Detecting Off-topic Responses to Visual Prompts

Automated methods for essay scoring have made great progress in recent years, achieving accuracies very close to human annotators. However, a known weakness of such automated scorers is not taking into account the semantic relevance of the submitted text. While there is existing work on detecting answer relevance given a textual prompt, very little previous research has been done to incorporate visual writing prompts. We propose a neural architecture and several extensions for detecting off-topic responses to visual prompts and evaluate it on a dataset of texts written by language learners

Mirroring to Build Trust in Digital Assistants

We describe experiments towards building a conversational digital assistant that considers the preferred conversational style of the user. In particular, these experiments are designed to measure whether users prefer and trust an assistant whose conversational style matches their own. To this end we conducted a user study where subjects interacted with a digital assistant that responded in a way that either matched their conversational style, or did not. Using self-reported personality attributes and subjects' feedback on the interactions, we built models that can reliably predict a user's preferred conversational style.Comment: Preprin

The Feasibility of Dynamically Granted Permissions: Aligning Mobile Privacy with User Preferences

Current smartphone operating systems regulate application permissions by prompting users on an ask-on-first-use basis. Prior research has shown that this method is ineffective because it fails to account for context: the circumstances under which an application first requests access to data may be vastly different than the circumstances under which it subsequently requests access. We performed a longitudinal 131-person field study to analyze the contextuality behind user privacy decisions to regulate access to sensitive resources. We built a classifier to make privacy decisions on the user's behalf by detecting when context has changed and, when necessary, inferring privacy preferences based on the user's past decisions and behavior. Our goal is to automatically grant appropriate resource requests without further user intervention, deny inappropriate requests, and only prompt the user when the system is uncertain of the user's preferences. We show that our approach can accurately predict users' privacy decisions 96.8% of the time, which is a four-fold reduction in error rate compared to current systems.Comment: 17 pages, 4 figure

From Chatbots to PhishBots? -- Preventing Phishing scams created using ChatGPT, Google Bard and Claude

The advanced capabilities of Large Language Models (LLMs) have made them invaluable across various applications, from conversational agents and content creation to data analysis, research, and innovation. However, their effectiveness and accessibility also render them susceptible to abuse for generating malicious content, including phishing attacks. This study explores the potential of using four popular commercially available LLMs - ChatGPT (GPT 3.5 Turbo), GPT 4, Claude and Bard to generate functional phishing attacks using a series of malicious prompts. We discover that these LLMs can generate both phishing emails and websites that can convincingly imitate well-known brands, and also deploy a range of evasive tactics for the latter to elude detection mechanisms employed by anti-phishing systems. Notably, these attacks can be generated using unmodified, or "vanilla," versions of these LLMs, without requiring any prior adversarial exploits such as jailbreaking. As a countermeasure, we build a BERT based automated detection tool that can be used for the early detection of malicious prompts to prevent LLMs from generating phishing content attaining an accuracy of 97\% for phishing website prompts, and 94\% for phishing email prompts

Pirate stealth or inattentional blindness?:the effects of target relevance and sustained attention on security monitoring for experienced and naïve operators

Closed Circuit Television (CCTV) operators are responsible for maintaining security in various applied settings. However, research has largely ignored human factors that may contribute to CCTV operator error. One important source of error is inattentional blindness--the failure to detect unexpected but clearly visible stimuli when attending to a scene. We compared inattentional blindness rates for experienced (84 infantry personnel) and naïve (87 civilians) operators in a CCTV monitoring task. The task-relevance of the unexpected stimulus and the length of the monitoring period were manipulated between participants. Inattentional blindness rates were measured using typical post-event questionnaires, and participants' real-time descriptions of the monitored event. Based on the post-event measure, 66% of the participants failed to detect salient, ongoing stimuli appearing in the spatial field of their attentional focus. The unexpected task-irrelevant stimulus was significantly more likely to go undetected (79%) than the unexpected task-relevant stimulus (55%). Prior task experience did not inoculate operators against inattentional blindness effects. Participants' real-time descriptions revealed similar patterns, ruling out inattentional amnesia accounts

Machine-assisted mixed methods: augmenting humanities and social sciences with artificial intelligence

