Hind Dadoun

Hervé Delingette

Anne-Laure Rousseau

Eric De Kerviler

Nicholas Ayache

JOINT REPRESENTATION LEARNING FROM FRENCH RADIOLOGICAL REPORTS AND ULTRASOUND IMAGES

Keywords: multimodal learning, deep clustering, natural language processing, ultrasound examinations, kidneys

In this study, we explore the value of using a recently proposed multimodal learning method as an initialization for anomaly detection in abdominal ultrasound images. The method efficiently learns visual concepts from radiological reports using natural language supervision and constrastive learning. The underlying requirement of the method is simply the availability of image and textual descriptions pairs. However, in abdominal ultrasound examinations, radiological reports are associated with several images and describe all organs observed during the examination. To address this shortcoming, we automatically construct image and text pairs using 1) deep clustering for abdominal organ classification on ultrasound images and 2) natural language processing tools to extract the corresponding description on the report. We show that pre-training the model with these constructed pairs yields representations that better separate normal classes from abnormal ones on ultrasound images for the kidneys, compared to ImageNet-based representations, with a 10% improvement in macro-average accuracy.

INTRODUCTION

In this study, we focus on abnormality detection in abdominal ultrasound images by considering a binary classification task (i.e. normal vs. abnormal organ) with access to limited labeled data. Diseases associated to a given organ may alter its shape, size, contour, position, or textural appearance, resulting in highly variable differences in ultrasound patterns, all grouped into a single "abnormal" category. For this reason, transferring the model weights from ImageNet [START_REF] Deng | ImageNet: A large-scale hierarchical image database[END_REF] pre-training can result in poor performance, as the features learned on natural images are not suited to capture the fine-grained visual features necessary to separate the normal class from the abnormal class. Alternatively, in the absence of large number of annotated datasets, the model can be pre-trained on an unlabeled set of ultrasound images using self-supervised learning methods. These image-based self-supervised methods were proven to enhance performance in specific settings [START_REF] Jiao | Self-supervised representation learning for ultrasound video[END_REF][START_REF] Chen | Selfsupervised learning for medical image analysis using image context restoration[END_REF][START_REF] Bai | Selfsupervised learning for cardiac mri image segmentation by anatomical position prediction[END_REF]. In the case of abdominal ultrasound, we observed that for tasks with high inter-class variability, these methods can be useful. However, on a task such as pathology detection, in which there is greater similarity between classes, and more variability within classes, these methods often fail to achieve better results. More recently, a study [START_REF] Zhang | Contrastive learning of medical visual representations from paired images and text[END_REF] proposed the use of multi-modal pre-training to learn fine-grained representations required by medical imaging tasks. They argue that medical reports, as opposed to image labels are often produced by medical experts in their routine workflow and are therefore easily accessible. The approach takes advantage of the medical reports associated to medical images, to learn better latent representations. The underlying assumption of the method is simply the presence of pairs of images and text describing the image. This method was evaluated on four different medical image classification tasks covering 2 different specialties with encouraging results. Yet, the considered hypothesis in which image pairs and a textual description are always available is not consistent with the abdominal ultrasound setting in which a textual radiology report describes a set of ultrasound images. In this work, we present a method to automatically build pairs of text descriptions and ultrasound images using deep clustering for images and named entity recognition for text. We evaluate the pre-trained image encoder on two criteria: its ability to extract discriminative features for the anomaly classification task, and its performance when fine tuning the model on a labeled set. We provide results for the normal/abnormal kidney detection task in an abdominal ultrasound examination.

IMAGE AND TEXT PAIRS GENERATION

Data

During an ultrasound examination, the sonographer performs a complete scan of the area of interest and takes captures, also known as freeze frames, of the standard scanning plane views and potential visible abnormalities. The freeze-frames along with a textual documentation of the examination form the ul- A subset of 1,051 ultrasound examinations was randomly selected to constitute the labeled sets of all freeze frames linked to the examinations. For each image the annotators had to assign a label, either normal kidneys, abnormal kidneys or "absent" if the image did not contain any kidneys. 873 of those examinations (n images = 3, 910 images of kidneys) were assigned to the training set, 47 examinations (n images = 240 images of kidneys) to the validation set and 131 examinations (n images = 603 images of kidneys) to the test set.

