Memes possess a humorous intent, yet they can also be used for malicious purposes. Analysing meme data has the potential to enhance content monitoring, identify emerging topics, and support content moderation in online platforms. Memes also represent an interesting use case for multimodal machine learning, as they combine text and image data. In this study, we explored the linguistic characteristics and analysed the convergent themes of five meme classes through common word extraction. Moreover, we compared the effectiveness of various machine learning models, i.e., unimodal (text or image) and multimodal (early fusion, late fusion) in binary and multiclass meme classification tasks. Our results on a large meme dataset showed that memes heavily adhered to current affairs, demonstrated by the high frequency of topical words across meme classes. Regarding model accuracy, early fusion achieved superior accuracy over late fusion in meme classification. Binary models outperformed multi-class classification methods. However, fusion models did not consistently surpass the accuracy of independent text or image-based models.

Recent advances in medical imaging and deep learning have enabled the efficient analysis of large databases of images. Notable examples include the analysis of computed tomography (CT), magnetic resonance imaging (MRI), and X-ray. While the automatic classification of images has proven successful, adopting such a paradigm in the medical healthcare setting is unfeasible. Indeed, the physician in charge of the detailed medical assessment and diagnosis of patients cannot trust a deep learning model’s decisions without further explanations or insights about their classification outcome. In this study, rather than relying on classification, we propose a new method that leverages deep neural networks to extract a representation of images and further analyze them through clustering, dimensionality reduction for visualization, and class activation mapping. Thus, the system does not make decisions on behalf of physicians. Instead, it helps them make a diagnosis. Experimental results on lung images affected by Pneumonia and Covid-19 lesions show the potential of our method as a tool for decision support in a medical setting. It allows the physician to identify groups of similar images and highlight regions of the input that the model deemed important for its predictions.

Analysis of restaurant location has significant value both for owners, and for consumers. In this paper we examine the statistical relationships of restaurant properties and qualities in order to establish their relevance for the success of a restaurant. For this purpose, we use information-based methods and a dataset obtained from the TripAdvisor platform containing data about restaurant facilities situated in London. The dataset is rich with numerous qualitative features describing the restaurants. Through the use of statistical and geographical methods we explore the usefulness of geographic visualisation of restaurant qualities, and examine the statistical relationships among restaurant descriptors. Our results demonstrate the effectiveness of information methods for researching economic and non-economic phenomena.

Stress is or could be one of the most talked-about and recurring things in recent years, because of the world that we live. Stress is our body's response to a pressure thing or situation in our life. In this way, countless things have a stressful impact on our lives. On the other hand, stress is usually due to something new or unexpected, that somehow is beyond our control. The effects on our body are evident, inevitably having symptoms. When we are exposed to stress, certain hormones are released in our body, and the immune system is working on self-defence. During this, breathing becomes faster, heart rate increases, muscles contract and blood pressure also increases. Thus, the organism is ready to act to protect itself. It is where our project comes in, because, with these symptoms of our body, they allow stress to be identified. This paper is focused on precisely that, because, by reading the person's vital data, we can establish standards of normality, which, when they suffer variation, may indicate to us in advance that the person is stressed and help him to control himself so that there is no more significant damage. With this, we hope to obtain positive results in people's lives and routine, causing the stress rate to drop worldwide.

The functional tests are essential to test the functionality of different types of people, and specialty for older adults. The primary purpose of this paper is to create a method for the automatic measurement of the results of the different functional tests. These are the Heel-rise Test, Functional Reach Test, Timed Up and Go Test, Ten Meter Walk Test, Eight hop test, Up-down hop test, Side hop test, Single hop test, Chair Stand Test, Arm Curl Test, and Chair Sit and Reach test. The use of sensors may increase the accuracy of the measurements of these tests. These tests may identify several diseases, and it will be subject to further research in the future.

The ability to analyze the available data is a valuable asset for any successful business, especially when the analysis yields meaningful knowledge. One of the key processes for acquiring such ability is the Extract-Transform-Load (ETL) process. For Big Data, ETL requires a significant effort and it is a very challenging task to be performed in a cost-effective way. There are quite a few examples in the literature that describe an architecture for cost-effective ETL but none of the available examples are complete enough and they are usually evaluated in narrow problem domains. The ones that are more general, require specific implementation details. In this paper we propose a cloud-based ETL framework where we use a general cluster-size optimization algorithm, while providing implementation details, and is able to perform the required job within a predefined, and thus known, time. We evaluated the algorithm by executing three scenarios regarding data aggregation during ETL: (i) ETL with no aggregation; (ii) aggregation based on predefined columns or time intervals; and (iii) aggregation within single user sessions spanning over arbitrary time intervals. The execution of the three ETL scenarios in a production setting showed that the cluster size could be optimized so it can process the required data volume within a predefined and thus, expected, latency. The scalability was evaluated on Amazon AWS Hadoop clusters by processing user logs collected with Kinesis streams with datasets ranging from 30 GB to 2.6 TB.