Geografisk profilering har blivit en av de mest kontroversiella och modernametoderna som används under brottsutredningar i nuläget. Framgången ochtillförlitligheten av metoden är ett debatterat ämne inom forskningsvärlden. Denhär studien ämnar att undersöka huruvida geografisk profilering är ett användbartverktyg för Polisen. Syftet med studien är att analysera hur väl metoden fungerarsom ett verktyg och komplement till en brottsutredning samt om geografiskprofilering är användbart inom bostadsinbrottutredningar. Genom att skapa ensystematisk litteraturöversikt och utföra nyckelpersonsintervjuer fann författarenatt geografisk profilering fungerar som ett utmärkt komplement till utredningar.Resultatet visade att de geografiska profileringsprogrammen inte alltid är merframgångsrika än andra metoder inom området men att de oftast är konsistenta itillförlitligheten. Resultatet visade även att metoden är användbar inombostadsinbrottutredningar så länge profilen är gjord ordentligt och utav enutbildad analytiker. Studiens slutsats är att geografisk profilering är mycket merän bara en gissningslek och kan identifiera gärningsmän om analysen är gjord aven erfaren utredare. Detta resultat diskuteras senare i studien, samt valet av metodoch möjligheter för framtida forskning. / Geographic profiling is considered as one of the most controversial andinnovative technologies used in criminal investigations today. The accuracy of themethodology has become a popular topic amongst scholars and has caused aheated debate regarding the success of geographic profiling. This study seeks toevaluate if geographic profiling is a useful tool for the police. Thus the aims ofthis study are to examine if the methodology is a viable tool during investigationsand further to establish to what extent geographic profiling has been successfullyapplied within the area of property crime, in particular burglary investigations. Byconducting a systematic literature review and key informant interviews this studyfound that geographic profiling can be a very useful tool for analysts. Further theresults showed that geographic profiling systems are not always more accuratethan simpler methods, however simpler strategies are not necessarily as consistentas a computerised system. Moreover the results indicate that geographic profilingcan be applied during burglary investigations, if done correctly and by a trainedinvestigator. The study concludes that geographic profiling is more than just aguessing game and if applied appropriately it will most likely identify theoffender. Lastly the results and shortcomings of this study, including the need forfuture research is discussed.

This thesis is a benchmark performed on three commercial Optical Character Recognition (OCR) engines. The purpose of this benchmark is to characterize the performance of the OCR engines with emphasis on the correlation of errors between each engine. The benchmarks are performed for the evaluation of the effect of a multi-OCR system employing a voting scheme to increase overall recognition accuracy. This is desirable since currently OCR systems are still unable to recognize characters with 100% accuracy. The existing error rates of OCR engines pose a major problem for applications where a single error can possibly effect significant outcomes, such as in legal applications. The results obtained from this benchmark are the primary determining factor in the decision of implementing a voting scheme. The experiment performed displayed a very high accuracy rate for each of these commercial OCR engines. The average accuracy rate found for each engine was near 99.5% based on a less than 6,000 word document. While these error rates are very low, the goal is 100% accuracy in legal applications. Based on the work in this thesis, it has been determined that a simple voting scheme will help to improve the accuracy rate.

Considering the diminishing availability of professional resources, increasing costs, and time requirements involved in early childhood mass screenings, parents are an essential source of information. In this study, the Survey of Speech Development (SSD) (Perry-Carson & Steel, 2001; Steel, 2000) was used to determine the accuracy of parents in reporting the speech sound inventories of their toddlers. Parents of 30 children, who were between the ages of 27 to 33 months old, completed the SSD prior to a speech and language assessment session. Based on assessment results, the children were classified as normal developing or language delayed. A 20-minute play interaction between the parent and child was recorded during the assessment and was transcribed later for analysis. Speech sounds (consonants) were coded as present or absent and comparisons were made between the parents results on the SSD and data from the 20-minute speech sample. A point-by-point reliability analysis of the speech sounds on the SSD compared to those produced in the speech sample revealed an overall parental accuracy of 75%. Further, no differences were found between parent reports and transcribed accounts for total number of different consonants. This was true for parents of both language delayed and language normal toddlers. Results suggest that if given a systematic means of providing information, parents are a reliable source of information regarding sounds their toddlers produce.

