Previous User Research Articles

Sosiaalialan asiakastietojärjestelmissä paljon parannettavaa: käyttäjäkokemukset 2019

Sosiaali- ja terveydenhuollon digitalisaatiolle on asetettu paljon odotuksia niin palveluiden laadun, saavutettavuuden kuin tehokkuuden osalta. Asiakas- ja potilastietojärjestelmiltä odotetaan paljon tavoitteiden saavuttamiseksi. Asiakastietojärjestelmät ovat laajasti käytössä sosiaalialalla, mutta niiden käyttöä työvälineenä ja niiden avulla saavutettuja hyötyjä on tutkittu ammattilaisten näkökulmasta vain vähän. Keväällä 2019 kartoitettiin ensimmäistä kertaa valtakunnallisesti sosiaalialan ammattilaisten käyttäjäkokemuksia asiakastietojärjestelmistä. Tässä artikkelissa kuvataan kyselytutkimuksen toteutus ja tulokset eniten käytettyjen tuotemerkkien osalta. Tutkimus toimii pilottina vuonna 2020 toteutettavalle sosiaalialan ammattilaisten käyttäjäkokemuskyselylle, joka on osa valtakunnallista Sosiaali- ja terveydenhuollon tietojärjestelmien seurannan ja arvioinnin hanketta (STePS 3.0).
 Tutkimusaineisto kerättiin keväällä 2019 sähköisellä kyselyllä, joka pohjautui lääkäreille ja hoitajille aiemmin toteutettuihin käyttäjäkokemuskyselyihin. Sosiaalialan kyselyn teemoina olivat tekninen toimivuus, käytettävyys, tuki asiakastapauksen hahmottamiselle ja oman työn hallinnalle, yhteistyö ja tiedonkulku sekä järjestelmien hyödyt ja haitat. Aineisto koostui 1145 eri tehtävässä ja toimintaympäristössä toimivan sosiaalialan ammattilaisen vastauksista. Tätä artikkelia varten aineistosta on analysoitu vastaajien eniten käyttämien asiakastietojärjestelmien tuotemerkkien osalta joukko edustavia väittämiä kyselyn eri teema-alueista.
 Kaikkien asiakastietojärjestelmien kouluarvosanojen keskiarvo oli 6,7. Asiakastietojärjestelmiä pidettiin teknisesti vakaina ja toimivina, mutta arviot tiedonkulussa ja monialaisen yhteistyön tuessa, asiakkuuksien hallinnassa ja asiakkaasta saatavan kokonaiskuvan hahmottamisessa tietojärjestelmissä olivat varsin kriittisiä.
 Käyttäjien kokemuksia tutkimalla on mahdollista saada arvokasta tietoa asiakastietojärjestelmien kehittämisen ja suunnittelun tueksi. Jatkotutkimuksen aiheena on yksityiskohtaisempia ja fokusoidumpia analyysejä laajasta kyselytutkimusaineistosta muun muassa eri vastaajajoukkojen osalta.

Book Recommendation System Development Using User-Based Collaborative Filtering

In general, a library has a Book Search System, both search books by title, author, publisher, and book subject. But with so many book search results displayed by the system, sometimes it makes users (library members) find it difficult to choose books that match their profile. For that, we need a system that can provide recommendations in the book search process to provide recommendations systematically based on previous user searches. This study aims to produce a book search recommendation system in a desktop-based library using the Python programming language and the MySQL database. recommendation system aims to reduce errors in getting the reference books needed. The information that comes out of the search process is right on target with user needs so that it can streamline time in searching for books. The book search recommendation system uses a user-based collaborative filtering method based on the similarity of one member to another member based on the lending pattern which is grouped based on the subject of the book being sought. This system will display the results of book search recommendations by ranking from highest to lowest and provide book title solutions to users according to their profile. The development of the book search recommendation system uses the System Development life cycle (SDLC) method to design a system with a case study research in the TasikmalayaPejuang University library. The first stage of system development is the Identification of Problems that occur in the University of Struggle library following the phenomena that occur in the field. The second stage is the analysis of software requirements, hardware, and information needed in developing a system. The third stage is the design of workflow system design recommendations for book search using Unified Modeling Language (UML), database design and designing the interface. The fourth stage is the implementation of the design results in real form using Python as a programming language and MySql as a database system. The last step is testing the book search recommendation system using the Blackbox testing method by Trial and Error.