The remaining 6,960 unlabeled examinations were used to construct the pre-training set. The image and text data of the pre-training set were processed in three steps which are summarized in Figure 2. First images containing the kidneys are selected, then sentences in the medical report describing the kidneys are selected. Finally, pairs of image and text are constructed for the kidneys.

Clustering U/S Images

Since we are only interested in kidney images in this study, we use deep clustering to identify a set of clusters linked to this organ. Specifically, we use the method presented in [START_REF] Hind Dadoun | Deep clustering for abdominal organ classification in US imaging[END_REF] where a framework for abdominal organ clustering using unlabeled ultrasound images is presented. Two different augmentation schemes (.) and (.) are applied to all input images before passing through the CNN. Then a loss term is used to encourage both augmented versions of the image to be assigned to the same class with high probability. Finally, to avoid all images being assigned to the same cluster, an additional loss term is introduced to constrain the distribution of the cluster size to follow a symmetric Dirichlet distribution. This method achieves reasonable performance with an F1-score weighted average of 66.75% and an F1-score of 71.5% for the kidney's class. All images (n = 87, 696) of our pre-training set are processed by the deep clustering method, and only images assigned to "Kidney" clusters are kept which amounts to a total 28,334 images from 6,531 examinations.

Text Data

To select the sentences mentioning kidneys in the medical report, we use a tagging tool based on the Unified Medical Language System (UMLS). UMLS is a meta-system that unifies concepts from several dozen terminologies in the biomedical domain. Each UMLS concept is assigned a unique concept identifier (CUI), a set of terms (or synonyms), possibly in multiple languages, and a semantic type. The labeling tool (QuickUMLS) [START_REF] Soldaini | QuickUMLS: a fast, unsupervised approach for medical concept extraction[END_REF], is a fast, unsupervised biomedical concept extraction tool from medical texts that works for multiple languages, including French. Given an unstructured textual med- ical report, we first divide it into sentences. For each sentence, we search for a word related to the semantic type "Body part, organ or organ component" and check whether its unique concept identifier refers to kidneys. If so, the sentence is added to the list of sentences assigned to the organ. A total of 5891 examinations, out of the original 6960 examinations, mention kidneys in the report.

Image-Text Pairing

For each image where the abdominal organ clustering predicted the presence of a kidney, we look if the associated report mentions the kidney. If so, the sentences mentioning the kidney in the report are all associated with the image. This means that multiple images from the same examination will have the same description. Of the 28,334 images, 3003 could not be linked to any textual description (i.e. the associated examination did not mention the kidneys), which leaves us with a final dataset of 25,331 pairs of text and images from 5,513 examinations.

JOINT REPRESENTATION LEARNING

Here we detail the model architecture for the renal anomaly detection task. First, the text and image encoders are trained jointly to project the data into the same dimensional space while ensuring coherence between text and image representations. The image encoder is then fine-tuned on a labeled dataset of renal ultrasound images.

Architecture

We base the pre-training approach on a model (ConVIRT) developed by Zhang et al [START_REF] Zhang | Contrastive learning of medical visual representations from paired images and text[END_REF]. The model is composed of a text encoder and an image encoder. For the image encoder, we use the ResNet50 architecture. The input images are randomly augmented with different data augmentations: cropping, horizontal flipping, affine transformation, color jittering and Gaussian blur before passing to the encoder. For the text encoder, we use the CamemBERT [START_REF] Martin | Camem-BERT: a tasty French language model[END_REF] model which is a state-of-the-art language model pre-trained on a French corpus OSCAR, based on the RoBERTa [9] architecture. Since the text encoder was pre-trained on generic text, it is essential to consider words that are specific to the domain on which we want to refine the model (abdominal ultrasound radiological reports). To do so, we re-train a word-piece Tokenizer to find the set of words that minimize the number of tokens needed to reconstruct the reports in our training set. Each input image and text are then converted into d-dimensional vector representation using a Linear Layer as shown in Figure 3.

