Submitted by Cláudia Bueno (claudiamoura18@gmail.com) on 2015-11-12T20:25:29Z No. of bitstreams: 2 Dissertação - Sérgio de Alencastro Veiga Filho - 2015.pdf: 13555500 bytes, checksum: 572004050c98fd5a1ab1d30f156cb9c4 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Rejected by Luciana Ferreira (lucgeral@gmail.com), reason: cOLOCAR ANEXOS on 2015-11-13T10:05:01Z (GMT) / Submitted by Cláudia Bueno (claudiamoura18@gmail.com) on 2015-11-13T16:17:24Z No. of bitstreams: 32 Dissertação - Sérgio de Alencastro Veiga Filho - 2015.pdf: 13555500 bytes, checksum: 572004050c98fd5a1ab1d30f156cb9c4 (MD5) Partitura-Vênus e Marte.pdf: 1880908 bytes, checksum: 3b30f3609682c418aa5ad0401d5506fa (MD5) Sinth-Rand-Harm.pdf: 38778 bytes, checksum: 3f783ea7da9395fb00c10f3e0cc7c33b (MD5) Sinth-Rand-Harm-FLUX.png: 89293 bytes, checksum: baee49fe4d4194b1bf2c85d73d65422e (MD5) Sinth-Rand-Harm-Sliders.csd: 3412 bytes, checksum: f84ce270e36a536bea3723120b9d0d39 (MD5) Theremin-Giroscopico.pdf: 49251 bytes, checksum: 24002e1a59e55adf0647b55cf04908a9 (MD5) Theremin-OSC-Giroscópio.png: 383932 bytes, checksum: 9afc697fd14003e59e7db6fe2f7b2236 (MD5) Caverna da Medusa - parte 1.mp3: 3883994 bytes, checksum: 5a1f60394fb1651adc76fec532728ce2 (MD5) Caverna da Medusa - parte 2.mp3: 5828909 bytes, checksum: 2ed0ff69bebea8b12d63ee37d0ee96bb (MD5) Harmon-RT.pdf: 43735 bytes, checksum: 6d6deea640989ffa00f46d125c89e219 (MD5) Harmon-RT-FLUX.png: 103925 bytes, checksum: f66caf4bf3c8753979d9d4a3e8b3f76e (MD5) XY-FM.pdf: 30703 bytes, checksum: ade9c718d7bbdc9c8b37ef5110940d1b (MD5) XY-FM-Fluxograma.png: 59305 bytes, checksum: 42d0a17fdef80db6d3942c8f98f4392d (MD5) Saigai to Higai-Fluxograma.png: 1074253 bytes, checksum: 942d30a1e0013db5965eceb2cb3d865e (MD5) Saigai-to-Higai.pdf: 55178 bytes, checksum: 888b66ef96b9459443928af3f5404dff (MD5) ANS Estudo Nº1 - Partitura.docx: 465853 bytes, checksum: e36547b4dc82510692fb9d38f6ba3216 (MD5) ANS Estudo Nº1 - Partitura.pdf: 2744081 bytes, checksum: 5aea52d6f697220930c7e4a3f2b587e9 (MD5) ANS-Estudo-N1.jpg: 9621613 bytes, checksum: 809e55ba48123bdef9bb7dd3d8608b3e (MD5) ANS-Estudo-N1.mp3: 6016625 bytes, checksum: 163f0129cc26167b86e50ec17ecdee0f (MD5) ANS-Estudo-N1.png: 15468732 bytes, checksum: 4b13a2bd4f6aa36d1fa49e29cf3de5db (MD5) ANS-Estudo-N1.png: 15468732 bytes, checksum: 4b13a2bd4f6aa36d1fa49e29cf3de5db (MD5) ANS-Estudo-N1-PhonoPaper.jpg: 465525 bytes, checksum: 4b09e4620d8699a246b8e07a2e2aee3e (MD5) ANS Estudo Nº2 - Partitura.docx: 196681 bytes, checksum: 0ca992bdcfbaad6e64dc855bfb729772 (MD5) ANS Estudo Nº2 - Partitura.pdf: 865465 bytes, checksum: 8f6bcff9351dd5cb7283c5dfb1069840 (MD5) ANS_Estudo-N2.ans: 8076441 bytes, checksum: fe9c2c327e63e6f0f97f67cef7a866e4 (MD5) ANS-Estudo-N2.jpg: 3221503 bytes, checksum: 2f568a5d4e65ab11fde9889aca4a6301 (MD5) ANS-Estudo-N2.mp3: 6748572 bytes, checksum: a965978985a483fb19db27257d9939c1 (MD5) ANS-Estudo-N2.png: 4496534 bytes, checksum: e620a09b125d537ecc4e73d5f98fb7ac (MD5) ANS-Estudo-N2-PhonoPaper.jpg: 362197 bytes, checksum: cdb143435502f8824e46939e5b550722 (MD5) Pincel-Ai.png: 792689 bytes, checksum: 6a07ef3411c7df43c40f9d077cec95c2 (MD5) Pincel-ongaku.png: 907221 bytes, checksum: 7126777d4389c59b94cccf8db66c9f96 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2015-11-16T09:22:33Z (GMT) No. of bitstreams: 32 Dissertação - Sérgio de Alencastro Veiga Filho - 2015.pdf: 13555500 bytes, checksum: 572004050c98fd5a1ab1d30f156cb9c4 (MD5) Partitura-Vênus e Marte.pdf: 1880908 bytes, checksum: 3b30f3609682c418aa5ad0401d5506fa (MD5) Sinth-Rand-Harm.pdf: 38778 bytes, checksum: 3f783ea7da9395fb00c10f3e0cc7c33b (MD5) Sinth-Rand-Harm-FLUX.png: 89293 bytes, checksum: baee49fe4d4194b1bf2c85d73d65422e (MD5) Sinth-Rand-Harm-Sliders.csd: 3412 bytes, checksum: f84ce270e36a536bea3723120b9d0d39 (MD5) Theremin-Giroscopico.pdf: 49251 bytes, checksum: 24002e1a59e55adf0647b55cf04908a9 (MD5) Theremin-OSC-Giroscópio.png: 383932 bytes, checksum: 9afc697fd14003e59e7db6fe2f7b2236 (MD5) Caverna da Medusa - parte 1.mp3: 3883994 bytes, checksum: 5a1f60394fb1651adc76fec532728ce2 (MD5) Caverna da Medusa - parte 2.mp3: 5828909 bytes, checksum: 2ed0ff69bebea8b12d63ee37d0ee96bb (MD5) Harmon-RT.pdf: 43735 bytes, checksum: 6d6deea640989ffa00f46d125c89e219 (MD5) Harmon-RT-FLUX.png: 103925 bytes, checksum: f66caf4bf3c8753979d9d4a3e8b3f76e (MD5) XY-FM.pdf: 30703 bytes, checksum: ade9c718d7bbdc9c8b37ef5110940d1b (MD5) XY-FM-Fluxograma.png: 59305 bytes, checksum: 42d0a17fdef80db6d3942c8f98f4392d (MD5) Saigai to Higai-Fluxograma.png: 1074253 bytes, checksum: 942d30a1e0013db5965eceb2cb3d865e (MD5) Saigai-to-Higai.pdf: 55178 bytes, checksum: 888b66ef96b9459443928af3f5404dff (MD5) ANS Estudo Nº1 - Partitura.docx: 465853 bytes, checksum: e36547b4dc82510692fb9d38f6ba3216 (MD5) ANS Estudo Nº1 - Partitura.pdf: 2744081 bytes, checksum: 5aea52d6f697220930c7e4a3f2b587e9 (MD5) ANS-Estudo-N1.jpg: 9621613 bytes, checksum: 809e55ba48123bdef9bb7dd3d8608b3e (MD5) ANS-Estudo-N1.mp3: 6016625 bytes, checksum: 163f0129cc26167b86e50ec17ecdee0f (MD5) ANS-Estudo-N1.png: 15468732 bytes, checksum: 4b13a2bd4f6aa36d1fa49e29cf3de5db (MD5) ANS-Estudo-N1.png: 15468732 bytes, checksum: 4b13a2bd4f6aa36d1fa49e29cf3de5db (MD5) ANS-Estudo-N1-PhonoPaper.jpg: 465525 bytes, checksum: 4b09e4620d8699a246b8e07a2e2aee3e (MD5) ANS Estudo Nº2 - Partitura.docx: 196681 bytes, checksum: 0ca992bdcfbaad6e64dc855bfb729772 (MD5) ANS Estudo Nº2 - Partitura.pdf: 865465 bytes, checksum: 8f6bcff9351dd5cb7283c5dfb1069840 (MD5) ANS_Estudo-N2.ans: 8076441 bytes, checksum: fe9c2c327e63e6f0f97f67cef7a866e4 (MD5) ANS-Estudo-N2.jpg: 3221503 bytes, checksum: 2f568a5d4e65ab11fde9889aca4a6301 (MD5) ANS-Estudo-N2.mp3: 6748572 bytes, checksum: a965978985a483fb19db27257d9939c1 (MD5) ANS-Estudo-N2.png: 4496534 bytes, checksum: e620a09b125d537ecc4e73d5f98fb7ac (MD5) ANS-Estudo-N2-PhonoPaper.jpg: 362197 bytes, checksum: cdb143435502f8824e46939e5b550722 (MD5) Pincel-Ai.png: 792689 bytes, checksum: 6a07ef3411c7df43c40f9d077cec95c2 (MD5) Pincel-ongaku.png: 907221 bytes, checksum: 7126777d4389c59b94cccf8db66c9f96 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2015-11-16T09:22:33Z (GMT). No. of bitstreams: 32 Dissertação - Sérgio de Alencastro Veiga Filho - 2015.pdf: 13555500 bytes, checksum: 572004050c98fd5a1ab1d30f156cb9c4 (MD5) Partitura-Vênus e Marte.pdf: 1880908 bytes, checksum: 3b30f3609682c418aa5ad0401d5506fa (MD5) Sinth-Rand-Harm.pdf: 38778 bytes, checksum: 3f783ea7da9395fb00c10f3e0cc7c33b (MD5) Sinth-Rand-Harm-FLUX.png: 89293 bytes, checksum: baee49fe4d4194b1bf2c85d73d65422e (MD5) Sinth-Rand-Harm-Sliders.csd: 3412 bytes, checksum: f84ce270e36a536bea3723120b9d0d39 (MD5) Theremin-Giroscopico.pdf: 49251 bytes, checksum: 24002e1a59e55adf0647b55cf04908a9 (MD5) Theremin-OSC-Giroscópio.png: 383932 bytes, checksum: 9afc697fd14003e59e7db6fe2f7b2236 (MD5) Caverna da Medusa - parte 1.mp3: 3883994 bytes, checksum: 5a1f60394fb1651adc76fec532728ce2 (MD5) Caverna da Medusa - parte 2.mp3: 5828909 bytes, checksum: 2ed0ff69bebea8b12d63ee37d0ee96bb (MD5) Harmon-RT.pdf: 43735 bytes, checksum: 6d6deea640989ffa00f46d125c89e219 (MD5) Harmon-RT-FLUX.png: 103925 bytes, checksum: f66caf4bf3c8753979d9d4a3e8b3f76e (MD5) XY-FM.pdf: 30703 bytes, checksum: ade9c718d7bbdc9c8b37ef5110940d1b (MD5) XY-FM-Fluxograma.png: 59305 bytes, checksum: 42d0a17fdef80db6d3942c8f98f4392d (MD5) Saigai to Higai-Fluxograma.png: 1074253 bytes, checksum: 942d30a1e0013db5965eceb2cb3d865e (MD5) Saigai-to-Higai.pdf: 55178 bytes, checksum: 888b66ef96b9459443928af3f5404dff (MD5) ANS Estudo Nº1 - Partitura.docx: 465853 bytes, checksum: e36547b4dc82510692fb9d38f6ba3216 (MD5) ANS Estudo Nº1 - Partitura.pdf: 2744081 bytes, checksum: 5aea52d6f697220930c7e4a3f2b587e9 (MD5) ANS-Estudo-N1.jpg: 9621613 bytes, checksum: 809e55ba48123bdef9bb7dd3d8608b3e (MD5) ANS-Estudo-N1.mp3: 6016625 bytes, checksum: 163f0129cc26167b86e50ec17ecdee0f (MD5) ANS-Estudo-N1.png: 15468732 bytes, checksum: 4b13a2bd4f6aa36d1fa49e29cf3de5db (MD5) ANS-Estudo-N1.png: 15468732 bytes, checksum: 4b13a2bd4f6aa36d1fa49e29cf3de5db (MD5) ANS-Estudo-N1-PhonoPaper.jpg: 465525 bytes, checksum: 4b09e4620d8699a246b8e07a2e2aee3e (MD5) ANS Estudo Nº2 - Partitura.docx: 196681 bytes, checksum: 0ca992bdcfbaad6e64dc855bfb729772 (MD5) ANS Estudo Nº2 - Partitura.pdf: 865465 bytes, checksum: 8f6bcff9351dd5cb7283c5dfb1069840 (MD5) ANS_Estudo-N2.ans: 8076441 bytes, checksum: fe9c2c327e63e6f0f97f67cef7a866e4 (MD5) ANS-Estudo-N2.jpg: 3221503 bytes, checksum: 2f568a5d4e65ab11fde9889aca4a6301 (MD5) ANS-Estudo-N2.mp3: 6748572 bytes, checksum: a965978985a483fb19db27257d9939c1 (MD5) ANS-Estudo-N2.png: 4496534 bytes, checksum: e620a09b125d537ecc4e73d5f98fb7ac (MD5) ANS-Estudo-N2-PhonoPaper.jpg: 