Expressing numerical uncertainty

In Russian, numeral expressions can be made approximate through Approximative Inversion, whereby the noun and the numeral appear to exchange positions. Approximative Inversion has been analyzed as head movement, where a head containing the noun raises to the left of the numeral, but this leads to incorrect semantics. I propose that Approximative Inversion involves post-nominal generation of the numeral in a reduced relative structure, where it is associated with a feature marking speaker uncertainty. This feature triggers a round-number reading of the numeral, resulting in what appears to be number approximation due to speaker uncertainty

Unified by degrees

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Rising intonation and uncertainty

Rising intonation and uncertaint

Hedging arguments

Hedges such as loosely speaking and sorta indicate a mismatch between what is said and what is actually meant. As demonstrated by the example in (1), sorta is often used when a speaker doesn't know a more appropriate word or phrase at the time of utterance.(1) I was running on concrete and accidentally sorta kicked the ground – that is to say, I didn't really kick the ground, but it was like kicking the ground. (Anderson 2014:02, ex.2)In this study, we investigated the readings that arise from sorta-hedging. We present results indicating the possibility of hedging objects, verbs, and whole sentences, and we show that verb type, definiteness of the object, and stress on sorta all influence the availability of an object hedge reading

The strong and the weak: evaluation in modal desideratives

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Evaluation and consumption

Singular indefinite objects of evaluative predicates (e.g. like) are interpreted specifically (1). But several constructions do not yield specific readings, (2). (1) # John likes a cookie. (specific reading/#kind reading) (2) a. John likes a cookie after dinner. b. John likes having a cookie. c. John likes a good cookie. d. John likes a cookie as much as the next guy. We propose that constructions with a minimal consumption-situation reading license a contextual operator which binds the object, giving it a non-specific reading

Malware in the Future? Forecasting of Analyst Detection of Cyber Events

There have been extensive efforts in government, academia, and industry to anticipate, forecast, and mitigate cyber attacks. A common approach is time-series forecasting of cyber attacks based on data from network telescopes, honeypots, and automated intrusion detection/prevention systems. This research has uncovered key insights such as systematicity in cyber attacks. Here, we propose an alternate perspective of this problem by performing forecasting of attacks that are analyst-detected and -verified occurrences of malware. We call these instances of malware cyber event data. Specifically, our dataset was analyst-detected incidents from a large operational Computer Security Service Provider (CSSP) for the U.S. Department of Defense, which rarely relies only on automated systems. Our data set consists of weekly counts of cyber events over approximately seven years. Since all cyber events were validated by analysts, our dataset is unlikely to have false positives which are often endemic in other sources of data. Further, the higher-quality data could be used for a number for resource allocation, estimation of security resources, and the development of effective risk-management strategies. We used a Bayesian State Space Model for forecasting and found that events one week ahead could be predicted. To quantify bursts, we used a Markov model. Our findings of systematicity in analyst-detected cyber attacks are consistent with previous work using other sources. The advanced information provided by a forecast may help with threat awareness by providing a probable value and range for future cyber events one week ahead. Other potential applications for cyber event forecasting include proactive allocation of resources and capabilities for cyber defense (e.g., analyst staffing and sensor configuration) in CSSPs. Enhanced threat awareness may improve cybersecurity.Comment: Revised version resubmitted to journa

SHERLOCK: Experimental evaluation of a conversational agent for mobile information tasks

Abstract—Controlled Natural Language (CNL) has great potential to support human-machine interaction (HMI) because it provides an information representation that is both human readable and machine processable.We investigated the effectiveness of a CNL-based conversational interface for HMI in a behavioural experiment called Simple Human Experiment Regarding Locally Observed Collective Knowledge (SHERLOCK). In SHERLOCK, individuals acted in groups to discover and report information to the machine using natural language (NL), which the machine then processed into CNL. The machine fused responses from different users to form a common operating picture, a dashboard showing the level of agreement for distinct information. To obtain information to add to this dashboard, users explored the real world in a simulated crowd-sourced sensing scenario. This scenario represented a simplified, controlled analogue for tactical intelligence (i.e., direct intelligence of the environment), which is key for rapidly planning military, law enforcement, and emergency operations. Overall, despite close to zero training, 74% of the users inputted NL that was machine interpretable and addressed the assigned tasks. An experimental manipulation aimed to increase user-machine interaction, however, did not improve performance as hypothesised. Nevertheless, results indicate the conversational interface may be effective in assisting humans with collection and fusion of information in a crowd-sourcing context

Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Training

Much work has been dedicated to the exploration of Multi-Agent Reinforcement Learning (MARL) paradigms implementing a centralized learning with decentralized execution (CLDE) approach to achieve human-like collaboration in cooperative tasks. Here, we discuss variations of centralized training and describe a recent survey of algorithmic approaches. The goal is to explore how different implementations of information sharing mechanism in centralized learning may give rise to distinct group coordinated behaviors in multi-agent systems performing cooperative tasks.Comment: This article appeared in the news at: https://www.army.mil/article/247261/army_researchers_develop_innovative_framework_for_training_a