Predicting online shopping behaviour from clickstream data using deep learning

Clickstream data is an important source to enhance user experience and pursue business objectives in e-commerce. The paper uses clickstream data to predict online shopping behavior and target marketing interventions in real-time. Such AI-driven targeting has proven to save huge amounts of marketing costs and raise shop revenue. Previous user behavior prediction models rely on supervised machine learning (SML). Conceptually, SML is less suitable because it cannot account for the sequential structure of clickstream data. The paper proposes a methodology capable of unlocking the full potential of clickstream data using the framework of recurrent neural networks (RNNs). An empirical evaluation based on real-world e-commerce data systematically assesses multiple RNN classifiers and compares them to SML benchmarks. To this end, the paper proposes an approach to measure the revenue impact of a targeting model. Estimates of revenue impact together with results of standard classifier performance metrics evidence the viability of RNN-based clickstream modeling and guide employing deep recurrent learners for campaign targeting. Given that the empirical analysis shows RNN-based and conventional classifiers to capture different patterns in clickstream data, a specific recommendation is to combine sequence and conventional classifiers in an ensemble. The paper shows such an ensemble to consistently outperform the alternative models considered in the study.

The use of YouTube by the Spanish press: A model to be defined

The instability in the newspaper business model since the emergence of the Internet disrupted the value chain and has led newspapers to turn to videos as an additional source of revenue. YouTube has become the content distribution channel of choice for most newspapers, which benefit from the ability to generate greater traffic in exchange for a blind contract imposed by YouTube’s algorithm. Advertising income from this source is still scarce and depends on engagement, previous user experience, video quality, video reputation and numerous other factors. Based on an analysis of 864 videos over three randomly selected weeks, conclusions are reached, and the consequences of the management of the YouTube channels of six Spanish newspapers are discussed.

Semi-Autonomous Robotic Arm Reaching With Hybrid Gaze-Brain Machine Interface.

Recent developments in the non-muscular human–robot interface (HRI) and shared control strategies have shown potential for controlling the assistive robotic arm by people with no residual movement or muscular activity in upper limbs. However, most non-muscular HRIs only produce discrete-valued commands, resulting in non-intuitive and less effective control of the dexterous assistive robotic arm. Furthermore, the user commands and the robot autonomy commands usually switch in the shared control strategies of such applications. This characteristic has been found to yield a reduced sense of agency as well as frustration for the user according to previous user studies. In this study, we firstly propose an intuitive and easy-to-learn-and-use hybrid HRI by combing the Brain–machine interface (BMI) and the gaze-tracking interface. For the proposed hybrid gaze-BMI, the continuous modulation of the movement speed via the motor intention occurs seamlessly and simultaneously to the unconstrained movement direction control with the gaze signals. We then propose a shared control paradigm that always combines user input and the autonomy with the dynamic combination regulation. The proposed hybrid gaze-BMI and shared control paradigm were validated for a robotic arm reaching task performed with healthy subjects. All the users were able to employ the hybrid gaze-BMI for moving the end-effector sequentially to reach the target across the horizontal plane while also avoiding collisions with obstacles. The shared control paradigm maintained as much volitional control as possible, while providing the assistance for the most difficult parts of the task. The presented semi-autonomous robotic system yielded continuous, smooth, and collision-free motion trajectories for the end effector approaching the target. Compared to a system without assistances from robot autonomy, it significantly reduces the rate of failure as well as the time and effort spent by the user to complete the tasks.

