1,720,983 research outputs found
In pursuit of deception: a journey through fake channels, conspiracies, wash trading, and stealth addresses
Get PDFThe rapid technological advancements and the widespread use of online platforms have profoundly changed the digital world, reshaping human interactions, information dissemination, and user engagement. As users navigate the intricate web of online platforms, the prevalence of deceptive practices emerges as a critical and pervasive theme. Nowadays, the range of deceptive techniques has broadened, encompassing many strategies to manipulate, misinform, or exploit users. This wide-ranging influence infiltrates various aspects of online interactions, profoundly shaping the dissemination and reception of information.
Digital platforms have become breeding grounds for misinformation campaigns, where false or misleading information is purposely spread to influence opinions, sow discord, or achieve particular agendas. Whether it is the deliberate spread of fake news articles on social media or the creation of deepfake videos to fabricate events, misinformation leverages the interconnectedness of digital platforms to deceive unsuspecting users. The 2016 US presidential election, marked by misinformation circulating on social media, clearly illustrates the impact of false stories, manipulated images, and misleading narratives in influencing voters.
Deceptive practices extend into social engineering, where attackers manipulate individuals into revealing sensitive information or undertaking actions compromising their privacy or security. In 2020, a widespread phishing campaign capitalized on the COVID-19 pandemic, leveraging emails impersonating health organizations to deceive recipients into clicking on malicious links or disclosing personal information.
Creating fictitious personas and forging online identities is a commonly utilized deceptive strategy, serving diverse objectives with nefarious intent. The proliferation of fake social media accounts to manipulate public opinion challenges the authenticity and reliability of digital interactions. An example is the "Sockpuppet" scandal on Wikipedia, which revealed that certain editors created multiple fake accounts to manipulate content and sway discussions to push specific narratives and biased articles.
The prevalence of these deceptive practices underscores the need to comprehend, analyze, and fight digital deception within the diverse contexts of online platforms.
This thesis aims to reveal the complexities of these deceptive schemes, shedding light on these activities, and contributing to a nuanced understanding of the challenges posed by deceptive practices in the digital domain
Estimation of distribution parameters as a tool for model-based system engineering and model identification
Get PDFThe estimation of the parameters of a probability distribution (e.g., moments) plays an important role both in the model-based system engineering (e.g., analysis and verification through Statistical Model Checking (SMC)) and in the identification of parameters of predictive models (e.g., systems biology, social networks). The contribution of this PhD thesis is both on the algorithm side and on the modeling side. On the algorithm side, we overview a set of Monte Carlo-based Statistical Model Checking tools and algorithms for the verification of Cyber-Physical Systems, and we provide selection criteria for the verification problem at hand. Furthermore, we present an efficient Monte Carlo-based algorithm to estimate the expected value of a multivariate random variable, when marginal density functions are not known. We prove the correctness of our algorithm, we give an Upper Bound and a Lower Bound to its complexity and we present experimental results confirming our evaluations. On the modeling side, we present a mechanistic and identifiable model to predict, at the node level and at a set of nodes level, the expected value of the retweeting rate of a message inside a social network, at a certain time. Our model parameters are random variables, whose distribution parameters are estimated from an available dataset. We experimentally show that our model reliably predicts both the qualitative and the quantitative time behavior of retweeting rates. This is confirmed by the high correlation between the predicted and the observed data. These results enable a simulation-based analysis of users or of a set of users' behaviors inside a network
Artificial intelligence and model checking methods for in silico clinical trials
Get PDFModel-based approaches to safety and efficacy assessment of pharmacological treatments (In Silico Clinical Trials, ISCT) hold the promise to decrease time and cost for the needed experimentations, reduce the need for animal and human testing, and enable personalised medicine, where treatments tailored for each single patient can be designed before being actually administered.
Research in Virtual Physiological Human (VPH) is harvesting such promise by developing quantitative mechanistic models of patient physiology and drugs. Depending on many parameters, such models define physiological differences among different individuals and different reactions to drug administrations. Value assignments to model parameters can be regarded as Virtual Patients (VPs). Thus, as in vivo clinical trials test relevant drugs against suitable candidate patients, ISCT simulate effect of relevant drugs against VPs covering possible behaviours that might occur in vivo.
Having a population of VPs representative of the whole spectrum of human patient behaviours is a key enabler of ISCT.
However, VPH models of practical relevance are typically too complex to be solved analytically or to be formally analysed. Thus, they are usually solved numerically within simulators. In this setting, Artificial Intelligence and Model Checking methods are typically devised. Indeed, a VP coupled together with a pharmacological treatment represents a closed-loop model where the VP plays the role of a physical subsystem and the treatment strategy plays the role of the control software. Systems with this structure are known as Cyber-Physical Systems (CPSs). Thus, simulation-based methodologies for CPSs can be employed within personalised medicine in order to compute representative VP populations and to conduct ISCT.
In this thesis, we advance the state of the art of simulation-based Artificial Intelligence and Model Checking methods for ISCT in the following directions.
First, we present a Statistical Model Checking (SMC) methodology based on hypothesis testing that, given a VPH model as input, computes a population of VPs which is representative (i.e., large enough to represent all relevant phenotypes, with a given degree of statistical confidence) and stratified (i.e., organised as a multi-layer hierarchy of homogeneous sub-groups). Stratification allows ISCT to adaptively focus on specific phenotypes, also supporting prioritisation of patient sub-groups in follow-up in vivo clinical trials.
Second, resting on a representative VP population, we design an ISCT aiming at optimising a complex treatment for a patient digital twin, that is the virtual counterpart of that patient physiology defined by means of a set of VPs. Our ISCT employs an intelligent search driving a VPH model simulator to seek the lightest but still effective treatment for the input patient digital twin.
Third, to enable interoperability among VPH models defined with different modelling and simulation environments and to increase efficiency of our ISCT, we also design an optimised simulator driver to speed-up backtracking-based search algorithms driving simulators.
Finally, we evaluate the effectiveness of our presented methodologies on state-of-the-art use cases and validate our results on retrospective clinical data
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
SPMIAC AS A MODELLING TOOL TO EVALUATE THE IMPACT OF EMISSION REDUCTION STRATEGIES IN THE NAPLES AREA
No full textDispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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