Expert system development for hard disk drive failure analysis

The Computer\u27s Hard Disk Drive is a complex high technology device, diagnosis of a failure is difficult even using sophisticated test equipments. These Drives are expensive and returning a new one when it fails within the warranty period is not an option. The failed drive must be tested and repaired. In the repair process the analysis and diagnosis of the failures is a key point in the process where decisions regarding the repair process that the failed Drive will follow. Therefore, there is a requirement to perform an effective analysis and provide an accurate diagnosis. The standard system utilized for diagnosis was based on human capacity, this study proves that the deficiencies of the standard failure analysis method does not provide satisfactory results. On the other hand, this study proposes alternatives that incorporate the expert\u27s knowledge and makes it available for analysis technicians, analyzing every Drive

The AGI Containment Problem

There is considerable uncertainty about what properties, capabilities and motivations future AGIs will have. In some plausible scenarios, AGIs may pose security risks arising from accidents and defects. In order to mitigate these risks, prudent early AGI research teams will perform significant testing on their creations before use. Unfortunately, if an AGI has human-level or greater intelligence, testing itself may not be safe; some natural AGI goal systems create emergent incentives for AGIs to tamper with their test environments, make copies of themselves on the internet, or convince developers and operators to do dangerous things. In this paper, we survey the AGI containment problem - the question of how to build a container in which tests can be conducted safely and reliably, even on AGIs with unknown motivations and capabilities that could be dangerous. We identify requirements for AGI containers, available mechanisms, and weaknesses that need to be addressed

An Incremental Learning Method to Support the Annotation of Workflows with Data-to-Data Relations

Workflow formalisations are often focused on the representation of a process with the primary objective to support execution. However, there are scenarios where what needs to be represented is the effect of the process on the data artefacts involved, for example when reasoning over the corresponding data policies. This can be achieved by annotating the workflow with the semantic relations that occur between these data artefacts. However, manually producing such annotations is difficult and time consuming. In this paper we introduce a method based on recommendations to support users in this task. Our approach is centred on an incremental rule association mining technique that allows to compensate the cold start problem due to the lack of a training set of annotated workflows. We discuss the implementation of a tool relying on this approach and how its application on an existing repository of workflows effectively enable the generation of such annotations

A web-based graphical food frequency assessment system: design, development and usability metrics

Background: Food frequency questionnaires (FFQs) are well established in the nutrition field, but there remain important questions around how to develop online tools in a way that can facilitate wider uptake. Also, FFQ user acceptance and evaluation have not been investigated extensively. Objective: This paper presents a Web-based graphical food frequency assessment system that addresses challenges of reproducibility, scalability, mobile friendliness,security, and usability and also presents the utilization metrics and user feedback from a deployment study. Methods: The application design employs a single-page application Web architecture with back-end services (database,authentication, and authorization) provided by Google Firebase’s free plan. Its design and responsiveness take advantage of the Bootstrap framework. The FFQ was deployed in Kuwait as part of the EatWellQ8 study during 2016. The EatWellQ8 FFQ contains 146 food items (including drinks). Participants were recruited in Kuwait without financial incentive. Completion time was based on browser timestamps and usability was measured using the System Usability Scale (SUS), scoring between 0 and 100. Products with a SUS higher than 70 are considered to be good. Results: A total of 235 participants created accounts in the system, and 163 completed the FFQ. Of those 163 participants, 142 reported their gender (93 female, 49 male) and 144 reported their date of birth (mean age of 35 years, range from 18-65 years). The mean completion time for all FFQs (n=163), excluding periods of interruption, was 14.2 minutes (95% CI 13.3-15.1 minutes). Female participants (n=93) completed in 14.1 minutes (95% CI 12.9-15.3 minutes) and male participants (n=49) completed in 14.3 minutes (95% CI 12.6-15.9 minutes). Participants using laptops or desktops (n=69) completed the FFQ in an average of 13.9 minutes (95% CI 12.6-15.1 minutes) and participants using smartphones or tablets (n=91) completed in an average of 14.5 minutes(95% CI 13.2-15.8 minutes). The median SUS score (n=141) was 75.0 (interquartile range [IQR] 12.5), and 84% of the participants who completed the SUS classified the system either “good” (n=50) or “excellent” (n=69). Considering only participants using smartphones or tablets (n=80), the median score was 72.5(IQR 12.5), slightly below the SUS median for desktops and laptops(n=58), which was 75.0 (IQR 12.5). No significant differences were found between genders or age groups (below and above the median) for the SUS or completion time. Conclusions: Taking into account all the requirements, the deployment used professional cloud computing at no cost, and the resulting system had good user acceptance. The results for smartphones/tablets were comparable with desktops/laptops. This work has potential to promote wider uptake of online tools that can assess dietary intake at scale