Pre-training Objective Function

The model is trained to predict which image goes with which description and conversely. This is achieved using two In-foNCE [START_REF] Van Den Oord | Representation learning with contrastive predictive coding[END_REF] losses based on the cosine similarity between the transformed image and text vectors. Let I, T be the ddimensional vectors of image and text respectively, and I, T be the cosine similarity between the two, the objective function introduced in [START_REF] Zhang | Contrastive learning of medical visual representations from paired images and text[END_REF] is as follows:

L = 1 N N i=1 (λ • L I→T i + (1 -λ) • L T →I i )
where L I→T i is a text-to-image contrastive loss, whose goal is to predict I i , T i as the true pair among all possible descriptions and L T →I i is an image-to-text contrastive loss, whose goal is to predict I i , T i as the true pair among all possible images.

L I←T i = -log exp( Ii,Ti /τ

Fine-tuning Objective Function

We evaluate our pretrained image encoder on the binary classification task of abnormality detection (normal vs. abnormal

RESULTS

T-SNE Visualization of Extracted Features during Pre-training

First we evaluate how joint representation pre-training impacts the resulting image features, in comparison to the same encoder trained on ImageNet, using t-SNE visualization [START_REF] Van Der Maaten | Visualizing data using t-SNE[END_REF]. t-SNE is a stochastic method for visualizing high-dimensional data. We use the scikit-learn [START_REF] Pedregosa | Scikit-learn: Machine learning in python[END_REF] implementation with the default parameters. We can see in Figure 4 that joint representation pre-training helps separate normal classes from abnormal ones in its encoding low-dimensional space.

Classification Results after Fine-tuning

In order to quantitatively assess the detection performance of this approach after fine-tuning, we evaluated the model on the test cohort presented in Section 3.1. We measured the algorithm's performance using the precision and recall rates as well as the F1-score. One can see in Table 1 that the Image-Text joint pre-traning using ConVIRT yields a 10% improvement in macro-average accuracy compared to the baseline with ImageNet pre-training. Specifically, we found that for ImageNet and ConVIRT pre-training, the negative predictive value (0.91 vs. 0.92) and sensitivity (0.85 vs. 0.84) respectively, were similar. On the other hand, the positive predictive value (0.49 vs. 0.60) and specificity (0.63 vs. 0.77) respectively, were both higher for ConVIRT compared to ImageNet pre-training.

CONCLUSION

In this study we explored the value of using unstructured radiological reports to pre-train a model to better separate normal and abnormal kidney ultrasound images. Although a direct link between images and their descriptions is not provided in abdominal ultrasound examinations, we were able to build pairs of images and text using different unsupervised methods. Finally, we showed that this matching strategy, combined with conVIRT pre-training, provided a 10% increase in accuracy during fine-tuning compared to ImageNet pre-training.

+ 0 -Fig. 1 .Fig. 2 .

 012 Fig. 1. Generation pipeline of Image-Text Kidney pairs for the pre-training set.

Fig. 3 .

 3 Fig. 3. Pre-training Pipeline: Images and texts are fed to an image encoder and a text encoder respectively. Each input image and text are then converted into d-dimensional vector representation using a Linear Layer. An asymmetric Image-to-text contrastive loss is used for each input modality.

  )

n

  j=1 exp( Ii,Tj ) and L T ←I i = -log exp( Ii,Ti /τ ) n j=1 exp( Ij ,Ti )

  Fig. 4. t-SNE visualizations of encoded image from different pre-training methods. Purple points correspond to images of healthy kidneys, and yellow to images of abnormal kidneys.

Table 1 .

 1

			Precision	Recall			F1-Score	support
		Baseline ConVIRT Baseline ConVIRT Baseline ConVIRT
	Normal Kidneys	0.91	0.92	0.63	0.77	0.74	0.84	427
	Abnormal Kidneys	0.49	0.60	0.85	0.84	0.62	0.70	176
	accuracy					0.69	0.79	603
	macro-average	0.70	0.76	0.74	0.80	0.68	0.77	603
	weighted average	0.79	0.83	0.69	0.79	0.71	0.80	603

COMPLIANCE WITH ETHICAL STANDARDS

IRB approval (IRB00011591) was obtained for this multicenter study and informed consent was waived. Data for this pilot study were obtained in collaboration with the Clinical Data Warehouse of Greater Paris University Hospitals registered at the National Commission for Data Protection and Liberties (CNIL-France) under the number 1980120.

