Abstract - This paper presents an in-depth description of a multi-modal security solution based on a blockchain architecture developed within the CHRISS (Critical infrastructure High accuracy and Robustness increase Integrated Synchronization Solutions) project. Specifically, the focus in the paper is on the description of the proposed security architecture, functionalities and security measures based on modern blockchain solutions developed for the CHRISS project.

Entity recognition, also known as named entity recognition (NER), is a fundamental task in natural language processing (NLP) that involves identifying and categorizing entities within text. These entities, such as names of people, organizations, locations, dates, and numerical values, provide structured information from unstructured text data. NER models, ranging from rule-based to machine learning-based approaches, decode linguistic patterns and contextual information to extract entities effectively. This article explores the roles of entities, tokens, and NER models in NLP, detailing their significance in various applications like information retrieval and border security. It delves into the practices of implementing NER in legal document analysis, travel history analysis, and document verification, showcasing its transformative impact in streamlining processes and enhancing security measures. Despite challenges such as ambiguity and data scarcity, ongoing research and emerging trends in multilingual NER and ethical considerations promise to drive innovation in the field. By addressing these challenges and embracing new developments, entity recognition is poised to continue advancing NLP capabilities and powering diverse real-world applications.

The paper presents an overview of Call Detail Records (CDRs), important datasets within the telecommunications industry that capture detailed information about telephonic activities. CDRs encompass a vast array of metadata, including the date, time, duration, source, destination of calls, and more specific details like service type, call status, and location data. This paper highlights the significant potential of CDRs in various applications, ranging from telecom fraud detection, urban planning, disaster readiness, to novel methodologies for predicting socio-economic metrics like poverty. We discuss specific use cases, demonstrating the critical role of CDRs in identifying SIM box fraud, analyzing urban mobility, and enhancing emergency response strategies through sophisticated data analysis techniques. Furthermore, we introduce a novel CDR Generator solution developed within the RITHMS project (G.A. 101073932) aimed at detecting suspicious activities around archaeological sites. The tool leverages modern technologies such as Python, Streamlit, and Folium, to generate synthetic CDRs based on selected geographic areas, demonstrating the applicability of CDRs in safeguarding cultural heritage. We find that the generated dataset largely preserves the properties of the real dataset, thus being of real use for the research community suffering from the limited availability of datasets from either practical simulations or experimental testbeds that can be considered as reference or standard datasets

The increase in the number of Internet users has also led to an increase in activities leading to a cyber threat and fraud intelligence. These activities include the use of the Dark Web for coordination and virtual currencies for funding. This article will present the main methods of cyber-attacks and crimes used nowadays, and how they can be prevented by using tools specialized in monitoring transactions with virtual currencies and detecting web pages that pose a threat to users. The tools that will be described in this article are Graphsense used to analyze virtual currency activities and SpiderFoot used to identify Cyber Threat, Attack Surfaces, Security Assessments and Asset Discovery.

In the current industrial century, smart grid is one of the technologies that has been proposed for efficient and quality distribution of electricity. However, this technology is exposed to many security threats and vulnerabilities. These challenges have led to the development of advanced technologies and sustainable solutions to make smart grids more secure and reliable. Blockchain is one of the recent technologies that has attracted a lot of attention in various applications, including smart grids. SealedGRID is a project designed, analyzed and implemented with the aim of providing a scalable and reliable Smart Grid security platform based on blockchain. In this paper, we present a scalable and secure solution for smart grids using Hyperledger Fabric and MQTT.

Over the past five years, different organisations have increasingly called for science to become more open and reproducible. They have endorsed a set of data-management principles known as the FAIR (Findable, Accessible, Interoperable, Reusable) principles. As such, there is a growing trend towards the open availability of research data, as researchers continue to enhance reproducibility by enabling sharing and opening of their findings and datasets. However, there is not yet a standardised way to openly enable access to datasets while keeping control of their final use, potentially obtaining benefits from their utilisation. This paper introduces Smardy, an EU-funded project which is deploying a traceable FAIR-compliant open innovation marketplace for data. Its innovative method for data exchange consists of the use of blockchain for controlling access rights to data, with data models able to grant access according to policies completely kept under the control of the data owner/producer. We also describe how Smardy employs dimensionality reduction techniques to automatically generate FAIR–compliant metadata, statistical fingerprinting to identify derivated datasets, and watermarking to help data owners trace the distribution of multiple copies of a dataset.