Kohti automaattisia vihjeitä visuaalisessa algoritmisimulaatiossa

Visual Algorithm Simulation (VAS) exercise is an interactive application, which teaches an algorithm or a data structure. The exercise shows the student a visual representation of a data structure with initial data. The student imitates the execution of the algorithm by interacting with the visual representation. The student's solution is graded automatically. A misconception about the algorithm being learned can manifest itself as systematic errors, which can be modelled as a new algorithm. It is assumed that a VAS exercise, which could detect automatically a misconception and give corrective feedback would further support learning. This thesis includes a literature review on algorithm misconceptions and an empirical study. Four VAS exercises of OpenDSA e-textbook are reviewed by their program code: Evaluating a postfix expression, Build heap, Quicksort, and Dijkstra's algorithm. A dataset of 1430 Build heap VAS submissions is analysed manually with ad-hoc software. The submissions are then automatically classified based on the misconceptions found. The main result extends the set of known misconceptions of the Build-heap VAS exercise. 52 percent of the submissions were correct, 17 percent were misconceptions and the rest 31 percent had a lo0gical explanation. 95 per cent of submissions classified as misconception have multiple explanations with heap size of 10. The thesis presents a Python software, which can automatically classify the known misconceptions. Theory on how to generate VAS inputs, which support detection of misconceptions is discussed. The theory is applied by improving the input generation of Dijkstra's algorithm exercise. The thesis concludes that studying misconceptions in the VAS exercises of OpenDSA currently requires exercise-dependent work. Not all OpenDSA VAS exercises record enough data for later analysis. Moreover, the player and analysis software must be written separately for each exercise. There is need to develop the OpenDSA related software libraries to produce detailed exercise recordings. It should be studied how the heap size in the Build-heap exercise affects detection of misconceptions.Visuaalinen algoritmisimulaatiotehtävä (VAS-tehtävä) on vuorovaikutteinen sovellus, joka opettaa algoritmin tai tietorakenteen. Tehtävä näyttää opiskelijalle kuvan tietorakenteesta lähtödatalla. Opiskelija mukailee algoritmin suoritusta vuorovaikuttamalla kuvaesityksen kanssa. Opiskelijan ratkaisu arvostellaan automaattisesti. Väärinkäsitys VAS-tehtävässä on opiskelijan järjestelmällinen väärinymmärrys, joka voidaan kuvata algoritmilla. On oletus, että VAS-tehtävä, joka tunnistaisi automaattisesti väärinkäsityksen ja antaisi korjaavaa palautetta, tukisi oppimista entisestään. Tämä opinnäytetyö sisältää kirjallisuustutkimuksen algoritmien väärinkäsityksistä sekä empiirisen tutkimuksen. Sähköisen OpenDSA-kirjan neljä VAS-tehtävää on tutkittu niiden ohjelmakoodiltaan: Postfix-lausekkeen evaluointi, binäärikeon rakentaminen, pikajärjestäminen ja Dijkstran algoritmi. Tietoaineisto, jossa on 1430 tallennetta Binäärikeon rakentaminen -tehtävästä, on analysoitu käsin tätä varten kehitetyllä ohjelmalla. Tallenteet on sitten automaattisesti luokiteltu löydettyjen väärinkäsitysten perusteella. Työn päätulos laajentaa binäärikeon rakentaminen -tehtävän väärinkäsityksien joukkoa. 52 prosenttia tehtäväpalautuksista oli oikein, 17 prosenttia väärinkäsityksiä ja loput 31 prosenttia voidaan selittää loogisesti. 95 prosentilla niistä palautuksista, jotka luokiteltiin väärinkäsitykseksi, oli useampi yhtä hyvä selitys, kun keon koko oli 10. Työ esittää Python-ohjelman, joka voi automaattisesti luokitella tunnettuja väärinkäsityksiä. Työ esittää myös teoriaa, kuinka tuottaa VAS-tehtävien lähtödataa siten, että se tukisi väärinkäsitysten tunnistamista. Teoriaa on sovellettu parantam alla Dijkstran algoritmi -tehtävän syötteen tuottamista. Johtopäätöksenä OpenDSA:n VAS-tehtävien väärinkäsitysten tutkiminen vaatii nykyisellään tehtäväkohtaista työtä. Kaikki OpenDSA:n VAS-tehtävät eivät tallenna riittävästi dataa myöhempää analyysiä varten. Lisäksi tehtävätoistin ja analyysiohjelma pitää kirjoittaa erikseen joka tehtävälle. On tarve kehittää OpenDSA:n ohjelmakirjastoja tuottamaan yksityiskohtaisia tehtävätallenteita. Binäärikeon rakentaminen -tehtävässä pitäisi tutkia keon koon vaikutusta väärinkäsitysten tunnistamiseen