Barcelona, Spain | September 3, 2018 MobileHCI 2018 Workshop on Socio-Technical Aspects of Text Entry Text Entry Does Not Imply “English Text Entry” Luis A. Leiva Abstract Sciling, SL The title above is an actual sentence borrowed from 46120 Valencia, Spain MacKenzie and Soukoreff’s seminal article on Text Entry name@sciling.com for Mobile Computing (2002), which I found to be enlightening many years ago, and has inspired my latest work on text entry. My latest work on text entry has been directed at improving the stimulus phrases when conducting evaluations with non-native English speakers. Together with my co-authors, I devised a couple of automated phrase sampling methods that produce phrases that are memorable (easy to remember), representative of the language or task, and easy to assimilate. This paper provides a brief overview of such methods as well as other contributions I have made to the text entry community. Author Keywords Text Entry; Phrases; Sampling; Multilingualism; Permission to make digital or hard copies of part or all of this work for personal or Memorability; Representativeness; Semantics. classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. ACM Classification Keywords For all other uses, contact the owner/author(s). H.5.m [Information interfaces and presentation]: Copyright held by the owner/author(s). MobileHCI, 2018 Barcelona, Spain. Miscellaneous. 11 Barcelona, Spain | September 3, 2018 MobileHCI 2018 Workshop on Socio-Technical Aspects of Text Entry Biography Luis Leiva is working as researcher and Chief Technological Officer at Sciling, a Machine Learning company based in Spain. He is a former research staff member of the PRHLT research center at the Technical University of Valencia (UPV), where he worked as Principal Investigator. He got his PhD degree (with honors, international mention, extraordinary doctorate award) in Computer Science in 2012. He also has two undergraduate degrees—Industrial Design and Figure 1: BoD Taps. Top row: shoulder surfer view. Bottom Industrial Engineering—and an M.Sc. in row: front view. Communications and Mobile Services Development (2006) from the UPV. His research interests include Human-Computer Interaction, Machine Learning, and Tiny QWERTY Keyboards the intersection between the two. His recent work on With the ongoing breakthrough of wearables, such as text entry includes Back-of-Device interaction [1, 3, 4, 5], smartwatches or digital jewelry, text entry on devices new miniaturized QWERTY keyboard layouts [6, 7], and with very small screens (1” wide or less) becomes multilingual phrase sampling methods [2, 8, 9]. increasingly relevant and a challenging issue, simply because space is at a premium. I contributed in this Overview of Past and Current Work regard with ZShift [7], a callout-based text entry While this position paper will focus on phrase sampling, technique for diminutive QWERTY keyboards. I also I believe it is worth mentioning my previous work on text investigated cost-efficient error auto-correction entry as well. mechanisms [6], that can be easily implemented in current wearables. Back-of-Device Interaction I have investigated two contexts of use for Back-of-Device (BoD) interaction. The first one is BoD Taps [1, 5], an unlock technique that was found to be very usable and theoretically more secure than their peers. The second one is βTap [3, 4], a tap-based BoD input technique that uses a low-cost yet highly discriminative set of features from commodity sensors, without the need to instrument the device. Figure 2: ZShift keyboard on a tiny touchscreen device. 12 [S] WIKI:EN:Journal_(1977_TV_series) Barcelona, Spain | September 3, 2018 MobileHCI 2018 Workshop on Socio-Technical Aspects of Text Entry [S] daybook#n#1 [S] take#v#6 binary_file#n#1 accounting_data#n#1 read-only_file#n#1 computer_file#n#1 interpret#v#1 [S] read#v#8 read-only_memory#n#1 [S] read#v#4 optical_disk#n#1 datum#n#1 person#n#1 firmware#n#1 memory#n#4 Multilingual Phrase Sampling web_site#n#1 operating_system#n#1 disk_cache#n#1 bulletin_board_system#n#1 web_log#n#1 home_computer#n#1 bit#n#6 magnetic_tape#n#1 [S] read#v#11 This is the current line of research I am mostly involved internet#n#1 random-access_memory#n#1 electronic_mail#n#1 on-line#a#2 homo#n#2 diskette#n#1 data_structure#n#1 algorithm#n#1 computer#n#1 pari-mutuel_machine#n#1 in, motivated by the fact that text entry is fundamentally cognition#n#1 [S] diary#n#1 [S] read#v#3 interaction#n#1 WIKI:EN:Digital program#n#7 [S] WIKI:EN:Interaction_(album) language#n#1 multilingual. Many text entry researchers are conducting least_squares#n#1 WIKI:EN:Human–computer_interaction WIKI:EN:Confidence_interval user studies in many languages different from English, [S] WIKI:EN:Interaction_(s WIKI:EN:Multivariate_normal_distribution WIKI:EN:Bias_of_an_estimator WIKI:EN:Mean_squared_error and the tasks given to participants are often computer_graphics#n#1 computer_network#n#1 radio#n#1 programming_language#n#1 engineering#n#2 regression_analysis#n#1 personal#a#1 covariance#n#1 analysis_of_variance#n#1 computer_science#n#1 telecommunication#n#1 language-sensitive. This suggests that, when resolution#n#7 virtual_reality#n#1 telephone#n#2 data#n#1 programmer#n#1 [S] understand#v#3 reading#n#1 read#v#1 literate#n#1 reader#n#1 WIKI:EN:Gauss–Markov_theorem variance#n#3 normal_distribution#n#1 conducting text entry experiments with