Showing papers by "University of Piraeus published in 2019"

A Survey on Machine-Learning Techniques for UAV-Based Communications

Abstract: Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new degree of freedom that will be included in the network will also add new challenges. In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security.

The dynamic linkage between renewable energy, tourism, CO2 emissions, economic growth, foreign direct investment, and trade

Abstract: Because of the lack of econometric studies in relevance to the link between tourism and renewable energy, the goal of this study is to remedy this lack and to explore the causal relationships between renewable energy consumption, the number of tourist arrivals, the trade openness ratio, economic growth, foreign direct investment (FDI), and carbon dioxide (CO2) emissions for a panel of 22 Central and South American countries, spanning the period 1995–2010. The empirical findings document that the variables under investigation are cointegrated. Short-run Granger causality tests illustrate unidirectional causalities running from: (i) renewable energy to CO2 emissions and trade; (ii) tourism to trade and FDI; and (iii) economic growth to renewable energy and tourism. In the long run, there is evidence of bidirectional causality between renewable energy, tourism, FDI, trade, and emissions. Thus, renewable energy and tourism are in a strong long-run causal relationship. Moreover, long-run estimates for the whole panel and for the three income panel groups considered (Lower Middle, Upper Middle, High) highlight that tourism, renewable energy, and FDI contribute to the reduction of emissions, while trade and economic growth lead to higher carbon emissions. Therefore, attracting foreign direct investment, encouraging the use of renewable energy, and tourism development, particularly green tourism, are good policies for this region to combat climate change.

Analytical Performance Assessment of THz Wireless Systems

Abstract: This paper is focused on providing the analytical framework for the quantification and evaluation of the joint effect of misalignment fading and hardware imperfections in the presence of multipath fading at terahertz (THz) wireless fiber extenders. In this context, we present the appropriate system model that incorporates the different operation, design, and environmental parameters. In more detail, it takes into account the transceivers antenna gains, the operation frequency, the distance between the transmitter (TX) and the receiver (RX), the environmental conditions, i.e., temperature, humidity, and pressure, the spatial jitter between the TX and RX antennas that results to antennas misalignment, the level of transceivers' hardware imperfections, and the stochastic characteristics of the wireless channel. Based on this model, we analyze and quantify the joint impact of misalignment and multipath fading by providing novel closed-form expressions for the probability and cumulative density functions of the composite channel. Moreover, we derive exact closed-form expressions for the outage probability for both cases of ideal and non-ideal radio frequency (RF) front-end. In addition, in order to quantify the detrimental effect of misalignment fading, we analytically obtain the outage probability in the absence of misalignment cases for both cases of ideal and non-ideal RF front-end. In addition, we extract the novel closed-form expressions for the ergodic capacity for the case of the ideal RF front-end and tight upper bounds for both the cases of ideal and non-ideal RF front-end. Finally, an insightful ergodic capacity ceiling for the non-ideal RF front-end case is provided.

A Survey on Long-Range Attacks for Proof of Stake Protocols

Abstract: Despite common arguments about the prevalence of blockchain technology, in terms of security, privacy, and immutability, in reality, several attacks can be launched against them. This paper provides a systematic literature review on long-range attacks for proof of stake protocols. If successful, these attacks may take over the main chain and partially, or even completely, rewrite the history of transactions that are stored in the blockchain. To this end, we describe how proof of stake protocols work, their fundamental properties, their drawbacks, and their attack surface. After presenting long-range attacks, we discuss possible countermeasures and their applicability.

