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Agents and agent-based systems are becoming essential in the development of various fields, such as artificial intelligence, ubiquitous computing, ambient intelligence, autonomous computing, and intelligent robotics. The concept of autonomous agents, inspired by the observed agency in living systems, is also central to current theories on the origin, development, and evolution of life. Therefore, it is crucial to develop an accurate understanding of agents and the concept of agency. This paper begins by discussing the role of agency in natural systems as an inspiration and motivation for agential technologies and then introduces the idea of artificial agents. A systematic approach is presented for the classification of artificial agents. This classification aids in understanding the existing state of the artificial agents and projects their potential future roles in addressing specific types of problems with dedicated agent types. [ABSTRACT FROM AUTHOR]

Analysing message streams in a dynamic environment is challenging. Various methods and metrics are used to evaluate message classification solutions, but often fail to realistically simulate the actual environment. As a result, the evaluation can produce overly optimistic results, rendering current solution evaluations inadequate for real-world environments. This paper proposes a framework based on the simulation of real-world message streams to evaluate classification solutions. The framework consists of four modules: message stream simulation, processing, classification and evaluation. The simulation module uses techniques and queueing theory to replicate a real-world message stream. The processing module refines the input messages for optimal classification. The classification module categorises the generated message stream using existing solutions. The evaluation module evaluates the performance of the classification solutions by measuring accuracy, precision and recall. The framework can model different behaviours from different sources, such as different spammers with different attack strategies, press media or social network sources. Each profile generates a message stream that is combined into the main stream for greater realism. A spam detection case study is developed that demonstrates the implementation of the proposed framework and identifies latency and message body obfuscation as critical classification quality parameters. [ABSTRACT FROM AUTHOR]

The pervasive spread of fake news in online social media has emerged as a critical threat to societal integrity and democratic processes. To address this pressing issue, this research harnesses the power of supervised AI algorithms aimed at classifying fake news with selected algorithms. Algorithms such as Passive Aggressive Classifier, perceptron, and decision stump undergo meticulous refinement for text classification tasks, leveraging 29 models trained on diverse social media datasets. Sensors can be utilized for data collection. Data preprocessing involves rigorous cleansing and feature vector generation using TF-IDF and Count Vectorizers. The models' efficacy in classifying genuine news from falsified or exaggerated content is evaluated using metrics like accuracy, precision, recall, and more. In order to obtain the best-performing algorithm from each of the datasets, a predictive model was developed, through which SG with 0.681190 performs best in Dataset 1, BernoulliRBM has 0.933789 in Dataset 2, LinearSVC has 0.689180 in Dataset 3, and BernoulliRBM has 0.026346 in Dataset 4. This research illuminates strategies for classifying fake news, offering potential solutions to ensure information integrity and democratic discourse, thus carrying profound implications for academia and real-world applications. This work also suggests the strength of sensors for data collection in IoT environments, big data analytics for smart cities, and sensor applications which contribute to maintaining the integrity of information within urban environments. [ABSTRACT FROM AUTHOR]

A procedure for a simplified evaluation of bridges is proposed based on census and visual inspections. The structural–foundational, seismic, landslide, and hydraulic risks are considered, the hazard, vulnerability, and exposure factors of which are quantified with an index that can assume integer values from 1 to 5. Polynomial functions are then defined combining these indices, calculating an index for each risk and finally a multi-risk index of attention. The procedure follows a mathematical approach, less influenced by subjective choices, leading to a more gradual and efficient classification that managers can directly utilize. Specific needs and requirements result in specific configuration and calibration of the mathematical model coefficients. In this study, the authors calibrated coefficients to obtain results that were compliant with the Italian guidelines for existing bridges. The procedure, tested on a set of 86 bridges, does not replace an accurate evaluation, which is necessary in some cases and represents a higher level of knowledge, nor does it claim to provide a definitive result. It provides a more efficient classification, useful for establishing a rational decision-making process to prioritize any subsequent retrofit interventions. [ABSTRACT FROM AUTHOR]

Children with developmental central hypotonia have reduced muscle tone secondary to non-progressive damage to the brain or brainstem. Children may have transient delays, mild or global functional impairments, and the lack of a clear understanding of this diagnosis makes evaluating appropriate interventions challenging. This overview aimed to systematically describe the best available evidence for tools to identify and evaluate children with developmental central hypotonia aged 2 months to 6 years. A systematic review of systematic reviews or syntheses was conducted with electronic searches in PubMed, Medline, CINAHL, Scopus, Cochrane Database of Systematic Reviews, Google Scholar, and PEDro and supplemented with hand-searching. Methodological quality and risk-of-bias were evaluated, and included reviews and tools were compared and contrasted. Three systematic reviews, an evidence-based clinical assessment algorithm, three measurement protocols, and two additional measurement tools were identified. For children aged 2 months to 2 years, the Hammersmith Infant Neurological Examination has the strongest measurement properties and contains a subset of items that may be useful for quantifying the severity of hypotonia. For children aged 2–6 years, a clinical algorithm and individual tools provide guidance. Further research is required to develop and validate all evaluative tools for children with developmental central hypotonia. [ABSTRACT FROM AUTHOR]