Social Context Modulates Tolerance For Pragmatic Violations In Binary But Not Graded Judgments

A common method for investigating pragmatic processing and its development in children is to have participants make binary judgments of underinformative (UI) statements such as Some elephants are mammals. Rejection of such statements indicates that a (not-all) scalar implicature has been computed. Acceptance of UI statements is typically taken as evidence that the perceiver has not computed an implicature. Under this assumption, the results of binary judgment studies in children and adults suggest that computing an implicature may be cognitively costly. For instance, children under 7 years of age are systematically more likely to accept UI statements compared to adults. This makes sense if children have fewer processing resources than adults. However, Katsos and Bishop (2011) found that young children are able to detect violations of informativeness when given graded rather than binary response options. They propose that children simply have a greater tolerance for pragmatic violations than do adults. The present work examines whether this pragmatic tolerance plays a role in adult binary judgment tasks. We manipulated social attributes of a speaker in an attempt to influence how accepting a perceiver might be of the speaker’s utterances. This manipulation affected acceptability rates for binary judgments (Experiment 1) but not for graded judgments (Experiment 2). These results raise concerns about the widespread use of binary choice tasks for investigating pragmatic processing and undermine the existing evidence suggesting that computing scalar implicatures is costly

A Novel Approach to Multimedia Ontology Engineering for Automated Reasoning over Audiovisual LOD Datasets

Multimedia reasoning, which is suitable for, among others, multimedia content analysis and high-level video scene interpretation, relies on the formal and comprehensive conceptualization of the represented knowledge domain. However, most multimedia ontologies are not exhaustive in terms of role definitions, and do not incorporate complex role inclusions and role interdependencies. In fact, most multimedia ontologies do not have a role box at all, and implement only a basic subset of the available logical constructors. Consequently, their application in multimedia reasoning is limited. To address the above issues, VidOnt, the very first multimedia ontology with SROIQ(D) expressivity and a DL-safe ruleset has been introduced for next-generation multimedia reasoning. In contrast to the common practice, the formal grounding has been set in one of the most expressive description logics, and the ontology validated with industry-leading reasoners, namely HermiT and FaCT++. This paper also presents best practices for developing multimedia ontologies, based on my ontology engineering approach

Social Context Modulates Tolerance for Pragmatic Violations in Binary but Not Graded Judgments

A common method for investigating pragmatic processing and its development in children is to have participants make binary judgments of underinformative (UI) statements such as Some elephants are mammals. Rejection of such statements indicates that a (not-all) scalar implicature has been computed. Acceptance of UI statements is typically taken as evidence that the perceiver has not computed an implicature. Under this assumption, the results of binary judgment studies in children and adults suggest that computing an implicature may be cognitively costly. For instance, children under 7 years of age are systematically more likely to accept UI statements compared to adults. This makes sense if children have fewer processing resources than adults. However, Katsos and Bishop (2011) found that young children are able to detect violations of informativeness when given graded rather than binary response options. They propose that children simply have a greater tolerance for pragmatic violations than do adults. The present work examines whether this pragmatic tolerance plays a role in adult binary judgment tasks. We manipulated social attributes of a speaker in an attempt to influence how accepting a perceiver might be of the speaker’s utterances. This manipulation affected acceptability rates for binary judgments (Experiment 1) but not for graded judgments (Experiment 2). These results raise concerns about the widespread use of binary choice tasks for investigating pragmatic processing and undermine the existing evidence suggesting that computing scalar implicatures is costly

CamDec: Advancing axis P1435-LE video camera security using honeypot-based deception

The explosion of online video streaming in recent years resulted in advanced services both in terms of efficiency and convenience. However, Internet-connected video cameras are prone to exploitation, leading to information security issues and data privacy concerns. The proliferation of video-capable Internet of Things devices and cloud-managed surveillance systems further extend these security issues and concerns. In this paper, a novel approach is proposed for video camera deception via honeypots, offering increased security measures compared to what is available on conventional Internet-enabled video cameras

A Unified Nanopublication Model for Effective and User-Friendly Access to the Elements of Scientific Publishing

Scientific publishing is the means by which we communicate and share scientific knowledge, but this process currently often lacks transparency and machine-interpretable representations. Scientific articles are published in long coarse-grained text with complicated structures, and they are optimized for human readers and not for automated means of organization and access. Peer reviewing is the main method of quality assessment, but these peer reviews are nowadays rarely published and their own complicated structure and linking to the respective articles is not accessible. In order to address these problems and to better align scientific publishing with the principles of the Web and Linked Data, we propose here an approach to use nanopublications as a unifying model to represent in a semantic way the elements of publications, their assessments, as well as the involved processes, actors, and provenance in general. To evaluate our approach, we present a dataset of 627 nanopublications representing an interlinked network of the elements of articles (such as individual paragraphs) and their reviews (such as individual review comments). Focusing on the specific scenario of editors performing a meta-review, we introduce seven competency questions and show how they can be executed as SPARQL queries. We then present a prototype of a user interface for that scenario that shows different views on the set of review comments provided for a given manuscript, and we show in a user study that editors find the interface useful to answer their competency questions. In summary, we demonstrate that a unified and semantic publication model based on nanopublications can make scientific communication more effective and user-friendly