Dedicated versus mainstreaming approaches in local climate plans in Europe

Abstract: Cities are gaining prominence committing to respond to the threat of climate change, e.g., by developing local climate plans or strategies. However, little is known regarding the approaches and processes of plan development and implementation, or the success and effectiveness of proposed measures. Mainstreaming is regarded as one approach associated with (implementation) success, but the extent of integration of local climate policies and plans in ongoing sectoral and/or development planning is unclear. This paper analyses 885 cities across the 28 European countries to create a first reference baseline on the degree of climate mainstreaming in local climate plans. This will help to compare the benefits of mainstreaming versus dedicated climate plans, looking at policy effectiveness and ultimately delivery of much needed climate change efforts at the city level. All core cities of the European Urban Audit sample were analyzed, and their local climate plans classified as dedicated or main-streamed in other local policy initiatives. It was found that the degree of mainstreaming is low for mitigation (9% of reviewed cities; 12% of the identified plans) and somewhat higher for adaptation (10% of cities; 29% of plans). In particular horizontal mainstreaming is a major effort for local authorities; an effort that does not https://doi. T necessarily pay off in terms of success of action implementation. This study concludes that climate change issues in local municipalities are best tackled by either, developing a dedicated local climate plan in parallel to a mainstreamed plan or by subsequently developing first the dedicated and later a mainstreaming plan (joint or subsequent "dual track approach"). Cities that currently provide dedicated local climate plans (66% of cities for mitigation; 26% of cities for adaptation) may follow-up with a mainstreaming approach. This promises effective implementation of tangible climate actions as well as subsequent diffusion of climate issues into other local sector policies. The development of only broad sustainability or resilience strategies is seen as critical.

Geopolitical Risks, Returns, and Volatility in Emerging Stock Markets: Evidence from a Panel GARCH Model

Abstract: In this article, we analyze the role of country-specific and global geopolitical risks (GPRs) on the returns and volatility of 18 emerging market economies over the monthly period of 1998:1...

Recognizing Occlusal Caries in Dental Intraoral Images Using Deep Learning

Abstract: Based on an image dataset of 88 in-vivo dental images taken with an intra-oral camera, we show that a Deep Learning model (Mask R-CNN) can detect and classify dental caries on occlusal surfaces across the whole 7-class ICDAS (International Caries Detection and Assessment System) scale. This is accomplished without any image pre-processing method and by utilizing superpixels segmentation for the experts' annotations and the evaluation of the classifier. In the proposed methodology, transfer learning and data augmentation are employed during the training of the model. The paper discusses technical details, provides initial results and denotes points for further improvement by fine-tuning the classifier along with an extended dataset.

Towards a secure automatic identification system (AIS)

Abstract: The Automatic Identification System (AIS) is the emerging system for automatic traffic control and collision avoidance services in the maritime transportation sector. It is one of the cornerstone systems for improved marine domain awareness and is embedded in e-navigation, e-bridging, and autonomous ships proposals. However, AIS has some security vulnerabilities that can be exploited to invade privacy of passengers, to launch intentional collision attacks by pirates and terrorists, etc. In this work, we explore how Identity-Based Public Cryptography and Symmetric Cryptography may enhance the security properties of the AIS.

IoT in Healthcare: Achieving Interoperability of High-Quality Data Acquired by IoT Medical Devices

Abstract: It is an undeniable fact that Internet of Things (IoT) technologies have become a milestone advancement in the digital healthcare domain, since the number of IoT medical devices is grown exponentially, and it is now anticipated that by 2020 there will be over 161 million of them connected worldwide. Therefore, in an era of continuous growth, IoT healthcare faces various challenges, such as the collection, the quality estimation, as well as the interpretation and the harmonization of the data that derive from the existing huge amounts of heterogeneous IoT medical devices. Even though various approaches have been developed so far for solving each one of these challenges, none of these proposes a holistic approach for successfully achieving data interoperability between high-quality data that derive from heterogeneous devices. For that reason, in this manuscript a mechanism is produced for effectively addressing the intersection of these challenges. Through this mechanism, initially, the collection of the different devices’ datasets occurs, followed by the cleaning of them. In sequel, the produced cleaning results are used in order to capture the levels of the overall data quality of each dataset, in combination with the measurements of the availability of each device that produced each dataset, and the reliability of it. Consequently, only the high-quality data is kept and translated into a common format, being able to be used for further utilization. The proposed mechanism is evaluated through a specific scenario, producing reliable results, achieving data interoperability of 100% accuracy, and data quality of more than 90% accuracy.