non-native information_technology#n#1 mathematics#n#1 written_record#n#1 journal#n#2 expert#n#1 diarist#n#1 reader#n#3 statistic#n#1 electronics#n#1 number_cruncher#n#1 [S] WIKI:EN:Computer_engineering artificial_intelligence#n#1 subtracter#n#1 English speakers or in very specialized domains (e.g. musical_instrument_digital_interface#n#1 electrical_engineering#n#1 energy#n#1 chemistry#n#1 physics#n#1 automaton#n#2 boson#n#1 computer_user#n#1 statistics#n#1 statistician#n#2 proportionality#n#1 adder#n#1 WIKI:EN:Will_Gregory [S] WIKI:EN:Computer_music medical devices, where technical vocabulary is [S] WIKI:EN:Human_(Goldfrapp_song) WIKI:EN:Electronic_music Figure 3: The Kaps technique uses knowledge graphs as a industrial_engineering#n#1 physical_phenomenon#n#1 electromagnetism#n#2 science#n#1 machine#n#1 personal_digital_assistant#n#1 fermion#n#1 force#n#2 WIKI:EN:Goldfrapp charge#n#4 cyborg#n#1 [S] WIKI:EN:Human_(1971_film) commonplace), we either use a standard phrase set common concept representation across languages. quantum_mechanics#n#1 emotion#n#1 android#n#1 genetics#n#1 society#n#1 diary#n#2 and accept that there will be differences in performance discipline#n#1 personal_computer#n#1 brain#n#1 reciprocal#a#1 man#n#3 record#n#5 character#n#4 WIKI:EN:Natural_environment male#n#2 across studies, or we have to develop language- or elementary_particle#n#1 [S] interaction#n#3 health#n#1 cell#n#2 soul#n#3 organism#n#1 creature#n#2 give#v#18 [S] read#v#9 stimulus phrases may have in current text entry general_relativity#n#1 domain-specific phrase sets. To solve this problem, [S] WIKI:EN:Human_(The_Killers_song) cosmic_background_radiation#n#1 phenotype#n#1 matter#n#3 life#n#11 human_body#n#1 annals#n#1 [S] journal#n#4 automated phrase sampling methods like MemRep and evaluations. Moreover, the phrase sampling techniques [S] learn#v#4 universe#n#1 animal#n#1 evolution#n#2 human#a#1 [S] journal#n#6 Kaps become necessary. I created so far have been tested with Indo-European ecosystem#n#1 nature#n#3 mind#n#1 virus#n#1 bacteria#n#1 humanness#n#1 object#n#1 languages only, which represent an important portion of spirit#n#4 On the one hand, MemRep [8, 9] was the first phrase the current living languages but certainly there are may study#n#2 hog#n#3 blood#n#1 sampling method that optimized both for memorability more languages worth of evaluation. I look forward to [S] read#v#5 and representativeness (dual-objective function). Our discussing this with the workshop attendees. intestine#n#1 [S] WIKI:EN:Human_(Stargate_Universe) experimental results showed that it performed significantly better than other sampling procedures. On [S] WIKI:EN:Computer_(magazine) REFERENCES the other hand, Kaps [2] sought a balance between 1. Catalá, A., and Leiva, L. A. Back-of-device memorability, representativeness, and complexity authentication with bod taps and bod shapes. In Proc. (grammar and semantics subtleties). We found that, by ACM Conf. on Human-computer interaction with introducing this third variable, the method not only had mobile devices and services (MobileHCI) (2014). better properties than MemRep but also that the[S] journal#n#5 2. Franco-Salvador, M., and Leiva, L. A. Multilingual resulting phrases exhibited better performance. phrase sampling for text entry evaluations. Proposed Scenario International Journal of Human-Computer Studies I believe my recent research on text entry is of special [S] WIKI:EN:Human_(band) 113, 1 (2018). [S] WIKI:EN:Human_(Brandy_Norwood_album) relevance to text entry researchers interested in 3. Granell, E., and Leiva, L. A. Less is more: Efficient conducting experiments tailored to the linguistic back-of-device tap input detection using built-in capabilities of their participants. Therefore, I would like smartphone sensors. In Proceedings of the ACM Intl. to contribute to the workshop with this topic. More [S] WIKI:EN:Human_(Babylon_5) Conf. on Interactive Surfaces and Spaces (ISS) concretely, I would like to dive in the importance that (2016). [S] WIKI:EN:Projected [S] WIKI:EN:Lights_(Ellie_Goulding_album) 13 Barcelona, Spain | September 3, 2018 MobileHCI 2018 Workshop on Socio-Technical Aspects of Text Entry 4. Granell, E., and Leiva, L. A. βTap: Back-of-device tap input with built-in sensors. In Proc. ACM Conf. on Human-computer interaction with mobile devices and services (MobileHCI) (2017). 5. Leiva, L. A., and Catalá, A. Bod taps: An improved back-of-device authentication technique on smartphones. In Proc. ACM Conf. on Human-computer interaction with mobile devices and services (MobileHCI) (2014). 6. Leiva, L. A., Sahami, A., Catalá, A., Henze, N., and Schmidt, A. Error auto-correction mechanisms on tiny qwerty soft keyboards. In CHI Workshop on Text Entry on the Edge (2015). 7. Leiva, L. A., Sahami, A., Catalá, A., Henze, N., and Schmidt, A. Text entry on tiny qwerty soft keyboards. In Proc. SIGCHI Conf. on Human Factors in Computing Systems (CHI) (2015). 8. Leiva, L. A., and Sanchis-Trilles, G. Representatively memorable: sampling the right phrase set to get the text entry experiment right. In Proc. SIGCHI Conf. on Human Factors in Computing Systems (CHI) (2014). 9. Sanchis-Trilles, G., and Leiva, L. A. A systematic comparison of 3 phrase sampling methods for text entry experiments in 10 languages. In Proc. ACM Conf. on Human-computer interaction with mobile devices and services (MobileHCI) (2014). 14