From politicians and nation states to terrorist groups, numerous organizations reportedly conduct explicit campaigns to influence opinions on social media, posing a risk to freedom of expression. Thus, there is a need to identify and eliminate "influence bots" - realistic, automated identities that illicitly shape discussions on sites like Twitter and Facebook - before they get too influential.

SUGILITE is a new programming-by-demonstration (PBD) system that enables users to create automation on smartphones. SUGILITE uses Android's accessibility API to support automating arbitrary tasks in any Android app (or even across multiple apps). When the user gives verbal commands that SUGILITE does not know how to execute, the user can demonstrate by directly manipulating the regular apps' user interface. By leveraging the verbal instructions, the demonstrated procedures, and the apps? UI hierarchy structures, SUGILITE can automatically generalize the script from the recorded actions, so SUGILITE learns how to perform tasks with different variations and parameters from a single demonstration. Extensive error handling and context checking support forking the script when new situations are encountered, and provide robustness if the apps change their user interface. Our lab study suggests that users with little or no programming knowledge can successfully automate smartphone tasks using SUGILITE.

In recent years, conversational agents (CAs) have become ubiquitous and are a presence in our daily routines. It seems that the technology has finally ripened to advance the use of CAs in various domains, including commercial, healthcare, educational, political, industrial, and personal domains. In this study, the main areas in which CAs are successful are described along with the main technologies that enable the creation of CAs. Capable of conducting ongoing communication with humans, CAs are encountered in natural-language processing, deep learning, and technologies that integrate emotional aspects. The technologies used for the evaluation of CAs and publicly available datasets are outlined. In addition, several areas for future research are identified to address moral and security issues, given the current state of CA-related technological developments. The uniqueness of our review is that an overview of the concepts and building blocks of CAs is provided, and CAs are categorized according to their abilities and main application domains. In addition, the primary tools and datasets that may be useful for the development and evaluation of CAs of different categories are described. Finally, some thoughts and directions for future research are provided, and domains that may benefit from conversational agents are introduced.

The problem of insider threat is receiving increasing attention both within the computer science community as well as government and industry. This paper starts by presenting a broad, multidisciplinary survey of insider threat capturing contributions from computer scientists, psychologists, criminologists, and security practitioners. Subsequently, we present the behavioral analysis of insider threat (BAIT) framework, in which we conduct a detailed experiment involving 795 subjects on Amazon Mechanical Turk (AMT) in order to gauge the behaviors that real human subjects follow when attempting to exfiltrate data from within an organization. In the real world, the number of actual insiders found is very small, so supervised machine-learning methods encounter a challenge. Unlike past works, we develop bootstrapping algorithms that learn from highly imbalanced data, mostly unlabeled, and almost no history of user behavior from an insider threat perspective. We develop and evaluate seven algorithms using BAIT and show that they can produce a realistic (and acceptable) balance of precision and recall.

Large language models have been shown useful in multiple domains including conversational agents, education, and explainable AI.ChatGPT is a large language model developed by OpenAI as a conversational agent. Being a large language model, ChatGPT is trained on massive amounts of data. Clearly, the characteristics of the data influence ChatGPT's responses. In this paper, we stress a surprising bias of ChatGPT related to the use of digits in numbers. Namely, we show a very high correlation between the frequency of digits generated by ChatGPT and humans' favorite numbers, with the most frequent digit generated by ChatGPT, matching humans' most favorable number, $7$. We also show some advantages of ChatGPT being developed as a conversational agent, and discuss some of its limitations.