Surveying the Perception of the Environmental Advantages of an Adaptable Product

The aim of adaptable design is to create products that can easily adapt to different needs. The objective if this study is to analyze the effectivenes in communication to promote an adaptable baby stroller, in order to know the user perception of the advantages derived from its adaptability, as well as the environmental ones, and if there is correlation between them. It is also intended to determine whether age or previous experience with this type of product can influence this perception. To this effect, a study with 54 participants has been conducted. Results show that users percieve the advantages and find the adaptable design interesting. Valuation of the advantages of the product is affected by previous user experience with the need for adaptability. Valuation of the environmental benefits is independent from the degree of experiense, as well as from the age of the participants (between 30 and 45 years old).

Jaro–Winkler Distance Improvement For Approximate String Search Using Indexing Data For Multiuser Application

Word searching method has been developed in many ways and named as: Hamming Distance, Jaccard Distance, Jaro Distance, Jaro-Winkler Distance, Levenshtein Distance, etc. Those methods are used for lexicographic comparison to find words according to the similarity of the words which searched. The time needed for searching by using these words distance method can cause overhead as some difference user might try to search the same words all over. If these method is used in a multi user application where the user generally searching for some keywords repeatedly, then the user might have a longer searching time compared to exact search. In spite of this problem, we try to propose a method where the first search result of the previous user, will be recorded to the database for future usage by indexing the search keywords. In order to try this method, we use Jaro-winkler Distance method to search words. From the test result show that combining indexing and similarity word searching by using Jaro-Winkler Distance method can decrease the searching time to 90-92% compared to just using the Jaro-Winkler Distance method only. As the searched data increased, the processing time can be shorten.

Cross PHY/APP Layer User Grouping and Power Allocation for Uplink Multiantenna NOMA Video Communication Systems

The previous suboptimal user pairing and optimal power control for uplink multiantenna NOMA systems considers the physical-layer-only to maximize the sum data rates. For video communications, the objective is changed to minimize the sum video distortion and then maximize the peak signal-to-noise ratio (PSNR). For uplink multiantenna NOMA systems, we propose 1) a novel weak set user selection scheme to maximize the ratio of signal power and the interference to the strong set users, 2) a novel iterative user substitution scheme based on video distortion in the application layer that increases the PSNR, and the video quality, 3) a novel optimal power allocation derived to minimize the video distortion in the application layer. 1) considers the physical-layer only and 2) and 3) consider the application layer too. The numerical results indicate that the proposed cross-layer scheme outperforms the previous physical-layer-only by 1.1 dB, and is only 0.8 dB away from the exhaustive upper bound in PSNR with 20 users and SNR = 15 dB. The complexity of the proposed scheme is slightly greater than that of the previous physical-layer-only scheme, but still much lower than that of the exhaustive upper bound.

Integrating breakdown detection into dialogue systems to improve knowledge management: encoding temporal utterances with memory attention

With the increasing pervasiveness of smart phones and smart devices, dialogue systems are gaining ever growing attention from both academic and industry. These systems can be broadly classified into two categories, one that is aimed at helping user to gain new knowledge and one that can chat with users without completing any specific tasks. Although dialogue systems are improving substantially, the user experience of such systems are still unsatisfactory as there are no specific rules covering all possible situations of real human–machine dialogue, resulting in breakdowns. There are two technical issues affecting the detection of dialogue breakdown in an open domain conversation: human resources to prepare and annotate a large chunk of conversation data and dialogue histories containing words that don’t appear directly in training data. To tackle these issues, we propose a novel encoding method for temporal utterances with memory attention based on end-to-end dialogue breakdown detection. Specifically, long short-term memory (LSTM) is employed to encode each word of all previous user and system utterances. Encoded vectors from LSTM (user and system utterances), along with system and user utterances from sentence embedding, are then stored in memory wherein an attention mechanism is applied to select the most relevant piece of words from system and user utterances for breakdown detection. An evaluation of the proposed approach on a breakdown detection task (DBDC3) showed that the model for single-labeled breakdown detection outperforms other state-of-the-art methods in a classification task. In conclusion, a more effective knowledge gain and management can be achieved by integration of our proposed breakdown detection into dialogue systems.

