Smart Product Design Process through the Implementation of a Fuzzy Kano-AHP-DEMATEL-QFD Approach

Product design has become a critical process for the healthcare technology industry, given the ever-changing demands, vague customer requirements, and interrelations among design criteria. This paper proposed a novel integration of fuzzy Kano, Analytic Hierarchy Process (AHP), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Quality Function Deployment (QFD) to translate customer needs into product characteristics and prioritize design alternatives considering interdependence and vagueness. First, the customer requirements were established. Second, the fuzzy KANO was applied to calculate the impact of each requirement, often vague, on customer satisfaction. Third,designalternativesweredefined,whiletherequirements’weightswerecalculated usingAHP.DEMATELwaslaterimplementedforevaluatingtheinterdependenceamongalternatives. Finally,QFDwasemployedtoselectthebestdesign. Ahipreplacementsurgeryaiddeviceforelderly people was used for validation. In this case, collateral issues were the most important requirement, while code change was the best-ranked design

User Behavior-Based Implicit Authentication

In this work, we proposed dynamic retraining (RU), wind vane module (WVM), BubbleMap (BMap), and reinforcement authentication (RA) to improve the efficacy of implicit authentication (IA). Motivated by the great potential of implicit and seamless user authentication, we have built an implicit authentication system with adaptive sampling that automatically selects dynamic sets of activities for user behavior extraction. Various activities, such as user location, application usage, user motion, and battery usage have been popular choices to generate behaviors, the soft biometrics, for implicit authentication. Unlike password-based or hard biometric-based authentication, implicit authentication does not require explicit user action or expensive hardware. However, user behaviors can change unpredictably, which renders it more challenging to develop systems that depend on them. In addition to dynamic behavior extraction, the proposed implicit authentication system differs from the existing systems in terms of energy efficiency for battery-powered mobile devices. Since implicit authentication systems rely on machine learning, the expensive training process needs to be outsourced to the remote server. However, mobile devices may not always have reliable network connections to send real-time data to the server for training. In addition, IA systems are still at their infancy and exhibit many limitations, one of which is how to determine the best retraining frequency when updating the user behavior model. Another limitation is how to gracefully degrade user privilege when authentication fails to identify legitimate users (i.e., false negatives) for a practical IA system.To address the retraining problem, we proposed an algorithm that utilizes Jensen-Shannon (JS)-dis(tance) to determine the optimal retraining frequency, which is discussed in Chapter 2. We overcame the limitation of traditional IA by proposing a W-layer, an overlay that provides a practical and energy-efficient solution for implicit authentication on mobile devices. The W-layer is discussed in Chapter 3 and 4. In Chapter 5, a novel privilege-control mechanism, BubbleMap (BMap), is introduced to provide fine-grained privileges to users based on their behavioral scores. In the same chapter, we describe reinforcement authentication (RA) to achieve a more reliable authentication

Innate talents: reality or myth?

Talents that selectively facilitate the acquisition of high levels of skill are said to be present in some children but not others. The evidence for this includes biological correlates of specific abilities, certain rare abilities in autistic savants, and the seemingly spontaneous emergence of exceptional abilities in young children, but there is also contrary evidence indicating an absence of early precursors of high skill levels. An analysis of positive and negative evidence and arguments suggests that differences in early experiences, preferences, opportunities, habits, training, and practice are the real determinants of excellence

Creation of a Project Portfolio for a Construction Company by Using Lean Six Sigma Principles : Change Management, as part of Clarifying the Case Company’s Strategy