362197 bytes, checksum: cdb143435502f8824e46939e5b550722 (MD5) Pincel-Ai.png: 792689 bytes, checksum: 6a07ef3411c7df43c40f9d077cec95c2 (MD5) Pincel-ongaku.png: 907221 bytes, checksum: 7126777d4389c59b94cccf8db66c9f96 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2015-04-27 / Fundação de Amparo à Pesquisa do Estado de Goiás - FAPEG / This is a work investigating the possibility of composing musical works with interactive interfaces created with algorithms and programs that are Free Software including the entire operating system. It also includes description of their elaborations, both musical compositions with existing interfaces as for interfaces built in the light of musical and technological training of this composer. The compositions were presented during the Master's degree in performing arts in SEMPEM XIII, XIV and SEMPEM qualification and defense presentations. This research therefore is divided into two parts: Part A, which consists of the artistic part where the composer presents his work to the public hearing, and Part B, which consists of all written work, the scores and algorithms in computer language to this article. / Este é um trabalho que investiga a possibilidade de composição de obras musicais com interfaces interativas criadas com algoritmos e programas que sejam S oftwares Livres desde o sistema operacional. Inclui também, descrição de suas elaborações, tanto para as composições musicais com interfaces já existentes como para as interfaces construídas à luz da formação musical e tecnológica deste compositor. As composições foram apresentadas durante o curso de mestrado em apresentações artísticas nos SEMPEM XIII, SEMPEM XIV e apresentações de qualificação e defesa. Este trabalho de pesquisa portanto se divide em duas partes: a Parte A, que consiste da parte artística onde o compositor apresenta sua obra para audição pública, e Parte B, que consiste em todo trabalho escrito, das partituras e algorítmos em linguagem computacional ao presente artigo.

This work deals with the interactive interface for remote controlled robots and examines some of the existing visualization and simulation tools and robotic platforms. It also designs and implements interactive elements suitable for representation of detected objects, such as bounding box or billboard, and proposes interactive elements to eliminate some of the problems associated with remote control of the robot, such as bad perception of distances and the orientation in the environment. The interactive interface is implemented in the Robot Operating System using offered means for visualization, communication and operations management. Graphics primitives are represented by Interactive Markers that, in addition to the visualization, offers also possibilities of interaction. With these markers, a simple tool for controlling the movement of the robot is designed.

