Dissecting Ponzi schemes on Ethereum: identification, analysis, and impact

Ponzi schemes are financial frauds which lure users under the promise of high profits. Actually, users are repaid only with the investments of new users joining the scheme: consequently, a Ponzi scheme implodes soon after users stop joining it. Originated in the offline world 150 years ago, Ponzi schemes have since then migrated to the digital world, approaching first the Web, and more recently hanging over cryptocurrencies like Bitcoin. Smart contract platforms like Ethereum have provided a new opportunity for scammers, who have now the possibility of creating "trustworthy" frauds that still make users lose money, but at least are guaranteed to execute "correctly". We present a comprehensive survey of Ponzi schemes on Ethereum, analysing their behaviour and their impact from various viewpoints

Study of gas-steam combined cycle power plants integrated with MCFC for carbon dioxide capture

Abstract In the field of fossil-fuel based technologies, natural gas combined cycle (NGCC) power plants are currently the best option for electricity generation, having an efficiency close to 60%. However, they produce significant CO2 emissions, amounting to around 0.4 tonne/MWh for new installations. Among the carbon capture and sequestration (CCS) technologies, the process based on chemical absorption is a well-established technology, but markedly reduces the NGCC performances. On the other side, the integration of molten carbonate fuel cells (MCFCs) is recognized as an attractive option to overcome the main drawbacks of traditional CCS technologies. If the cathode side is fed by NGCC exhaust gases, the MCFC operates as a CO2 concentrator, beside providing an additional generating capacity. In this paper the integration of MCFC into a two pressure levels combined cycle is investigated through an energy analysis. To improve the efficiency of MCFC and its integration within the NGCC, plant configurations based on two different gas recirculation options are analyzed. The first is a traditional recirculation of exhaust gases at the compressor inlet; the second, mainly involving the MCFC stack, is based on recirculating a fraction of anode exhaust gases at the cathode inlet. Effects of MCFC operating conditions on energy and environmental performances of the integrated system are evaluated

Similarity and diversity: two sides of the same coin in the evaluation of data streams

The Information Systems represent the primary instrument of growth for the companies that operate in the so-called e-commerce environment. The data streams generated by the users that interact with their websites are the primary source to define the user behavioral models. Some main examples of services integrated in these websites are the Recommender Systems, where these models are exploited in order to generate recommendations of items of potential interest to users, the User Segmentation Systems, where the models are used in order to group the users on the basis of their preferences, and the Fraud Detection Systems, where these models are exploited to determine the legitimacy of a financial transaction. Even though in literature diversity and similarity are considered as two sides of the same coin, almost all the approaches take into account them in a mutually exclusive manner, rather than jointly. The aim of this thesis is to demonstrate how the consideration of both sides of this coin is instead essential to overcome some well-known problems that affict the state-of-the-art approaches used to implement these services, improving their performance. Its contributions are the following: with regard to the recommender systems, the detection of the diversity in a user profile is used to discard incoherent items, improving the accuracy, while the exploitation of the similarity of the predicted items is used to re-rank the recommendations, improving their effectiveness; with regard to the user segmentation systems, the detection of the diversity overcomes the problem of the non-reliability of data source, while the exploitation of the similarity reduces the problems of understandability and triviality of the obtained segments; lastly, concerning the fraud detection systems, the joint use of both diversity and similarity in the evaluation of a new transaction overcomes the problems of the data scarcity, and those of the non-stationary and unbalanced class distribution

The management of clinical risk in a sample of hospital wards and in the service centers for the elderly the Region of Veneto [La gestione del rischio clinico in un campione di reparti ospedalieri e nei centri di servizi per anziani della Regione del Veneto]

Negli ultimi decenni \ue8 cresciuta la cultura del rischio correlato agli errori di terapia farmacologica quale causa di eventi potenzialmente dannosi per i pazienti. Il Progetto \u201cLa gestione del rischio clinico in un campione di reparti ospedalieri e nei Centri di Servizi per anziani della Regione del Veneto\u201d \ue8 stato condotto con l\u2019obiettivo di rilevare lo stato di applicazione delle indicazioni ritenute pi\uf9 rilevanti tra quelle contenute nelle Raccomandazioni Ministeriali n. 1-7-12 presso un campione di Reparti ospedalieri e di Centri di Servizi per anziani non autosufficienti del territorio regionale. La rilevazione ha coinvolto tutte le aziende pubbliche (21 Aziende ULSS, 2 Aziende Ospedaliere, 2 IRCCS) e 16 strutture private; i Centri di Servizi per Anziani sono stati selezionati dalle Aziende ULSS. La rilevazione \ue8 stata effettuata dal farmacista locale, direttamente in reparto attraverso interviste di audit al personale medico/infer- mieristico, osservazione diretta e controlli a campione. Il progetto ha mostrato, sia nei reparti che nei Centri di Servizi per anziani, alcune criticit\ue0 tra cui la bassa ado- zione di procedure sulla Ricognizione e Riconciliazione terapeutica (rispettivamente 34% e 30%), la non com- pleta attenzione alla registrazione in cartella clinica delle allergie dei pazienti (74% e 69%), una bassa segnalazione delle reazioni avverse a farmaci

