The Dark Web, known for its anonymity and illicit activities, presents considerable challenges for Law Enforcement Agencies (LEAs) due to the complexity and volume of data generated within it. Online marketplaces on the Dark Web are notorious for facilitating illegal activities such as drug trafficking, counterfeit goods, and weapons sales while using advanced obfuscation techniques to avoid detection. The unstructured nature of data on these platforms and their constantly evolving operations make manual extraction and analysis exceedingly difficult.This paper addresses the pressing need for structured information extraction from Dark Web marketplaces, with a specific focus on firearm-related listings. Traditional rule-based methods have proven inadequate due to their reliance on HTML tags and pattern recognition, necessitating more adaptive solutions. Thus, the application of Large Language Models (LLMs) and Prompt Engineering to tackle these challenges is explored. By leveraging the capabilities of LLMs, this study aims to transform the extraction process into a more efficient and accurate system. Various generative models and prompt formulations are tested, to determine the most effective approach for extracting detailed information such as product specifications, pricing, and seller details.The proposed pipeline involves feeding crawled marketplace pages into a generative model, which then identifies Product Details Pages (PDPs) and consequently extracts relevant information from them. The use of LLMs marks a significant advancement over traditional methods, enhancing the accuracy and comprehensiveness of data extraction. Additionally, this research highlights the effectiveness of prompt engineering in improving information retrieval.This work underscores the critical need for sophisticated tools to monitor and combat illegal activities on the Dark Web, particularly in the context of firearm trafficking. By refining techniques for automated data extraction and applying cutting-edge LLM and prompt engineering methods, this study aims to support LEAs in their efforts to disrupt and dismantle criminal networks and enhance public safety.

We report on the development of a cutting-edge brain-computer interface (BCI) network that leverages the sensor output of a low cost electroencephalogram (EEG) headband to detect specific eye and head movements, enabling intelligent wheelchair navigation. Using a hybrid CNN-LSTM architecture, our method achieves high accuracy classification of these movements while maintaining low inference time on the complete EEG signal. To validate our approach, we conducted a comprehensive experimental evaluation.

Online discussion boards have become a tool for traffickers to widen their reach in the illegal firearm trade. This provides opportunities for authorities to detect firearm trafficking networks, as well as interdict arms shipments arranged by such networks. The present work offers a fresh solution to the pressing problem of spotting dubious firearm transaction chats on online discussion boards. The suggested methodology integrates state-of-the-art capabilities, such as natural language understanding and unsupervised clustering methods, to support the creation of more efficient cybercrime solutions. The necessity to target and flag questionable discussions about the trading of firearms in Internet forums is the main issue this work addresses. The major claim in this study centers on the use of intent recognition sequence patterns within dialogues and unsupervised clustering of such sequences. This method groups conversations based on the general direction and subject. The research methodology involves several key steps: i) data collection and manual annotation from the subreddit r/GunDeals, chosen due to its proximity to the primary subject of illicit firearm tracking, ii) training an intent classification Transformer-based model to generate intent sequences for each conversation, iii) preprocessing the intent sequences for clustering and encoding them using a polynomial method. iv) implementing clustering techniques while tuning various hyperparameters to optimize the results. The proposed methodology effectively classifies conversations into meaningful clusters, providing actionable insights. This research contributes to the body of knowledge by presenting a novel approach to detecting suspicious firearm trade conversations online. The practical implications are significant, as this work can be leveraged by law enforcement agencies to enhance their Internet scanning capabilities. The ability to efficiently process large volumes of text conversations and flag specific content can aid in addressing illicit firearm trading more effectively.

Europe, during the last decade, is facing major migration flows, due to several crises out the border of its territory. In this regard, the prediction of migration flows place a significant r ole. The development of the internet and the rise of Big Data pave the path for the development of early warning systems with promising forecasting power. This paper presents a novel approach to migration prediction, using neural network architectures, in combination with Natural Language Processing techniques, that monitor and search for early signals in the national press of countries of origin and destination. This is the first study to use topic modeling to monitor the national press to predict migration. The pipeline proposed in this paper uses the following three major datasets: EUROSTAT, GDELT and ALL-NEWS. The topic classifier, which is proposed, consists of a Latent Dirichlet Allocation(LDA) model trained using the ALL-NEWS dataset. Big Data in the form of national press articles can be of tremendous value to the problem of migration prediction.