The acquisition of data and its analysis has become a common yet critical task in many areas of modern economy and research. Unfortunately, the ever-increasing scale of datasets has long outgrown the capacities and abilities humans can muster to extract information from them and gain new knowledge. For this reason, research areas like data mining and knowledge discovery steadily gain importance. The algorithms they provide for the extraction of knowledge are mandatory prerequisites that enable people to analyze large amounts of information. Among the approaches offered by these areas, clustering is one of the most fundamental. By finding groups of similar objects inside the data, it aims to identify meaningful structures that constitute new knowledge. Clustering results are also often used as input for other analysis techniques like classification or forecasting. As clustering extracts new and unknown knowledge, it obviously has no access to any form of ground truth. For this reason, clustering results have a hypothetical character and must be interpreted with respect to the application domain. This makes clustering very challenging and leads to an extensive and diverse landscape of available algorithms. Most of these are expert tools that are tailored to a single narrowly defined application scenario. Over the years, this specialization has become a major trend that arose to counter the inherent uncertainty of clustering by including as much domain specifics as possible into algorithms. While customized methods often improve result quality, they become more and more complicated to handle and lose versatility. This creates a dilemma especially for amateur users whose numbers are increasing as clustering is applied in more and more domains. While an abundance of tools is offered, guidance is severely lacking and users are left alone with critical tasks like algorithm selection, parameter configuration and the interpretation and adjustment of results. This thesis aims to solve this dilemma by structuring and integrating the necessary steps of clustering into a guided and feedback-driven process. In doing so, users are provided with a default modus operandi for the application of clustering. Two main components constitute the core of said process: the algorithm management and the visual-interactive interface. Algorithm management handles all aspects of actual clustering creation and the involved methods. It employs a modular approach for algorithm description that allows users to understand, design, and compare clustering techniques with the help of building blocks. In addition, algorithm management offers facilities for the integration of multiple clusterings of the same dataset into an improved solution. New approaches based on ensemble clustering not only allow the utilization of different clustering techniques, but also ease their application by acting as an abstraction layer that unifies individual parameters. Finally, this component provides a multi-level interface that structures all available control options and provides the docking points for user interaction. The visual-interactive interface supports users during result interpretation and adjustment. For this, the defining characteristics of a clustering are communicated via a hybrid visualization. In contrast to traditional data-driven visualizations that tend to become overloaded and unusable with increasing volume/dimensionality of data, this novel approach communicates the abstract aspects of cluster composition and relations between clusters. This aspect orientation allows the use of easy-to-understand visual components and makes the visualization immune to scale related effects of the underlying data. This visual communication is attuned to a compact and universally valid set of high-level feedback that allows the modification of clustering results. Instead of technical parameters that indirectly cause changes in the whole clustering by influencing its creation process, users can employ simple commands like merge or split to directly adjust clusters. The orchestrated cooperation of these two main components creates a modus operandi, in which clusterings are no longer created and disposed as a whole until a satisfying result is obtained. Instead, users apply the feedback-driven process to iteratively refine an initial solution. Performance and usability of the proposed approach were evaluated with a user study. Its results show that the feedback-driven process enabled amateur users to easily create satisfying clustering results even from different and not optimal starting situations.

The acquisition of data and its analysis has become a common yet critical task in many areas of modern economy and research. Unfortunately, the ever-increasing scale of datasets has long outgrown the capacities and abilities humans can muster to extract information from them and gain new knowledge. For this reason, research areas like data mining and knowledge discovery steadily gain importance. The algorithms they provide for the extraction of knowledge are mandatory prerequisites that enable people to analyze large amounts of information. Among the approaches offered by these areas, clustering is one of the most fundamental. By finding groups of similar objects inside the data, it aims to identify meaningful structures that constitute new knowledge. Clustering results are also often used as input for other analysis techniques like classification or forecasting. As clustering extracts new and unknown knowledge, it obviously has no access to any form of ground truth. For this reason, clustering results have a hypothetical character and must be interpreted with respect to the application domain. This makes clustering very challenging and leads to an extensive and diverse landscape of available algorithms. Most of these are expert tools that are tailored to a single narrowly defined application scenario. Over the years, this specialization has become a major trend that arose to counter the inherent uncertainty of clustering by including as much domain specifics as possible into algorithms. While customized methods often improve result quality, they become more and more complicated to handle and lose versatility. This