How Do Women Interpret the NHS Information Leaflet about Cervical Cancer Screening?

Background. Organized screening programs often rely on written materials to inform the public. In the United Kingdom, women invited for cervical cancer screening receive a leaflet from the National Health Service (NHS) to support screening decisions. However, information about screening may be too complex for people to understand, potentially hindering informed decision making. Objectives. We aimed to identify women’s difficulties in interpreting the leaflet used in England and negative and positive responses to the leaflet. Methods. We used a sequential mixed-methods design involving 2 steps: cognitive think-aloud interviews (n = 20), followed by an England-wide survey (n = 602). Data were collected between June 2017 and December 2018, and participants included women aged 25 to 64 y with varying sociodemographics. Results. Interview results revealed misunderstandings concerning screening results, benefits, and additional tests and treatment, although participants tended to react positively to numerical information. Participants were often unfamiliar with the potential harms associated with screening (i.e., screening risks), key aspects of human papillomavirus, and complex terms (e.g., dyskaryosis). Survey results indicated that interpretation difficulties were common (M correct items = 12.5 of 23). Lower understanding was associated with lower educational level (β’s >0.15, P’s <0.001), lower numeracy scores (β = 0.36, P < 0.001), and nonwhite ethnicity (β = 0.10, P = 0.007). The leaflet was evaluated positively overall. Conclusions. Despite previous user testing of the leaflet, key information may be too complex for some recipients. As a consequence, they may struggle to make informed decisions about screening participation based on the information provided. We discuss implications for the improvement of communications about screening and decision support.

The Effects of Interactivity on the Attitudes and Intentions about Second-hand Smoke: Comparison of Two Mechanisms

ABSTRACTThis study examines whether interactive affordances of an anti-secondhand-smoking website can induce college students’ behavioral and attitudinal changes. A single factor experiment (the interactive vs. control condition) with 239 non-smoking college students was conducted to investigate the mechanism by which modality interactivity enhances user attitudes toward the website and their intention to avoid second-hand smoke (SHS). Built upon previous interactivity and user interface literature, interface assessment and perceived contingency were hypothesized to mediate the effects of interactivity on persuasive outcomes. The results demonstrated the main effect of modality interactivity on attitudes towards the website and its indirect effect on SHS avoidance intention after controlling for issue involvement and self-efficacy in avoiding SHS. Both relationships were significantly and positively mediated by interface assessment but not by perceived contingency. Theoretical and practical implications are discussed.

Interview

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Mobile Recommendation System for Culinary Tourism Destination using KNN (K-nearest neighbor)

Currently culinary tourism is becoming very popular among the Indonesian people. People make visits to interesting culinary places. They want to visit a culinary place that is strategic, comfortable, and cheap as their culinary destination. The culinary destination in Yogyakarta that continues to grow every year makes the tourists who will visit be confused to determine the location of culinary tourism that they will choose. Travelers need references and recommendations to help them determine culinary destinations that meet their expectations. Mobile devices and application guides are able to gather information about an environment and suggest certain places such as tourism locations, based on context factors such as location, weather conditions, and the time needed to get there. The purpose of this study is to develop a recommendation system for culinary tourism destinations in Yogyakarta based on mobile using the KNN (K-nearest neighbor) algorithm. The method used in this study consists of five main steps: literature study, identification, data collection, implementation, and evaluation. The recommendation system is based on previous user ratings of food taste, environmental atmosphere, price, service and distance between users and location. The weight of each criterion is tailored to the needs of the user. The results showed that the mobile application recommendation system was able to provide recommendations to users to determine culinary tourism destinations in Yogyakarta in accordance with the parameters of user desires.