ACKNOWLEDGMENTS

This work has been supported by the French government, through the 3IA Côte d'Azur Investments in the Future project managed by the National Research Agency (ANR) with the reference number ANR-19-P3IA-0002. The authors are grateful to the OPAL infrastructure from Université Côte d'Azur, the Radiology team from Saint Louis Hospital of Greater Paris University Hospitals and the French Health Data Hub for providing resources and support. We thank Guillaume Oules, engineer at GoPro, for his help in the design of the NHance project. We thank the Clinical Data Warehouse of Greater Paris University Hospitals and especially the imaging team. The authors declare that they have no conflicts of interest.


Keywords: Adaptation, User Interface, Mobile Application, Usage Behavior, Clustering, User Groups

Adapting Mobile User Interfaces based on users' application usage behaviors 1

Adapting Mobile User Interfaces based on users' application usage behaviors

Introduction

The smartphone is considered an essential component of modern life [START_REF] Pérez-Torres | Power management techniques in smartphone-based mobility sensing systems: A survey[END_REF]. It offers a wide range of functions, and users can benefit from mobile phone apps to achieve many affordable goals [START_REF] Nicholas D Lane | A survey of mobile phone sensing[END_REF][START_REF] Conti | The internet of people (iop): A new wave in pervasive mobile computing[END_REF]. It can be transformed into GPS, musical instruments, and credit cards, among others [START_REF] Böhmer | Falling asleep with angry birds, facebook and kindle: a large scale study on mobile application usage[END_REF], and it helps to achieve a variety of activities such as ordering food, shopping online, and managing health [START_REF] Böhmer | What's in the apps for context? extending a sensor for studying app usage to informing context-awareness[END_REF][START_REF] Jesdabodi | Understanding usage states on mobile devices[END_REF][START_REF] Srinivasan | Mobileminer: Mining your frequent patterns on your phone[END_REF][START_REF] Radosavljevic | Smartphone app categorization for interest targeting in advertising marketplace[END_REF].

Generally, a smartphone is associated with one person. Thus, the usage of smartphone apps can communicate a lot of personal information such as needs, interests, habits, and activities. This can provide new insight to better profile users.

In the last few years, several studies have tried to profile users from various points of view based on their app usage behavior, such as inferring demographics and personality [START_REF] Qin | Demographic information prediction: A portrait of smartphone application users[END_REF][START_REF] Al-Molegi | Regions-ofinterest discovering and predicting in smartphone environments[END_REF], and identifying the interests, preferences, and habits of users [START_REF] Micallef | Investigating login features in smartphone apps[END_REF][START_REF] Ouyang | Modeling and forecasting the popularity evolution of mobile apps: a multivariate hawkes process approach[END_REF][START_REF] Miguel Pires | Identification of activities of daily living through data fusion on motion and magnetic sensors embedded on mobile devices[END_REF].

Understanding app usage behavior has the potential to improve the user experience (abbr., UX) and the user's life quality. Many researchers have in recent years invested in understanding users' behavior with their smartphone apps, known as "app usage behavior".

However, they either scratched the surface to distinguish different categories of users, examined the usage of the apps, or discovered the variety of users. They made no attempt to investigate any differences in app usage behavior amongst user groups. Furthermore, they have not explored the app's usage behavior for further purposes, such as adapting the mobile user interface (abbr., UI). Despite the remarkable technological evolution of smartphones, it continues to fall short of users' expectations.

The mobile UI is a critical component of the UX for mobile devices and applications. User-friendly, efficient, and responsive mobile UIs are essential. In today's fast-paced, author competitive mobile industry, users expect to be able to accomplish their tasks quickly and easily. Thus, understanding and adapting to mobile app users' behavior is crucial.

Adapting the interface of a mobile device based on the user's app usage behavior is crucial for several reasons. Firstly, mobile UIs are typically static and do not self-adapt to the user's needs, meaning that the app groupings and icons on the current screen are determined by the device manufacturer. Besides, the user's most used or most time-consuming apps are not considered either. This can lead to a disjointed UX, where the user has to navigate through multiple screens to access the apps they use most frequently. Regardless of user behavior, they remain unchanged.

The significance of adapting the mobile device's interface extends beyond a single app and encompasses several key benefits. In fact, adapting the interface for a particular app is not enough. A user's behavior can vary significantly between different apps, and their needs and preferences may change over time. A mobile UI that is tailored to the user's app usage behavior across all applications will provide a more seamless and consistent experience, making it easier for users to complete their tasks quickly and efficiently.