Project Portfolio Management (PPM) is an important tool for an industrial companies to achieve a competitive advantage. Companies in the Construction Industry, especially in Infrastructure construction, where the majority of companies are small or medium-sized, generally do not have the capacity or knowledge for the strategic selection of projects as part of the company's future strategy, as there is a dearth of knowledge on project level PPM and Lean Six Sigma. This causes companies to blindly bid on different projects, hoping for a good outcome, as the selection of projects is not based on knowledge or facts. This causes companies to offer projects, that are negatively correlated to their own strategy, which in the long run will worsen the company's ability to generate prof-it, as projects do not create synergies between the companies' different business areas. In the ever-tightening competition in the market, where the cheapest offer invariably wins, the company must consider the Lean Six Sigma -mentality in its production, where it strives to optimize its own processes to achieve a situation, where it produces more added value with its processes, more efficiently and at lower costs. Only by learning from its own production can company achieve a competitive edge in a market. The methodology of the research has been based on the Case Company's interviews and internal docu-ments, on which the analysis has been formed by using the literature of the field. This study focuses on analyzing the production processes of an influential infrastructure construction company in Finland, aiming to optimize the company's strategy-, production-, and bidding processes, by developing a Project Portfolio Management -tool, so that the company has a better competitive position in future.Project Portfolio Management (PPM) on tärkeä työkalu teollisuusyrityksille kilpailuedun saavuttamisessa. Erityisesti infra-alaan erikoistuneista rakennusalan yrityksistä suurin osa on pieniä tai keskisuuria. Tämän takia yrityksillä ei yleisesti ottaen ole kapasiteettia, tietoa tai projektitason PPM- ja Lean Six Sigma -osaamista, strategisesta projektien valinnasta osana yhtiön strategisia tavoitteita. Infrarakentamisen parissa toimivat yritykset perustavat tarjouslaskentansa eri rakennusprojekteissa arvioihin, toivoen hyvää lopputulosta. Hankkeiden tarjouslaskenta perustuu tällöin arvailuun, eikä tietoon tai tosiasioihin. Tämä saa yritykset tarjoamaan omaan strategiaansa negatiivisesti vaikuttavia projekteja, jotka eivät luo synergiaetuja yritysten eri liiketoiminta-alueiden välille ja pitkällä aikavälillä heikentävät yrityksen kannattavuutta. Alalla vallitsevan kilpailun jatkuvasti kiristyessä, jossa halvin tarjous poikkeuksetta voittaa, tulee yrityksen huomioida Lean Six Sigma -mentaliteetti tuotannossaan. Yrityksen tulee pyrkiä optimoimaan omia prosessejaan saavuttaakseen tilanteen, jossa se tuottaa enemmän lisäarvoa resursseillaan tehokkaammin ja edullisemmin. Vain omasta tuotannostaan oppimalla yritys voi saavuttaa merkittävää kilpailuetua markkinoilla. Tämä tutkimus keskittyy Suomessa toimivan infrarakentamiseen erikoistuneen yrityksen tuotantoprosessien analysointiin; tavoitteena optimoida yrityksen strategia-, tuotanto- ja tarjousprosesseja. Tutkimuksen lopputuotteena kehitettiin Project Portfolio Management -työkalu, jota hyödyntämällä yrityksellä on mahdollisuus parempaan kilpailuasemaan tulevaisuudessa. Tutkimuksen metodologia on perustunut Case Yrityksen haastatteluihin sekä sisäisiin asiakirjoihin, joiden perusteella analyysi on muodostettu alan kirjallisuutta hyödyntäen

Learning Analytics for Teacher’s Dashboard in a Course Result System

With the digital transformation of learning and the spread of online learning under the impact of COVID-19, huge amounts of learning data are being generated. How to utilize and analyze this learning data has led to a new field -- Learning Analytics (LA). LA demonstrates the collection, use and analysis of data generated by students in the learning process to predict student behavior and provide feedback. The goal of this thesis is to redesign a course result system (OSR) with LA functions. OSR is a results registration system used by the Department of Computer Science at Aalto University for over a decade. The development technology used by OSR is outdated and does not have the satisfying LA features for teachers. Meanwhile, this thesis tries to build a prototype of the new system based on the cloud platform (Salesforce). The research questions are: what is the state of art of the old system, what LA functions the new system needs, and what are the requirements of the new system. In order to answer these three research questions, this thesis first conducts a literature review. This thesis reviews the literature on OSR, A+, LA, cloud service models, and Salesforce. Through literature review and practical experience of OSR, this thesis obtains the state of art of the existing system as well as possible functional requirements and LA requirements. This thesis uses the constructive research method to design the new system. To gather functional needs and LA requirements from users, this thesis performed semi-structured interviews with teachers. After analyzing and summarizing the interview results, this thesis concludes the user needs. After comparing, merging and screening the requirements from the OSR and LA reviews in the Literature Review chapter, the requirements of the new system are summarized in this thesis. As for final requirements, the following three findings are made in this thesis. First of all, teachers are satisfied with the final grade calculation function and database function provided by the old system. The new system can follow the design of final grade calculation function of the old system. Second, although teachers were satisfied with most of the features of the old system, they spent a lot of time verifying the results. This suggests that they need a more reassuring system. Third, the old system provided only some statistical functions, and few teachers used them. Teachers hope the new system will provide them with more LA features, such as producing periodic reports for them to monitor students' progress in real time