During the last decades, the dark web has become the ground for criminal activities, enabling for illegal content sharing, as well as marketplaces selling drugs and firearms. The 2023 Internet Organised Crime Threat Assessment (IOCTA) of Europol’s European Cybercrime Centre (EC3) presented the dark web as the one of the top crime ecosystems. Analyzing the .onion sites hosting marketplaces is of interest to law enforcement, security researchers, and big data analysts. The capability of automatically harvesting web content from web servers enables Law Enforcement Agencies (LEAs) to collect and preserve data prone to serve as potential clues or evidence in investigations. The use of sophisticated protocols and the inherent complexity of the Dark Web makes it difficult for security agencies to identify and investigate these activities through conventional methods. The sheer size, unpredictable ecosystem, and anonymity provided by the Dark Web are the essential confrontations to trace the criminals. Therefore, it is a crucial step to discover the potential solutions towards cyber-crimes evaluating the sailing Dark Web crime threats. In this paper, we devise Artificial Intelligence and Big Data processing to extract insights and investigate how the Dark Web facilitates crime and dynamically maintains marketplaces with illegal goods exchange. The scientific contribution of this paper entails novel textual and multimedia analytics over large collections of data to collect evidence for further investigations and support visual reporting and alerting mechanisms. The conclusions include practical implications of Dark Web content retrieval and archival, such as investigation clues and evidence, and related future research topics.

During the last decades, Dark Web content has risen in necessity in an increasingly connected world, where international anonymous networks provide access to data marketplaces and illicit multimedia material through the TOR or I2P networks. The motivation behind this paper is to gauge the current state and growth of the Dark Web in relation to the role it plays with special focus on Small and Medium-sized Enterprises (SMEs and MEs). More specifically, we devise Machine Learning and specialised Information Retrieval techniques to extract insights and investigate how the Dark Web enables cybercrime, maintains marketplaces with breached enterprise data collections and pawned email accounts. The research questions that we address concern: a) the role that the Dark Web plays for SMEs, MEs, and society in general; b) the criticality of cybercriminal activities and operations in the Dark Web exploiting threat taxonomies and scoring schemes; and c) the maturity and efficiency of technical tools and methods to curb illegal activities on the Dark Web through raising awareness via efficient text analytics, visual reporting and alerting mechanisms.

The emergence of the Industrial Internet of Things paves the way for enhancing the real-time monitoring capabilities of contemporary manufacturing enterprises through the extensive utilization of physical and virtual sensors. This paradigm enables the detection of early warning signals concerning systems' degradation and facilitates the prompt decision making and actions performed ahead of time. Currently, even large manufacturing companies have not yet developed a complete Predictive Maintenance strategy and appropriate sensor-driven, real-time systems in order to utilize these benefits. In this paper, we propose a failure prediction system for complex IT systems in the steel industry. The novelty of our work lies in the exploitation of Deep Learning techniques from streaming operational sensor data, enabling earlier failure predictions through a Neural Networks approach. To evaluate the proposed framework, real-life data are collected and analyzed based on daily operational and maintenance activities within the production line. We further demonstrate the framework's potential by presenting some early results in modeling and predicting the complex and dynamic behavior in the manufacturing settings.

In this paper, an architecture is presented that allows the extraction of argumentation clauses that might exist in publications, in order to perform molecular clustering on referenced molecules. Grammar rules are defined and used to identify sentences corresponding to argumentation being present in publications. The references of those molecules are then compiled as lists that include their structure definition in SMILES format. These lists are given as input to virtual screening tools and then to a molecular clustering tool, with the ultimate goal to classify molecules that are known to be prone to specific diseases, thus leading to the discovery of new drugs.

Electronic Health Records (EHRs) contain an increasing wealth of medical information. They have the potential to help significantly in advancing medical research, as well as improve health policies, providing society with additional benefits. However, the European healthcare information space is fragmented due to the lack of legal and technical standards, cost effective platforms, and sustainable business models. The vision of Linked2Safety is to advance clinical practice and accelerate medical research, by providing pharmaceutical companies, healthcare professionals and patients with an innovative secure semantic interoperability framework facilitating the efficient and homogenized access to anonymised distributed EHRs in an aggregate form that enables merging multiple data sources into a single analyses. In this paper a first public introduction to the project is provided along with a clear definition of the problems, and proposed architecture. Three usage scenarios are used to demonstrate the potential impact of the outcomes of the project.

A dramatic increase of demand for provided treatment quality has occurred during last decades. The main challenge to be confronted, so as to increase treatment quality, is the personalization of treatment, since each patient constitutes a unique case. Healthcare provision encloses a complex environment since healthcare provision organizations are highly multidisciplinary. In this paper, we present the conceptualization of the domain of clinical pathways (CP). The SEMPATH (SEMantic PATHways) Oontology comprises three main parts: 1) the CP part; 2) the business and finance part; and 3) the quality assurance part. Our implementation achieves the conceptualization of the multidisciplinary domain of healthcare provision, in order to be further utilized for the implementation of a Semantic Web Rules (SWRL rules) repository. Finally, SEMPATH Ontology is utilized for the definition of a set of SWRL rules for the human papillomavirus) disease and its treatment scheme.

An adaptive joint process structure is described which rejects under-ice reverberation by taking advantage of the spatial separation between acoustic backscatter returning from small elevation angles and transmitted energy reflected off the sea surface.

An application of the adaptive joint complex least-squares lattice algorithm to the rejection of sea surface reverberation is described. A specific dual channel noise cancelling structure is considered along with the results of processing experimental ocean reverberation data.

The application of adaptive linear predictor structures to the prewhitening of acoustic reverberation data is discussed. The results of processing experimental ocean reverberation data constitutes the bulk of the material. In specific, a comparison between three adaptive linear predictor structures is made: (1) complex gradient transversal filter (CLMS), (2) complex gradient lattice (CGL), and (3) complex least-squares-lattice (CLSL). Figures include: (1) input range-Doppler map (conventional), (2) input range-Doppler map (inverse of the spectrum of the prewhitening filter), and (3) output range-Doppler map (conventional). Trade-offs between the three adaptive structures are illustrated as a function of their respective adaptation rate parameters.

The optimal design and performance analysis of nonuniform quantizers are considered in the context of data quantization in detection systems for stochastic signals. The case of detection with small signal-to-noise ratio is treated using two different statistical models for the presence of the signal in this situation. A general data-quantized detection system is proposed, and analytical expressions are presented for the asymptotic performance comparison of such systems within each of the two signal models. A design procedure for the optimal choice of quantizer parameters as also discussed, and optimal parameter values are presented for the particular case of four-level quantization for a class of noise propability densities exhibiting generalized exponential decay. Two suboptimal quantizer-detectors are also proposed, one based on minimum-distortion quantization and the other based on an approximation to the quadratic detector, and the performance of the optimal quantization scheme relative to these is analyzed with the result that the optimal quantizer-detector generally exhibits considerable performance improvement over the suboptimal schemes.

Two design criteria for the quantization of signal-detection data are considered. These criteria, one of which is based on asymptotic efficiency and the other on a mean-squared error quantity, are shown to be closely related for quantizer design in a general parametric decision framework.