Photonic circuits pave the way to ultrafast computing and real-time inference of applications with paramount importance, such as imaging flow cytometry (IFC). However, current implementations exhibit inherent restrictions that consequently diminish the neural networks (NNs) complexity that can be supported.

This work presents a comparative analysis of human-crafted and automated feature extraction approaches for the discrimination of hand-based activities among eating, drinking and smoking. In this scheme, accelerometer and gyroscope sensors were utilised to capture activity signals. For this reason, wearable devices that embed the aforementioned sensors were employed to collect activity data from 12 office workers. The two approaches that were developed for feature mapping were evaluated equally on the collected dataset. Both the proposed schemes achieved to classify the hand-based activities. However, based on the experimental process, this study shows that the human-crafted features that extracted valuable information from the time and frequency domain of the raw signal measurements outperformed the automated feature mapping that utilised deep learning advances. The relevant results prove that the human-crafted features can recognise hand-based activities with 0.9109 and, on the other hand, automated features with a 0.907 F1 weighted score over the dataset.

Complex multimedia data handling is as a hugechallenge for the computer science community. Social networks, manufacturing, retail, national and cyber-security, medicine, computational biology, etc. are related to a variety of heterogeneousimages and videos that create unique methodologicalchallenges. Data complexity arising from these large-scale applicationsseek for high-performance processing to obtain datainsights. Dimensionality reduction techniques reveal a managementexcellence towards multimedia data manipulation. Manydimensionality reduction techniques can be described within theframework of graph embedding, where a neighborhood graph isconstructed from the data, in order to reveal their structure. Existingtechniques usually handle only a single data representation, so-called modality, resulting in a single neighborhood graph. However, data often are multimodal, i.e. the same semantic conceptis described by various diverse representations. In the contextof graph embedding, the multiple modalities can be representedas multiple neighborhood graphs among the data. In this paper, an extension of the graph embedding framework is presented, where a multimodal graph is constructed as a weighted sumof the multiple unimodal graphs. Different from other methods, the weights of this sum are adaptively calculated as the solutionto an optimization problem, where an introduced measure ofgraph consistency, based on a rational assumption regardingthe similarities between objects, is optimized. The experimentalresults of the comparison of the proposed Multimodal GraphEmbedding (MGE) dimensionality reduction method to existingwork prove that this adaptive weighting scheme leads to superiorperformance in a number of experimental settings and datasets.

A novel big data building performance evaluation knowledge processing and mining system utilizing visual analytics is going to be presented in this paper. A large dataset comprised of building information, energy consumption, environmental measurements, human presence and behavior and business processes is going to be exploited for the building performance evaluation. Building performance evaluation is one of the most important factors in engineering that leads to building renovation and construction with low energy consumption and gas emissions in conjunction with comfort, utility and durability. For this purpose, business processes occurring in the building are correlated with the energy consumption and the human flows in the spatiotemporal domain modeling the dynamic behavior of the building. These models lead to the extraction of useful semantic information and the detection of spatiotemporal patterns that are important for the evaluation of the building performance. Furthermore, a number of novel visual analytics techniques allow the end-users to process data in different temporal resolutions and with different temporal filters, assisting them to detect patterns that may be difficult to be detected otherwise. The proposed visual analytics techniques support design and energy management decisions by visualizing the building measurements regarding business and comfort aspects. To do so, the proposed system includes a variety of techniques and components, properly selected to offer quick identification of focal points and evaluation of the building performance. Considering the increasing interest and the green building goals of almost all world governments including EU, the suggested methodology and application could be rendered a very useful tool for the Architecture and Engineering Community working on Building Performance Simulation and Analysis, and all related communities in Architect, Engineering and Construction (AEC) industry.