Adapting the mobile device's interface is crucial as it enhances the overall UX, increases consistency and coherence, addresses the challenges of the mobile form factor, and accommodates the needs of diverse user groups. In fact, an adaptive interface can cater to the diverse needs and preferences of different user groups, such as elderly users, those with disabilities, or those with different cultural backgrounds.

This requires first identifying user behavior groups based on mobile app usage and then adapting mobile UIs for each group.

We believe that the concept of adaptive UI based on the app usage behaviors of users and their clustered groups is an interesting research direction. The findings could help in designing, developing, and recommending apps for specific user groups, and improving the overall smartphone usage experience. The goal of this study is to identify various groups of users in order to adapt the UI for the different groups of users, and then for each user individually.

The remainder of the paper is organized as follows: Section 2 gives a synopsis of the related works. This section covers the exploited user information in profiling as well as the data of apps for user profiling. Section 3 presents our two-phased approach for adapting the UI based on the user app usage behavior. Section 4 is dedicated to the first phase of the approach where we present our method for collecting log files. We analyze the collected data and expose the experiment results of the trained model and we present our analysis of the obtained clusters. In Section 5, we present the proposed two levels of adaptation and the adaptation rules. Section 6 discusses the benefits and limits of our research work. Finally, Section 7 concludes the paper and outlines future perspectives.

Related work

This section presents the most relevant research on collecting data, app usage behavior, and UI adaptation.

Data Collection

Adaptation requires data collection. Log studies provide the most complete and accurate view of mobile user activity [START_REF] Dumais | Understanding user behavior through log data and analysis[END_REF]. Logs are captured easily at scale. Log files record what users do rather than why. They can also test researchers' hypotheses about user behavior. Table 1 presents the findings of several studies involving log files and data collection. The table presents several key features, including the platform of the participants (Android or iOS), the format of the collected data, and the storage location of the data. The feature of Instrumentation refers to the process of adding extra code to a software program to gather and measure data. This code can log data to a file, send data to a remote server, or perform real-time analysis of the collected data.

The Test-evaluation feature indicates the evaluation approach used by the authors. In a Lab study, participants are brought into a laboratory setting to perform pre-determined tasks. In contrast, Log studies (i.e., Real usage) are more natural observations as the systems are used without any influence from experimenters or observers.

The Scaling feature highlights whether the research works consider the process of increasing the data collection, storage, and analysis efforts. This table also covers whether the data collection process is initiated automatically or triggered by the user, the type of collected data, the purpose of the studies, and the study object.

We conclude that the vast majority of studies that make use of log files are applicationoriented. They are mostly concerned with determining the usefulness of a certain app. Even though the work of [START_REF] Riegler | Measuring visual user interface complexity of mobile applications with metrics[END_REF] focused on mobile UI, they were limited to creating an Android prototype for calculating the user interface complexity of any mobile application.

Apps usage behavior studies

The use of smartphone apps has the potential to transmit a considerable amount of personal information. As indicated previously, an increasing number of studies have been conducted in recent years to detect how users utilize smartphone apps.

The purpose of studying app usage behavior varies among research works. Some work such as [START_REF] Lekkas | Using smartphone app use and lagged-ensemble machine learning for the prediction of work fatigue and boredom[END_REF] used mobile app use and lagged-ensemble machine learning to predict work fatigue and boredom. Other works such as [START_REF] Huseynov | Understanding usage behavior of different mobile application categories based on personality traits[END_REF] assessed the relationship between different author personality traits and mobile app usage behavior, and this by using the well-known Big Five personality traits taxonomy. Other researchers such as [START_REF] Huang | Behavioral intention to continuously use learning apps: A comparative study from taiwan universities[END_REF] conducted an empirical study in order to identify the types of learners based on the use of learning apps. The COVID-19 pandemic served as a catalyst for this study since it has shifted people's attention back to digital learning, and many online courses and digital language learning systems have attracted numerous language learners.

We synthesize in the table 2 below that there are many more complex and diverse behaviors that make up the smartphone user population. The table includes the used data type, population size, the operating system type of the user's smartphone, and the time period of the conducted experiment. The main goal of each research work as well as the disadvantages of the methods are listed.