HEDGE: Efficient Traffic Classification of Encrypted and Compressed Packets

Abstract: As the size and source of network traffic increase, so does the challenge of monitoring and analysing network traffic. Therefore, sampling algorithms are often used to alleviate these scalability issues. However, the use of high entropy data streams, through the use of either encryption or compression, further compounds the challenge as current state of the art algorithms cannot accurately and efficiently differentiate between encrypted and compressed packets. In this work, we propose a novel traffic classification method named HEDGE (High Entropy DistinGuishEr) to distinguish between compressed and encrypted traffic. HEDGE is based on the evaluation of the randomness of the data streams and can be applied to individual packets without the need to have access to the entire stream. Findings from the evaluation show that our approach outperforms current state of the art. We also make available our statistically sound dataset, based on known benchmarks, to the wider research community.

Defining granularity levels for supply chain traceability based on IoT and blockchain

Abstract: Identifying the optimal granularity level of traceable units is necessary when implementing traceability, particularly in food supply chains. In this paper we propose a generic framework for defining granularity levels based on product's unique characteristics, supply chain processes and stakeholders engagement. We determine different levels of granularity by using smart contracts within a blockchain-enabled supply chain traceability architecture. The granularity relates to different levels depending on the application of information within a company and between companies across complex supply chain networks. The applicability of the proposed framework is demonstrated with a food supply chain use case scenario by developing a local private blockchain-enabled architecture. The overall benefits of the proposed model are discussed along with several fruitful areas for further research directions.

Internet of Medical Things (IoMT): Acquiring and Transforming Data into HL7 FHIR through 5G Network Slicing

Abstract: The Healthcare 4.0 era is surrounded by challenges varying from the Internet of Medical Things (IoMT) devices’ data collection, integration and interpretation. Several techniques have been developed that however do not propose solutions that can be applied to different scenarios or domains. When dealing with healthcare data, based on the severity and the application of their results, they should be provided almost in real-time, without any errors, inconsistencies or misunderstandings. Henceforth, in this manuscript a platform is proposed for efficiently managing healthcare data, by taking advantage of the latest techniques in Data Acquisition, 5G Network Slicing and Data Interoperability. In this platform, IoMT devices’ data and network specifications can be acquired and segmented in different 5G network slices according to the severity and the computation requirements of different medical scenarios. In sequel, transformations are performed on the data of each network slice to address data heterogeneity issues, and provide the data of the same network slices into HL7 FHIR-compliant format, for further analysis.

Hydras and IPFS: a decentralised playground for malware

Abstract: Modern malware can take various forms and has reached a very high level of sophistication in terms of its penetration, persistence, communication and hiding capabilities. The use of cryptography, and of covert communication channels over public and widely used protocols and services, is becoming a norm. In this work, we start by introducing Resource Identifier Generation Algorithms. These are an extension of a well-known mechanism called domain generation algorithms, which are frequently employed by cybercriminals for bot management and communication. Our extension allows, beyond DNS, the use of other protocols. More concretely, we showcase the exploitation of the InterPlanetary File System (IPFS). This is a solution for the “permanent web”, which enjoys a steadily growing community interest and adoption. The IPFS is, in addition, one of the most prominent solutions for blockchain storage. We go beyond the straightforward case of using the IPFS for hosting malicious content and explore ways in which a botmaster could employ it, to manage her bots, validating our findings experimentally. Finally, we discuss the advantages of our approach for malware authors, its efficacy and highlight its extensibility for other distributed storage services.

Composite Event Recognition for Maritime Monitoring

Abstract: Maritime monitoring systems support safe shipping as they allow for the real-time detection of dangerous, suspicious and illegal vessel activities. We present such a system using the Run-Time Event Calculus, a composite event recognition system with formal, declarative semantics. For effective recognition, we developed a library of maritime patterns in close collaboration with domain experts. We present a thorough evaluation of the system and the patterns both in terms of predictive accuracy and computational efficiency, using real-world datasets of vessel position streams and contextual geographical information.

