Sexual murderers' implicit theories

Interviews with 28 sexual murderers were subjected to grounded theory analysis. Five implicit theories (ITs) were identified: dangerous world, male sex drive is uncontrollable, entitlement, women as sexual objects, and women as unknowable. These ITs were found to be identical to those identified in the literature as being present in rapists. The presence of dangerous world and male sex drive is uncontrollable were present, or absent, such that three groups could be identified: (a) dangerous world plus male sex drive is uncontrollable; (b) dangerous world, in the absence of male sex drive is uncontrollable; (c) male sex drive is uncontrollable in the absence of dangerous world. These three groups were found to differ in motivation: (a) were motivated by urges to rape and murder; (b) were motivated by grievance, resentment and/or anger toward women; (c) were motivated to sexually offend but were prepared to kill to avoid detection, or secure compliance.

Identification of five fundamental implicit theories underlying cognitive distortions in child abusers : a preliminary study

Qualitative analysis of interviews with 22 child abusers found strong evidence for Ward and Keenan's (1999) proposal that there are five implicit theories in child abusers that account for the majority of their cognitive distortions/thinking errors. These implicit theories are: Child as a sexual being where children are perceived as being able to and wanting to engage in sexual activity with adults and also are not be harmed by such sexual contact; Nature of harm where the offender perceives that sexual activity does not cause harm (and may in fact be beneficial) to the child; Entitlement where the child abuser perceives that he is superior and more important than others: and hence is able to have sex with whoever, and whenever, he wants; Dangerous world where the offender perceives that that others are abusive and rejecting and he must fight to regain control; and Uncontrollable where the offender perceives the world as uncontrollable and hence he believes that circumstances are outside of his control. There was no evidence for any other type of implicit theory. Results of the study also indicated that there was a significant difference in terms of the endorsement of the Dangerous world implicit theory between participants reporting a history of child sexual abuse and those who did not. Offenders against male victims were significantly more likely to endorse the Child as a sexual being and Dangerous world implicit theories compared to men who had offended against female children.

The identification of implicit theories in domestic violence perpetrators

An understanding of how the beliefs of domestically violent offenders might influence their abusive behavior is central to the development and delivery of any intervention program that aims to reduce the risk of further violence against women and children. This article reports the results of a preliminary investigation into the core beliefs of a sample of domestically violent men. Three major themes emerged from an analysis of the accounts of their violence, which were understood in relation to three implicit theories that participants held about themselves, their relationships, and the world. These are discussed in terms of previous studies of offender cognition, how domestic violence programs might be conceptualized, and their implications for practice.

Human action categorization using conditional random field

Automatic human action recognition has been a challenging issue in the field of machine vision. Some high-level features such as SIFT, although with promising performance for action recognition, are computationally complex to some extent. To deal with this problem, we construct the features based on the Distance Transform of body contours, which is relatively simple and computationally efficient, to represent human action in the video. After extracting the features from videos, we adopt the Conditional Random Field for modeling the temporal action sequences. The proposed method is tested with an available standard dataset. We also testify the robustness of our method on various realistic conditions, such as body occlusion or intersection.

Stressor categorization : acute physical and psychological stressors elicit distinctive recruitment patterns in the amygdala and in medullary noradrenergic cell groups

It has been hypothesized that the brain categorizes stressors and utilizes neural response pathways that vary in accordance with the assigned category. If this is true, stressors should elicit patterns of neuronal activation within the brain that are category-specific. Data from previous immediate–early gene expression mapping studies have hinted that this is the case, but interstudy differences in methodology render conclusions tenuous. In the present study, immunolabelling for the expression of c-fos was used as a marker of neuronal activity elicited in the rat brain by haemorrhage, immune challenge, noise, restraint and forced swim. All stressors elicited c-fos expression in 25–30% of hypothalamic paraventricular nucleus corticotrophin-releasing-factor cells, suggesting that these stimuli were of comparable strength, at least with regard to their ability to activate the hypothalamic–pituitary–adrenal axis. In the amygdala, haemorrhage and immune challenge both elicited c-fos expression in a large number of neurons in the central nucleus of the amygdala, whereas noise, restraint and forced swim primarily elicited recruitment of cells within the medial nucleus of the amygdala. In the medulla, all stressors recruited similar numbers of noradrenergic (A1 and A2) and adrenergic (C1 and C2) cells. However, haemorrhage and immune challenge elicited c-fos expression in subpopulations of A1 and A2 noradrenergic cells that were significantly more rostral than those recruited by noise, restraint or forced swim. The present data support the suggestion that the brain recognizes at least two major categories of stressor, which we have referred to as ‘physical’ and ‘psychological’. Moreover, the present data suggest that the neural activation footprint that is left in the brain by stressors can be used to determine the category to which they have been assigned by the brain.