The increasing capacities of large language models (LLMs) present an unprecedented opportunity to scale up data analytics in the humanities and social sciences, augmenting and automating qualitative analytic tasks previously typically allocated to human labor. This contribution proposes a systematic mixed methods framework to harness qualitative analytic expertise, machine scalability, and rigorous quantification, with attention to transparency and replicability. 16 machine-assisted case studies are showcased as proof of concept. Tasks include linguistic and discourse analysis, lexical semantic change detection, interview analysis, historical event cause inference and text mining, detection of political stance, text and idea reuse, genre composition in literature and film; social network inference, automated lexicography, missing metadata augmentation, and multimodal visual cultural analytics. In contrast to the focus on English in the emerging LLM applicability literature, many examples here deal with scenarios involving smaller languages and historical texts prone to digitization distortions. In all but the most difficult tasks requiring expert knowledge, generative LLMs can demonstrably serve as viable research instruments. LLM (and human) annotations may contain errors and variation, but the agreement rate can and should be accounted for in subsequent statistical modeling; a bootstrapping approach is discussed. The replications among the case studies illustrate how tasks previously requiring potentially months of team effort and complex computational pipelines, can now be accomplished by an LLM-assisted scholar in a fraction of the time. Importantly, this approach is not intended to replace, but to augment researcher knowledge and skills. With these opportunities in sight, qualitative expertise and the ability to pose insightful questions have arguably never been more critical

Investigating speech and language impairments in delirium: a preliminary case-control study

<div><p>Introduction</p><p>Language impairment is recognized as as part of the delirium syndrome, yet there is little neuropsychological research on the nature of this dysfunction. Here we hypothesized that patients with delirium show impairments in language formation, coherence and comprehension.</p><p>Methods</p><p>This was a case-control study in 45 hospitalized patients (aged 65–97 years) with delirium, dementia without delirium, or no cognitive impairment (N = 15 per group). DSM-5 criteria were used for delirium. Speech was elicited during (1) structured conversational questioning, and (2) the "Cookie Theft" picture description task. Language comprehension was assessed through standardized verbal and written commands. Interviews were audio-recorded and transcribed.</p><p>Results</p><p>Delirium and dementia groups scored lower on the conversational assessment than the control group (p<0.01, moderate effect sizes (r) of 0.48 and 0.51, resp.). In the Cookie Theft task, the average length of utterances (i.e. unit of speech), indicating language productivity and fluency, distinguished patients with delirium from those with dementia (p<0.01, r = 0.50) and no cognitive impairment (p<0.01, r = 0.55). Patients with delirium performed worse on written comprehension tests compared to cognitively unimpaired patients (p<0.01, r = 0.63), but not compared to the dementia group.</p><p>Conclusions</p><p>Production of spontaneous speech, word quantity, speech content and verbal and written language comprehension are impaired in delirious patients compared to cognitively unimpaired patients. Additionally, patients with delirium produced significantly less fluent speech than those with dementia. These findings have implications for how speech and language are evaluated in delirium assessments, and also for communication with patients with delirium. A study limitation was that the delirium group included patients with co-morbid dementia, which precludes drawing conclusions about the specific language profile of delirium.</p></div

Personalization via collaboration in web retrieval systems: a context based approach

World Wide Web is a source of information, and searches on the Web can be analyzed to detect patterns in Web users' search behaviors and information needs to effectively handle the users' subsequent needs. The rationale is that the information need of a user at a particular time point occurs in a particular context, and queries are derived from that need. In this paper, we discuss an extension of our personalization approach that was originally developed for a traditional bibliographic retrieval system but has been adapted and extended with a collaborative model for the Web retrieval environment. We start with a brief introduction of our personalization approach in a traditional information retrieval system. Then, based on the differences in the nature of documents, users and search tasks between traditional and Web retrieval environments, we describe our extensions of integrating collaboration in personalization in the Web retrieval environment. The architecture for the extension integrates machine learning techniques for the purpose of better modeling users' search tasks. Finally, a user-oriented evaluation of Web-based adaptive retrieval systems is presented as an important aspect of the overall strategy for personalization

Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilities

Spurred by the recent rapid increase in the development and distribution of large language models (LLMs) across industry and academia, much recent work has drawn attention to safety- and security-related threats and vulnerabilities of LLMs, including in the context of potentially criminal activities. Specifically, it has been shown that LLMs can be misused for fraud, impersonation, and the generation of malware; while other authors have considered the more general problem of AI alignment. It is important that developers and practitioners alike are aware of security-related problems with such models. In this paper, we provide an overview of existing - predominantly scientific - efforts on identifying and mitigating threats and vulnerabilities arising from LLMs. We present a taxonomy describing the relationship between threats caused by the generative capabilities of LLMs, prevention measures intended to address such threats, and vulnerabilities arising from imperfect prevention measures. With our work, we hope to raise awareness of the limitations of LLMs in light of such security concerns, among both experienced developers and novel users of such technologies.Comment: Pre-prin