The Design of Pre-Processing Multidimensional Data Based on Component Analysis

Increased implementation of new databases related to multidimensional data involving techniques to support efficient query process, create opportunities for more extensive research. Pre-processing is required because of lack of data attribute values, noisy data, errors, inconsistencies or outliers and differences in coding. Several types of pre-processing based on component analysis will be carried out for cleaning, data integration and transformation, as well as to reduce the dimensions. Component analysis can be done by statistical methods, with the aim to separate the various sources of data into a statistical pattern independent. This paper aims to improve the quality of pre-processed data based on component analysis. RapidMiner is used for data pre-processing using FastICA algorithm. Kernel K-mean is used to cluster the pre-processed data and Expectation Maximization (EM) is used to model. The model was tested using wisconsin breast cancer datasets, lung cancer datasets and prostate cancer datasets. The result shows that the performance of the cluster vector value is higher and the processing time is shorter

The NoiseFiltersR Package: Label Noise Preprocessing in R

In Data Mining, the value of extracted knowledge is directly related to the quality of the used data. This makes data preprocessing one of the most important steps in the knowledge discovery process. A common problem affecting data quality is the presence of noise. A training set with label noise can reduce the predictive performance of classification learning techniques and increase the overfitting of classification models. In this work we present the NoiseFiltersR package. It contains the first extensive R implementation of classical and state-of-the-art label noise filters, which are the most common techniques for preprocessing label noise. The algorithms used for the implementation of the label noise filters are appropriately documented and referenced. They can be called in a R-user-friendly manner, and their results are unified by means of the "filter" class, which also benefits from adapted print and summary methods.Spanish Research ProjectAndalusian Research PlanBrazilian grant-CeMEAI-FAPESPFAPESPUniv Granada, Dept Comp Sci & Artificial Intelligence, E-18071 Granada, SpainUniv Sao Paulo, Inst Ciencias Matemat & Comp, Trabalhador Sao Carlense Av 400, BR-13560970 Sao Carlos, SP, BrazilUniv Fed Sao Paulo, Inst Ciencia & Tecnol, Talim St 330, BR-12231280 Sao Jose Dos Campos, SP, BrazilUniv Fed Sao Paulo, Inst Ciencia & Tecnol, Talim St 330, BR-12231280 Sao Jose Dos Campos, SP, BrazilSpanish Research Project: TIN2014-57251-PAndalusian Research Plan: P11-TIC-7765CeMEAI-FAPESP: 2013/07375-0FAPESP: 2012/22608-8FAPESP: 2011/14602-7Web of Scienc

An intelligent alarm management system for large-scale telecommunication companies

This paper introduces an intelligent system that performs alarm correlation and root cause analysis. The system is designed to operate in large- scale heterogeneous networks from telecommunications operators. The pro- posed architecture includes a rules management module that is based in data mining (to generate the rules) and reinforcement learning (to improve rule se- lection) algorithms. In this work, we focus on the design and development of the rule generation part and test it using a large real-world dataset containing alarms from a Portuguese telecommunications company. The correlation engine achieved promising results, measured by a compression rate of 70% and as- sessed in real-time by experienced network administrator staff

Investigating the attainment of optimum data quality for EHR Big Data: proposing a new methodological approach