To date, the vast majority of research regarding personality in IO Psychology has relied on self-report assessments. Despite support for the utility of third-party assessments, IO Psychologists have only just begun extensive research in this area. Connelly and Ones (2010) conducted a meta-analysis that demonstrated that accuracy of third-party ratings improved as intimacy between the judge and the target grew. This remained true with the exception of predicting behavioral criteria, where non-intimates maintained superior predictability (Connelly & Ones, 2010). This was later contradicted by a recent investigation that found the best predictive validity for third-party assessments when they are taken from personal acquaintances as opposed to work colleagues (Connelly & Hulsheger, 2012). The current study is intended to investigate how the depth of the relationship and breadth of behavioral observations differentially moderate the relationship between third-party personality assessments and accuracy criteria (i.e., self-other overlap, discriminant validity and behavior). Results indicate that both depth and breadth impact accuracy criteria and they do so differentially based on trait visibility and evaluativeness. These findings will be discussed along with practical implications and limitations of the following research.

The presence of outlying anchor items is an issue faced by many testing agencies. The decision to retain or remove an item is a difficult one, especially when the content representation of the anchor set becomes questionable by item removal decisions. Additionally, the reason for the aberrancy is not always clear, and if the performance of the item has changed due to improvements in instruction, then removing the anchor item may not be appropriate and might produce misleading conclusions about the proficiency of the examinees. This study is conducted in two parts consisting of both a simulation and empirical data analysis. In these studies, the effect on examinee classification was investigated when the decision was made to remove or retain aberrant anchor items. Three methods of detection were explored; (1) delta plot, (2) IRT b-parameter plots, and (3) the RPU method. In the simulation study, degree of aberrancy was manipulated as well as the ability distribution of examinees and five aberrant item schemes were employed. In the empirical data analysis, archived statewide science achievement data that was suspected to possess differential opportunity to learn between administrations was re-analyzed using the various item parameter drift detection methods. The results for both the simulation and empirical data study provide support for eliminating the use of flagged items for linking assessments when a matrix-sampling design is used and a large number of items are used within that anchor. While neither the delta nor the IRT b-parameter plot methods produced results that would overwhelmingly support their use, it is recommended that both methods be employed in practice until further research is conducted for alternative methods, such as the RPU method since classification accuracy increases when such methods are employed and items are removed and most often, growth is not misrepresented by doing so.

Background: Every country has seen an increase in motorcycle accidents over the years due to social and economic differences as well as regional variations in transportation circumstances. One common mode of transportation for those in the middle class is a motorbike.  Every motorbike rider is legally required to wear a helmet when driving a bike. However, some people on bikes used to ignore their safety, which resulted in them violating traffic rules by driving the bike without a helmet. The policeman tried to address this issue manually, but it was ineffective and proved to be quite challenging in practical circumstances. Therefore, automating this procedure is essential if we are to effectively enforce road safety. As a result, an automated system was created employing a variety of techniques, including Convolutional Neural Networks (CNN), the Haar Cascade Classifier, the You Only Look Once (YOLO), the Single Shot multi-box Detector (SSD), etc. In this study, YOLOv3 and Haar Cascade Classifier are used to compare motorcycle helmet and license plate detection.  Objectives: This thesis aims to compare the machine learning algorithms that detect motorcycles’ license plates and helmets. Here, the Haar Cascade Classifier and YOLO algorithms are used on the US License Plates and Helmet Detection datasets to train the models. The accuracy is obtained in detecting the helmets and license plates of the motorcycles and analyzed.  Methods: The experiment method is chosen to answer the research question. An experiment is performed to find the accuracy of the models in detecting the helmets and license plates of motorcycles. The datasets utilized for this are from Kaggle, which included 764 pictures of two distinct classes, i.e., with and without a helmet, along with 447 unique license plate images. Before training the model, preprocessing techniques are performed on US License Plates and Helmet Detection datasets. Now the datasets are divided into test and train datasets where the test data set size is considered to be 20% and the train data set size is 80%. The models are trained using 80% pre-processed training datasets and using the Haar Cascade Classifier and YOLOv3 algorithms. An observation is made by giving the 20% test data to the trained models. Finally, the prediction results of these two models are recorded and the accuracy is measured by generating a confusion matrix.   Results: The efficient and best algorithm for detecting the helmets and license plates of motorcycles is identified from the experiment method. The YOLOv3 algorithm is considered more accurate in detecting motorcycles' helmets and license plates based on the results.  Conclusions: Models are trained using Haar Cascade and YOLOv3 algorithms on US License Plates and Helmet Detection training datasets. The accuracy of the models in detecting the helmets and license plates of motorcycles is checked by using the testing datasets. The model trained using the YOLOv3 algorithm has high accuracy; hence, the Neural network-based YOLOv3 technique is thought to be the best and most efficient.