creates a dilemma especially for amateur users whose numbers are increasing as clustering is applied in more and more domains. While an abundance of tools is offered, guidance is severely lacking and users are left alone with critical tasks like algorithm selection, parameter configuration and the interpretation and adjustment of results. This thesis aims to solve this dilemma by structuring and integrating the necessary steps of clustering into a guided and feedback-driven process. In doing so, users are provided with a default modus operandi for the application of clustering. Two main components constitute the core of said process: the algorithm management and the visual-interactive interface. Algorithm management handles all aspects of actual clustering creation and the involved methods. It employs a modular approach for algorithm description that allows users to understand, design, and compare clustering techniques with the help of building blocks. In addition, algorithm management offers facilities for the integration of multiple clusterings of the same dataset into an improved solution. New approaches based on ensemble clustering not only allow the utilization of different clustering techniques, but also ease their application by acting as an abstraction layer that unifies individual parameters. Finally, this component provides a multi-level interface that structures all available control options and provides the docking points for user interaction. The visual-interactive interface supports users during result interpretation and adjustment. For this, the defining characteristics of a clustering are communicated via a hybrid visualization. In contrast to traditional data-driven visualizations that tend to become overloaded and unusable with increasing volume/dimensionality of data, this novel approach communicates the abstract aspects of cluster composition and relations between clusters. This aspect orientation allows the use of easy-to-understand visual components and makes the visualization immune to scale related effects of the underlying data. This visual communication is attuned to a compact and universally valid set of high-level feedback that allows the modification of clustering results. Instead of technical parameters that indirectly cause changes in the whole clustering by influencing its creation process, users can employ simple commands like merge or split to directly adjust clusters. The orchestrated cooperation of these two main components creates a modus operandi, in which clusterings are no longer created and disposed as a whole until a satisfying result is obtained. Instead, users apply the feedback-driven process to iteratively refine an initial solution. Performance and usability of the proposed approach were evaluated with a user study. Its results show that the feedback-driven process enabled amateur users to easily create satisfying clustering results even from different and not optimal starting situations.

Det ställs allt högre krav på interaktiva system idag. Centralt är att de ska tillgodose användarens behov – inte minst användarens känslor och upplevelser. Enligt tidigare forskning kan starka känslomässiga ögonblick under ett händelseförlopp och upplevelsen av dess slut påverka hur vi bedömer en tidigare upplevelse (Kahneman, Fredrickson, Schreiber & Redelmeier, 1993; Redelmeier, Katz & Kahneman, 2003). Syftet med denna studie var att undersöka huruvida peak-end rule har en effekt på den retrospektiva användarupplevelsen av ett digitalt gränssnitt. Detta genom att jämföra två versioner av en prototyp vars interaktion var avsedd att efterlikna ett formulär på en webbsida. De två versionerna var identiska förutom att slutet manipulerades i syfte att framkalla ett positivt slut i den ena versionen och ett neutralt slut i den andra versionen. Totalt 22 deltagare skattade sin generella användarupplevelse efter att ha interagerat med varje version. Därefter svarade de på frågor rörande val av preferens samt upplevd ansträngning. Resultatet visade att peak-end rule hade en signifikant påverkan på val av preferens. Däremot fanns inget stöd som talar för att den totala användarupplevelsen eller upplevda ansträngningen påverkades av slutets utformning. Detta innebär att slutet av en interaktion kan påverka användarupplevelsen i viss mån men att det samtidigt råder en viss tvetydighet kring resultatet. Studerandet av peak-end rule är ännu i en tidig fas inom sammanhanget människa-datorinteraktion. Det krävs således vidare forskning för bättre förståelse om dess effekt för användarupplevelse av interaktiva system. / Today there are ever higher demands on interactive systems. Central is that they should meet the user's needs – not least the user's feelings and experiences. According to previous research, strong emotional moments during the course of events and the experience of its end can affect how we assess previous experiences (Kahneman, Fredrickson, Schreiber & Redelmeier, 1993; Redelmeier, Katz & Kahneman, 2003). The purpose of this study was to investigate whether peak-end rule has an effect on the retrospective user experience of a digital interface. This is done by comparing two versions of a prototype whose interaction was intended to resemble a form on a web page. The two versions were identical except that the ending was manipulated in one version in order to evoke a positive ending and a neutral ending in the other version. A total of 22 participants rated their overall user experience after interacting with each version. They then answered questions regarding the choice of preference and perceived effort. The results showed that the peak-end rule had a significant influence on the choice of preference. On the other hand, there was no evidence to suggest that the overall user experience or perceived effort was affected by the design of the end. This means that the end of an interaction can affect the user experience to a certain extent, but that at the same time there is a certain ambiguity about the result. The study of peak-end rule is still in an early phase within the field of human-computer interaction. Further research is thus required for a better understanding of its effect on the user experience of interactive systems.