As we can see from table 2, the study scale can vary in a large range, from 14 users to more than hundreds of users. The data duration also varies, from 1 week to around 1 year or more. The most commonly used data are user interactions and app usage records, with several works emphasizing the significance of location and time of use. The user's phone in the observed studies varies, ranging from iPhone to Android to Windows Mobile.

While some have demonstrated considerable variety in app usage [START_REF] Xu | Identifying diverse usage behaviors of smartphone apps[END_REF][START_REF] Falaki | Diversity in smartphone usage[END_REF], others have concentrated on examining and reporting app usage averages or ranges of app use across users. Jones et al. [START_REF] Simon L Jones | Revisitation analysis of smartphone app use[END_REF] identified three distinct clusters of users based on their app revisitation patterns (Checkers, Waiters and Responsives) while Banovic et al. [START_REF] Banovic | Proactivetasks: the short of mobile device use sessions[END_REF] identified four types of users based on their actions on emails displayed on their lock screen (Non-users, Normal users, Power users, and Cleaners). However, their scope is limited by only examining relatively simple behaviors such as app revisitation and actions performed on emails.

UI adaptation

When it comes to adapting mobile interfaces, we realize that the main research question revolves around the development of adaptive mobile apps that can be used in context-aware environments. The majority of research focuses on examining the impact of different design mechanisms in assisting the development of mobile apps. These apps can be adaptable to fit different user needs, situations, modes of interactive design, and technological limitations. Indeed, numerous approaches address the continuous adaptation of app interfaces. They demonstrate the wide range of software artifacts that can be used to adapt mobile app interfaces dynamically.

Mobile UI adaptation can have a significant impact on a wide range of industries (e.g., healthcare, finance, education, retail, travel, entertainment, etc.). For instance, mobile UI adaption can enable healthcare providers to access patient data, track vital signs, and collaborate with other healthcare professionals.

Kellett et al. [START_REF] Kyle | A mobile app for the precise measurement of healthcare provider activity times to support time-driven activity-based costing studies[END_REF] examine the use of a smartphone mobile app technology to record activity times. The purpose is to support time-driven activity-based costing (abbr., TDABC) studies, which are used to determine the cost of providing healthcare services (e.g., time spent on patient care, administrative tasks, and training). The adaption of UI allows for making informed decisions about how to allocate resources and improve the costeffectiveness of healthcare delivery. However, this study has some potential operational barriers to implementation and does not consider adapting the overall interface of the mobile device itself. It is dedicated to a particular app. Some research work aimed to improve mental healthcare by introducing new adaptive intervention technology. Lamo et al. [START_REF] Lamo | Towards adaptive technology in routine mental health care[END_REF] actions on emails displayed on their lock screens.

They only look at one pretty straightforward behavior (actions taken on emails) The adaptation of user interfaces can enhance the field of Extended Reality (XR) by adapting interface elements to the user's context, resulting in improved user experiences. Evangelista et al. [START_REF] Marcelo | Auit-the adaptive user interfaces toolkit for designing xr applications[END_REF] introduced a toolkit to simplify the design and implementation of optimization-based XR adaptation policies. The toolkit addresses implementation challenges such as manual scripting and limitations in realistic XR environments. The toolkit streamlines the design process, making adaptation policy implementation more manageable and efficient.

In [START_REF] Coninx | Dygimes: Dynamically generating interfaces for mobile computing devices and embedded systems[END_REF], the authors employed XML-based UI descriptions to create UIs that adapt to the context of use at run-time. Authors in [START_REF] Mitrović | Dynamic user interface architecture for mobile applications based on mobile agents[END_REF] suggested a UI architecture for run-time adaptations in mobile apps based on mobile agents, and authors in [START_REF] Bisignano | Dynamic user interface adaptation for mobile computing devices[END_REF] created an XML vocabulary to model dynamic UI adaptations for mobile computing devices. Abrahao et al. [START_REF] Abrahão | Model-based intelligent user interface adaptation: challenges and future directions[END_REF] developed an ontology-based framework for representing, designing, and supporting the adaptation of UIs in mobile apps using design patterns that are adjusted to the user's needs or preferences as well as the context in which they are used. Abrahao et al. [START_REF] Abrahão | Model-based intelligent user interface adaptation: challenges and future directions[END_REF] defines a conceptual reference framework for intelligent UI adaptation for model-based UI adaptation.

