Specific User Research Articles

Chair Size Design Based on User Height.

General principles derived from anatomical studies of human body sizes should be applied to chair designs. Chairs can be designed for a specific user or a particular group of users. Universal chairs for public spaces should be comfortable for the largest possible group of users and should not be adjustable, such as office chairs. However, the fundamental problem is that the anthropometric data available in the literature either come from many years ago and are out of date or do not provide a complete set of all the dimensional parameters of a sitting human body position. This article proposes a way to design chair dimensions solely based on the height range of the intended chair users. For this purpose, based on literature data, the main structural dimensions of the chair were assigned to the appropriate anthropometric body measurements. Furthermore, calculated average body proportions for the adult population overcome the incompleteness, outdated and burdensome access to anthropometric data and link the main chair design dimensions to one easily accessible anthropometric parameter: human height. This is achieved by seven equations describing the dimensional relations between the chair's essential design dimensions and human height or even a height range. The result of the study is a method of determining the optimal functional dimensions of a chair for a chosen range of sizes of its future users based only on users' height range. Limitations of the presented method: the calculated body proportions are correct only for people with a standard body proportion characteristic of adults, i.e., they exclude children and adolescents up to 20 years of age, seniors, and people with a body mass index exceeding 30.

Finite element implementation of the aortic double-dispersion fibre model and development of a predictive damage model

With the development of next-generation cardiovascular prosthesis, the requirement for more advanced simulations for predicting the mechanical response of biological tissue has arisen. Finite-element analysis represents a powerful tool capable of simulating complex behaviours within this framework. The present work proposes the computational implementation of an advanced constitutive model for the fibre-reinforced tissue making up the aortic wall. The formulation available in literature is numerically implemented within the FE software Abaqus© through specific user subroutines, and model predictions are compared with the already available single-dispersion-model results. The influence of each dispersion parameter on the macroscopic behaviour is analysed and discussed. Finally, the model is extended by a damage regime based on strain invariants, which is capable of considering the influence of patient ageing on the mechanical response failure strain. The user subroutines are here provided in the Appendix for applications.

Occupants’ Decision-Making of Their Energy Behaviours in Office Environments: A Case of New Zealand

Understanding how occupants behave and interact with building systems is vital to energy efficiency in buildings. The building occupants’ behaviours are complex and influenced by diverse factors. A deep understanding of the underlying environmental, contextual, social, and psychological factors is the first step of many in establishing the relationship between the indoor environment and occupants’ behaviours. The current study investigates the influence of occupants’ perceived indoor environmental comfort, the availability of control, and the social-psychological impacts on occupant behaviours in a New Zealand context. The data were collected through online surveys, and 99 office occupants responded. A machine learning technique was applied to identify the critical factors influencing the decision-making of occupant behaviours. Of the occupant behaviours considered in the study, adjusting windows, doors, shades and blinds, and drinking beverages were mostly practised (>70%) while adjusting lighting, personal fans, thermostats/heaters, and computers (40–70%) was moderately practised by occupants. The availability of specific user controls was the main predictor of most occupant behaviours, followed by social-psychological factors such as actual knowledge, perceived knowledge, behavioural interventions, subjective norms, organisational support, personal norms, attitudes, and perceived behavioural control. The indoor environmental parameters such as indoor temperature, indoor air quality, natural light, and inside noise were highlighted as most influential in decision-making for occupant behaviours. Additionally, the demographic factors: gender, work duration, days at work, and permanence/temporariness of workspace, were also impactful. Knowing the complexity of occupants’ decision-making with respect to their behaviours helps building managers use this sensitive information to enhance building energy performance and enable more energy feedback to the occupants to raise their awareness. Such information is helpful for creating an intelligent environmental control system loop with eco-feedback and establishing occupant-centric buildings or features.

Cooperative affordances: How instant messaging apps afford learning, resistance and solidarity among food delivery workers

