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Algorithm selection (AS) tasks are dedicated to find the optimal algorithm for an unseen problem instance. With the knowledge of problem instances’ meta-features and algorithms’ landmark performances, Machine Learning (ML) approaches are applied to solve AS problems. However, the standard training process of benchmark ML approaches in AS either needs to train the models specifically for every algorithm or relies on the sparse one-hot encoding as the algorithms’ representation. To escape these intermediate steps and form the mapping function directly, we borrow the learning to rank framework from Recommender System (RS) and embed the bi-linear factorization to model the algorithms’ performances in AS. This Bi-linear Learning to Rank (BLR) has proven to work with competence in some AS scenarios and thus is also proposed as a benchmark approach. Thinking from the evaluation perspective in the modern AS challenges, precisely predicting the performance is usually the measuring goal. Though approaches’ inference time also needs to be counted for the running time cost calculation, it’s always overlooked in the evaluation process. The multi-objective evaluation metric Adjusted Ratio of Root Ratios (A3R) is therefore advocated in this paper to balance the trade-off between the accuracy and inference time in AS. Concerning A3R, BLR outperforms other benchmarks when expanding the candidates range to TOP3. The better effect of this candidates expansion results from the cumulative optimum performance during the AS process. We take the further step in the experimentation to represent the advantage of such TOPK expansion, and illustrate that such expansion can be considered as the supplement for the convention of TOP1 selection during the evaluation process.
Within many real-world networks, the links between pairs of nodes change over time. Thus, there has been a recent boom in studying temporal graphs. Recognizing patterns in temporal graphs requires a proximity measure to compare different temporal graphs. To this end, we propose to study dynamic time warping on temporal graphs. We define the dynamic tem- poral graph warping (dtgw) distance to determine the dissimilarity of two temporal graphs. Our novel measure is flexible and can be applied in various application domains. We show that computing the dtgw-distance is a challenging (in general) NP-hard optimization problem and identify some polynomial-time solvable special cases. Moreover, we develop a quadratic programming formulation and an efficient heuristic. In experiments on real-world data, we show that the heuristic performs very well and that our dtgw-distance performs favorably in de-anonymizing networks compared to other approaches.
Barrett's esophagus figured a swift rise in the number of cases in the past years. Although traditional diagnosis methods offered a vital role in early-stage treatment, they are generally time- and resource-consuming. In this context, computer-aided approaches for automatic diagnosis emerged in the literature since early detection is intrinsically related to remission probabilities. However, they still suffer from drawbacks because of the lack of available data for machine learning purposes, thus implying reduced recognition rates. This work introduces Generative Adversarial Networks to generate high-quality endoscopic images, thereby identifying Barrett's esophagus and adenocarcinoma more precisely. Further, Convolution Neural Networks are used for feature extraction and classification purposes. The proposed approach is validated over two datasets of endoscopic images, with the experiments conducted over the full and patch-split images. The application of Deep Convolutional Generative Adversarial Networks for the data augmentation step and LeNet-5 and AlexNet for the classification step allowed us to validate the proposed methodology over an extensive set of datasets (based on original and augmented sets), reaching results of 90% of accuracy for the patch-based approach and 85% for the image-based approach. Both results are based on augmented datasets and are statistically different from the ones obtained in the original datasets of the same kind. Moreover, the impact of data augmentation was evaluated in the context of image description and classification, and the results obtained using synthetic images outperformed the ones over the original datasets, as well as other recent approaches from the literature. Such results suggest promising insights related to the importance of proper data for the accurate classification concerning computer-assisted Barrett's esophagus and adenocarcinoma detection.
Shadow IT describes covert/hidden IT systems that are managed by business entities themselves. Additionally, there are also overt forms in practice, so-called Business-managed IT, which share most of the characteristics of Shadow IT. To better understand this phenomenon, we interviewed 29 executive IT managers about positive and negative cases of Shadow IT and Business-managed IT. By applying qualitative comparative analysis (QCA), we derived four conditions that characterize these cases: Aligned, local, simple, and volatile. The results show that there are three sufficient configurations of conditions that lead to a positive outcome; one of them even encompasses Shadow IT. The most important solution indicates that IT systems managed by business entities are viewed as being positive if they are aligned with the IT department and limited to local requirements. This allows to balance local responsiveness to changing requirements and global standardization. In contrast, IT systems that are not aligned and permanent (and either organization-wide or simple) are consistently considered as negative. Our study is the first empirical quantitative–qualitative study to shed light on the success and failure of Shadow IT and Business-managed IT.
IT-Backsourcing
(2020)
IT-Backsourcing bezeichnet die Re-Integration vormals outgesourcter IT-Services zurück zum auslagernden Unternehmen. Die vorliegende Studie stellt die Ergebnisse einer Umfrage unter IT-Managern zu Einflussfaktoren von IT-Backsourcing-Entscheidungen vor. Es lässt sich feststellen, dass IT-Backsourcing von Unternehmen in unterschiedlichen Branchen und für eine Vielzahl an unterschiedlichen IT-Services, wie z. B. Applikationsentwicklung oder Infrastruktur-Leistungen, durchgeführt wird. Hauptgründe für die Entscheidung für ein IT-Backsourcing sind mangelnde Qualität, hohe Kosten sowie eine schlechte Beziehung zum IT-Dienstleister. Die Ergebnisse der Umfrage zeigen, dass diejenigen Unternehmen, die sich für ein Backsourcing entschieden haben, nach der Entscheidung insgesamt zufriedener waren als diejenigen, die in einer Outsourcing-Beziehung geblieben sind. Zudem werden verschiedene Handlungsempfehlungen für IT-Manager aufgezeigt. Zur Ausarbeitung einer zukunftsorientieren IT-Sourcing-Strategie empfiehlt es sich, eine service-spezifische Sourcing-Strategie zu verfolgen und entsprechend spezialisierte Dienstleister einzusetzen. Im Falle einer Entscheidung für ein IT-Backsourcing sollten verschiedene Erfolgsfaktoren beachtetet werden, wie z. B. eine frühzeitige Neubesetzung von Schlüsselstellen sowie ein lückenloser Wissenstransfer.