Speak before you listen: Pragmatic reasoning in multi-trial language games

Rational Speech Act theory (Frank & Goodman, 2012) has been successfully applied in numerous communicative settings, including studies using one-shot web-based language games. Several follow-up studies of the latter, however, suggest that listeners may not behave as pragmatically as originally suggested in those tasks. We investigate whether, in such reference games, listeners’ pragmatic reasoning about an informative speaker is improved by greater exposure to the task, and/or prior experience with being a speaker in this task. While we find limited evidence that increased exposure results in more pragmatic responses, listeners do show increased pragmatic reasoning after playing the role of the speaker. Moreover, we find that only in the Speaker-first condition, participant’s tendency to be an informative speaker predicts their degree of pragmatic behavior as a listener. These findings demonstrate that, in these settings, experience as a speaker enhances the ability of listeners to reason pragmatically, as modeled by RSA

Reevaluating pragmatic reasoning in language games.

The results of a highly influential study that tested the predictions of the Rational Speech Act (RSA) model suggest that (a) listeners use pragmatic reasoning in one-shot web-based referential communication games despite the artificial, highly constrained, and minimally interactive nature of the task, and (b) that RSA accurately captures this behavior. In this work, we reevaluate the contribution of the pragmatic reasoning formalized by RSA in explaining listener behavior by comparing RSA to a baseline literal listener model that is only driven by literal word meaning and the prior probability of referring to an object. Across three experiments we observe only modest evidence of pragmatic behavior in one-shot web-based language games, and only under very limited circumstances. We find that although RSA provides a strong fit to listener responses, it does not perform better than the baseline literal listener model. Our results suggest that while participants playing the role of the Speaker are informative in these one-shot web-based reference games, participants playing the role of the Listener only rarely take this Speaker behavior into account to reason about the intended referent. In addition, we show that RSA's fit is primarily due to a combination of non-pragmatic factors, perhaps the most surprising of which is that in the majority of conditions that are amenable to pragmatic reasoning, RSA (accurately) predicts that listeners will behave non-pragmatically. This leads us to conclude that RSA's strong overall correlation with human behavior in one-shot web-based language games does not reflect listener's pragmatic reasoning about informative speakers

What Do You Know? ERP Evidence For Immediate Use Of Common Ground During Online Reference Resolution

Recent evidence on the time-course of conversational perspective taking is mixed. Some results suggest that listeners rapidly incorporate an interlocutor’s knowledge during comprehension, while other findings suggest that listeners initially interpret language egocentrically. A key finding in support of the egocentric view comes from visual-world eye-tracking studies — listeners systematically look at potential referents that are known to them but unknown to the speaker. An alternative explanation is that eye movements might be driven by attentional processes that are unrelated to referent identification. To address this question, we assessed the time-course of perspective taking using event-related potentials (ERP). Participants were instructed to select a referent from a display of four animals (e.g., “Click on the brontosaurus with the boots”) by a speaker who could only see three of the animals. A competitor (e.g., a brontosaurus with a purse) was either mutually visible, visible only to the listener, or absent from the display. Results showed that only the mutually visible competitor elicited an ERP signature of referential ambiguity. Critically, ERPs exhibited no evidence of referential confusion when the listener had privileged access to the competitor. Contra the egocentric hypothesis, this pattern of results indicates that listeners did not consider privileged competitors to be candidates for reference. These findings are consistent with theories of language processing that allow socio-pragmatic information to rapidly influence online language comprehension. The results also suggest that eye-tracking evidence in studies of online reference resolution may include distraction effects driven by privileged competitors and highlight the importance of using multiple measures to investigate perspective use

A Logic Programming Approach to Predict Enterprise-Targeted Cyberattacks

Although cybersecurity research has demonstrated that many of the recent cyberattacks targeting real-world organizations could have been avoided, proactively identifying and systematically understanding when and why those events are likely to occur is still challenging. It has earlier been shown that monitoring malicious hacker discussions about software vulnerabilities in the Dark web and Deep web platforms (D2web) is indicative of future cyberattack incidents. Based on this finding, a system generating warnings of cyberattack incidents was previously developed. However, key limitations to this approach are (1) the strong reliance on explicit software vulnerability mentions from malicious hackers, and (2) the inability to adapt to the ephemeral, constantly changing nature of D2web sites. In this chapter, we address those limitations by leveraging indicators that capture aggregate discussion trends identified from the context of hacker discussions across multiple hacker community websites. Our approach is evaluated on real-world, enterprise-targeted attack events of malicious emails. Compared to a baseline statistical prediction model, our approach provides better precision-recall tradeoff. In addition, it produces actionable, transparent predictions that provide details about the observed hacker activity and reasoning led to certain decision. Moreover, when the predictions of our approach are fused with the predictions of the statistical prediction model, recall can be improved by over 14% while maintaining precision.Fil: Almukaynizi, Mohammed. Arizona State University; Estados UnidosFil: Marin, Ericsson. Arizona State University; Estados UnidosFil: Shah, Malay. Cyber Reconnaissance Inc.; Estados UnidosFil: Nunes, Eric. Arizona State University; Estados UnidosFil: Simari, Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Shakarian, Paulo. Arizona State University; Estados Unido