The value derivable from the use of data is continuously increasing since some years. Both commercial and non-commercial organisations have realised the immense benefits that might be derived if all data at their disposal could be analysed and form the basis of decision taking. The technological tools required to produce, capture, store, transmit and analyse huge amounts of data form the background to the development of the phenomenon of Big Data. With Big Data, the aim is to be able to generate value from huge amounts of data, often in non-structured format and produced extremely frequently. However, the potential value derivable depends on general level of governance of data, more precisely on the quality of the data. The field of data quality is well researched for traditional data uses but is still in its infancy for the Big Data context. This dissertation focused on investigating effective methods to enhance data quality for Big Data. The principal deliverable of this research is in the form of a methodological approach which can be used to optimize the level of data quality in the Big Data context. Since data quality is contextual, (that is a non-generalizable field), this research study focuses on applying the methodological approach in one use case, in terms of the Electronic Health Records (EHR). The first main contribution to knowledge of this study systematically investigates which data quality dimensions (DQDs) are most important for EHR Big Data. The two most important dimensions ascertained by the research methods applied in this study are accuracy and completeness. These are two well-known dimensions, and this study confirms that they are also very important for EHR Big Data. The second important contribution to knowledge is an investigation into whether Artificial Intelligence with a special focus upon machine learning could be used in improving the detection of dirty data, focusing on the two data quality dimensions of accuracy and completeness. Regression and clustering algorithms proved to be more adequate for accuracy and completeness related issues respectively, based on the experiments carried out. However, the limits of implementing and using machine learning algorithms for detecting data quality issues for Big Data were also revealed and discussed in this research study. It can safely be deduced from the knowledge derived from this part of the research study that use of machine learning for enhancing data quality issues detection is a promising area but not yet a panacea which automates this entire process. The third important contribution is a proposed guideline to undertake data repairs most efficiently for Big Data; this involved surveying and comparing existing data cleansing algorithms against a prototype developed for data reparation. Weaknesses of existing algorithms are highlighted and are considered as areas of practice which efficient data reparation algorithms must focus upon. Those three important contributions form the nucleus for a new data quality methodological approach which could be used to optimize Big Data quality, as applied in the context of EHR. Some of the activities and techniques discussed through the proposed methodological approach can be transposed to other industries and use cases to a large extent. The proposed data quality methodological approach can be used by practitioners of Big Data Quality who follow a data-driven strategy. As opposed to existing Big Data quality frameworks, the proposed data quality methodological approach has the advantage of being more precise and specific. It gives clear and proven methods to undertake the main identified stages of a Big Data quality lifecycle and therefore can be applied by practitioners in the area. This research study provides some promising results and deliverables. It also paves the way for further research in the area. Technical and technological changes in Big Data is rapidly evolving and future research should be focusing on new representations of Big Data, the real-time streaming aspect, and replicating same research methods used in this current research study but on new technologies to validate current results

Forced to Make Amends: An Evaluation of the Community Reparation Order Pilots

This report sets out the findings of the evaluation of the pilot Community Reparation Order schemes ( CROs) operating in Dundee, Highland and Inverclyde from April 2005 to March 2007. The research has been conducted by the University of Edinburgh’s Criminal Justice Social Work Development Centre for Scotland in partnership with DTZ. The Scottish Executive introduced Community Reparation Orders ( CROs) in May 2005 as one of a range of new measures for tackling antisocial behaviour under the Antisocial Behaviour etc (Scotland) Act 2004

Fairness: from the ethical principle to the practice of Machine Learning development as an ongoing agreement with stakeholders

This paper clarifies why bias cannot be completely mitigated in Machine Learning (ML) and proposes an end-to-end methodology to translate the ethical principle of justice and fairness into the practice of ML development as an ongoing agreement with stakeholders. The pro-ethical iterative process presented in the paper aims to challenge asymmetric power dynamics in the fairness decision making within ML design and support ML development teams to identify, mitigate and monitor bias at each step of ML systems development. The process also provides guidance on how to explain the always imperfect trade-offs in terms of bias to users

Feeding identity: The critical hermeneutics of contemporary Kleinian psychoanalytically oriented psychotherapy from the perspective of a novice