Access and exclusion: case of transnational ‘land grabbing’ in Pakistan

In recent years, hundreds of thousands of hectares of land in developing countries is being granted to the governments and private investors of developed countries through leases, land purchase and contract farming. Based on the notion of ‘power of exclusion’ this paper explores the process and identifies which social class has more access to new income generating opportunities and who is excluded in the context of transnational land acquisition in Sindh province of Pakistan. Quantitative and qualitative data shows that poor segments of society such as previous land user, women, agricultural labor and landless farmers were the most excluded groups regarding TLA process and associated benefits while elite social groups such as land leaser/politicians and some irrigation department officials have increased access to positive outcomes of transnational land acquisition. Decreasing in the acreages under food crops also have a detrimental impact on local food security.

User Models for Personalized Physical Activity Interventions: Scoping Review.

BackgroundFitness devices have spurred the development of apps that aim to motivate users, through interventions, to increase their physical activity (PA). Personalization in the interventions is essential as the target users are diverse with respect to their activity levels, requirements, preferences, and behavior.ObjectiveThis review aimed to (1) identify different kinds of personalization in interventions for promoting PA among any type of user group, (2) identify user models used for providing personalization, and (3) identify gaps in the current literature and suggest future research directions.MethodsA scoping review was undertaken by searching the databases PsycINFO, PubMed, Scopus, and Web of Science. The main inclusion criteria were (1) studies that aimed to promote PA; (2) studies that had personalization, with the intention of promoting PA through technology-based interventions; and (3) studies that described user models for personalization.ResultsThe literature search resulted in 49 eligible studies. Of these, 67% (33/49) studies focused solely on increasing PA, whereas the remaining studies had other objectives, such as maintaining healthy lifestyle (8 studies), weight loss management (6 studies), and rehabilitation (2 studies). The reviewed studies provide personalization in 6 categories: goal recommendation, activity recommendation, fitness partner recommendation, educational content, motivational content, and intervention timing. With respect to the mode of generation, interventions were found to be semiautomated or automatic. Of these, the automatic interventions were either knowledge-based or data-driven or both. User models in the studies were constructed with parameters from 5 categories: PA profile, demographics, medical data, behavior change technique (BCT) parameters, and contextual information. Only 27 of the eligible studies evaluated the interventions for improvement in PA, and 16 of these concluded that the interventions to increase PA are more effective when they are personalized.ConclusionsThis review investigates personalization in the form of recommendations or feedback for increasing PA. On the basis of the review and gaps identified, research directions for improving the efficacy of personalized interventions are proposed. First, data-driven prediction techniques can facilitate effective personalization. Second, use of BCTs in automated interventions, and in combination with PA guidelines, are yet to be explored, and preliminary studies in this direction are promising. Third, systems with automated interventions also need to be suitably adapted to serve specific needs of patients with clinical conditions. Fourth, previous user models focus on single metric evaluations of PA instead of a potentially more effective, holistic, and multidimensional view. Fifth, with the widespread adoption of activity monitoring devices and mobile phones, personalized and dynamic user models can be created using available user data, including users’ social profile. Finally, the long-term effects of such interventions as well as the technology medium used for the interventions need to be evaluated rigorously.

Towards providing middleware-level proactive resource reorganisation for elastic HPC applications in the cloud

Elasticity is one of the most important features of cloud computing, referring to the ability to add or remove resources according to the needs of the application or service. Particularly for High Performance Computing (HPC), elasticity can provide a better use of resources and also a reduction in the execution time of applications. Today, we observe the emergence of proactive initiatives to handle the elasticity and HPC duet, but they present at least one problem related to the need of a previous user experience, large processing time or completion of parameters. Concerning the aforesaid context, this paper presents ProElastic - a lightweight model that uses proactive elasticity to drive resource reorganisation decisions for HPC applications. Our idea is to explore both performance and adaptivity at middleware level in an effortless way at user perspective. The results showed performance gains and a competitive cost (application time × consumed resources).