This paper is about a multilingual chatbot developed for public administration within the CEF funded project ENRICH4ALL. We argue for multi-lingual chatbots empowered through MT and discuss the integration of the CEF eTranslation service in a chatbot solution.

Wireless low-power, multi-hop networks are exposed to numerous attacks also due to their resource-constraints. While there has been a lot of work on intrusion detection systems for such networks, most of these studies have considered only a few topologies, scenarios and attacks. One of the reasons for this shortcoming is the lack of sufficient data traces that are required to train many machine learning algorithms. In contrast to other wireless networks, multi-hop networks do not contain one entity that can capture all the traffic which makes it more difficult to acquire such traces. In this paper we present Multi-Trace. Multi-Trace extends the Cooja simulator with multi-level tracing facilities that enable data logging at different levels while maintaining a global time. We discuss the opportunities that traces generated by Multi-Trace enable for researchers interested in input for their machine learning algorithms. We present experiments that show the efficiency with which Multi-Trace generates traces. We expect Multi-Trace to be a useful tool for the research community.

Businesses, particularly small and medium-sized enterprises, aiming to start up in Model-Based Design (MBD) face difficult choices from a wide range of methods, notations and tools before making the significant investments in planning, procurement and training necessary to deploy new approaches successfully. In the development of Cyber-Physical Systems (CPSs) this is exacerbated by the diversity of formalisms covering computation, physical and human processes. In this paper, we propose the use of a cloud-enabled and open collaboration platform that allows businesses to offer models, tools and other assets, and permits others to access these on a pay-per-use basis as a means of lowering barriers to the adoption of MBD technology, and to promote experimentation in a sandbox environment.

Air pollution is a key environmental and social issue and it is also a complex problem posing multiple challenges in terms of management and mitigation of pollutants. The evaluation of the status of air quality is based mainly on ambient air measurements. Although the emissions of principal air pollutants are highly regulated, there is a lack of information about the real extent of emissions generated by the traffic and made difficult the quantification of the effects of policies and measures to reduce air pollution. To tackle these challenges, local air pollution measurements near main streets, based on small IoT devices became necessary. The aim of the paper is to present the way in which low-cost sensors in combination with Artificial Intelligence algorithms could be used for prediction of PM10and PM-2.5concentration. The data were collected using IoT devices based on Optical Particle Counter technologies, statistically analyzed and corrected (using a specific algorithm) to reduce the influence of the air humidity. Comparison with measurement from reference station were presented. For PM10and PM-2.5concentration forecasting was developed an ARIMA algorithm which was tested for time series registered in Bucharest. The results show that in 89% of cases the predicted values are within the accepted uncertainty limit, while the Pearson correlation coefficients have significant values.

The paper proposes a Machine Learning methodology for Android malware detection and recognition, including crypto-mining applications using the blockchain. The design is based on a hierarchical classification method, with several decision stages. A combination of functional and statistical features is proposed to be applied for data classification in order to provide a high-performance malware recognition process. The specific contribution of this design methodology is the hierarchical classifier with detection and discrimination stages, respectively. Further works should be done for various features sets in order to achieve an optimized and high-accuracy modeling process supporting an innovative Machine Learning-based solution for Android malware detection.

