The focus of this paper is a construction which is surprisingly rarely scrutinised: the impersonal passive of the intransitive. Although it sometimes receives a brief mention in discussions of the passive, and although the impersonal passive label is often wrongly given to morpholexical impersonal constructions, there are as yet no thorough analyses of the impersonal passive available for any l...

Traditional methods of user authentication in distributed systems suuer from an important weakness which is due to the low degree of randomness in secrets that human beings can use for identiication. Even though weak secrets (passwords and PINs) are typically not exposed in the clear over the communication lines, they can be discovered with oo-line brute force attacks based on exhaustive trials...

1. The impersonal passive In this paper I apply the phase-based model outlined in Chomsky 1998, 1999 to the pattern of impersonal passives in English and the Scandinavian languages, with supporting evidence drawn from French and Spanish. The basic observation is that the location of an ‘associate’ DP in impersonal passives varies cross-linguistically. The basic order can be taken to be that in ...

Recognition of human activities, using smart phones and wearable devices, has attracted much attention recently. The machine learning (ML) approach to human activity recognition can broadly be classified into two categories: training an ML model on (i) an impersonal dataset or (ii) a personal dataset. Previous research shows that models learned from personal datasets can provide better activity...

This article aims to put Latin impersonal passive into the context of covert categories, specificallypluractionality. I try reanalyse six passages from Roman grammatical texts, mostly compiled in Heinrich Keil’s Grammatici Latini, which meaning passives is considered. There are two groups evidence. The first one (passages Diomedes, Priscian, and frg. Bobiense de verbo) presents as a linguistic ...

Smartphones incorporate many diverse and powerful sensors, which creates exciting new opportunities for data mining and human-computer interaction. In this paper we show how standard classification algorithms can use labeled smartphone-based accelerometer data to identify the physical activity a user is performing. Our main focus is on evaluating the relative performance of impersonal and perso...