NIK Norsk informatikkonferanse. Universitetet i Nordland november NIK-styret og redaksjonskomité. Høgskolen i Oslo og Akershus

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1 NIK 2012 Norsk informatikkonferanse Norsk informatikkonferanse NIK 2012 Universitetet i Nordland november 2012 NIK-styret og redaksjonskomité Lars Ailo Bongo Siri Fagernes Dag Haugland Erik Hjelmås Hein Meling Birger Møller-Pedersen Andreas Prinz Trond Aalberg ISSN ISBN Universitetet i Tromsø Høgskolen i Oslo og Akershus Universitetet i Bergen Høgskolen i Gjøvik (styreleder) Universitetet i Stavanger Universitetet i Oslo Universitetet i Agder NTNU (redaktør)

2 Norsk informatikkonferanse NIK 2012 Universitetet i Nordland november 2012 NIK-styre og redaksjonskomité Lars Ailo Bongo Siri Fagernes Dag Haugland Erik Hjelmås Hein Meling Birger Møller-Pedersen Andreas Prinz Trond Aalberg Universitetet i Tromsø Høgskolen i Oslo og Akershus Universitetet i Bergen Høgskolen i Gjøvik (styreleder) Universitetet i Stavanger Universitetet i Oslo Universitetet i Agder NTNU (redaktør)

3 NIK-stiftelsen og Akademika forlag, Trondheim 2012 ISSN ISSN (online) ISBN Det må ikke kopieres fra denne boka ut over det som er tillatt etter bestemmelser i lov om opphavsrett til åndsverk, og avtaler om kopiering inngått med Kopinor. Dette gjelder også filer, kode eller annen gjengivelse tilknyttet e- bok. Redaktør: Trond Aalberg, NTNU Digital trykk og innbinding: AIT Oslo AS Vi bruker miljøsertifiserte trykkerier Akademika forlag Postboks 2461 Sluppen 7005 Trondheim Tlf.: E- post: forlag@akademika.no Forlagsredaktør: Lasse Postmyr (lasse.postmyr@akademika.no)

4 Forord Norsk informatikkonferanse har vært arrangert hvert år siden 1988 ved de forskjellige universitetene og høyskolene i Norge. I år er turen igjen kommet til Bodø. Formålet har hele tiden vært det samme: å være et møtested for datafaglig ansatte og studenter i høyere undervisningsinstitusjoner, forskningsinstitutter og næringsliv, å gi et representativt bilde av hva som foregår av forsknings- og avansert utviklingsarbeid i hele det norske fagmiljøet, dvs forsøke å vise bredden i forskningsmiljøet, både faglig, organisatorisk og geografisk, samt å holde et høyt faglig nivå og åpne for debatt om emner som opptar det datafaglige miljøet. Årets innsendte bidrag holder et normalt nivå. 21 av 43 innsendte bidrag ble akseptert som ordinære bidrag, mens ytterligere 7 bidrag ble akseptert til en egen short paper session. Det er flere studentarbeider blant de aksepterte bidragene og disse får økonomisk støtte fra NIK for å komme til konferansen og presentere sine bidrag. I vurderingsarbeidet har programkomiteen også fått uvurderlig hjelp av frivillige faglige konsulenter, nemlig Anders Andersen, Ivar Austvoll, Abdulrahman Azab, Anya Helene Bagge, Joanna Bauer, John Markus Bjørndalen, Reggie Davidrajuh, Ingvar Eidhammer, Pål Evensen, Bård Fjukstad, Ivar Farup, Phuong Ha, Geir Hasle, Jo Herstad, Magnus Lie Hetland, Rune Hjelsvold, Kjell Jørgen Hole, Anders Kofod-Petersen, Stein Krogdahl, Hanno Langweg, Mass Soldal Lund, Fredrik Manne, Arne Maus, Ellen Munthe-Kaas, Georg Muntingh, Morten Mossige, Khalid Mughal, Tor Anders Nygaard, Erlend Tøssebro, Julius Parulek, Jonathan Read, Tom Ryen, Gerhard Skagestein, Karl Skretting, Daniel Stødle, Ragnar Stølsmark, Nils Kalstad Svendsen, Gunnar Syrrist, Lars Tiede, Yngve Villanger, Sule Yildirim, Weihai Yu, Anne-Siri Øyen og Brit Helle Aarskog. På vegne av redaksjonskomiteen vil jeg gjerne få takke disse. I likhet med tidligere år har ikke NIK opphavsrett til artiklene i denne samlingen. Den enkelte forfatter må kontaktes om rettighetene til eventuell videre gjengivelse av artiklene. I år inviterer vi Bran Selic fra Malina Software Corp. som keynote til å fortelle oss om model-based engineering. Artiklene finnes også på nettet på NIKs adresse der man også finner annen informasjon om NIK. Programkomiteen for NIK Erik Hjelmås Gjøvik, oktober 2012.