This paper aims to understand the practices and meanings associated with the creation and use of private chat groups on instant messaging services such as WhatsApp, Telegram, Messenger and WeChat that are accessible only to platform workers of online food delivery services. We draw on participant observation in five countries (Italy, Spain, Mexico, China, and India), in-depth interviews with 68 food delivery couriers and digital ethnography ( Pink et al., 2015 ) within dozens of online private chat groups of food delivery workers. Our fieldwork shows that private chat groups are extremely relevant in the daily work of delivery workers and are appropriated to restore forms of mutualism not afforded by the food delivery apps. Following Costa (2018) and her concept of affordances- in-practice, we describe how the practice of online private chat groups created by platform workers affords: (1) the emergence of communities of practice; (2) resistance and contempt; (3) mutualism and solidarity. We argue that these workers ‘enact’ the affordances of instant messaging apps, to supplement – from below – the affordances of food delivery apps that were denied or ignored by food delivery companies. We argue that these affordances constitute cooperative affordances. This concept captures the cooperative nature of peer-to-peer communication that occurs within the informal online chat groups created by the workers themselves. Finally, this article contributes to affordance theory by highlighting how affordances are not immanent properties of artifacts, or ‘invariants’, as argued by Gibson (1979) , but can be ‘enacted’ by specific users, like food delivery workers, within specific social and cultural contexts.

RESOLVING SMART HEALTH SECURITY ISSUES USING ONTOLOGIES AND BLOCKCHAIN SERVICES

IoT is one of the most commonly used and emerging technology. It has been used for resolving many issues in daily life. In this paper, we are going to address the security issues in smart health and how we can resolve them by using blockchain technology. Blockchain is evolving rapidly which deals in blocks of data. By using this attribute, we can partially grant access to data to a specific user by identifying their roles in that certain activity. Although there are already many derived solutions by other researchers, we will follow the combination of RFID and EHR techniques of blockchain to propose a better solution to solve this security challenge.

Automatic modal identification of bridges based on free vibration response and variational mode decomposition technique

Free vibration tests are often performed for the dynamic characterization of structural systems and components. In the field of road bridges monitoring, such tests require minimal equipment (i.e., a sparse network of few sensors, even a single one) in order to gather – quickly and at low cost – some essential information about the dynamic behavior of the structure. The experimental free-vibration response is thus processed for estimating structural modal features which, in turn, can be useful to corroborate or update available numerical models as well as to evaluate current bridge conditions. Within this framework, an efficient computational strategy is here developed for the modal identification of bridges based on their free vibration response. The procedure combines the variational mode decomposition technique with the area ratio-based damping identification method, which ensures enhanced robustness to noise-related effects in field-test signals. A novel procedure is implemented to make the whole identification process fully automatic, in such a way that it does not depend on specific structural features (e.g., to identify even closely-spaced modes) or subjective user’s settings. After a preliminary validation with a synthetic multi-modal signal, the proposed procedure is applied to two real case-studies. The first case-study deals with the identification of natural frequencies, modal damping ratios and mode shapes of prestressed concrete girder bridge decks. Particularly, experimental natural frequencies and mode shapes are compared with numerical predictions obtained from a finite element model of the bridge. The dynamic characterization of the cables of a cable-stayed bridge is considered in the second case-study. Herein, experimental natural frequencies of all the stay-cables are initially analyzed for estimating their stress levels, which have been critically analyzed to detect the existence of relaxation losses. Moreover, modal damping ratios of all the stay-cables are identified and compared to estimates obtained by alternative ambient-vibration-based techniques.

Digital Ethnic Enclaves: Mate Preferences and Platform Choices Among Chinese Immigrant Online Daters in Vancouver

In light of the growing racialized immigrant population in Canada and advances in dating technologies, this study examines Chinese immigrants' partner preferences and mate selection processes through the lens of online dating. We draw on in-depth interviews with 31 Chinese immigrants who have used online dating services in Metro Vancouver to search for different-sex partners. Chinese immigrant online daters show strong preferences for dating Chinese. They emphasize permanent residency status and similarity in age at arrival when evaluating potential partners. Given their preferences, Chinese immigrants strategically choose the dating platforms they primarily use. Men exhibit higher selectivity in their preferences and choices of platforms. Notably, platforms catering to Chinese users create "digital ethnic enclaves" where Chinese immigrant daters congregate. The findings illuminate the intersection of race, gender, immigrant status, and age at arrival in shaping divergent experiences of mate selection and immigrant assimilation in the digital era.

