(DEV) 🤖 VTeX: Solutions for Science Publishing

By its nature, data science uses ideas and methodologies from computer science and statistics, along with field-specific knowledge, to describe, learn and predict. Recently, storytelling has been highlighted as an important extension of more traditional data science skills such as coding and modeling. Three courses in our new Master in Data Science and Analytic Storytelling program were designed to include interdisciplinary modules, mainly taught by faculty in storytelling-related disciplines, such as Communication and Art & Design. These courses were PDAT 622: Narrative, Argument, and Persuasion in Data Science; PDAT 624: Principles of Design in Data Visualization; and PDAT 625: Big Data Ethics and Security.

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Over the past several years, as the development of Internet, social media websites such as Twitter and Weibo have received much attention due to their enormous users. A lot of research has been done on sentiment analysis and opinion mining in these websites. However the number of research on using the data in the social media websites to predict the stock market price movement is limited. Behavioral economics and behavioral finance believe that public mood is correlated with economic indicators and financial decisions are significantly driven by emotions. This paper first presents a Chinese emotion mining approach and discusses whether the public emotions or opinions in the Chinese social media websites could be used to predict the stock market price in China. The experimental results demonstrate that the emotions automatically extracted from the large scale Weibo posts represent the real public opinions about some special topics of the stock market in China. Some public mood states extracted such as the “Happiness” and “Disgust” states are highly correlated with the change of stock price according to the Granger causality analysis. Finally, a nonlinear autoregressive model with exogenous sentiment inputs is proposed to predict the stock price movement.

In this paper we investigate on detecting opinion spammer groups through analyzing how users interact with each other. More specifically, our approaches are based on 1) discovering strong vs. weak implicit communities by mining user interaction patterns, and 2) revealing positive vs. negative communities through sentiment analysis on user interactions. Through extensive experiments over various datasets collected from Amazon, we found that the discovered strong, positive communities are significantly more likely to be opinion spammer groups than other communities. Interestingly, while our approach focused mainly on the characteristics of user interactions, it is comparable to the state of the art content-based classifier that mainly uses various content-based features extracted from user reviews. More importantly, we argue that our approach can be more robust than the latter in that if spammers superficially alter their review contents, our approach can still reliably identify them while the content-based approaches may fail.

We introduce a new class of analysis problems, called Scenario Finding Problems (SFPs), for security-sensitive business processes that – besides execution constraints on tasks – define access control policies (constraining which users can execute which tasks) and authorization constraints (such as Separation of Duty). The solutions to SFPs are concrete execution scenarios that assist customers in the reuse and deployment of security-sensitive workflows. We study the relationship of SFPs to well-known properties of security-sensitive processes such as Workflow Satisfiability and Resiliency together with their complexity. Finally, we present a symbolic approach to solving SFPs and describe our experience with a prototype implementation on real-world business process models taken from an on-line library.

Root is the administrative privilege on Android, which is however inaccessible on stock Android devices. Due to the desire for privileged functionalities and the reluctance of rooting their devices, Android users seek for no-root approaches, which provide users with part of root privileges without rooting their devices. Existing no-root approaches require users to launch a separate service via Android Debug Bridge (ADB) on an Android device, which would perform user-desired tasks. However, it is unusual for a third-party Android application to work with a separate native service via sockets, and it requires the application developers to have extra knowledge such as Linux programming in application development. In this paper, we propose a feasible no-root approach based on new functionalities added on Android, which creates no separate service but an ADB loopback. To ensure such no-root approach is not misused in a proactive instead of reactive manner, we examine its dark side. We find out that while this approach makes it easy for no-root applications to work, it may lead to a “permission explosion,” which enables any third-party application to attain shell permissions beyond its granted permissions. The permission explosion can further lead to exploits including privacy leakage, account takeover, application UID abuse, and user input inference. A practical experiment is carried out to evaluate the situation in the real world, which shows that many real-world applications from Google Play and four third-party application markets are indeed vulnerable to these exploits. To mitigate the dark side of the new no-root approach and make it more suitable for users to adopt, we identify the causes of the exploits, and propose a permission-based solution. We also provide suggestions to application developers and application markets on how to prevent these exploits.

Root is the administrative privilege on Android, which is however inaccessible on stock Android devices. Due to the desire for privileged functionalities and the reluctance of rooting their devices, Android users seek for no-root approaches, which provide users with part of root privileges without rooting their devices. Existing no-root approaches require users to launch a separate service via Android Debug Bridge (ADB) on an Android device, which would perform user-desired tasks. However, it is unusual for a third-party Android application to work with a separate native service via sockets, and it requires the application developers to have extra knowledge such as Linux programming in application development. In this paper, we propose a feasible no-root approach based on new functionalities added on Android, which creates no separate service but an ADB loopback. To ensure such no-root approach is not misused in a proactive instead of reactive manner, we examine its dark side. We find out that while this approach makes it easy for no-root applications to work, it may lead to a “permission explosion,” which enables any third-party application to attain shell permissions beyond its granted permissions. The permission explosion can further lead to exploits including privacy leakage, account takeover, application UID abuse, and user input inference. A practical experiment is carried out to evaluate the situation in the real world, which shows that many real-world applications from Google Play and four third-party application markets are indeed vulnerable to these exploits. To mitigate the dark side of the new no-root approach and make it more suitable for users to adopt, we identify the causes of the exploits, and propose a permission-based solution. We also provide suggestions to application developers and application markets on how to prevent these exploits.

Existing CAPTCHA solutions are a major source of user frustration on the Internet today, frequently forcing companies to lose customers and business. Game CAPTCHAs are a promising approach which may make CAPTCHA solving a fun activity for the user. One category of such CAPTCHAs – called Dynamic Cognitive Game (DCG) CAPTCHA – challenges the user to perform a game-like cognitive (or recognition) task interacting with a series of dynamic images. Specifically, it takes the form of many objects floating around within the images, and the user’s task is to match the objects corresponding to specific target(s), and drag/drop them to the target region(s).

We discuss the problem of deciding when a metrisable topological group G has a canonically defined local Lipschitz geometry. This naturally leads to the concept of minimal metrics on G, that we characterise intrinsically in terms of a linear growth condition on powers of group elements.

We study the multiplicative Hilbert matrix, i.e. the infinite matrix with entries ${(\sqrt{mn}log(mn))}^{\mathbf{-}1}$ for $m,n\ge 2$. This matrix was recently introduced within the context of the theory of Dirichlet series, and it was shown that the multiplicative Hilbert matrix has no eigenvalues and that its continuous spectrum coincides with $[0,\mathrm{\pi }]$. Here we prove that the multiplicative Hilbert matrix has no singular continuous spectrum and that its absolutely continuous spectrum has multiplicity one. Our argument relies on spectral perturbation theory and scattering theory. Finding an explicit diagonalisation of the multiplicative Hilbert matrix remains an interesting open problem.