5 Innhold An Empirical Evaluation of a Metric Index for Approximate String Matching... 1 Bilegsaikhan Naidan, Magnus Lie Hetland Image Metadata as a Means to Gather Image Relevant Information Randi Karlsen, Roger Bruun Asp Hansen Efficient Rule-Matching for Automated Coherent Logic Bjarne Holen, Dag Hovland, Martin Giese Mining of Association Relations in Text Gleb Sizov, Pinar Öztürk Bruk av PC-sal under eksamen i videregående programmering Erlend Tøssebro Vurderingsform og karakterbruk Christian F. Heide, Monica Kristiansen, Tom Heine Nätt Course Improvement the TQM way Tor Stålhane, Svein Erik Bratsberg, Roger Midtstraum Language and Metamorphosis Supervising Computer Science Students diagnosed with Asperger Syndrome Kirsten Ribu Computing the Optimal Layout of a Wind Farm Jan Kristian Haugland, Dag Haugland Optimization of Wood Fuel Chip Production in Denmark Assimizele Brice, Arne Løkketangen Optimization and Parallelization of Ptychography Reconstruction Code Thomas L. Falch, Jostein B. Fløystad, Anne C. Elster, Dag W. Breiby GPU-Based Airway Segmentation and Centerline Extraction for Image Guided Bronchoscopy Erik Smistad, Anne C. Elster, Frank Lindseth Real-Time Surface Extraction and Visualization of Medical Images using OpenCL and GPUs Erik Smistad, Anne C. Elster, Frank Lindseth Towards Efficient Simulation of Task Based Parallel Applications Alexandru C. Iordan, Magnus Jahre, Lasse Natvig A Child s Voice Miriam E. N. Begnum, Synnøve Flatebø Hoelseth, Britt Johnsen, Frank Hansen Making Touch-Based Mobile Phones Accessible for the Visually Impaired Alexander Dreyer Johnsen, Tor-Morten Grønli, Bendik Bygstad

6 Mobile IT Support for Multisport Competitions Steinar Kristoffersen, Manish Shrestha Public Review of E-voting Source Code: Lessons Learnt from E-vote Bjarte M. Østvold, Edvard K. Karlsen Konstruksjon av databaser for aktuelle og historiske addresser Kjell Bratbergsengen On the Truthfulness of Petal Graphs for Visualisation of Data Frode Eika Sandnes Tekstur og dekkval i biletsteganografi Hans Georg Schaathun NFC Experiments in the NFC City Project Anders Andersen, Randi Karlsen GeStore Incremental Computation for Metagenomic Pipelines Edvard Pedersen, Nils Peder Willassen, Lars Ailo Bongo BSV Window Visualization System Lars Ailo Bongo Towards Cross-Lingual Information Retrieval using Random Indexing Hans Moen, Erwin Marsi Eye Contact in Leisure Video Conferencing Annick Van der Hoest, Simon McCallum A Personal Assistant A Push System to Offer Relevant Information with a Minimum of Input Kai A. Olsen, Alessio Malizia, Teresa Onorati A Memory Aid for Reduced Cognitive Load in Manually Entered Online Bank Transactions Frode Eika Sandnes