Let IoT Know You Better: User Identification and Emotion Recognition Through Millimeter-Wave Sensing

Emotion recognition, particularly contactless recognition via wireless sensing, has shown its promise in diverse applications. However, the previous works only focus on emotions rather than the person, i.e., the premise is already knowing who the subject is, without considering the issue of identifying subjects. We envision that user identification and emotion recognition together will bring more adaptive and personalized Internet of Things applications, e.g., a smart home system can react to specific emotions of a specific user, independently. In this work, we move forward to investigate the problem of simultaneous user identification, using only physiological indicators embedded in wireless signals reflected off from targets. Toward the objective, in this article, we first carry out a comprehensive measurement study, which validates the feasibility of simultaneous user identification and emotion recognition. Moreover, the measurement also discovers that the key challenge lies in the limitation of artificial features and the substantial emotion feature deviation across different days, which hinders accurate and robust sensing. To resolve the challenge, we design two multiscale neural networks, incorporated with a custom-built feature attention mechanism, so as to obtain rich feature expression and, thus, enhance the important features for accurate recognition. We prototype mmEMO using a commercial off-the-shelf millimeter-wave radar and experimental evaluation shows that mmEMO can achieve 87.68% user identification accuracy and 80.59% emotion recognition accuracy, respectively.

Using Deep Learning to Recognize Therapeutic Effects of Music Based on Emotions

Music is important in everyday life, and music therapy can help treat a variety of health issues. Music listening is a technique used by music therapists in various clinical treatments. As a result, music therapists must have an intelligent system at their disposal to assist and support them in selecting the most appropriate music for each patient. Previous research has not thoroughly addressed the relationship between music features and their effects on patients. The current paper focuses on identifying and predicting whether music has therapeutic benefits. A machine learning model is developed, using a multi-class neural network to classify emotions into four categories and then predict the output. The neural network developed has three layers: (i) an input layer with multiple features; (ii) a deep connected hidden layer; (iii) an output layer. K-Fold Cross Validation was used to assess the estimator. The experiment aims to create a machine-learning model that can predict whether a specific song has therapeutic effects on a specific person. The model considers a person's musical and emotional characteristics but is also trained to consider solfeggio frequencies. During the training phase, a subset of the Million Dataset is used. The user selects their favorite type of music and their current mood to allow the model to make a prediction. If the selected song is inappropriate, the application, using Machine Learning, recommends another type of music that may be useful for that specific user. An ongoing study is underway to validate the Machine Learning model. The developed system has been tested on many individuals. Because it achieved very good performance indicators, the proposed solution can be used by music therapists or even patients to select the appropriate song for their treatment.

Three levels at which the user's cognition can be represented in artificial intelligence.

Artificial intelligence (AI) plays an important role in modern society. AI applications are omnipresent and assist many decisions we make in daily life. A common and important feature of such AI applications are user models. These models allow an AI application to adapt to a specific user. Here, we argue that user models in AI can be optimized by modeling these user models more closely to models of human cognition. We identify three levels at which insights from human cognition can be-and have been-integrated in user models. Such integration can be very loose with user models only being inspired by general knowledge of human cognition or very tight with user models implementing specific cognitive processes. Using AI-based applications in the context of education as a case study, we demonstrate that user models that are more deeply rooted in models of cognition offer more valid and more fine-grained adaptations to an individual user. We propose that such user models can also advance the development of explainable AI.

Virtual Reality and Spatial Augmented Reality for Social Inclusion: The “Includiamoci” Project

Extended Reality (XR) technology represents an innovative tool to address the challenges of the present, as it allows for experimentation with new solutions in terms of content creation and its fruition by different types of users. The potential to modulate the experience based on the target audience’s needs and the project’s objectives makes XR suitable for creating new accessibility solutions. The “Includiamoci” project was carried out with the aim of creating workshops on social inclusion through the combination of art and technology. Specifically, the experimentation involved ten young people between the ages of 28 and 50, with cognitive disabilities, who participated in Extended Reality workshops and Art Therapy workshops. In the course of these activities, the outputs obtained were two: a virtual museum, populated by the participants’ works, and a digital set design for a theatrical performance. Through two tests, one on user experience (UX) and one on the degree of well-being, the effectiveness of the entire project was evaluated. In conclusion, the project demonstrated how the adopted solutions were appropriate to the objectives, increasing our knowledge of UX for a target audience with specific user needs and using XR in the context of social inclusion.

An improved constrained Bayesian probabilistic matrix factorization algorithm

Given the increasing growth of the Web and consequently the growth of e-commerce, the application of recommendation systems becomes more and more extensive. A good recommendation algorithm can provide a better user experience. In the collaborative filtering algorithm recommendation system, many existing approaches to collaborative filtering can neither handle very large datasets nor easily deal with users who have very few ratings, this paper proposes an improved constrained Bayesian probability matrix factorization algorithm. The algorithm introduces a potential similarity constraint matrix for specific sparsely scored users to affect the user’s feature vector, and uses the Logistic function to express the nonlinear relationship of the potential factors, combined with the Markov chain Monte Carlo method for training. Finally, the data set is used for testing and comparative evaluation. This experiment proves that the algorithmic model can be efficiently trained using Markov chain Monte Carlo methods by applying them to the MovieLens and Netflix dataset. The experimental results show that the algorithm has better predictive performance and is suitable for solving the problem of sparse rating matrix of specific users.

