Cognitive-behavioural interventions for children who have been sexually abused

Background: Despite differences in how it is defined, there is a general consensus amongst clinicians and researchers that the sexual abuse of children and adolescents (’child sexual abuse’) is a substantial social problem worldwide. The effects of sexual abuse manifest in a wide range of symptoms, including fear, anxiety, post-traumatic stress disorder and various externalising and internalising behaviour problems, such as inappropriate sexual behaviours. Child sexual abuse is associated with increased risk of psychological problems in adulthood. Cognitive-behavioural approaches are used to help children and their non-offending or ’safe’ parent tomanage the sequelae of childhood sexual abuse. This review updates the first Cochrane review of cognitive-behavioural approaches interventions for children who have been sexually abused, which was first published in 2006.

Objectives: To assess the efficacy of cognitive-behavioural approaches (CBT) in addressing the immediate and longer-term sequelae of sexual abuse on children and young people up to 18 years of age.

Search methods: We searched the Cochrane Central Register of Controlled Trials (CENTRAL) (2011 Issue 4); MEDLINE (1950 to November Week
3 2011); EMBASE (1980 to Week 47 2011); CINAHL (1937 to 2 December 2011); PsycINFO (1887 to November Week 5 2011); LILACS (1982 to 2 December 2011) and OpenGrey, previously OpenSIGLE (1980 to 2 December 2011). For this update we also searched ClinicalTrials.gov and the International Clinical Trials Registry Platform (ICTRP).

Selection criteria: We included randomised or quasi-randomised controlled trials of CBT used with children and adolescents up to age 18 years who had experienced being sexually abused, compared with treatment as usual, with or without placebo control.

Data collection and analysis: At least two review authors independently assessed the eligibility of titles and abstracts identified in the search. Two review authors independently extracted data from included studies and entered these into Review Manager 5 software. We synthesised and presented data in both written and graphical form (forest plots).

Main results: We included 10 trials, involving 847 participants. All studies examined CBT programmes provided to children or children and a nonoffending parent. Control groups included wait list controls (n = 1) or treatment as usual (n = 9). Treatment as usual was, for the most part, supportive, unstructured psychotherapy. Generally the reporting of studies was poor. Only four studies were judged ’low risk of bias’ with regards to sequence generation and only one study was judged ’low risk of bias’ in relation to allocation concealment. All studies were judged ’high risk of bias’ in relation to the blinding of outcome assessors or personnel; most studies did not report on these, or other issues of bias. Most studies reported results for study completers rather than for those recruited.

Depression, post-traumatic stress disorder (PTSD), anxiety and child behaviour problems were the primary outcomes. Data suggest that CBT may have a positive impact on the sequelae of child sexual abuse, but most results were not statistically significant. Strongest evidence for positive effects of CBT appears to be in reducing PTSD and anxiety symptoms, but even in these areas effects tend to be 'moderate’ at best. Meta-analysis of data from five studies suggested an average decrease of 1.9 points on the Child Depression Inventory immediately after intervention (95% confidence interval (CI) decrease of 4.0 to increase of 0.4; I2 = 53%; P value for heterogeneity = 0.08), representing a small to moderate effect size. Data from six studies yielded an average decrease of 0.44 standard deviations on a variety of child post-traumatic stress disorder scales (95% CI 0.16 to 0.73; I2 = 46%; P value for heterogeneity = 0.10). Combined data from five studies yielded an average decrease of 0.23 standard deviations on various child anxiety scales (95% CI 0.3 to 0.4; I2=0%; P value for heterogeneity = 0.84). No study reported adverse effects.

Authors’ conclusions: The conclusions of this updated review remain the same as those when it was first published. The review confirms the potential of CBT to address the adverse consequences of child sexual abuse, but highlights the limitations of the evidence base and the need for more carefully conducted and better reported trials.