Clinical supervision is required for every graduate student pursuing a clinical track in psychology. Yet empirical research has focused almost exclusively on identifying efficacious supervision techniques in terms of altering trainee characteristics rather than investigating in depth the effects of supervision as changes in the characteristics of the trainee\u27s communicative behavior. Moreover, the question of how the specialized communicative behavior practiced by the trainee works to facilitate change for the client/patient has not been addressed by quantitative empirical studies of supervision or psychotherapy process. This study addresses these problems by asking: (1) What characterizes the author\u27s specialized communicative competence after two years of supervision in contemporary Kleinian psychoanalytically oriented psychotherapy, and (2) How does this communicative competence facilitate subject-forming processes with emancipatory potential? Through answering these questions, a case study of contemporary Kleinian psychoanalytically oriented psychotherapy (CKPP) is presented that describes some of the history of this approach, explains some of its major concepts, demonstrates some of its techniques, and articulates its distinct communicative features from the perspective of the author as a participant-researcher. A hermeneutic case study method is explained and employed to analyze the verbatim process notes written during one psychotherapy session with one patient under the supervision of a contemporary Kleinian psychoanalytic psychologist. Drawing on Habermas\u27 theory of communicative competence, Conversation Analysis, methodological hermeneutic and critical hermeneutic theory as expounded by Kögler, nine characteristics of CKPP are articulated and shown to meet the criteria of a critical hermeneutics. As a critical hermeneutic language game CKPP facilitates the (re)formation of the subject by discursively subjecting the client/patient to a continuous displacement of longing in the face of the Other\u27s difference. In Habermas\u27 and Kögler\u27s terms, it is a productive dialogue of asymmetrical power and dependence where knowledge about the self becomes knowledge for the self through the alterity of the Other by means of the experience of a difference in a relational repetition. Finally, this subject (re)forming work of CKPP is examined and discussed in terms of the paradox of subjection as argued by the post-structuralist writings of Judith Butler

Psychological, social and welfare interventions for psychological health and well-being of torture survivors

Background: Torture is widespread, with potentially broad and long-lasting impact across physical, psychological, social and other areas of life. Its complex and diverse effects interact with ethnicity, gender, and refugee experience. Health and welfare agencies offer varied rehabilitation services, from conventional mental health treatment to eclectic or needs-based interventions. This review is needed because relatively little outcome research has been done in this field, and no previous systematic review has been conducted. Resources are scarce, and the challenges of providing services can be considerable. Objectives: To assess beneficial and adverse effects of psychological, social and welfare interventions for torture survivors, and to comp are these effects with those reported by active and inactive controls. Search methods: Randomised controlled trials (RCTs) were identified through a search of PsycINFO, MEDLINE, EMBASE, Web of Science, the Cumulative Index to Nursing and Allied Health Literature (CINA HL), the Cochrane Central Register of Controlled Trials (CENTR AL) and the Cochrane Depression, Anxiety and Neurosis Specialise d Register (CCDANCTR), the Latin American and Caribbean Health Science Information Database (LILACS), the Open System for Information on Grey Literature in Europe (OpenSIGLE), the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP) and Published International Literature On Traumatic Stress (PILOTS) all years to 11 April 2013; searches of Cochrane resources, international trial registries and the main biomedical databases were updated on 20 June 2014. We also searched the On line Library of Dignity (Danish Institute against Torture), reference lists of reviews and included studies and the most frequently cited journals, up to April 2013 but not repeated for 2014. Investigators were contacted to provide updates or details as necessary. Selection criteria: Full publications of RCTs or quasi-RCTs of psychological, social or welfare interventions for survivors of torture against any active or inactive comparison condition. Data collection and analysis: We included all major sources of grey literature in our search and used standard methodological procedures as expected by The Cochrane Collaboration for collecting data, evaluating risk of bias and using GRADE (Grades of Recommendation, Assessment, Development and Evaluation) methods to assess the quality of evidence. Main results: Nine RCTs were included in this review. All were of psychological interventions; none provided social or welfare interventions. The nine trials provided data for 507 adults; none involved children or adolescents. Eight of the nine studies described individual treatment, and one discussed group treatment. Six trials were conducted in Europe, and three in different African countries. Most people were refugees in their thirties and forties; most met the criteria for post-traumatic stress disorder (PTSD) at the outset. Four trials used narrative exposure therapy (NET), one cognitive-behavioural therapy (CBT ) and the other four used mixed methods for trauma symptoms, one of which included reconciliation methods. Five interventions were compared with active controls, such as psychoeducation; four used treatment as usual or waiting list/no treatment; we analysed all control conditions together. Duration of therapy varied from one hour to longer than 20 hours with a median of around 12 to 15 hours. All trials reported effects on distress and on PTSD, and two reported on quality of life. Five studies followed up participants for at least six months. No immediate benefits of psychological therapy were noted in comparison with controls in terms of our primary outcome of distress (usually depression), nor for PTSD symptoms, PTSD caseness, or quality of life. At six-month follow-up, three NET and one CBT study (86 participants) showed moderate effect sizes for intervention over control in reduction of distress (standardised me an difference (SMD) -0.63, 95% confidence interval (CI) -1.07 to -0.19) and of PTSD symptoms (SMD -0.52, 95% CI -0.97 to -0.07). However, the quality of evidence was very low, and risk of bias resulted from researcher/therapist allegiance to treatment methods, effects of uncertain asylum status of some people and real-time non-standardised translation of assessment measures. No measures of adverse events were described, nor of participation, social functioning, quantity of social or family relationships, proxy measures by third parties or satisfaction with treatment. Too few studies were identified for review authors to attempt sensitivity analyses. Authors’ conclusions: Very low-quality evidence suggests no differences between psychological therapies and controls in terms of immediate effects on post- traumatic symptoms, distress or quality of life; however, NET and CBT were found to confer moderate benefits in reducing dis tress and PTSD symptoms over the medium term (six months after treatment). Evidence was of very low quality, mainly because non- standardised assessment methods using interpreters were applied, and sample sizes were very small. Most eligible trials also revealed medium to high risk of bias. Further, attention to the cultural appropriateness of interventions or to their psychometric qualities was inadequate, and assessment measures used were unsuitable. As such, these findings should be interpreted with caution. No data were available on whether symptom reduction enabled improvements in quality of life, participation in community life, or in social and family relationships in the medium term. Details of adverse events and treatment satisfaction were not available immediately after treatment nor in the medium term. Future research should aim to address these gaps in the evidence and should include larger sample sizes when possible. Problems of torture survivors need to be defined far more broadly than by PTSD symptoms, and re cognition given to the contextual influences of being a torture survivor, including as an asylum seeker or refugee, on psychological and social health