7 A Memory Aid for Reduced Cognitive Load in Manually Entered Online Bank Transactions Frode Eika Sandnes Faculty of Technology, Art and Design Oslo and Akershus University College of Applied Sciences Abstract Paper based invoices are still commonly used in Norway where users manually copy large amounts of digits in order to set up transactions. Such digit copying tasks are cognitively and visually demanding, errors are easily introduced and the process is often perceived as laborious. This study proposes an alternative scheme based on dictionary coding that reduces the cognitive load on the user by a factor of five. The strategy has improved error detection characteristics and the scheme is easy to implement. 1 Introduction In Norway online banking has nearly replaced manual banking. Despite the convenience of direct debit and electronic invoices paper-based invoiced and invoices are still commonly used. Paper based invoices require the recipient to manually set up the transaction, which involves manually copying up to 50 digits. The digit copying task is problematic for several reasons. First, copying digits is cognitively demanding. Humans short term memory is usually limited to 7+/-2 pieces of information at one time [1]. The task therefore has to be split up into several readinput cycles. Given 50 digits up to 10 such read-input cycles are needed, each cycle requiring shifting the visual attention from the source invoice to the transaction input form. Complicating matters further, the invoice identification code is usually the longest sequence of digits and is always presented without separators. The lack of chunking means that additional cognitive and visual effort is required for users to remember and locate where in the string they were when shifting attention between the invoice and the form. Another issue is that invoices and forms used by the online banks do not follow the Norwegian invoice layout convention and often there is a mismatch in both information sequence and information position making it harder for users to pair invoice data with the required input form fields. Second, the digit copying task is error-prone. Only the account number and the invoice identification codes utilize simple error detection mechanisms and earlier internet banking implementations did not implement the error correction schemes leading to severe problems [2]. This paper was presented at the NIK-2012 conference; see 273

8 Third, the digit copying task is perceived as time-consuming and laborious. Many users own inexpensive portable laptop computers without a numeric keypad. It has been demonstrated that digit input without numeric keypads are significantly slower than with numeric keypads [3]. Approximately 10% of the population suffers from some form of reduced cognitive functioning or learning disorders such as dyslexia due to reduced memory capacity and effort should be made to reduce the cognitive load on users. 2 Invoice structure Norwegian invoices have a standardized structure comprising an 11-digit account number, a date, amount and the invoice identification code (KID) with 2 to 25 digits. The last digit in the account number is a modulus-11 error detection code. In this study the bank account parity digit is discarded and an error detection mechanism that applies to the entire transaction is used instead. The last digit of the KID is an error detection code based on modulus-10 or modulus-11. Since KIDs can use one of two error detection schemes the full KID is included in subsequent discussions. The date indicates the day, month and year of the due date. In this discussion a scheme based on five digits is used comprising two digits for the day of the month, two digits for the month, and one digit representing the least significant digit of the year. The amount is specified in terms of kroner and øre. In this study the øre part is discarded and amounts are rounded up to the nearest krone since øre no longer is legal tender in Norway. In this study we assume a maximum transaction amount of 99,999 NOK requiring 5 digits. 3 Memory aids According to cognitive psychology it takes practice to move information from working memory into long term memory. The brain filters and forgets unimportant information and information that we are repeatedly exposed to are gradually fixed to our long term memory such as our personal ID-number. One memory aid employed by many students involves remembering number sequences required to enter a word on a mobile keypad such as a four letter word representing a year in history. Instead of recalling the individual digits where each digit counts as one piece of information, the student remembers the word which counts as one piece of information. This paper proposes to use a fixed dictionary on the encoding and decoding sides. The principle is to split the sequences of digits into chunks, where each chunk is converted to a linguistic word. Then, the user is presented with a word sequence instead of a number sequence and will thus be able to copy more information per copy-input cycle. For example, take the number sequence It is first split into chunks of 5 digits namely and Each number is then looked up in the wordlist. The digits could correspond to the Norwegian word våg (dare) and the digits correspond to the word porsjoner meaning portions. Thus, the word sequence våg porsjoner is presented to the user, who is able to hold both words in working memory and copy the words into the form. Next, the words identified in the wordlist on the receiving end and the indices of the two words are found. Another advantage of words compared to digit sequences are that experienced readers do not read words character by character, but a word at a time, while digit sequences are 274