It is expected that drones will play a major role in connecting the world in future. They will be delivering packages and merchandise, serving as mobile hotspots for broadband wireless access, will deal with surveillance purposes and security of smart cities. Drones, while can be used for the betterment of the community, can also be used by malevolent entities to gather physical and cyber-attacks, and threaten the society. However, we took a different approach using Parrot Bebop 2 by doing 3D modeling of our building. In this paper the main problems and the available solutions are addressed for the generation of 3D models from drone images. Close range photogrammetry has dealt for many years with manual or automatic image measurements for precise 3D modelling. Nowadays drones are also becoming a major source for scanning and snapping, but image-based modelling remains the most complete, portable, flexible and widely used approach. In this article we used 3D modeling as a process of establishing a mathematical representation of a 3-dimensional building by using the software Pix4D or Pix4D cloud. 3D models are now widely used on industrial scale and real estate dealers, architecture, construction, dealing with hazardous situations and product development using 3D models for visualizing, simulating and depiction graphic designs.

Wi-Fi communication is becoming popular for offloading mobile networks. However, there are several radio communication problems, such as spectrum sharing and fading. The purpose of this paper is to define a communication scheme that implements a random fluctuation of the natural environment of communication channels. Only the transmitter and the receiver share the communication channel features. From mutuality among a transmitter and a receiver, it might be required for them to share one-time information of their fluctuating channel. This can deliver a secret key agreement scheme without key management and key distribution processes. In this article, we propose a new secret key generation and agreement scheme that utilizes the fluctuation of channel features with an electronically steerable parasitic array radiator (ESPAR) antenna. The antenna, which has been proposed and prototyped, is a smart antenna designed for users. Using the beamforming technique of the ESPAR antenna, one can intensify the fluctuation of the channel features. For the communication analysis we used Ekahau Heatmapper to create a propagation pattern and to highlight the effects of fading in a wireless distribution system (WDS) network built on Lancom Wi-Fi access points. From the experimental results, we conclude that the proposed scheme can generate secret keys from the received signal strength indicator (RSSI) profile with sufficient independence.

Nowadays, in order to be competitive in the context of dynamic market changes, companies concern themselves with the adoption of novel business models for the optimization of resource consumption. The decision to adopt energy efficiency measures within a company is often hard to substantiate, given the multitude of factors that influence the feasibility of the project, such as regional laws and regulations, ambient weather conditions, energy pricing, company's activity profile, building structure and characteristics, consumption infrastructure and availability of funding sources. The combination of these factors is unique for each company, thus the adoption of any energy efficiency measures, especially when the investment costs are high, should be rigorously evaluated. The purpose of this paper is to present the conceptual model of a Cloud-based energy management platform that aims to help SMEs monitor energy consumption and associated costs in real time, analyze consumption patterns, assess the economic efficiency of EPCbased projects, benefit from recommendations, generate energy reports in compliance with international standards and find suitable business partners in the field of energy.

Pesticides are substances or different mixtures of substances, which are used in order to eliminate pests of agricultural crops. The scientific work that successfully implemented electrochemical devices to detect pesticide residues is coming to the aid of the population. The purpose of this paper is to describe a telemetry solution for detecting and monitoring these substances using few resources. The practical tool developed is represented by a potentiostat - an electronic device with three electrodes - and an efficient method for processing data. The paper describes the experiments done with the proposed cost-effective and stand-alone IoT system, which can be easily used in remote areas using M2M telemetry.

To respond to current consumer demands and lifestyle challenges with agility, agri-food practitioners have to evolve quickly and remain competitive. Unfortunately, in many situations, development and proliferation of innovative foods in the market act as a barrier to consumers instead of a driving force, because food manufacturers tend to proceed according to profit maximization and cost minimization approach. These foods generally do not promote health and even may contribute to the development of non-communicable diseases, leading to lack of consumer trust. To help modern consumers to make correct food choices on a daily basis with long-term beneficial effects on their body, this paper proposes a concept of a novel e-platform on healthy eating behavior. By accessing the platform, every consumer will have access not only to safe, high-quality, sufficient and affordable food in his own region, but will also be able to select the “healthiest” one in terms of his own preferences and physiological needs.

