of empirical methods and enables examining the dynamic impacts. The results reveal that the "special platform + generic complementor" pattern leads to the highest innovation output. The impact of component correlations on the innovation performance follows an inverted U-shape. The small world network structure promotes innovation versus regular or random networks. The results provide novel theoretical insights into strategically configuring platform partnerships and network connections to optimize innovation. The findings offer practical guidance for firms to choose beneficial collaboration pattern and design proper network structure. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the impact of pattern leakage during data preprocessing on the reliability of Machine Learning (ML) based intrusion detection systems (IDS). Data leakage, also known as pattern leakage, occurs during data preprocessing when information from the testing set is used in training, leading to overfitting and inflated accuracy scores. Our study uses three well-known intrusion detection datasets: NSL-KDD, UNSW-NB15, and KDDCUP99. We preprocess the data to create versions with and without pattern leakage and train and test six ML models: Decision Tree (DT), Gradient Boosting (GB), K-neighbours (KNN), Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR). Our results show that building IDS models with data leakage leads to higher accuracy but is unreliable. Additionally, we find that some algorithms are more sensitive to data leakage than others, as seen by the drop in model accuracy when built without leakage. To address this problem, we provide suggestions for mitigating data leakage in the training process and analyzing the sensitivity of different algorithms. Overall, our study emphasizes the importance of addressing data leakage in the training process to ensure the reliability of ML-based IDS models. [ABSTRACT FROM AUTHOR]

A detailed and extensive empirical study of dynamic selection (DS) and random under-sampling (RUS) for the class imbalance problem is conducted in this paper. Total 20 state of the art DS methods are compared on 54 datasets. The empirical results clearly answer the following six key research questions in this direction, (1) how performances of ensembles with and without RUS compare with respect to different DS and static ensemble (SE) methods (2) how whether RUS is used affects performances of ensembles with respect to different DS/SE methods (3) how performances of different DS/SE methods compare with respect to ensembles with and without RUS (4) whether DS methods perform better than SE methods no matter whether RUS is used and what types of ensembles are used (5) how numbers of base classifiers affect how performances of ensembles with and without RUS compare with respect to different DS/SE methods (6) how numbers of base classifiers affect how performances of different DS/SE methods compare with respect to ensembles with and without RUS. The answers to the six research questions based on the experimental results in this study and the experimental findings are the main contributions of this work. • An extensive empirical study of DES with RUS for class imbalance is conducted. • Total 20 state of the art dynamic selection methods are compared on 54 datasets. • Three types of ensembles, bagging, random forests and AdaBoosting, are compared. • The experimental results and findings are important for the DES research community. [ABSTRACT FROM AUTHOR]

Sine cosine algorithm (SCA) is a recently developed and widely used metaheuristic to perform global optimization tasks. Due to its simplicity in structure and reasonable performance, it has been utilized to solve several real-world applications. This paper proposes an alternate version of the SCA by adopting the greedy approach of search, crossover and exponentially decreased transition control parameter to overcome the issues of low exploitation, insufficient diversity and premature convergence. The proposed algorithm, called ECr-SCA, is validated and compared with the original SCA using computational time, diversity, performance index, statistical and convergence analysis on a set of 23 standard benchmark problems. Later, the proposed ECr-SCA is compared with seventeen other algorithms including improved versions of the SCA and state-of-the-art algorithms. Furthermore, the ECr-SCA is used to train multi-layer perceptron and the results are compared with variants of SCA and other metaheuristics. Overall comparison based on several different metrics illustrates the significant improvement in the search strategy of the SCA by the proposal of the ECr-SCA. • The enhanced sine cosine algorithm (ECr-SCA) is proposed for global optimization. • A greedy search and modified transition parameter are used to increase exploitation. • The multi-parent crossover is embedded to enhance diversity and global search. • The excellent performance of the ECr-SCA is validated on benchmark problems. • The ECr-SCA is also used to train multilayer perceptron. [ABSTRACT FROM AUTHOR]