ISA-standardien mukaisen tilintarkastusprosessin tehostaminen – evidenssiä Suomesta

Tilintarkastuksen sääntely on lisääntynyt merkittävästi 2000-luvulla. Samalla kilpailu on kiristynyt, tilintarkastuspalkkiot alentuneet ja raportointiaikataulut tiukentuneet. Näistä tekijöistä johtuen tilintarkastuksen tehostamiselle on tarvetta. Tämän tutkielman tavoitteena on selvittää ja tutkia, miten tilintarkastusprosessia voidaan tehostaa sekä arvioida eri tehostamiskeinojen käyttöarvoa punnitsemalla niiden mahdollisuuksia ja haasteita. Tutkielman empiirinen osa on toteutettu laadullisena tutkimuksena. Aineisto on kerätty haastattelemalla suomalaisia tilintarkastajia ja analyysimenetelmänä on käytetty teemoittelua. Empiirisessä osassa tutkitaan, miltä teoriasta löydettyjen tehostamiskeinojen käyttömahdollisuudet näyttävät käytännössä ja selvitetään, millä muilla keinoilla tilintarkastusprosessia voidaan tehostaa. Tutkimuksen perusteella tilintarkastuksen suunnitteluvaihetta voidaan tehostaa tilintarkastajien erikoistumisen, dokumentoinnin standardoinnin, sähköisen materiaalin tehokkaamman hyödyntämisen ja palvelukeskusten hyödyntämisen avulla. Toteutusvaihetta voidaan tehostaa näiden lisäksi big data -menetelmillä ja kontrollien tarkastuksella.

INFO2009 2012-13 Resource Group 6 Flashcards

An inspirational and educational flashcard resource for secondary school children. Can be used as flashcards or as a matching activity (depending on how cards are cut out).

COMP1205 Student Presentations M-P

Tuesday 13th May Building 34 Room 3001, 16.15-17.45 Elena & Rikki/Jian Presenting: Groups: M, N, O, P Marking Groups: Q, R, S, T Schedule and Topics 16.15-16.20: Introduction and protocol for the session 16.20 Group M: Serious games – gaming as a driver for applications online 16.40 Group N: Open Education OERs 17.00 Group O: Big Data – the big picture 17.20 Group P: Rights and equality in the workplace 17.40-18.00: Wash-up: feedback session for presentation groups

WAIS Seminar:Let's SoFWIReD!

Title: Let’s SoFWIReD! Time: Wed, 21 May 2014 11:00-11:50 Location: Building 32, Room 3077 Speaker: Dr Sepi Chakaveh Abstract The information age as we know it has its roots in several enabling technologies – most of all the World Wide Web – for the provision of truly global connectivity. The emergence of a Web of Big Data in terms of the publication and analysis of Open Data provides new insights about the impact of the Web in our society. The second most important technology in this regard has been the emergence of streaming processes based on new and innovative compression methods such as MP3 so that audio and video content becomes accessible to everyone on the Web. The SoFWIReD team is developing comprehensive, interoperable platforms for data and knowledge driven processing of Open Data and will investigate aspects of collective intelligence. Insights generated in the project will form the basis for supporting companies through consulting, organisational development, and software solutions so that they can master the collective intelligence transition. The seminar will present how the project addresses the research topics of web observatory, dynamic media objects, crowd-sourced open data and Internet services. At the end of a talk a number of demos will be shown in the context of SoFWIReD’s Dynamic Media Object.

Ciencias de la complejidad: estado actual

Los aportes teóricos y aplicados de la complejidad en economía han tomado tantas direcciones y han sido tan frenéticos en las últimas décadas, que no existe un trabajo reciente, hasta donde conocemos, que los compile y los analice de forma integrada. El objetivo de este proyecto, por tanto, es desarrollar un estado situacional de las diferentes aplicaciones conceptuales, teóricas, metodológicas y tecnológicas de las ciencias de la complejidad en la economía. Asimismo, se pretende analizar las tendencias recientes en el estudio de la complejidad de los sistemas económicos y los horizontes que las ciencias de la complejidad ofrecen de cara al abordaje de los fenómenos económicos del mundo globalizado contemporáneo.