Automatic genre identification for content-based video categorization

This paper presents a set of computational features originating from our study of editing effects, motion, and color used in videos, for the task of automatic video categorization. These features besides representing human understanding of typical attributes of different video genres, are also inspired by the techniques and rules used by many directors to endow specific characteristics to a genre-program which lead to certain emotional impact on viewers. We propose new features whilst also employing traditionally used ones for classification. This research, goes beyond the existing work with a systematic analysis of trends exhibited by each of our features in genres such as cartoons, commercials, music, news, and sports, and it enables an understanding of the similarities, dissimilarities, and also likely confusion between genres. Classification results from our experiments on several hours of video establish the usefulness of this feature set. We also explore the issue of video clip duration required to achieve reliable genre identification and demonstrate its impact on classification accuracy.

Video genre categorization using audio wavelet coefficients

In this paper, we investigate the use of a wavelet transform-based analysis of audio tracks accompanying videos for the problem of automatic program genre detection. We compare the classification performance based on wavelet-based audio features to that using conventional features derived from Fourier and time analysis for the task of discriminating TV programs such as news, commercials, music shows, concerts, motor racing games, and animated cartoons. Three different classifiers namely the Decision Trees, SVMs, and k-Nearest Neighbours are studied to analyse the reliability of the performance of our wavelet features based approach. Further, we investigate the issue of an appropriate duration of an audio clip to be analyzed for this automatic genre determination. Our experimental results show that features derived from the wavelet transform of the audio signal can very well separate the six video genres studied. It is also found that there is no significant difference in performance with varying audio clip durations across the classifiers.

The social life of categories : an empirical study of term categorization

Most classification schemes in common use are actually categorization schemes which fall somewhere on a continuum between unstructured, uncontrolled lists of terms and formal classifications. Over time, terms change meaning and acquire new definitions. This paper presents the results of an approach that used the librarianship principle of consensus to form categories of terms and to relate those categories using a domain reference group. Boisot's Social Learning Cycle (SLC) was then used as a model with which to explain category variations. The single study undertaken in this investigation demonstrated the value of the SLC for explaining the variations between reference group members, and showed the potential for explaining category changes over time. This identifies areas in which consensus is breaking down or emerging, allowing for focused maintenance of categorical schemes.

Object categorization via sparse representation of local features

Sparse representation has been introduced to address many recognition problems in computer vision. In this paper, we propose a new framework for object categorization based on sparse representation of local features. Unlike most of previous sparse coding based methods in object classification that only use sparse coding to extract high-level features, the proposed method incorporates sparse representation and classification into a unified framework. Therefore, it does not need a further classifier. Experimental results show that the proposed method achieved better or comparable accuracy than the well known bag-of-features representation with various classifiers.

A manifold approach to face recognition from low quality video across illumination and pose using implicit super-resolution

We consider the problem of matching a face in a low resolution query video sequence against a set of higher quality gallery sequences. This problem is of interest in many applications, such as law enforcement. Our main contribution is an extension of the recently proposed Generic Shape-Illumination Manifold (gSIM) framework. Specifically, (i) we show how super-resolution across pose and scale can be achieved implicitly, by off-line learning of subsampling artefacts; (ii) we use this result to propose an extension to the statistical model of the gSIM by compounding it with a hierarchy of subsampling models at multiple scales; and (iii) we describe an extensive empirical evaluation of the method on over 1300 video sequences – we first measure the degradation in performance of the original gSIM algorithm as query sequence resolution is decreased and then show that the proposed extension produces an error reduction in the mean recognition rate of over 50%.

Does implicit motor imagery ability predict reaching correction efficiency? A test of recent models of human motor control

Neurocomputational models of reaching indicate that efficient purposive correction of movement midflight (e.g., online control) depends on one's ability to generate and monitor an accurate internal (neural) movement representation. In the first study to test this empirically, the authors investigated the relationship between healthy young adults’ implicit motor imagery performance and their capacity to correct their reaching trajectory. As expected, after controlling for general reaching speed, hierarchical regression demonstrated that imagery ability was a significant predictor of hand correction speed; that is, faster and more accurate imagery performance associated with faster corrections to reaching following target displacement at movement onset. They argue that these findings provide preliminary support for the view that a link exists between an individual's ability to represent movement mentally and correct movement online efficiently.

Multi-label classification with clustering for image and text categorization

This paper explores effective multi-label classification methods for multi-semantic image and text categorization. We perform an experimental study of clustering based multi-label classification (CBMLC) for the target problem. Experimental evaluation is conducted for identifying the impact of different clustering algorithms and base classifiers on the predictive performance and efficiency of CBMLC. In the experimental setting, three widely used clustering algorithms and six popular multi-label classification algorithms are used and evaluated on multi-label image and text datasets. A multi-label classification evaluation metrics, micro F1-measure, is used for presenting predictive performances of the classifications. Experimental evaluation results reveal that clustering based multi-label learning algorithms are more effective compared to their non-clustering counterparts.