A Co-Evolutionary Scheme for Multi-Objective Evolutionary Algorithms Based on $\epsilon$ -Dominance

Convergence and diversity of solutions play an essential role in the design of multi-objective evolutionary algorithms (MOEAs). Among the available diversity mechanisms, the $\epsilon $ -dominance has shown a proper balance between convergence and diversity. When using $\epsilon $ -dominance, diversity is ensured by partitioning the objective space into boxes of size $\epsilon $ and, typically, a single solution is allowed at each of these boxes. However, there is no easy way to determine the precise value of $\epsilon $ . In this paper, we investigate how this goal can be achieved by using a co-evolutionary scheme that looks for the proper values of $\epsilon $ along the search without any need of a previous user’s knowledge. We include the proposed co-evolutionary scheme into an MOEA based on $\epsilon $ -dominance giving rise to a new MOEA. We evaluate the proposed MOEA solving standard benchmark test problems. According to our results, it is a promising alternative for solving multi-objective optimization problems because three main reasons: 1) it is competitive concerning state-of-the-art MOEAs, 2) it does not need extra information about the problem, and 3) it is computationally efficient.

The Short-term User Modeling for Predictive Applications

One of the important purposes of data mining on the web is to reveal hidden characteristics of users including their behavior. These characteristics are often used to analyze previous user actions, his/her preferences, and also to predict the future behavior. An average user session consists of only few actions, which brings several complications for the user modeling and also for subsequent prediction tasks. Such tasks are usually researched from the long-term point of view (e.g., contract renewal or course quit). On the contrary, the short-term user modeling plays an important role in the context of web applications, where it helps to improve user experience. Its shortcoming is that it often requires rich data, which availability is rather rare. For this reason, we propose a novel user model focused on the capturing changes in the user’s behavior on the level of specific actions. The model idea is based on the enrichment of user actions by a comparison of actual user session data with previous sessions. As the model basis on generally available data sources, the approach is applicable to wide scale of existing systems. We evaluate our model by the task of session end intent prediction in the e-learning and news domain. Thanks to reflecting differences in user behavior we are able to predict the intent to end the session for particular user in the scale of his/her next couple of actions. Obtained results clearly show that the proposed model brings higher precision, accuracy and session hit ratio than baseline models.

Analyzing and Predicting User Navigation Pattern from Weblogs using Modified Classification Algorithm

&lt;p&gt;A Weblogs contains the history of User Navigation Pattern while user accessing the websites. The user navigation pattern can be analyzed based on the previous user navigation that is stored in weblog. The weblog comprises of various entries like IP address, status code and number of bytes transferred, categories and time stamp. The user interest can be classified based on categories and attributes and it is helpful in identifying user behavior. The aim of the research is to identifying the interested user behavior and not interested user behavior based on classification. The process of identifying user interest, it consists of Modified Span Algorithm and Personalization Algorithm based on the classification algorithm user prediction can be analyzed. The research work explores to analyze user prediction behavior based on user personalization that is captured from weblogs. &lt;/p&gt;

Study of car-sharing diffusion criticality conditions based on human traveling network

Car-sharing program, like Car2go, is an innovative urban transportation mode where the car-sharing company provides a car fleet to offer people with the short-term access of car traveling. As a new traveling service, car-sharing platforms have been struggling hard to trigger initial users and speed up their diffusion process. Unlike new product spreading via geographical proximity people, car-sharing users usually drive sharing cars to different destinations and influence people there, and potential user decision also depends on previous user activity at all their destinations. Car-sharing user connections are mainly affected by their traveling behaviors. The influence of user traveling network on new service/product spreading process has been rarely studied before. Here, we find that the infective rate between users with the same destination is critical to the minimum user base of car-sharing diffusion. Moreover, a city with central user network is more appropriate for car-sharing. It leads to a small critical infective rate for diffusion, and a large stable market size of car-sharing service. Our study can impact car-sharing market strategies ranging from market expansion in one city to optimal market selection among different cities.