Del re-ensamblaje de lo social a la movilización pública: caso Karina del Pozo

El presente trabajo investigativo tiene como objetivo principal dar respuesta a la interrogante acerca de en qué casos y condiciones las interacciones virtuales contribuyen a conformar movilizaciones reales a partir de la información que circula en redes sociales. Para cumplirlo, en primer lugar, se partió de una revisión teórica de autores trascendentales como Howard Rheingold, Manuel Castells, Antonio Damasio, Pierre Levy o Antonio Negri, lo que dio como resultado un primer hallazgo: el vínculo entre la comunicación, las nuevas redes sociales, los medios tradicionales y las emociones que se gestan en ellos y que pueden hacer eco en el individuo hasta promover su movilización y la acción social. Sobre esta base teórica, el siguiente paso fue determinar cómo aquello se presentaba en un grupo definido de usuarios de redes sociales, concretamente el colectivo que hizo que el caso de Karina del Pozo fuera tendencia. Para ello, con la aplicación de las herramientas que arrojaron datos cuantificables como el Big Data y el Important Data, se procedió al trabajo de campo que constó de dos momentos. El primero de ellos, la fase de recopilación de datos; y el segundo, de análisis e interpretación sobre los resultados obtenidos. Como deducciones del estudio a partir del planteamiento teórico y de la investigación, la movilización hacia un determinado objetivo más allá de las redes sociales, es el discurso y del relato periodístico en medios tradicionales que generaron una empatía narrativa, situando al espectador en un lugar virtualmente cercano al hecho. Estos elementos además de ser una respuesta al asesinato de Karina del Pozo fueron un cuestionamiento a la sociedad y a sus prácticas, al machismo, a la violencia de género, pero además significaron la manifestación de estas mismas condiciones cuando se empezó a culpabilizar a la víctima, atribuyéndole la responsabilidad de los hechos que acabaron con su vida. Estas emociónes, valores, pensamientos o sentimientos similares, reflejados en la adopción de una determinada posición frente a un mismo hecho. En el caso de quienes se movilizaron, se estaría cumpliendo la presencia de emociones que van desde el miedo, la ira, la indignación y en contraposición, la solidaridad y la esperanza por cambiar una realidad a través de la acción.

Unified framework for construction of rule based classification systems

Automatic generation of classification rules has been an increasingly popular technique in commercial applications such as Big Data analytics, rule based expert systems and decision making systems. However, a principal problem that arises with most methods for generation of classification rules is the overfit-ting of training data. When Big Data is dealt with, this may result in the generation of a large number of complex rules. This may not only increase computational cost but also lower the accuracy in predicting further unseen instances. This has led to the necessity of developing pruning methods for the simplification of rules. In addition, classification rules are used further to make predictions after the completion of their generation. As efficiency is concerned, it is expected to find the first rule that fires as soon as possible by searching through a rule set. Thus a suit-able structure is required to represent the rule set effectively. In this chapter, the authors introduce a unified framework for construction of rule based classification systems consisting of three operations on Big Data: rule generation, rule simplification and rule representation. The authors also review some existing methods and techniques used for each of the three operations and highlight their limitations. They introduce some novel methods and techniques developed by them recently. These methods and techniques are also discussed in comparison to existing ones with respect to efficient processing of Big Data.

A scalable expressive ensemble learning using Random Prism: a MapReduce approach

The induction of classification rules from previously unseen examples is one of the most important data mining tasks in science as well as commercial applications. In order to reduce the influence of noise in the data, ensemble learners are often applied. However, most ensemble learners are based on decision tree classifiers which are affected by noise. The Random Prism classifier has recently been proposed as an alternative to the popular Random Forests classifier, which is based on decision trees. Random Prism is based on the Prism family of algorithms, which is more robust to noise. However, like most ensemble classification approaches, Random Prism also does not scale well on large training data. This paper presents a thorough discussion of Random Prism and a recently proposed parallel version of it called Parallel Random Prism. Parallel Random Prism is based on the MapReduce programming paradigm. The paper provides, for the first time, novel theoretical analysis of the proposed technique and in-depth experimental study that show that Parallel Random Prism scales well on a large number of training examples, a large number of data features and a large number of processors. Expressiveness of decision rules that our technique produces makes it a natural choice for Big Data applications where informed decision making increases the user’s trust in the system.

Sex and STEM occupation predict Autism-spectrum Quotient (AQ) scores in half a million people

This study assesses Autism-Spectrum Quotient (AQ) scores in a ‘big data’ sample collected through the UK Channel 4 television website, following the broadcasting of a medical education program. We examine correlations between the AQ and age, sex, occupation, and UK geographic region in 450,394 individuals. We predicted that age and geography would not be correlated with AQ, whilst sex and occupation would have a correlation. Mean AQ for the total sample score was m = 19.83 (SD = 8.71), slightly higher than a previous systematic review of 6,900 individuals in a non-clinical sample (mean of means = 16.94) This likely reflects that this big-data sample includes individuals with autism who in the systematic review score much higher (mean of means = 35.19). As predicted, sex and occupation differences were observed: on average, males (m = 21.55, SD = 8.82) scored higher than females (m = 18.95; SD = 8.52), and individuals working in a STEM career (m = 21.92, SD = 8.92) scored higher than individuals non-STEM careers (m = 18.92, SD = 8.48). Also as predicted, age and geographic region were not meaningfully correlated with AQ. These results support previous findings relating to sex and STEM careers in the largest set of individuals for which AQ scores have been reported and suggest the AQ is a useful self-report measure of autistic traits