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In this paper we apply a series of Machine Learning models to a recently published unique dataset on the mortality of COVID19 patients. We use a dataset consisting of blood samples of 375 patients admitted to a hospital in the region of Wuhan, China. There are 201 patients who survived hospitalisation and 174 patients who died whilst in hospital. The focus of the paper is not only on seeing which Machine Learning model is able to obtain the absolute highest accuracy but more on the interpretation of what the Machine Learning models provides. We find that age, days in hospital, Lymphocyte and Neutrophils are important and robust predictors when predicting a patients mortality. Furthermore, the algorithms we use allows us to observe the marginal impact of each variable on a case-by-case patient level, which might help practicioneers to easily detect anomalous patterns. This paper analyses the global and local interpretation of the Machine Learning models on patients with COVID19.

Nowadays, the range of cloud services offered by cloud providers varies sharply and poses a challenge for cloud consumers aiming for the most cost-effective and compliant solutions, especially when operating highly scalable microservice architectures. A remedy can be provided by a cloud brokerage intelligent mechanism selecting cloud services across multiple clouds on behalf of consumers by considering individual goals and requirements. In this paper, we present the Cloud Service Purchasing Problem (CSPP) which aims to minimize costs while incorporating specific consumer and application task requirements. To solve this problem, we propose a mixed-integer programming model and two large neighborhood search approaches. Using well-defined problem instances incorporating data from real cloud providers, we conduct several computational experiments to evaluate the performance of the proposed algorithms. They exhibit a competitive performance providing solutions for all scenarios within short computational times. Finally, the significance of this problem and decision support approaches is analyzed by comparing usage patterns with respect to different nowadays cloud providers and offered virtual machine types for various scenarios.

In benthic macroinvertebrate biomonitoring systems, the target is to determine the status of ecosystems based on several biological indices. To increase cost-efficiency, computer-based taxa identification for image data has recently been developed. Taxa identification errors can, however, have strong effects on the indices and thus on the determination of the ecological status. In order to shift the biomonitoring process towards automated expert systems, we need a clear understanding on the bias caused by automation. In this paper, we examine eleven classification methods in the case of macroinvertebrate image data and show how their classification errors propagate into different biological indices. We evaluate 14 richness, diversity, dominance and similarity indices commonly used in biomonitoring. Besides the error rate of the classification method, we discuss the potential effect of different types of identification errors. Finally, we provide recommendations on indices that are least affected by the automatic identification errors and could be used in automated biomonitoring. We thank the Academy of Finland (projects 288584 (Kiranyaz), 295854 (Iosidis), 289364 (Gabbouj), 289076 (rje, Krkkinen) and 289104 (Meissner)) and the Ellen and Artturi Nyyssnen foundation for the grant of rje. The authors would like to thank Marko Vikstedt for the preparation of the monitoring data and Tuomas Turpeinen for the image data. We kindly thank Antti Penttinen for fruitful discussions and support. Scopus

The wide spread of fake news has caused huge losses to both governments and the public. Many existing works on fake news detection utilized spreading information like propagators profiles and the propagation structure. However, such methods face the difficulty of data collection and cannot detect fake news at the early stage. An alternative approach is to detect fake news solely based on its content. Early content-based methods rely on manually designed linguistic features. Such shallow features are domain-dependent, and cannot easily be generalized to cross-domain data. Recently, many natural language processing tasks resort to deep learning methods to learn word, sentence, and document representations. In this paper, we propose a novel graph-based neural network model named SemSeq4FD for early fake news detection based on enhanced text representations. In SemSeq4FD, we model the global pair-wise semantic relations between sentences as a complete graph, and learn the global sentence representations via a graph convolutional network with self-attention mechanism. Considering the importance of local context in conveying the sentence meaning, we employ a 1D convolutional network to learn the local sentence representations. The two representations are combined to form the enhanced sentence representations. Then a LSTM-based network is used to model the sequence of enhanced sentence representations, yielding the final document representation for fake news detection. Experiments conducted on four real-world datasets in English and Chinese, including cross-source and cross-domain datasets, demonstrate that our model can outperform the state-of-the-art methods., Highlights • An enhanced text representation model for content-based early fake news detection. • Global semantic relations between sentences modeled with graph convolution network. • Local sentence representation extracted by applying 1D convolution on local context. • Document representation obtained by incorporating the sentence order through LSTM. • Generalization ability verified on cross-source and cross-domain datasets.

