Safety, the Preface Paradox and Possible Worlds Semantics

This paper contains an argument to the effect that possible worlds semantics renders semantic knowledge impossible, no matter what ontological interpretation is given to possible worlds. The essential contention made is that possible worlds semantic knowledge is unsafe and this is shown by a parallel with the preface paradox

Semantic Knowledge for Famous Names in Mild Cognitive Impairment

Person identification represents a unique category of semantic knowledge that is commonly impaired in Alzheimer\u27s disease (AD), but has received relatively little investigation in patients with mild cognitive impairment (MCI). The current study examined the retrieval of semantic knowledge for famous names from three time epochs (recent, remote, and enduring) in two participant groups: 23 amnestic MCI (aMCI) patients and 23 healthy elderly controls. The aMCI group was less accurate and produced less semantic knowledge than controls for famous names. Names from the enduring period were recognized faster than both recent and remote names in both groups, and remote names were recognized more quickly than recent names. Episodic memory performance was correlated with greater semantic knowledge particularly for recent names. We suggest that the anterograde memory deficits in the aMCI group interferes with learning of recent famous names and as a result produces difficulties with updating and integrating new semantic information with previously stored information. The implications of these findings for characterizing semantic memory deficits in MCI are discussed. (JINS, 2009, 15, 9-18.

Integrating Semantic Knowledge to Tackle Zero-shot Text Classification

Insufficient or even unavailable training data of emerging classes is a big challenge of many classification tasks, including text classification. Recognising text documents of classes that have never been seen in the learning stage, so-called zero-shot text classification, is therefore difficult and only limited previous works tackled this problem. In this paper, we propose a two-phase framework together with data augmentation and feature augmentation to solve this problem. Four kinds of semantic knowledge (word embeddings, class descriptions, class hierarchy, and a general knowledge graph) are incorporated into the proposed framework to deal with instances of unseen classes effectively. Experimental results show that each and the combination of the two phases achieve the best overall accuracy compared with baselines and recent approaches in classifying real-world texts under the zero-shot scenario.Comment: Accepted NAACL-HLT 201

Investigation of Attitudes Towards Security Behaviors

Cybersecurity attacks have increased as Internet technology has proliferated. Symantec’s 2013 Internet Security Report stated that two out of the top three causes of data breaches in 2012 were attributable to human error (Pelgrin, 2014). This suggests a need to educate end users so that they engage in behaviors that increase their cybersecurity. This study researched how a user’s knowledge affects their engagement in security behaviors. Security behaviors were operationalized into two categories: cyber hygiene and threat response behaviors. A sample of 194 San José State University students were recruited to participate in an observational study. Students completed a card sort, a semantic knowledge quiz, and a survey of their intention to perform security behaviors. A personality inventory was included to see if there would be any effects of personality on security behaviors. Multiple regression was used to see how card sorting and semantic knowledge quiz scores predicted security behaviors, but the results were not significant. Despite this, there was a correlation between cyber hygiene behaviors and threat response behaviors, as well as the Big Five personality traits. The results showed that many of the Big Five personality traits correlated with each other, which is consistent with other studies’ findings. The only personality trait that had a correlation with one of the knowledge measures was neuroticism, in which neuroticism had a negative correlation with the semantic knowledge quiz. Implications for future research are discussed to understand how knowledge, cyber hygiene behaviors, and threat response behaviors relate

Combining link and content-based information in a Bayesian inference model for entity search

An architectural model of a Bayesian inference network to support entity search in semantic knowledge bases is presented. The model supports the explicit combination of primitive data type and object-level semantics under a single computational framework. A flexible query model is supported capable to reason with the availability of simple semantics in querie

More than skin deep: body representation beyond primary somatosensory cortex

The neural circuits underlying initial sensory processing of somatic information are relatively well understood. In contrast, the processes that go beyond primary somatosensation to create more abstract representations related to the body are less clear. In this review, we focus on two classes of higher-order processing beyond somatosensation. Somatoperception refers to the process of perceiving the body itself, and particularly of ensuring somatic perceptual constancy. We review three key elements of somatoperception: (a) remapping information from the body surface into an egocentric reference frame (b) exteroceptive perception of objects in the external world through their contact with the body and (c) interoceptive percepts about the nature and state of the body itself. Somatorepresentation, in contrast, refers to the essentially cognitive process of constructing semantic knowledge and attitudes about the body, including: (d) lexical-semantic knowledge about bodies generally and one’s own body specifically, (e) configural knowledge about the structure of bodies, (f) emotions and attitudes directed towards one’s own body, and (g) the link between physical body and psychological self. We review a wide range of neuropsychological, neuroimaging and neurophysiological data to explore the dissociation between these different aspects of higher somatosensory function

Walking across Wikipedia: a scale-free network model of semantic memory retrieval.

Semantic knowledge has been investigated using both online and offline methods. One common online method is category recall, in which members of a semantic category like "animals" are retrieved in a given period of time. The order, timing, and number of retrievals are used as assays of semantic memory processes. One common offline method is corpus analysis, in which the structure of semantic knowledge is extracted from texts using co-occurrence or encyclopedic methods. Online measures of semantic processing, as well as offline measures of semantic structure, have yielded data resembling inverse power law distributions. The aim of the present study is to investigate whether these patterns in data might be related. A semantic network model of animal knowledge is formulated on the basis of Wikipedia pages and their overlap in word probability distributions. The network is scale-free, in that node degree is related to node frequency as an inverse power law. A random walk over this network is shown to simulate a number of results from a category recall experiment, including power law-like distributions of inter-response intervals. Results are discussed in terms of theories of semantic structure and processing