An investigation of implicit measurement techniques amongst low risk and forensic samples

There are difficulties with utilising self- report and physiological measures of assessment amongst forensic populations. This study investigates implicit based measures amongst sexual offenders, nonsexual offenders and low risk samples. Implicit measurement is a term applied to measurement methods that makes it difficult to influence responses through conscious control. The test battery includes the Implicit Association Test (IAT), Rapid Serial Visual Presentation (RSVP), Viewing Time (VT) and the Structured Clinical interview for disorders. The IAT proposes that people will perform better on a task when they depend on well-practiced cognitive associations. The RSVP task requires participants to identify a single target image that is presented amongst a series of rapidly presented visual images. RSVP operates on the premise that if two target images are presented within 500milliseconds of each other, the possibility that the participant will recognize the second target is significantly reduced when the first target is of salience to the individual. This is the attentional blink phenomenon. VT is based on the principle that people will look longer at images that are of salience. Results showed that on the VT task, child sexual offenders took longer to view images of children than low risk groups. Nude over clothed images induced a greater attentional blink amongst low risk and offending samples on the RSVP task. Sexual offenders took longer than low risk groups on word pairing tasks where sexual words were paired with adult words on the IAT. The SCID highlighted differences between the offending and non offending groups on the sub scales for personality disorders. More erotic stimulus items on the VT and RSVP measures is recommended to better differentiate sexual preference between offending and non offending samples. A pictorial IAT is recommended. Findings provide the basis for further development of implicit measures within the assessment of sexual offenders.

ALD: adaptive layer distribution for scalable video

Recent years have witnessed a rapid growth in the demand for streaming video over the Internet and mobile networks, exposes challenges in coping with heterogeneous devices and varying network throughput. Adaptive schemes, such as scalable video coding, are an attractive solution but fare badly in the presence of packet losses. Techniques that use description-based streaming models, such as multiple description coding (MDC), are more suitable for lossy networks, and can mitigate the effects of packet loss by increasing the error resilience of the encoded stream, but with an increased transmission byte cost. In this paper, we present our adaptive scalable streaming technique adaptive layer distribution (ALD). ALD is a novel scalable media delivery technique that optimises the tradeoff between streaming bandwidth and error resiliency. ALD is based on the principle of layer distribution, in which the critical stream data are spread amongst all packets, thus lessening the impact on quality due to network losses. Additionally, ALD provides a parameterised mechanism for dynamic adaptation of the resiliency of the scalable video. The Subjective testing results illustrate that our techniques and models were able to provide levels of consistent high-quality viewing, with lower transmission cost, relative to MDC, irrespective of clip type. This highlights the benefits of selective packetisation in addition to intuitive encoding and transmission.

ALD: adaptive layer distribution for scalable video

Bandwidth constriction and datagram loss are prominent issues that affect the perceived quality of streaming video over lossy networks, such as wireless. The use of layered video coding seems attractive as a means to alleviate these issues, but its adoption has been held back in large part by the inherent priority assigned to the critical lower layers and the consequences for quality that result from their loss. The proposed use of forward error correction (FEC) as a solution only further burdens the bandwidth availability and can negate the perceived benefits of increased stream quality. In this paper, we propose Adaptive Layer Distribution (ALD) as a novel scalable media delivery technique that optimises the tradeoff between the streaming bandwidth and error resiliency. ALD is based on the principle of layer distribution, in which the critical stream data is spread amongst all datagrams thus lessening the impact on quality due to network losses. Additionally, ALD provides a parameterised mechanism for dynamic adaptation of the scalable video, while providing increased resilience to the highest quality layers. Our experimental results show that ALD improves the perceived quality and also reduces the bandwidth demand by up to 36% in comparison to the well-known Multiple Description Coding (MDC) technique.