Social media and online discussion forums have allowed people to hide behind a veil of anonymity, which has made the platforms feel unsafe for people with a different opinion than the majority. Recent research on robots and bots have found that they are a good option when it comes to inducing cooperation or acting as a conversation partner to encourage critical thinking. These robots and bots are based on an algorithm that is able to identify and classify comments, usually into positive and negative comments, left by users. The problem attended to in this thesis is to explore the possibility of creating an algorithm that can classify and pick out a minority opinion with an accuracy of at least 90%. The purpose is to create one of the vital algorithms for a larger project. The goal of this thesis is to provide a functioning algorithm with an accuracy of at least 90% for future implementations. In this thesis, the research approach is quantitative. The results show that it is possible to create an algorithm with the ability to classify and identify comments that also can pick out a minority opinion. Furthermore, the algorithm also achieved an accuracy of at least 90% when it comes to classification of comments, which makes the search for a minority opinion much easier. / Sociala medier och diskussionsforum online har tillåtit människor att gömma sig bakom sin datorskärm och vara anonym. Detta har gjort sociala medier till en osäker plats för människor som inte delar samma åsikt som majoriteten om olika diskussionsämnen. Ny forskning om robotar och sociala botar har funnit att dem är effektiva med att få människor att samarbeta samt att dem är en bra konversationspartner som framkallar mer kritiskt tänkande. Dessa robotar och sociala botar är baserade på en algoritm som kan identifiera och klassificera kommentarer, oftast till positiva eller negativa kommentarer som användare av sociala medier har lämnat. Problemet som avhandlingen försöker lösa är om det är möjligt att skapa en algoritm som kan identifiera och klassificera kommentarer, men även hitta och ta fram en åsikt som inte är en del av majoriteten med en träffsäkerhet på minst 90%. Ändamålet är att skapa en viktig byggsten för ett större forskningsprojekt. Målet med avhandlingen är att skapa en funktionerande algoritm för framtida undersökning som förhoppningsvis kan motarbeta partiskhet i sociala medier. Avhandlingens ståndpunkt är kvantitativ. Resultaten från avhandlingen visar att det är möjligt att skapa en algoritm som kan klassificera samt hitta en åsikt som inte är en del av majoriteten. Dessutom har algoritmen hög noggrannhet när det gäller klassificeringen vilket underlättar sökandet av en åsikt.

