TAILIEUCHUNG - Event Category Learning

Hidden malicious circuits provide an attacker with a stealthy attack vector. As they occupy a layer below the entire software stack, malicious circuits can bypass tra- ditional defensive techniques. Yet current work on trojan circuits considers only simple attacks against the hard- ware itself, and straightforward defenses. More complex designs that attack the software are unexplored, as are the countermeasures an attacker may take to bypass pro- posed defenses. We present the design and implementation of Illinois Malicious Processors (IMPs). There is a substantial de- sign space in malicious circuitry; we show that an at- tacker, rather than designing one specific attack, can in- stead design hardware to support attacks. Such flexi- ble hardware. | Journal of Experimental Psychology Learning Memory and Cognition 1997 Vol. 23 No 3 638-658 Copyright 1997 by the American Psychological Association Inc. 0278-7393 97 300 Event Category Learning Alan w. Kersten and Dorrit Billman Georgia Institute of Technology This research investigated the learning of event categories in particular categories of simple animated events each involving a causal interaction between 2 characters. Four experiments examined whether correlations among attributes of events are easier to learn when they form part of a rich correlational structure than when they are independent of other correlations. Event attributes . state change path of motion were chosen to reflect distinctions made by verbs. Participants were presented with an unsupervised learning task and were then tested on whether the organization of correlations affected learning. Correlations forming part of a system of correlations were found to be better learned than isolated correlations. This finding of facilitation from correlational structure is explained in terms of a model that generates internal feedback to adjust the salience of attributes. These experiments also provide evidence regarding the role of object information in events suggesting that this role is mediated by object category representations. Events unfolding over time have regularity and structure just as do the enduring objects participating in those events. Adapting to a dynamic world requires not only knowledge of objects but also knowledge of the events in which those objects participate. Capturing this knowledge in event categories requires a highly complex representation because events can often be decomposed into a number of smaller yet meaningful spatial entities . objects as well as temporal entities . subevents . Unlike object knowledge this complex event knowledge must often be acquired in an unsupervised context because parents seldom label events for children while the events are .

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