A systematic review of instruments for assessment of capacity in activities of daily living in children with developmental co-ordination disorder

Children with developmental co-ordination disorder (DCD) face evident motor difficulties in activities of daily living (ADL). Assessment of their capacity in ADL is essential for diagnosis and intervention, in order to limit the daily consequences of the disorder. The aim of this study is to systematically review potential instruments for standardized and objective assessment of children's capacity in ADL, suited for children with DCD. As a first step, databases of MEDLINE, EMBASE, CINAHL and PsycINFO were searched to identify studies that described instruments with potential for assessment of capacity in ADL. Second, instruments were included for review when two independent reviewers agreed that the instruments: (1) are standardized and objective; (2) assess at activity level and comprise items that reflect ADL, and; (3) are applicable to school-aged children that can move independently. Out of 1507 publications, 66 publications were selected, describing 39 instruments. Seven of these instruments were found to fulfil the criteria and were included for review: the Bruininks-Oseretsky Test of Motor Performance-2 (BOT2); the Do-Eat (Do-Eat); the Movement Assessment Battery for Children-2 (MABC2); the school-Assessment of Motor and Process Skills (schoolAMPS); the Tuffts Assessment of Motor Performance (TAMP); the Test of Gross Motor Development (TGMD); and the Functional Independence Measure for Children (WeeFIM). As a third step, for the included instruments, suitability for children with DCD was discussed based on the ADL comprised, ecological validity and other psychometric properties. We concluded that current instruments do not provide comprehensive and ecologically valid assessment of capacity in ADL as required for children with DCD.

Visual detection of occluded crop: For automated harvesting

This paper presents a novel crop detection system applied to the challenging task of field sweet pepper (capsicum) detection. The field-grown sweet pepper crop presents several challenges for robotic systems such as the high degree of occlusion and the fact that the crop can have a similar colour to the background (green on green). To overcome these issues, we propose a two-stage system that performs per-pixel segmentation followed by region detection. The output of the segmentation is used to search for highly probable regions and declares these to be sweet pepper. We propose the novel use of the local binary pattern (LBP) to perform crop segmentation. This feature improves the accuracy of crop segmentation from an AUC of 0.10, for previously proposed features, to 0.56. Using the LBP feature as the basis for our two-stage algorithm, we are able to detect 69.2% of field grown sweet peppers in three sites. This is an impressive result given that the average detection accuracy of people viewing the same colour imagery is 66.8%.

Automated feature engineering for HTTP tunnel detection

Generating discriminative input features is a key requirement for achieving highly accurate classifiers. The process of generating features from raw data is known as feature engineering and it can take significant manual effort. In this paper we propose automated feature engineering to derive a suite of additional features from a given set of basic features with the aim of both improving classifier accuracy through discriminative features, and to assist data scientists through automation. Our implementation is specific to HTTP computer network traffic. To measure the effectiveness of our proposal, we compare the performance of a supervised machine learning classifier built with automated feature engineering versus one using human-guided features. The classifier addresses a problem in computer network security, namely the detection of HTTP tunnels. We use Bro to process network traffic into base features and then apply automated feature engineering to calculate a larger set of derived features. The derived features are calculated without favour to any base feature and include entropy, length and N-grams for all string features, and counts and averages over time for all numeric features. Feature selection is then used to find the most relevant subset of these features. Testing showed that both classifiers achieved a detection rate above 99.93% at a false positive rate below 0.01%. For our datasets, we conclude that automated feature engineering can provide the advantages of increasing classifier development speed and reducing development technical difficulties through the removal of manual feature engineering. These are achieved while also maintaining classification accuracy.

Automated Discovery of Structured Process Models: Discover Structured vs. Discover and Structure

This paper addresses the problem of discovering business process models from event logs. Existing approaches to this problem strike various tradeoffs between accuracy and understandability of the discovered models. With respect to the second criterion, empirical studies have shown that block-structured process models are generally more understandable and less error-prone than unstructured ones. Accordingly, several automated process discovery methods generate block-structured models by construction. These approaches however intertwine the concern of producing accurate models with that of ensuring their structuredness, sometimes sacrificing the former to ensure the latter. In this paper we propose an alternative approach that separates these two concerns. Instead of directly discovering a structured process model, we first apply a well-known heuristic technique that discovers more accurate but sometimes unstructured (and even unsound) process models, and then transform the resulting model into a structured one. An experimental evaluation shows that our “discover and structure” approach outperforms traditional “discover structured” approaches with respect to a range of accuracy and complexity measures.

Review of Diagnostic Screening Instruments for Alcohol and Other Drug Use and Other Psychiatric Disorders

In recent years there has been a growing recognition that many people with drug or alcohol problems are also experiencing a range of other psychiatric and psychological problems. The presence of concurrent psychiatric or psychological problems is likely to impact on the success of treatment services. These problems vary greatly, from undetected major psychiatric illnesses that meet internationally accepted diagnostic criteria such as those outlined in the Diagnostic and Statistical Manual (DSM-IV) of the American Psychiatric Association (1994), to less defined feelings of low mood and anxiety that do not meet diagnostic criteria but nevertheless impact on an individual’s sense of wellbeing and affect their quality of life. Similarly, the presence of a substance misuse problem among those suffering from a major psychiatric illness, often goes undetected. For example, the use of illicit drugs such as cannabis and amphetamine is higher among those individuals suffering from schizophrenia (Hall, 1992) and the misuse of alcohol in people suffering from schizophrenia is well documented (e.g., Gorelick et al., 1990; Searles et al., 1990; Soyka et al., 1993). High rates of alcohol misuse have also been reported in a number of groups including women presenting for treatment with a primary eating disorder (Holderness, Brooks Gunn, & Warren, 1994), individuals suffering from post-traumatic stress disorder (Seidel, Gusman and Aubueg, 1994), and those suffering from anxiety and depression. Despite considerable evidence of high levels of co-morbidity, drug and alcohol treatment agencies and mainstream psychiatric services often fail to identify and respond to concurrent psychiatric or drug and alcohol problems, respectively. The original review was conducted as a first step in providing clinicians with information on screening and diagnostic instruments that may be used to assess previously unidentified co-morbidity. The current revision was conducted to extend the original review by updating psychometric findings on measures in the original review, and incorporating other frequently used measures that were not previously included. The current revision has included information regarding special populations, specifically Indigenous Australians, older persons and adolescents. The objectives were to: ● update the original review of AOD and psychiatric screening/diagnostic instruments, ● recommend when these instruments should be used, by whom and how they should be interpreted, ● identify limitations and provide recommendations for further research, ● refer the reader to pertinent Internet sites for further information and/or purchasing of assessment instruments.