Summary When mobile devices involved in a communication process are unable to establish a direct connection, or when communication should be offloaded to cope with large throughputs, mobile collaboration can be used to enable communication through opportunistic networks. These types of networks are formed when mobile devices communicate only using short‐range transmission protocols, usually when users are close. Routes are built dynamically, because each mobile device is acting according to the store‐carry‐and‐forward paradigm. Thus, contacts are seen as opportunities to move data towards the destination. In such networks, the routing protocol is of vital importance, and today, we witness quite a number of routing algorithms that have been proposed to maximize the success rate of message delivery whilst minimizing the communication cost. Such protocols take advantage of the devices history of contacts, or information about users carrying the mobile devices, to make their forwarding decision. This paper extends our previous work with the following: First, we describe a new simplified, fast simulator, designed to minimize the work needed to conduct extensive tests for opportunistic routing algorithm on multiple traces; next, we analyze extensively several of the most popular routing algorithms through extensive simulations conducted using our simulation platform. We highlight their pros and cons in different scenarios, considering different real‐world mobility data traces, such as Global Positioning System traces. The raw Global Positioning System traces are converted to a format based on encounters between participating entities. Copyright © 2016 John Wiley & Sons, Ltd.

Time critical applications have very high requirements on network and computing services, in particular on well-tuned software architecture with sophisticated optimisation on data communication. Their development is often customised to dedicated infrastructure, and system performance is difficult to maintain when infrastructure changes. This fatal weakness in existing architecture and software tools causes very high development costs, and makes it difficult to fully utilise the virtualised, programmable and quality-on-demand services provided by networked Clouds to improve the system productivity. The Software Workbench for Interactive, Time Critical and Highly self-adaptive Cloud applications (SWITCH) is a newly funded project by EU H2020 to address this urgent industrial need, it aims at improving the existing development and execution model of time critical applications by introducing a novel conceptual model called application-infrastructure co-programming and control model, in which application QoS/QoE together with the programmability and controllability of Cloud environments can be all included in the complete lifecycle of applications.

Customer price knowledge has been the object of considerable research in the past decades since the advent of online shopping. Furthermore, customers express online their personal opinions regarding the products or services they purchase, this activity becoming a habit for many people nowadays. However, due to the difficulty of analyzing such large datasets, extracting price knowledge from big data presents unique systems engineering and architectural challenges. The purpose of this paper is to analyze several existing solutions used for search and analysis of large volumes of data, with applicability in the retail field, and to present the results for price knowledge extraction from Big Data using Exalead Cloud View technology. The main contribution of this paper consists in the development of several connectors and a data model based on properties and patterns specific for price calculations.

When mobile devices are unable to establish direct communication, or when communication should be offloaded to cope with large throughputs, mobile collaboration can be used to facilitate communication through opportunistic networks. These types of networks are formed when mobile devices communicate only using short-range transmission protocols, usually when users are close, can help applications exchange data. Routes are built dynamically, since each mobile device is acting according to the store-carry-and-forward paradigm. Thus, contacts are seen as opportunities to move data towards the destination. In such networks the routing protocol is of vital importance -- and today we witness quite a number of routing algorithms that have been proposed to maximize the success rate of message delivery whilst minimizing the communication cost. Such protocols take advantage of the devices' history of contacts, or information about users carrying the mobile devices, to make their forwarding decision. Our contribution in this paper is two-fold: First, we present a new simplified, fast simulator, designed to minimize the work needed to conduct extensive tests for opportunistic routing algorithm on multiple traces, next we present an extensive analysis of several of the most popular routing algorithms through extensive simulations conducted using our simulation platform. We highlight their pros and cons in different scenarios, considering different real-world mobility data traces.

Current M2M communication platforms are being integrated in cloud IoT applications for providing remote sensing and actuating. Nevertheless, requirements for energy efficiency and resilience in severe operating environments are driving the development of new algorithms and infrastructures. This paper presents a survey of the measurement results for the winegrowing season 2014, as it was seen by an M2M remote telemetry station in cooperation with a big data processing platform and several sensors. We demonstrate the use of recent technologies such as Cloud IoT systems and Big Data processing in order to implement disease prediction and alerting application for viticulture. Finally, the extension of the proposed system for other agriculture applications is discussed.