Early-stage fusion of EEG and fNIRS improves classification of motor imagery.

Many research papers have reported successful implementation of hybrid brain-computer interfaces by complementarily combining EEG and fNIRS, to improve classification performance. However, modality or feature fusion of EEG and fNIRS was usually designed for specific user cases, which were generally customized and hard to be generalized. How to effectively utilize information from the two modalities was still unclear. In this paper, we conducted a study to investigate the stage of bi-modal fusion based on EEG and fNIRS. A Y-shaped neural network was proposed and evaluated on an open dataset, which fuses the bimodal information in different stages. The results suggests that the early-stage fusion of EEG and fNIRS have significantly higher performance compared to middle-stage and late-stage fusion network configuration (N = 57, P < 0.05). With the proposed framework, the average accuracy of 29 participants reaches 76.21% in the left-or-right hand motor imagery task in leave-one-out cross-validation, using bi-modal data as network inputs respectively, which is in the same level as the state-of-the-art hybrid BCI methods based on EEG and fNIRS data.

Stance4Health Nutritional APP: A Path to Personalized Smart Nutrition.

Access to good nutritional health is one of the principal objectives of current society. Several e-services offer dietary advice. However, multifactorial and more individualized nutritional recommendations should be developed to recommend healthy menus according to the specific user's needs. In this article, we present and validate a personalized nutrition system based on an application (APP) for smart devices with the capacity to offer an adaptable menu to the user. The APP was developed following a structured recommendation generation scheme, where the characteristics of the menus of 20 users were evaluated. Specific menus were generated for each user based on their preferences and nutritional requirements. These menus were evaluated by comparing their nutritional content versus the nutrient composition retrieved from dietary records. The generated menus showed great similarity to those obtained from the user dietary records. Furthermore, the generated menus showed less variability in micronutrient amounts and higher concentrations than the menus from the user records. The macronutrient deviations were also corrected in the generated menus, offering a better adaptation to the users. The presented system is a good tool for the generation of menus that are adapted to the user characteristics and a starting point to nutritional interventions.

Qualitative analysis to identify determinants of use among different occupational settings and channels of communication to address smokeless tobacco use in Sri Lanka.

Smokeless tobacco (SLT) use is a leading cause for oral and pharyngeal cancers in the Southeast Asian region which leads to considerable morbidity and mortality. This study aims to Identify the determinants of use and channels of communication to address smokeless tobacco use among specific user groups in Sri Lanka. The study uses a qualitative approach with purposive, snowballing sampling among groups. Specific high-risk demographic and occupational groups that are known to have high prevalence of SLT use were identified in five of the 25 administrative districts of Sri Lanka, were approached. Sixty-two in-depth interviews and 10 focus group discussions were carried out among different occupational groups in five districts. Thematic analysis coding each reported determinants and communication methods was carried out. Users of SLT of different groups revealed different determinants of use. Some of the determinants were common to several groups. When investigated the preferred methods to address SLT, it is found that the media use was also different among these groups. All preferred group level awareness programmes to individual level awareness programmes. Most groups watch specific television channels at specific times of the day. Most groups stated that they accepted the messages of religious leaders and cancer victim groups. Radio and newspapers were used rarely by these groups that were studied. Smart phones were used only by urban youth and others used basic mobile phones only for calling. Different risk groups were identified allowing preparation of an overall communication approach to address use of SLT. The findings here Identify determinants of use and the communication approaches that can be used to prepare an effective communication strategy to address SLT use among different and sometimes hidden groups in resource poor settings in low a middle-income country.

A novel Internet of Things-supported intelligent education management system implemented via collaboration of knowledge and data.

