A Region-based Training Data Segmentation Strategy to Credit Scoring

The rating of users requesting financial services is a growing task, especially in this historical period of the COVID-19 pandemic characterized by a dramatic increase in online activities, mainly related to e-commerce. This kind of assessment is a task manually performed in the past that today needs to be carried out by automatic credit scoring systems, due to the enormous number of requests to process. It follows that such systems play a crucial role for financial operators, as their effectiveness is directly related to gains and losses of money. Despite the huge investments in terms of financial and human resources devoted to the development of such systems, the state-of-the-art solutions are transversally affected by some well-known problems that make the development of credit scoring systems a challenging task, mainly related to the unbalance and heterogeneity of the involved data, problems to which it adds the scarcity of public datasets. The Region-based Training Data Segmentation (RTDS) strategy proposed in this work revolves around a divide-and-conquer approach, where the user classification depends on the results of several sub-classifications. In more detail, the training data is divided into regions that bound different users and features, which are used to train several classification models that will lead toward the final classification through a majority voting rule. Such a strategy relies on the consideration that the independent analysis of different users and features can lead to a more accurate classification than that offered by a single evaluation model trained on the entire dataset. The validation process carried out using three public real-world datasets with a different number of features. samples, and degree of data imbalance demonstrates the effectiveness of the proposed strategy. which outperforms the canonical training one in the context of all the datasets

Influencing brain waves by evoked potentials as biometric approach: taking stock of the last six years of research

The scientific advances of recent years have made available to anyone affordable hardware devices capable of doing something unthinkable until a few years ago, the reading of brain waves. It means that through small wearable devices it is possible to perform an electroencephalography (EEG), albeit with less potential than those offered by high-cost professional devices. Such devices make it possible for researchers a huge number of experiments that were once impossible in many areas due to the high costs of the necessary hardware. Many studies in the literature explore the use of EEG data as a biometric approach for people identification, but, unfortunately, it presents problems mainly related to the difficulty of extracting unique and stable patterns from users, despite the adoption of sophisticated techniques. An approach to face this problem is based on the evoked potentials (EPs), external stimuli applied during the EEG reading, a noninvasive technique used for many years in clinical routine, in combination with other diagnostic tests, to evaluate the electrical activity related to some areas of the brain and spinal cord to diagnose neurological disorders. In consideration of the growing number of works in the literature that combine the EEG and EP approaches for biometric purposes, this work aims to evaluate the practical feasibility of such approaches as reliable biometric instruments for user identification by surveying the state of the art of the last 6 years, also providing an overview of the elements and concepts related to this research area

Leveraging the Training Data Partitioning to Improve Events Characterization in Intrusion Detection Systems

The ever-increasing use of services based on computer networks, even in crucial areas unthinkable until a few years ago, has made the security of these networks a crucial element for anyone, also in consideration of the increasingly sophisticated techniques and strategies available to attackers. In this context, Intrusion Detection Systems (IDSs) play a primary role since they are responsible for analyzing and classifying each network activity as legitimate or illegitimate, allowing us to take the necessary countermeasures at the appropriate time. However, these systems are not infallible due to several reasons, the most important of which are the constant evolution of the attacks (e.g., zero-day attacks) and the problem that many of the attacks have behavior similar to those of legitimate activities, and therefore they are very hard to identify. This work relies on the hypothesis that the subdivision of the training data used for the IDS classification model definition into a certain number of partitions, in terms of events and features, can improve the characterization of the network events, improving the system performance. The non-overlapping data partitions train independent classification models, classifying the event according to a majority-voting rule. A series of experiments conducted on a benchmark real-world dataset support the initial hypothesis, showing a performance improvement with respect to a canonical training approach

Cryptocurrency scams: analysis and perspectives

Since the inception of Bitcoin in 2009, the market of cryptocurrencies has grown beyond the initial expectations, as witnessed by the thousands of tokenised assets available on the market, whose daily trades amount to dozens of USD billions. The pseudonymity features of these cryptocurrencies have attracted the attention of cybercriminals, who exploit them to carry out potentially untraceable scams. The wide range of cryptocurrency-based scams observed over the last ten years has fostered the research on the analysis of their effects, and the development of techniques to counter them. However, doing research in this field requires addressing several challenges: for instance, although a few data sources about cryptocurrency scams are publicly available, they often contain incomplete or misclassified data. Further, there is no standard taxonomy of scams, which leads to ambiguous and incoherent interpretations of their nature. Indeed, the unavailability of reliable datasets makes it difficult to train effective automatic classifiers that can detect and analyse cryptocurrency scams. In this paper, we perform an extensive review of the scientific literature on cryptocurrency scams, which we systematise according to a novel taxonomy. By collecting and homogenising data from different public sources, we build a uniform dataset of thousands of cryptocurrency scams.We devise an automatic tool that recognises scams and classifies them according to our taxonomy.We assess the effectiveness of our tool through standard performance metrics.We also give an in-depth analysis of the classification results, offering several insights into threat types, from their features to their connection with other types. Finally, we provide a set of guidelines that policymakers could follow to improve user protection against cryptocurrency scams

Integrated geophysical and aerial sensing methods for archaeology: A case history in the Punic site of Villamar (Sardinia, Italy)