A Qualitative Exploration of the Use of Contraband Cell Phones in Secured Facilities

Offenders accepting contraband cell phones in secured facilities violate state corrections law, and the possession of these cell phones is a form of risk taking behavior. When offenders continue this risky behavior, it affects their decision making in other domains where they are challenging authorities; and may impact the length of their incarceration. This qualitative phenomenological study examined the lived experience of ex-offenders who had contraband cell phones in secured correctional facilities in order to better understand their reasons for taking risks with contraband cell phones. The theoretical foundation for this study was Trimpop\u27s risk-homeostasis and risk-motivation theories that suggest an individual\u27s behaviors adapt to negotiate between perceived risk and desired risk in order to achieve satisfaction. The research question explored beliefs and perceptions of ex-offenders who chose to accept the risk of using contraband cell phones during their time in secured facilities. Data were collected anonymously through recorded telephone interviews with 8 male adult ex-offenders and analyzed using thematic content analysis. Findings indicated participants felt empowered by possession of cell phones in prison, and it was an acceptable risk to stay connected to family out of concern for loved ones. The study contributes to social change by providing those justice system administrators, and prison managers responsible for prison cell phone policies with more detailed information about the motivations and perspectives of offenders in respect to using contraband cell phones while imprisoned in secured facilities

Unbalanced data classification in fraud detection by introducing a multidimensional space analysis

The problem of frauds is becoming increasingly important in this E-commerce age, where an enormous number of financial transactions are carried out by using electronic instruments of payment such as credit cards. In this scenario it is not possible to adopt human-driven solutions due to the huge number of involved operations. The only approach is therefore to adopt automatic solutions able to discern the legitimate transactions from the fraudulent ones. For this reason, today the development of techniques capable of carrying out this task efficiently represents a very active research field that involves a large number of researchers around the world. Unfortunately, this is not an easy task, since the definition of effective fraud detection approaches is made difficult by a series of well-known problems, the most important of them being the non-balanced class distribution of data that leads towards a significant reduction of the machine learning approaches performance. Such limitation is addressed by the approach proposed in this paper, which exploits three different metrics of similarity in order to define a three-dimensional space of evaluation. Its main objective is a better characterization of the financial transactions in terms of the two possible target classes (legitimate or fraudulent), facing the information asymmetry that gives rise to the problem previously exposed. A series of experiments conducted by using real-world data with different size and imbalance level, demonstrate the effectiveness of the proposed approach with regard to the state-of-the-art solutions

A discrete wavelet transform approach to fraud detection

The exponential growth in the number of operations carried out in the e-commerce environment is directly related to the growth in the number of operations performed through credit cards. This happens because practically all commercial operators allow their customers to make their payments by using them. Such scenario leads toward an high level of risk related to the potential fraudulent activities that the fraudsters can perform by exploiting this powerful instrument of payment illegitimately. A large number of state-of-the-art approaches have been designed to address this problem, but they must face some common issues, the most important of them are the imbalanced distribution and the heterogeneity of data. This paper presents a novel fraud detection approach based on the Discrete Wavelet Transform, which is exploited in order to define an evaluation model able to address the aforementioned problems. Such objective is achieved by using only legitimate transactions in the model definition process, an operation made possible by the more stable data representation offered by the new domain. The performed experiments show that our approach performance is comparable to that of one of the best state-of-the-art approaches such as random forests, demonstrating how such proactive strategy is also able to face the cold-start problem

An Entropy Based Algorithm for Credit Scoring

Part 6: Decision Support in EISInternational audienceThe request of effective credit scoring models is rising in these last decades, due to the increase of consumer lending. Their objective is to divide the loan applicants into two classes, reliable or unreliable, on the basis of the available information. The linear discriminant analysis is one of the most common techniques used to define these models, although this simple parametric statistical method does not overcome some problems, the most important of which is the imbalanced distribution of data by classes. It happens since the number of default cases is much smaller than that of non-default ones, a scenario that reduces the effectiveness of the machine learning approaches, e.g., neural networks and random forests. The in Maximum Entropy (DME) approach proposed in this paper leads toward two interesting results: on the one hand, it evaluates the new loan applications in terms of maximum entropy difference between their features and those of the non-default past cases, using for the model training only these last cases, overcoming the imbalanced learning issue; on the other hand, it operates proactively, overcoming the cold-start problem. Our model has been evaluated by using two real-world datasets with an imbalanced distribution of data, comparing its performance to that of the most performing state-of-the-art approach: random forests