A Forensics-by-Design Management Framework for Medical Devices Based on Blockchain

Abstract: The Internet of Medical Things (IoMT) provides ubiquitous healthcare services for patient monitoring and treatment. However, the interaction between doctors, patients, healthcare personnel and device manufacturers, with different and often conflicting security and privacy objectives, make such services vulnerable and subject to exploitation. In addition, since parties may require different access levels and the IoMT devices involve different functionalities, access control can be challenging. In this paper, we propose a blockchain-enabled authorization framework for managing both IoMT devices and medical files by creating a distributed chain of custody and health data privacy scheme. The core idea is to build trust domains for the various stakeholders and IoMT devices, in such a way that fine-grain access is enabled by taking into account critical attributes of the IoMT ecosystem such as a) the different roles and capabilities of the IoMT devices and b) their interaction with the users/stakeholders. A private blockchain is used in combination with on-chain smart contracts to allow for a forensics-by-design management architecture with audit trails for integrity and provenance guarantees as well as health data privacy. The private blockchain ecosystem is authenticated by a proof-of-medical-stake consensus mechanism that is tailored for medical applications.

An EWMA-type sign chart with exact run length properties

Abstract: In this paper, a new phase II EWMA-type chart for count data, based on the sign statistic, is proposed and applied to the monitoring of the location of an unknown continuous distribution. The most ...

Improving Vendor-managed Inventory Strategy Based on Internet of Things (IoT) Applications and Blockchain Technology

Abstract: Vendor-managed inventory (VMI) is a widely used collaborative inventory management policy in which manufacturers/vendors manage the inventory of retailers and take responsibility for making decisions related to the timing and extent of inventory replenishment. There exist several prerequisites for a successful VMI strategy implementation like information sharing, trust, systems integration and long-term collaboration. However, in nowadays logistics flows, complex processes, high number of participants and complex distribution channels impede the successful adoption of VMI strategies. In this paper, we propose a new interaction framework based on smart contracts and blockchain for governing the relationship between the vendor and the buyer. A use-case VMI scenario is presented along with a functional smart contract. Tests performed using a local private blockchain illustrate the applicability of the proposed architecture along with the significant benefits for each participant.

An intelligent adaptive fuzzy-based inference system for computer-assisted language learning

Abstract: Adaptive e-learning employs algorithmic mechanisms in order to orchestrate the pace of instruction and provide tailored learning objects to support the unique educational experience of each learner. Taking this into consideration, this research work presents a fully operating and evaluated adaptive and intelligent e-learning system for second language acquisition. This system uses a hybrid model for misconception detection and identification (MDI) and an inference system for the dynamic delivery of the learning objects tailored to learners’ needs. More specifically, the MDI mechanism incorporates the Fuzzy String Searching and The String Interpreting Resemblance algorithms in order to reason between possible learners’ misconceptions. Furthermore, the inference system utilizes the knowledge inference relationship between the learning objects and creates a personalized learning environment for each student. The paper presents examples of operation and the system is evaluated using an evaluation model. The results are very encouraging and promising since they reveal that the hybrid model for misconception detection and identification and the inference system operate collaboratively and enhance the adaptivity of the students’ needs and preferences.

Increased Functional Foods’ Consumption and Mediterranean Diet Adherence May Have a Protective Effect in the Appearance of Gastrointestinal Diseases: A Case–Control Study

Abstract: Background: Epidemiological studies have suggested a possible correlation between nutritional factors and gastrointestinal diseases. Methods: A case–control study was designed in order to investigate if functional foods consumption and Mediterranean diet adherence have a positive effect in ulcerative colitis, Crohn’s disease, irritable bowel syndrome, and gastroesophageal reflux disease. In total, 142 patients (cases) and 147 gender-matched healthy people (controls) participated in the study. Functional food consumption was screened by using a Food Frequency Questionnaire based on the NHANES study, while Mediterranean diet adoption was evaluated by a 14-item Med Diet Assessment tool based on the PREDIMED study. The statistical analysis was performed with SPSS-22. Results: In the previous 2–3 years, the controls had more frequently consumed some categories (probiotics, prebiotics-enriched, and low-fat foods) and some kinds of functional foods (mountain tea, berries, pomegranate, oats, mastics, turmeric, soybeans, and raisins) compared to the cases (p < 0.05). Healthy people were more adherent to the Mediterranean diet than patients (p < 0.05). A multifactor analysis showed that the augmented score of the Mediterranean diet and the augmented consumption of categories and kinds of functional foods were protective factors in the appearance of gastrointestinal diseases. Conclusions: More studies should be conducted in order to further investigate the possible association between specific food components and gastrointestinal diseases’ pathophysiology.