In this paper, the authors present a recent integrated survey carried out on an archaeological urban site, generally free of buildings, except some temporary structures related to excavated areas where multi-chamber tombs were found. The two methods used to investigate this site were thermal infrared and ground penetrating radar (GPR). The thermography was carried out with the sensor mounted under a helium balloon simultaneously with a photographic camera. In order to have a synthetic view of the surface thermal behavior, a simplified version of the existing night thermal gradient algorithm was applied. By this approach, we have a wide extension of thermal maps due to the balloon oscillation, because we are able to compute the maps despite collecting few acquisition samples. By the integration of GPR and the thermal imaging, we can evaluate the depth of the thermal influence of possible archaeological targets, such as buried Punic tombs or walls belonging to the succeeding medieval buildings, which have been subsequently destroyed. The thermal anomalies present correspondences to the radar time slices obtained from 30 to 50 cm. Furthermore, by superimposing historical aerial pictures on the GPR and thermal imaging data, we can identify these anomalies as the foundations of the destroyed building

A blockchain-based distributed paradigm to secure localization services

In recent decades, modern societies are experiencing an increasing adoption of interconnected smart devices. This revolution involves not only canonical devices such as smartphones and tablets, but also simple objects like light bulbs. Named the Internet of Things (IoT), this ever-growing scenario offers enormous opportunities in many areas of modern society, especially if joined by other emerging technologies such as, for example, the blockchain. Indeed, the latter allows users to certify transactions publicly, without relying on central authorities or intermediaries. This work aims to exploit the scenario above by proposing a novel blockchain-based distributed paradigm to secure localization services, here named the Internet of Entities (IoE). It represents a mechanism for the reliable localization of people and things, and it exploits the increasing number of existing wireless devices and blockchain-based distributed ledger technologies. Moreover, unlike most of the canonical localization approaches, it is strongly oriented towards the protection of the users’ privacy. Finally, its implementation requires minimal efforts since it employs the existing infrastructures and devices, thus giving life to a new and wide data environment, exploitable in many domains, such as e-health, smart cities, and smart mobility

Session Types with Linearity in Haskell

Type systems with parametric polymorphism can encode a significant pro- portion of the information contained in session types. This allows concurrent programming with session-type-like guarantees in languages like ML and Java. However, statically enforcing the linearity properties of session types, in a way that is also natural to program with, is more challenging. Haskell provides various language features that can capture concurrent programming with session types, with full linearity invariants and in a mostly idiomatic style. This chapter overviews various approaches in the literature for session typed programming in Haskell. As a starting point, we use polymorphic types and simple type-level functions to provide session-typed communication in Haskell without linearity. We then overview and compare the varying approaches to implementing session types with static linearity checks. We conclude with a discussion of the remaining open problems. The code associated with this chapter can be found at http://github. com/dorchard/betty-book-haskell-sessions

An overview of blockchain-based systems and smart contracts for digital coupons

Among the accessory applications of the blockchain, the idea of using it as an immutable register for tracking and certifying documents is recently gaining interest in research and industry. The problems of traceability, non-counterfeiting and unique usage of digital coupons fall within this area; many couponing platforms are hence exploring the possibility of addressing the above limitations with blockchain technologies. In view of the foregoing, in this work we analyse and compare several blockchain-based couponing systems. To do so, we first propose a general schema of digital coupon and define the desirable properties of a couponing system. Then, we select a sample of these systems and we examine them, describing their design choices and summarizing their relevant properties. Finally, we inspect their code and study how the notion of couponing system is interpreted in their smart contracts. We also highlight their distinctive features and relevant implementation solutions. We conclude by discussing what emerged from our analysis and proposing some possible future investigations

Brain Waves and Evoked Potentials as Biometric User Identification Strategy: An Affordable Low-cost Approach

The relatively recent introduction on the market of low-cost devices able to perform an Electroencephalography (EEG) has opened a stimulating research scenario that involves a large number of researchers previously excluded due to the high costs of such hardware. In this regard, one of the most stimulating research fields is focused on the use of such devices in the context of biometric systems, where the EEG data are exploited for user identification purposes. Based on the current literature, which reports that many of these systems are designed by combining the EEG data with a series of external stimuli (Evoked Potentials) to improve the reliability and stability over time of the EEG patterns, this work is aimed to formalize a biometric identification system based on low-cost EEG devices and simple stimulation instruments, such as images and sounds generated by a computer. In other words, our objective is to design a low-cost EEG-based biometric approach exploitable on a large number of real-world scenarios

From Payment Services Directive 2 (PSD2) to Credit Scoring: A Case Study on an Italian Banking Institution

The Payments Systems Directive 2 (PSD2), recently issued by the European Union, allows the banks to share their customer data if they authorize the operation. On the one hand, this opportunity offers interesting perspectives to the financial operators, allowing them to evaluate the customers reliability (Credit Scoring) even in the absence of the canonical information typically used (e.g., age, current job, total incomes, or previous loans). On the other hand, the state-of-the-art approaches and strategies still train their Credit Scoring models using the canonical information. This scenario is further worsened by the scarcity of proper datasets needed for research purposes and the class imbalance between the reliable and unreliable cases, which biases the reliability of the classification models trained using this information. The proposed work is aimed at experimentally investigating the possibility of defining a Credit Scoring model based on the bank transactions of a customer, instead of using the canonical information, comparing the performance of the two models (canonical and transaction-based), and proposing an approach to improve the performance of the transactions-based model. The obtained results show the feasibility of a Credit Scoring model based only on banking transactions, and the possibility of improving its performance by introducing simple meta-features