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There is much debate on the best way to educate students on how to generate hypotheses to enhance clinical reasoning in nursing education. To increase opportunities for nursing programs to promote the discovery of accurate and broad-level hypotheses, scholars recommend abductive reasoning which offers an alternative approach to hypothetico-deductive reasoning. This study explored the effects of abductive reasoning training on hypothesis generation abilities (accuracy, expertise, breadth) of first and second year baccalaureate nursing students in a problem-based learning curriculum. A quasi-experiment with 64 participants (29 control, 35 experimental) was conducted. Based on their allocation, study participants either took part in abductive reasoning training or informal group discussion. Three different test questionnaires, each with a unique care scenario, were used to assess participants’ hypothesis generation abilities at baseline, immediate post-test and one-week follow-up. Content validity for care scenarios and other study materials was obtained from content academic experts. Compared to control participants, experimental participants showed significant improvements at follow-up on hypothesis accuracy (p=0.05), expertise (p=0.006), and breadth (p=0.003). While control participants’ hypotheses displayed a superficial understanding of care situations, experimental participants’ hypotheses reflected increased accuracy, expertise and breadth. This study shows that abductive reasoning, as a scaffolding teaching and learning strategy, can allow nursing students to discover underlying salient patterns in order to better understand and explain the complex realities of care situations. Educating nursing students in abductive reasoning could enable them to adapt existing competencies when trying to accurately and holistically understand newer complex care situations. This could lead to a more holistic, person-based approach to care which will allow nursing students to see various health-related issues as integrated rather than separate. / Thesis / Doctor of Philosophy (PhD) / This study explored the effects of a training program on hypothesis generation abilities of nursing students. The training program aimed to teach students how to think more broadly about care situations. Student’s hypothesis generation abilities were measured through the use of three care scenarios, each of which was presented before, immediately after and one-week after the training program. Only first and second year nursing students were included in the study. About half of the students were provided with the training while the other half were provided with informal discussion about hypothesis generation. After one-week, it was discovered that students who received the training had improved significantly in their ability to generate broad hypotheses. These students also generated hypotheses that were more accurate than the other group of students who did not receive the training. Due to the training, students’ abilities in discovering the important aspects of the care situation also improved.

Title: Clinical Decision Making in Paramedicine Author(s) & affiliation(s): Michael Eby – McMaster University, Hamilton, ON, Canada Sandra Monteiro – McMaster University, Hamilton, ON, Canada Geoffrey Norman – McMaster University, Hamilton, ON, Canada Walter Tavares – McMaster University, Hamilton, ON, Canada Background: Paramedics are frequently required to make rapid decisions in an uncontrolled, dynamic environment, often with limited diagnostic information. In Ontario, paramedic practice is based on a set of provincial medical directives that provide diagnostic and treatment criteria. Unsupervised deviation from these directives is classified as a form of error and highly discouraged. To date, there is little known about how years of clinical experience or level of certification affect the way these medical directives are used. The purpose of this study was to examine the relationship between paramedic experience, training and accuracy of treatment decisions when faced with patients who meet and fall outside of the existing medical directives. Methods: Thirty-one participants (16 experienced / 15 novice) were recruited from two paramedic services in Ontario. “Experienced” was defined as in-practice for 5 years or more. Participants were presented with 9 scenarios; in 6 scenarios, the patient presentation fit within the existing directives, while in 3 scenarios, the patient presentation fell outside the medical directives. Multiple-choice responses were used to capture participants’ decisions to treat or not treat the patients. Responses were scored and submitted to a mixed-factorial ANOVA to evaluate differences in accuracy between case types, years of experience and level of training. Results: There was a significant effect of case type (p < 0.004). Accuracy was lower when the patient presentation did not meet the criteria of the medical directive (76.34% (CI = 67.15% to 85.53%) vs. 98.35% (CI = 96.55% to 100%) when they did. There was no effect of years of clinical practice or level of certification. Conclusion: The results suggest both novice and experienced paramedics are able to accurately apply medical directives, however, there is a significant decrease in accuracy when the patient presentation does not fit one. This variation in practice may have a significant impact on patient safety, and further research is required to determine what factors may be causing this decreased accuracy. / Thesis / Master of Science (MSc) / Paramedics work in a fast-paced, dynamic environment. The types of patients, and the situations paramedics encounter are different every day. Paramedic practice is based on a series of provincial medical directives that outline the different proceedures, medications and types of patients that can be treated. While these directives cover many of the cases paramedics encounter, there will always be cases that don’t “fit”. The purose of this study is to see if paramedics approach those types of cases in a different way, and if their years of experience or level of training change how good they are at idenfiying what patients require treatment. As there is very little paramedic specific research on this topic, this study will serve as a starting point for future research and hopefully stimulate discussion about paramedic practice, and how to support paramedics getting better at their jobs.