Towards an understanding of the causes and effects of software requirements change: two case studies

Changes to software requirements not only pose a risk to the successful delivery of software applications but also provide opportunity for improved usability and value. Increased understanding of the causes and consequences of change can support requirements management and also make progress towards the goal of change anticipation. This paper presents the results of two case studies that address objectives arising from that ultimate goal. The first case study evaluated the potential of a change source taxonomy containing the elements ‘market’, ‘organisation’, ‘vision’, ‘specification’, and ‘solution’ to provide a meaningful basis for change classification and measurement. The second case study investigated whether the requirements attributes of novelty, complexity, and dependency correlated with requirements volatility. While insufficiency of data in the first case study precluded an investigation of changes arising due to the change source of ‘market’, for the remainder of the change sources, results indicate a significant difference in cost, value to the customer and management considerations. Findings show that higher cost and value changes arose more often from ‘organisation’ and ‘vision’ sources; these changes also generally involved the co-operation of more stakeholder groups and were considered to be less controllable than changes arising from the ‘specification’ or ‘solution’ sources. Results from the second case study indicate that only ‘requirements dependency’ is consistently correlated with volatility and that changes coming from each change source affect different groups of requirements. We conclude that the taxonomy can provide a meaningful means of change classification, but that a single requirement attribute is insufficient for change prediction. A theoretical causal account of requirements change is drawn from the implications of the combined results of the two case studies.

Software-defined sphere decoding for FPGA-based MIMO detection

Sphere Decoding (SD) is a highly effective detection technique for Multiple-Input Multiple-Output (MIMO) wireless communications receivers, offering quasi-optimal accuracy with relatively low computational complexity as compared to the ideal ML detector. Despite this, the computational demands of even low-complexity SD variants, such as Fixed Complexity SD (FSD), remains such that implementation on modern software-defined network equipment is a highly challenging process, and indeed real-time solutions for MIMO systems such as 4 4 16-QAM 802.11n are unreported. This paper overcomes this barrier. By exploiting large-scale networks of fine-grained softwareprogrammable processors on Field Programmable Gate Array (FPGA), a series of unique SD implementations are presented, culminating in the only single-chip, real-time quasi-optimal SD for 44 16-QAM 802.11n MIMO. Furthermore, it demonstrates that the high performance software-defined architectures which enable these implementations exhibit cost comparable to dedicated circuit architectures.

Evaluation of Four Designed Virtual Agent Personalities

Convincing conversational agents require a coherent set of behavioral responses that can be interpreted by a human observer as indicative of a personality. This paper discusses the continued development and subsequent evaluation of virtual agents based on sound psychological principles. We use Eysenck's theoretical basis to explain aspects of the characterization of our agents, and we describe an architecture where personality affects the agent's global behavior quality as well as their back-channel productions. Drawing on psychological research, we evaluate perception of our agents' personalities and credibility by human viewers (N = 187). Our results suggest that we succeeded in validating theoretically grounded indicators of personality in our virtual agents, and that it is feasible to place our characters on Eysenck's scales. A key finding is that the presence of behavioral characteristics reinforces the prescribed personality profiles that are already emerging from the still images. Our long-term goal is to enhance agents' ability to sustain realistic interaction with human users, and we discuss how this preliminary work may be further developed to include more systematic variation of Eysenck's personality scales. © 2012 IEEE.


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Reaxys Database Information|

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INTCAL radiocarbon database

Radiocarbon data comprising the 2009 and 2013 internationally agreed radiocarbon calibration curves has been put in a format which makes it available to the climate and statistics research communities
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Illumination invariant facial recognition using a piecewise-constant lighting model

In this paper we demonstrate a simple and novel illumination model that can be used for illumination invariant facial recognition. This model requires no prior knowledge of the illumination conditions and can be used when there is only a single training image per-person. The proposed illumination model separates the effects of illumination over a small area of the face into two components; an additive component modelling the mean illumination and a multiplicative component, modelling the variance within the facial area. Illumination invariant facial recognition is performed in a piecewise manner, by splitting the face image into blocks, then normalizing the illumination within each block based on the new lighting model. The assumptions underlying this novel lighting model have been verified on the YaleB face database. We show that magnitude 2D Fourier features can be used as robust facial descriptors within the new lighting model. Using only a single training image per-person, our new method achieves high (in most cases 100%) identification accuracy on the YaleB, extended YaleB and CMU-PIE face databases.