After decades of massive digitisation, an unprecedented number of historical documents are available in digital format, along with their machine-readable texts. While this represents a major step forward with respect to preservation and accessibility, it also opens up new opportunities in terms of content mining and the next fundamental challenge is to develop appropriate technologies to efficiently search, retrieve, and explore information from this ‘big data of the past’. Among semantic indexing opportunities, the recognition and classification of named entities are in great demand among humanities scholars. Yet, named entity recognition (NER) systems are heavily challenged with diverse, historical, and noisy inputs. In this survey, we present the array of challenges posed by historical documents to NER, inventory existing resources, describe the main approaches deployed so far, and identify key priorities for future developments.

Melanoma, one of the most dangerous types of skin cancer, results in a very high mortality rate. Early detection and resection are two key points for a successful cure. Recent researches have used artificial intelligence to classify melanoma and nevus and to compare the assessment of these algorithms to that of dermatologists. However, training neural networks on an imbalanced dataset leads to imbalanced performance, the specificity is very high but the sensitivity is very low. This study proposes a method for improving melanoma prediction on an imbalanced dataset by reconstructed appropriate CNN architecture and optimized algorithms. The contributions involve three key features as custom loss function, custom mini-batch logic, and reformed fully connected layers. In the experiment, the training dataset is kept up to date including 17,302 images of melanoma and nevus which is the largest dataset by far. The model performance is compared to that of 157 dermatologists from 12 university hospitals in Germany based on the same dataset. The experimental results prove that our proposed approach outperforms all 157 dermatologists and achieves higher performance than the state-of-the-art approach with area under the curve of 94.4%, sensitivity of 85.0%, and specificity of 95.0%. Moreover, using the best threshold shows the most balanced measure compare to other researches, and is promisingly application to medical diagnosis, with sensitivity of 90.0% and specificity of 93.8%. To foster further research and allow for replicability, we made the source code and data splits of all our experiments publicly available.

The main objectives of massive open online courses (MOOC) are to foster knowledge through free high quality learning materials procurement; to create new knowledge through diverse users' interactions with the providing platform; and to empower research on learning. However, MOOC providers are also businesses (either profit or not-for-profit). They are still in the early stages of their development, but sooner or later, in order to secure their existence and assure their longterm growth, they will have to adapt a business model and monetize the services they provide. Nevertheless, despite their popularity MOOCs are characterized by a very high drop-out rate (about 90%), which may turn out to be a problem regardless of the adapted business model. Hence, MOOC providers can either assume the scale benefits to be sufficiently high to ignore the problem of low MOOC completion rate or tackle this problem. In this paper we explore the problem of the high drop-out rate in massive open online courses. First, we identify its main cause by conducting an online survey, namely bad time organization. Secondly, we provide suggestions to reduce the rate. Specifically, we argue that MOOC platforms should not only provide their users with high quality educational materials and interaction facilities. But they should also support and assist the users in their quest for knowledge. Thus, MOOC platforms should provide tools helping them optimize their time usage and subsequently develop metacognitive skills indispensable in proper time management of learning processes.

Skin cancer is one of the most common cancers in the world. However, the disease is curable if detected in the beginning stage. Early detection of malignant lesions through accurate techniques and innovative technologies has a significant impact on reducing skin cancer mortality rates. Recently, artificial intelligence has come to the forefront to facilitate skin cancer diagnosis based on medical images. Many deep learning models have been studied and developed, but the imbalance of performance among classes in the multi-class classification is still a challenging problem. This study proposes a hybrid method for handling class imbalance of skin-disease classification. This method combines the data level method of balanced mini-batch logic followed by real-time image augmentation with the algorithm level method of designing new loss function. The training dataset includes 24,530 dermoscopic images of seven skin disease categories, which is by far the largest dataset of skin cancer. The performance metrics of six proposed methods are evaluated on a test dataset of 2,453 images. Our proposed EfficientNetB4-CLF model achieves the highest accuracy of 89.97% and also the highest mean recall of 86.13% with the smallest recalls’ standard deviations of 7.60%. Compared to the original methods, our proposed solution not only surpasses 4.65% (86.13% vs 81.48%) of mean recalls but also reduces 4.24% of the recalls’ standard deviations (from ±11.84% to ±7.60%). This result indicates that our hybrid method is highly effective in training the Deep CNN network on the skin-disease imbalanced dataset. It addresses the problem of slow learning of the minority classes in the networks by combining the data level method of balanced mini-batch logic followed by the real-time image augmentation with the algorithm level method of the newly designed loss function.

This article considers the interdisciplinary opportunities and challenges of working with digital cultural heritage, such as digitized historical newspapers, and proposes an integrated digital hermeneutics workflow to combine purely disciplinary research approaches from computer science, humanities, and library work. Common interests and motivations of the above‐mentioned disciplines have resulted in interdisciplinary projects and collaborations such as the NewsEye project, which is working on novel solutions on how digital heritage data is (re)searched, accessed, used, and analyzed. We argue that collaborations of different disciplines can benefit from a good understanding of the workflows and traditions of each of the disciplines involved but must find integrated approaches to successfully exploit the full potential of digitized sources. The paper is furthermore providing an insight into digital tools, methods, and hermeneutics in action, showing that integrated interdisciplinary research needs to build something in between the disciplines while respecting and understanding each other's expertise and expectations.

Post-OCR is an important processing step that follows optical character recognition (OCR) and is meant to improve the quality of OCR documents by detecting and correcting residual errors. This paper describes the results of a statistical analysis of OCR errors on four document collections. Five aspects related to general OCR errors are studied and compared with human-generated misspellings, including edit operations, length effects, erroneous character positions, real-word vs. non-word errors, and word boundaries. Based on the observations from the analysis we give several suggestions related to the design and implementation of effective OCR post-processing approaches.