This paper deals with the topic of learning from unlabeled or noisy-labeled data in the context of a classification problem. In the classification problem the outcome yields one of a discrete set of values thus, assumptions on them could be established to obtain the most likely prediction model at the training stage . In this paper, a novel case-based model selection method is proposed, which combines hypothesis testing from a discrete set of expected outcomes and feature extraction within a cross-validated classification stage. This wrapper-type procedure acts on fully-observable variables under hypothesis-testing and improves the classification accuracy on the test set, or keeps its performance at least at the level of the statistical classifier. The model selection strategy in the cross validation loop allows building an ensemble classifier that could improve the performance of any expert and intelligence system, particularly on small sample-size datasets. Experiments were carried out on several databases yielding a clear improvement on the baseline, i.e., SPECT dataset A c c = 86.35 ± 1.51 , with S e n = 91.10 ± 2.77 , and S p e = 81.11 ± 1.61 . In addition, the CV error estimate for the classifier under our approach was found to be an almost unbiased estimate (as the baseline approach) of the true error that the classifier would incur on independent data.

To propose a flexible knowledge representation.To propose a new similarity measure.To use taxonomies in a conceptualization mechanism.To test some proposed mechanisms with a case-based reasoning for waste treatment. In Case Based Reasoning the representation of a case and the similarity measures are two difficult steps in the conception of a system. Often, these steps are developed to resolve one kind of problem. However, in some of them such as recovery treatment processes generation, it is necessary for the system to be able to modify and adapt the representation of a case and the similarity measures with respect of the context and also the kind of solutions proposed. In this paper, authors introduce a new method to represent cases with a flexibility based on a structure in a connectionist model. This flexibility is needed due to the complexity of cases, the number of possible options and to ensure the durability of the system. In a second main contribution, authors introduce a method for the selection of source cases using abstraction, conceptualisation and inference mechanisms. Finally, authors test their system in a CBR developed on SWI-Prolog with different problems. The CBR is applied to find new recovery processes and try to estimate the new upgraded product generated.

International audience; The increasing importance of ontologies has resulted in the development of a large number of ontologies in both coordinated and non-coordinated efforts. The number and complexity of such ontologies make hard to ontology and tool developers to select which ontologies to use and reuse. So far, there are no mechanism for making such decisions in an informed manner. Consequently, methods for evaluating ontology quality are required. OQuaRE is a method for ontology quality evaluation which adapts the SQuaRE standard for software product quality to ontologies. OQuaRE has been applied to identify the strengths and weaknesses of different ontologies but, so far, this framework has not been evaluated itself. Therefore, in this paper we present the evaluation of OQuaRE, performed by an international panel of experts in ontology engineering. The results include the positive and negative aspects of the current version of OQuaRE, the completeness and utility of the quality metrics included in OQuaRE and the comparison between the results of the manual evaluations done by the experts and the ones obtained by a software implementation of OQuaRE.

Social networks have become an essential meeting point for millions of individuals willing to publish and consume huge quantities of heterogeneous information. Some studies have shown that the data published in these platforms may contain sensitive personal information and that external entities can gather and exploit this knowledge for their own benefit. Even though some methods to preserve the privacy of social networks users have been proposed, they generally apply rigid access control measures to the protected content and, even worse, they do not enable the users to understand which contents are sensitive. Last but not least, most of them require the collaboration of social network operators or they fail to provide a practical solution capable of working with well-known and already deployed social platforms. In this paper, we propose a new scheme that addresses all these issues. The new system is envisaged as an independent piece of software that does not depend on the social network in use and that can be transparently applied to most existing ones. According to a set of privacy requirements intuitively defined by the users of a social network, the proposed scheme is able to: (i) automatically detect sensitive data in users' publications; (ii) construct sanitized versions of such data; and (iii) provide privacy-preserving transparent access to sensitive contents by disclosing more or less information to readers according to their credentials toward the owner of the publications. We also study the applicability of the proposed system in general and illustrate its behavior in two case studies., Comment: The content of this version is compliant with arXiv's license agreement