Professional doctoral students and the doctoral supervision relationship: negotiating difficulties

This research considers the experiences and difficulties that professional doctoral students face and the supervision relationship. Winnicott’s psychoanalytical ideas are used to understand and make sense of the less visible dynamics that shape the professional doctoral students’ narratives. Semi-structured interviews are used to sensitively explore in-depth the nature of difficult experiences. The method of analysis was both compatible with the psychoanalytical theoretical perspective and with the qualitative interview method. The analysis provided an opportunity to listen to and make sense of the professional doctoral students’ narratives in four different ways. The thesis begins with a review of the wider doctoral education research context. Changes, taking place in that context, are considered, looking particularly at the impact of the knowledge economy on doctoral educational research in general and, more specifically, on professional doctoral educational research. Literature within doctoral education highlights supervision models and psychoanalytical supervision models designed for doctoral supervision practice and doctoral student support. Key findings relate to the professional doctoral students’ expectations and the perceptions that shape their difficult experiences. Firstly, professional doctoral students have little knowledge of doctoral supervision before beginning their first doctoral supervision relationship. The professional doctoral students’ expectations and perceptions influence their supervision relationships. When the professional doctoral students negotiate their expectations, they experience a productive working supervision relationship. However, when professional doctoral students exclude difficult experiences from their supervision relationships they do not get an opportunity to make sense of their experiences. Informal pastoral support, such as cohorts, peer groups and families, provide additional space for the professional doctoral students to talk about their difficult experiences. However, this thesis shows that informal support does not provide an academic framework for the professional doctoral student to understand their difficult experience within a doctoral research context. In contrast, this research suggests that the supervision relationship between the professional doctoral student and the supervisor can offer a supervision space informed by Winnicott’s psychoanalytical ideas. In this space supervisors and supervisees can explore difficult professional doctoral student experiences in a creative, playful and academic environment. The thesis concludes by considering the implications for doctoral supervisors and for professional doctoral students. In doing so, I offer recommendations that include points to consider for Higher Education policy, professional doctoral education and supervision training