Data Science, Machine learning and big data in Digital Journalism: A survey of state-of-the-art, challenges and opportunities

Digital journalism has faced a dramatic change and media companies are challenged to use data science algo-rithms to be more competitive in a Big Data era. While this is a relatively new area of study in the media landscape, the use of machine learning and artificial intelligence has increased substantially over the last few years. In particular, the adoption of data science models for personalization and recommendation has attracted the attention of several media publishers. Following this trend, this paper presents a research literature analysis on the role of Data Science (DS) in Digital Journalism (DJ). Specifically, the aim is to present a critical literature review, synthetizing the main application areas of DS in DJ, highlighting research gaps, challenges, and op-portunities for future studies. Through a systematic literature review integrating bibliometric search, text min-ing, and qualitative discussion, the relevant literature was identified and extensively analyzed. The review reveals an increasing use of DS methods in DJ, with almost 47% of the research being published in the last three years. An hierarchical clustering highlighted six main research domains focused on text mining, event extraction, online comment analysis, recommendation systems, automated journalism, and exploratory data analysis along with some machine learning approaches. Future research directions comprise developing models to improve personalization and engagement features, exploring recommendation algorithms, testing new automated jour-nalism solutions, and improving paywall mechanisms.Acknowledgements This work was supported by the FCT-Funda?a ? o para a Ciência e Tecnologia, under the Projects: UIDB/04466/2020, UIDP/04466/2020, and UIDB/00319/2020

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

A First Look Into Users’ Perceptions of Facial Recognition in the Physical World

Facial recognition (FR) technology is being adopted in both private and public spheres for a wide range of reasons, from ensuring physical safety to providing personalized shopping experiences. It is not clear yet, though, how users perceive this emerging technology in terms of usefulness, risks, and comfort. We begin to address these questions in this paper. In particular, we conducted a vignette-based study with 314 participants on Amazon Mechanical Turk to investigate their perceptions of facial recognition in the physical world, based on thirty-five scenarios across eight different contexts of FR use. We found that users do not have a binary answer towards FR adoption. Rather, their perceptions are grounded in the specific contexts in which FR will be applied. The participants considered a broad range of factors, including control over facial data, the utility of FR, the trustworthiness of organizations using FR, and the location and surroundings of FR use to place the corresponding privacy risks in context. They weighed the privacy risks with the usability, security, and economic gain of FR use as they reported their perceptions. Participants also noted the reasons and rationals behind their perceptions of facial recognition, which let us conduct an in-depth analysis of their perceived benefits, concerns, and comfort with using this technology in various scenarios. Through this first systematic look into users’ perceptions of facial recognition in the physical world, we shed light on the tension between FR adoption and users’ concerns. Taken together, our findings have broad implications that advance the Privacy and Security community’s understanding of FR through the lens of users, where we presented guidelines for future research in these directions

On Enhancing Security of Password-Based Authentication

Password has been the dominant authentication scheme for more than 30 years, and it will not be easily replaced in the foreseeable future. However, password authentication has long been plagued by the dilemma between security and usability, mainly due to human memory limitations. For example, a user often chooses an easy-to-guess (weak) password since it is easier to remember. The ever increasing number of online accounts per user even exacerbates this problem. In this dissertation, we present four research projects that focus on the security of password authentication and its ecosystem. First, we observe that personal information plays a very important role when a user creates a password. Enlightened by this, we conduct a study on how users create their passwords using their personal information based on a leaked password dataset. We create a new metric---Coverage---to quantify the personal information in passwords. Armed with this knowledge, we develop a novel password cracker named Personal-PCFG (Probabilistic Context-Free Grammars) that leverages personal information for targeted password guessing. Experiments show that Personal-PCFG is much more efficient than the original PCFG in cracking passwords. The second project aims to ease the password management hassle for a user. Password managers are introduced so that users need only one password (master password) to access all their other passwords. However, the password manager induces a single point of failure and is potentially vulnerable to data breach. To address these issues, we propose BluePass, a decentralized password manager that features a dual-possession security that involves a master password and a mobile device. In addition, BluePass enables a hand-free user experience by retrieving passwords from the mobile device through Bluetooth communications. In the third project, we investigate an overlooked aspect in the password lifecycle, the password recovery procedure. We study the password recovery protocols in the Alexa top 500 websites, and report interesting findings on the de facto implementation. We observe that the backup email is the primary way for password recovery, and the email becomes a single point of failure. We assess the likelihood of an account recovery attack, analyze the security policy of major email providers, and propose a security enhancement protocol to help securing password recovery emails by two factor authentication. \newline Finally, we focus on a more fundamental level, user identity. Password-based authentication is just a one-time checking to ensure that a user is legitimate. However, a user\u27s identity could be hijacked at any step. For example, an attacker can leverage a zero-day vulnerability to take over the root privilege. Thus, tracking the user behavior is essential to examine the identity legitimacy. We develop a user tracking system based on OS-level logs inside an enterprise network, and apply a variety of techniques to generate a concise and salient user profile for identity examination