A divide-and-conquer method classifying sentence types before sentiment analysis.Classifying sentence types by the number of opinion targets a sentence contain.A data-driven approach automatically extract features from input sentences. Different types of sentences express sentiment in very different ways. Traditional sentence-level sentiment classification research focuses on one-technique-fits-all solution or only centers on one special type of sentences. In this paper, we propose a divide-and-conquer approach which first classifies sentences into different types, then performs sentiment analysis separately on sentences from each type. Specifically, we find that sentences tend to be more complex if they contain more sentiment targets. Thus, we propose to first apply a neural network based sequence model to classify opinionated sentences into three types according to the number of targets appeared in a sentence. Each group of sentences is then fed into a one-dimensional convolutional neural network separately for sentiment classification. Our approach has been evaluated on four sentiment classification datasets and compared with a wide range of baselines. Experimental results show that: (1) sentence type classification can improve the performance of sentence-level sentiment analysis; (2) the proposed approach achieves state-of-the-art results on several benchmarking datasets.

Lenders, such as banks andcredit card companies, use credit scoring models to evaluate the potential risk posed by lending money to customers, and therefore to mitigate losses due to bad credit. Theprofitabilityof the banks thus highly depends on the models used to decide on the customer’s loans. State-of-the-art credit scoring models are based onmachine learningand statistical methods. One of the major problems of this field is that lenders often deal with imbalanced datasets that usually contain many paid loans but very few not paid ones (calleddefaults). Recently, dynamic selection methods combined withensemble methodsandpreprocessing techniqueshave been evaluated to improveclassification modelsin imbalanced datasets presenting advantages over the staticmachine learning methods. In a dynamic selection technique, samples in the neighborhood of each query sample are used to compute the local competence of eachbase classifier. Then, the technique selects only competentclassifiersto predict the query sample. In this paper, we evaluate the suitability of dynamic selection techniques for credit scoring problem, and we present Reduced Minority k-Nearest Neighbors (RMkNN), an approach that enhances state of the art in defining the local region of dynamic selection techniques for imbalanced credit scoring datasets. This proposed technique has a superior prediction performance in imbalanced credit scoring datasets compared to state of the art. Furthermore, RMkNN does not need any preprocessing or sampling method to generate the dynamic selection dataset (called DSEL). Additionally, we observe an equivalence between dynamic selection and static selection classification. We conduct a comprehensive evaluation of the proposed technique against state-of-the-art competitors on six real-world public datasets and one private one. Experiments show that RMkNN improves the classification performance of the evaluated datasets regarding AUC, balanced accuracy, H-measure, G-mean, F-measure, and Recall.

Natural Language Processing (NLP) is the sub-field of Artificial Intelligence that represents and analyses human language automatically. NLP has been employed in many applications, such as information retrieval, information processing and automated answer ranking. Semantic analysis focuses on understanding the meaning of text. Among other proposed approaches, Latent Semantic Analysis (LSA) is a widely used corpus-based approach that evaluates similarity of text based on the semantic relations among words. LSA has been applied successfully in diverse language systems for calculating the semantic similarity of texts. LSA ignores the structure of sentences, i.e., it suffers from a syntactic blindness problem. LSA fails to distinguish between sentences that contain semantically similar words but have opposite meanings. Disregarding sentence structure, LSA cannot differentiate between a sentence and a list of keywords. If the list and the sentence contain similar words, comparing them using LSA would lead to a high similarity score. In this paper, we propose xLSA, an extension of LSA that focuses on the syntactic structure of sentences to overcome the syntactic blindness problem of the original LSA approach. xLSA was tested on sentence pairs that contain similar words but have significantly different meaning. Our results showed that xLSA alleviates the syntactic blindness problem, providing more realistic semantic similarity scores.

Fire disaster throughout the globe causes social, environmental, and economical damage, making its early detection and instant reporting essential for saving human lives and properties. Smoke detection plays a key role in early fire detection but majority of the existing methods are limited to either indoor or outdoor surveillance environments, with poor performance for hazy scenarios. In this paper, we present a Convolutional Neural Network (CNN)-based smoke detection and segmentation framework for both clear and hazy environments. Unlike existing methods, we employ an efficient CNN architecture, termed EfficientNet, for smoke detection with better accuracy. We also segment the smoke regions using DeepLabv3+, which is supported by effective encoders and decoders along with a pixel-wise classifier for optimum localization. Our smoke detection results evince a noticeable gain up to 3% in accuracy and a decrease of 0.46% in False Alarm Rate (FAR), while segmentation reports a significant increase of 2% and 1% in global accuracy and mean Intersection over Union (IoU) scores, respectively. This makes our method a best fit for smoke detection and segmentation in real-world surveillance settings.