The past decade has witnessed great progress in the Internet of Things (IoT), which can provide integrated platforms for data with various formats and to serve different parts of human society. Although IoT-supported education management systems have achieved some successful applications, most existing systems cannot perform intelligent information processing, such as autonomous planning and optimal scheduling. To remedy this gap, this work proposes a novel IoT-supported intelligent education management system that is implemented via collaboration of knowledge and data. First, the macroscopic architecture is designed according to field knowledge of education management, and a clustering-based data analysis algorithm is utilized to visualize real-time classroom characteristics. Then, statistics of learning status are generated, and personalized following plans are accordingly suggested to different specific users. Finally, the functions of the designed smart education management system are tested via computer simulation operations. The obtained results show that the proposal can work well under a real-time data stream and is expected to serve as a typical education management application in smart cities. Through verification, it is found that the integration of general education and professional courses is the ideal starting point for the design of the elements of an optimal course structure for engineering practice majors. In particular, we should strengthen the reform of the following course types: introduction to design, general education, concentrated practice, comprehensive design and peak courses.

Supporting the life cycle of household appliances during the operation stage

The last decade of the twentieth century was characterized by a rapid digitalization of all areas of activity from traditional research to the automation of production, trade, commerce, banking, and other activities. The CALS technology has become largely popular in the world, including Russia. Continuous Acquisition and Lifecycle Support (CALS) stands for the continuous information support of supplies and the life cycle of products, or the information support for product life cycle (IPS) The product life cycle comprises a set of processes performed from the identification of needs for specific products to the satisfaction of such needs and the scrapping of the products. The analysis of the household appliance service companies helped identify the key causes of equipment failure, one of which is failure to meet the operating requirements. We also established that servicing household appliances takes too much of the consumers’ time. They have to understand what consumables they need, when and where to buy them, and how to carry out the maintenance (themselves or through an agent). This article presents an instrument for the operation of products and services required by average consumers in their everyday lives. The tool is made up of a database that stores information about the useful life of different products by specific users as well as information about the maintenance, adjustment, and other actions required for the rational operation of the said products. The database will be used by specific vendors who will keep a database of their clients and products. Alternatively, a retail chain may keep a register of its clients and products sold to them and offer household appliance registration and maintenance services. The third option is creating a cloud service to register consumers, vendors, and service agencies.

PHASES OF CONSTRUCTING A LANGUAGE PROCESSOR FOR THE .NET PLATFORM

The article presents a comprehensive approach to programming language development for the .NET platform. The authors explore the stages of language creation, starting from defining its goals and objectives, designing syntax and semantics, to implementing a language processor with CIL code generation for the .NET virtual machine. The article employs a class hierarchy for operations on regular languages and provides a specific specification for the Vlan language. The research findings underscore the importance of a systematic approach to programming language development and their adaptation to specific tasks and user requirements in the .NET environment.

Identifying Influential Communities Using IID for a Multilayer Networks

In online social networks (OSN), they generate several specific user activities daily, corresponding to the billions of data points shared. However, although users exhibit significant interest in social media, they are uninterested in the content, discussions, or opinions available on certain sites. Therefore, this study aims to identify influential communities and understand user behavior across networks in the information diffusion process. Social media platforms, such as Facebook and Twitter, extract data to analyze the information diffusion process, based on which they cascade information among the individuals in the network. Therefore, this study proposes an influential information diffusion model that identifies influential communities across these two social media sites. Moreover, it addresses site migration by visualizing a set of overlapping communities using hyper-edge detection. Thus, the overlapping community structure is used to identify similar communities with identical user interests. Furthermore, the community structure helps in determining the node activation and user influence from the information cascade model. Finally, the Fraction of Intra/Inter-Layer (FIL) diffusion score is used to evaluate the efficiency of the influential information diffusion model by analyzing the trending influential communities in a multilayer network. However, from the experimental result, it observes that the FIL diffusion score for the proposed method achieves better results in terms of accuracy, precision, recall and efficiency of community detection than the existing methods.

IOSPReD: I/O Specialized Packaging of Reduced Datasets and Data-Intensive Applications for Efficient Reproducibility

The data generated by large scale scientific systems such as NASA’s Earth Observing System Data and Information System is expected to increase substantially. Consequently, applications processing these huge volumes of data suffer from lack of storage space at the execution site. This poses a critical challenge while sharing data and reproducing application executions w.r.t. specific user inputs in data-intensive applications. To address this issue, we propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IOSPReD</monospace> (I/O Specialized Packaging of Reduced Datasets), a data-based debloating framework, designed to automatically track and package only necessary chunks of data (along with the application) in a container. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IOSPReD</monospace> uses the specific inputs provided by the user to identify the necessary data chunks. To do so, the high level user inputs are mapped down to low level data file offsets. We evaluate <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IOSPReD</monospace> on different realistic NASA datasets to assess (i) the amount of data reduction, (ii) the reproducibility of results across multiple application executions and also (iii) the impact on performance.