Search

Artificial Intelligence (AI) solutions have been widely used in healthcare, and recent developments in deep neural networks have contributed to significant advances in medical image processing. Much ongoing research is aimed at helping medical practitioners by providing automated systems to analyze images and diagnose acute diseases, such as brain tumors, bone cancer, breast cancer, bone fracture, and many others. This comprehensive review delivers an overview of recent advances in medical imaging using deep neural networks. In addition to the comprehensive literature review, a summary of openly available data sources and future research directions are outlined.

Reddit is the largest topically structured social network. Existing literature, reporting results of Reddit-related research, considers different phenomena, from social and political studies to recommender systems. The most common techniques used in these works, include natural language processing, e.g., named entity recognition, as well as graph networks representing online social networks. However, large-scale studies that take into account Reddit’s unique structure are scarce. In this contribution, similarity between subreddits is explored. Specifically, subreddit posts (from 3189 subreddits, spanning the year 2022) are processed using NER to build graph networks which are further mined for relations between subreddits. The evaluation of obtained results follows the state-of-the-art approaches used for a similar problem, i.e., recommender system metrics, and applies recall and AUC. Overall, the use of Reddit crossposts discloses previously unknown relations between subreddits. Interestingly, the proposed approach may allow for researchers to better connect their study topics with particular subreddits and shows promise for subreddit similarity mining.

Cloud infrastructures operate in highly dynamic environments, and today, energy-focused optimization become crucial. Moreover, the concept of extended cloud infrastructure, which, among others, uses green energy, started to gain traction. This introduces a new level of dynamicity to the ecosystem, as “processing components” may “disappear” and “come back”, specifically in scenarios where the lack/return of green energy leads to shutting down/booting back servers at a given location. Considered use cases may involve introducing new types of resources (e.g., adding containers with server racks with “next-generation processors”). All such situations require the dynamic adaptation of “system knowledge”, i.e., runtime system adaptation. In this context, an agent-based digital twin of the extended green cloud infrastructure is proposed. Here, knowledge management is facilitated with an explainable Rule-Based Expert System, combined with Expression Languages. The tests were run using Extended Green Cloud Simulator, which allows the modelling of cloud infrastructures powered (partially) by renewable energy sources. Specifically, the work describes scenarios in which: (1) a new hardware resource is introduced in the system; (2) the system component changes its resource; and (3) system user changes energy-related preferences. The case study demonstrates how rules can facilitate control of energy efficiency with an example of an adaptable compromise between pricing and energy consumption.

As the largest open social medium on the Internet, Reddit is widely studied in the scientific literature. Due to its structured form and division into topical subfora (subreddits), conducted research often concerns connections and interactions between users and/or whole, subreddit-structure-based communities. Overall, the relations between communities are most often studied by applying graph networks, with various creation algorithms. In this work, a novel approach is proposed to build and understand the structure of Reddit. It is based on crossposts—posts that appeared on one subreddit and then were crossposted to another. After capturing one year of crossposts, a directed weighted graph network, using seven million posts from over 10,000 of the most popular subreddits, has been created. Using graph network algorithms, its characteristics are captured and compared to similar studies. We identify the information “sinks” and “sources”—the most active crossposting subreddits. Moreover, we obtained graph network metrics: the degree (modeled with the Power Law), clustering, community detection algorithms, and connected components structure network are compared to previous studies on Reddit network(s), yielding consistent, but also novel results. Finally, the relations between extensively studied subreddits (e.g., r/AITA, r/Parenting, r/politics) and new ones, which were not accounted for in previous research, opening new paths for data-driven studies, are summarized.

Continuous, real-time monitoring of occupational health and safety in high-risk workplaces such as construction sites can substantially improve the safety of workers. However, introducing such systems in practice is associated with a number of challenges, such as scaling up the solution while keeping its cost low. In this context, this work investigates the use of an off-the-shelf, low-cost smartwatch to detect health issues based on heart rate monitoring in a privacy-preserving manner. To improve the smartwatch’s low measurement quality, a novel, frugal machine learning method is proposed that corrects measurement errors, along with a new dataset for this task. This method’s integration with the smartwatch and the remaining parts of the health and safety monitoring system (built on the ASSIST-IoT reference architecture) are presented. This method was evaluated in a laboratory environment in terms of its accuracy, computational requirements, and frugality. With an experimentally established mean absolute error of 8.19 BPM, only 880 bytes of required memory, and a negligible impact on the performance of the device, this method meets all relevant requirements and is expected to be field-tested in the coming months. To support reproducibility and to encourage alternative approaches, the dataset, the trained model, and its implementation on the smartwatch were published under free licenses.

Agent-based computing remains an active field of research with the goal of building (semi-)autonomous software for dynamic ecosystems. Today, this task should be realized using dedicated, specialized frameworks. Over almost 40 years, multiple agent platforms have been developed. While many of them have been “abandoned”, others remain active, and new ones are constantly being released. This contribution presents a historical perspective on the domain and an up-to-date review of the existing agent platforms. It aims to serve as a reference point for anyone interested in developing agent systems. Therefore, the main characteristics of the included agent platforms are summarized, and selected links to projects where they have been used are provided. Furthermore, the described platforms are divided into general-purpose platforms and those targeting specific application domains. The focus of the contribution is on platforms that can be judged as being under active development. Information about “historical platforms” and platforms with an unclear status is included in a dedicated website accompanying this work.

There are many areas where conventional supervised machine learning does not work well, for instance, in cases with a large, or systematically increasing, number of countably infinite classes. Zero-shot learning has been proposed to address this. In generalized settings, the zero-shot learning problem represents real-world applications where test instances are present during inference. Separately, recently, there has been increasing interest in meta-classifiers, which combine the results from individual classifications to improve the overall classification quality. In this context, the purpose of the present paper is two-fold: First, the performance of five state-of-the-art, generalized zero-shot learning methods is compared for five popular benchmark datasets. Second, six standard meta-classification approaches are tested by experiment. In the experiments undertaken, all meta-classifiers were applied to the same datasets; their performance was compared to each other and to the original classifiers.

In a recent paper cite{zara} a parallel direct solver for the linear systems arising from elliptic partial differential equations has been proposed. The aim of this note is to present the initial evaluation of the performance characteristics of this algorithm on Beowulf-type cluster. In this context the performance of PVM and MPI based implementations is compared.