9 read a digit at a time. The reader recognizes the height signature of text set in lower case letters. This greatly adds to the reading speed. 4 Error detection and correction This strategy proposes three levels of error detection. The first level of error detection catches misspelled words [4] not found in the wordlist. A second level of error detection is achieved by introducing a modulus 10 parity check [5, 6]. This parity check is performed by summing each 5-digit chunk and computing modulus 10 of the total. If there are any mistakes in one or several of the words then this is likely to be caught since the parity will not match. For example, the misspelled words urt and tur that both are valid words in the dictionary are represented by the digits and 01155, respectively. This difference would flag a modulus-10 parity error. The third level of error is introduced by taking modulo 2 of each number chunk and representing the result as a bit pattern with the leftmost word representing the least significant bit, etc. This will allow the location of the error to be found in 50% of the cases. More elaborate error detection schemes could be employed. However, optimal codes rely on intricate implementations. Note that no attempt is made correcting errors in our approach. The philosophy is that manual intervention is needed if errors occur to avoid risking erroneous automatic corrections that can lead to financial risks for the user. The linguistic representation of digits can also assist detecting mistakes when comparing numbers. For instance imagine a customer comparing the two dates and Each date comprises three information parts, day, month and year. If users focus on the day they might overlook the difference in month since the shape of 6 is similar to 8. However, when comparing the linguistic representations it is obvious that the dates are different. 5 Implementation A Norwegian wordlist found in most Linux distributions containing 181,731 entries with lengths ranging from 2 to 33 characters were used. The entries were sorted according to increasing word length and the 100,000 shortest words were kept. The average length is 8.4 with the longest words being 12 characters long. A proof of concept coder and decoder were implemented in a Microsoft Excel workbook 1. Excel was chosen in order to prove the simplicity of the approach. The implementation is demonstrated using an invoice issued by NRK sent to each household with a television: Accout no Date 31 Jul, 2012 Amount 1290,06 KID This is first converted to the digit sequence which would have to be copied in eight cycles if done manually. This sequence gives the following eight Norwegian words 1 The encoder and decoder prototype can be downloaded from frodes/kid- DNSv2.3.xlsx 275

10 vas kuwaiterne logopeden tomtens Acta fjernelse dytte lise This sequence is easily remembered in two chunks, that is vas kuwaiterne logopeden and tomtens Acta fjernelse dytte lise. When this phrase is input to the decoder the original invoice information is detected successfully. Imagine that one of the words in incorrectly spelled, for instance vkuwaiterne instead of kuwaiterne. The mistake and the location of this mistake would easily be detected as this word is not present in the dictionary. Next, imagine we misspell dytte as the valid word dytta. This mistake is both detected and located since both the modulus-10 and modulus-2 tests fire. Instead, imagine the word dytte is misspelled as dytter. This mistake is detected as the modulus-10 test fires, while the location is not detected since the modulus 2 test does not fire. Finally, imagine that we misspell dytte as the valid word dyttet this mistake is not detected with the following setup as the parity tests pass despite the error. The error detection capability could be drastically improved if the modulus-10 test is replaced with a modulus-100,000 test. Such tests will reduce the chances of missing errors from approximately 10% to 0.001%. These tests require 5 parity digits captured in one linguistic word. 6 Conclusions A memory aid for simplifying the task of manually setting up bank transactions through digit copy tasks is proposed. The strategy converts the digits sequences to sequence of linguistic words allowing transactions to be more easily remembered and compared. The approach has better error-detection capabilities than the digit-based counterpart. References [1] Miller, G. A. The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review 63, (1956). [2] Olsen, K. Customer Errors in Internet Banking. in Proceedings of Norsk Informatikkonferanse 2008, Trondheim: Tapir Akademisk Forlag (2008). [3] Sandnes, F. E. Effects of common keyboard layouts on physical effort: Implications for kiosks and Internet banking. in Proceedings of Unitech 2010, Trondheim: Tapir Akademisk Forlag, pp (2010). [4] Kukich, K. Techniques for automatically correcting words in text. ACM Computing Surveys 24(4), (1992). [5] Wagner, N. R. and Putter, P. S. Error Detection Decimal Digits. Communications of the ACM 32(1), (1989). [6] Gallian, J. A. Error detection methods. ACM Computing Surveys 28(3), (1996). 276