The referenced research, however, exclusively dealt with personalizing the UI for a specific app. Furthermore, the majority of UI adaptation studies focused evaluate existing and new web app designs and provide developers and designers with a benchmark for choosing the most user-friendly and effective design. To the best of our knowledge, there haven't been any studies that cover mobile UI adaptation based on an app's usage behavior for different groups of users.

Review

Despite the fact that many studies have been conducted on mobile app usage behaviors, they have only scratched the surface in terms of identifying distinct types of users, analyzing app usage in the aggregate, or examining the range of users. They haven't looked into any variations in app usage behavior among different user groups.

Most of the mobile computing studies made no attempt to investigate any differences in app usage behavior across user groups. They investigate the ways users download, install, and use various apps [START_REF] Li | Characterizing smartphone usage patterns from millions of android users[END_REF], the length of the average app session [START_REF] Banovic | Proactivetasks: the short of mobile device use sessions[END_REF][START_REF] Böhmer | Falling asleep with angry birds, facebook and kindle: a large scale study on mobile application usage[END_REF], the number of daily interactions individuals have with apps [START_REF] Falaki | Diversity in smartphone usage[END_REF] and the way context affects the use of apps [START_REF] Minh | Smartphone usage in the wild: a large-scale analysis of applications and context[END_REF][START_REF] Ferreira | Contextual experience sampling of mobile application micro-usage[END_REF].

Furthermore, many studies have been conducted to investigate smartphone apps usage patterns, such as the frequency with which apps are used together [START_REF] Srinivasan | Mobileminer: Mining your frequent patterns on your phone[END_REF] and the frequency with which a particular app is revisited [START_REF] Simon L Jones | Revisitation analysis of smartphone app use[END_REF]. Other works have focused on predicting which apps users are intending to install [START_REF] Zhang | Nihao: A predictive smartphone application launcher[END_REF], and use [START_REF] Huang | Predicting mobile application usage using contextual information[END_REF][START_REF] Leroux | Mobile application usage prediction through context-based learning[END_REF][START_REF] Liao | On mining mobile apps usage behavior for predicting apps usage in smartphones[END_REF]. They treat all smartphone users as though they are the same, which is a convenient and simplifying assumption for app developers and phone manufacturers, besides the fact that it does not correspond to reality.

Adapting the mobile UI based on app usage behavior, especially after identifying various groups of users, is crucial for providing an optimized and personalized user experience. By first identifying user behavior groups based on mobile app usage and then adapting the mobile UI for each group, the interface becomes more responsive to the needs and preferences of each user. This can lead to a more intuitive, efficient, and seamless experience, where the user's most frequently used or time-consuming apps are given priority in the interface design. Therefore, adapting the mobile UI based on app usage behavior and user behavior groups is an essential step in creating a user-friendly and successful mobile device By identifying distinct types of mobile users based on their use of smartphone apps, it is possible to develop more accurate user profiles and, as a result, a more accurate interface adaptation. Profiling a user through a smartphone app involves a number of steps, such as collecting data, preprocessing it, and modeling the user. The quality of the user profile is affected by the data that is collected, and informational data helps to build an accurate user profile.

In this study, we aim to identify various types of users and redesign the mobile UI for the different groups of users in a dynamic and customizable way. From this point of view, we decided to collect our own data using an open-source Android toolkit that we upgraded so that log files could be sent automatically and periodically to a predefined Gmail box. We processed and analyzed the data we collected, and we used a few machine learning algorithms on our dataset to find out what kinds of smartphone users there are based on how they use apps.

The proposed approach

Our research purpose is to adapt the mobile UI based on the interactions of users with their mobile apps. To achieve our goal, we propose an approach based on two phases. We provide two levels of adaptation: primary adaptation and advanced adaptation. The first is performed automatically. This type of adaptation is associated with the user's cluster. It is reliant upon the cluster's characteristics on the one hand, and the UI state on the other (i.e., an overloaded interface). Each cluster has its own adaptation according to its author characteristics. This adaptation is performed once the user's cluster has been determined. This is covered in detail in section 5.1.1. The second type of adaptation no longer concerns all users in a given cluster. It requires user approval. The user is involved in the UI adaptation process, selecting the adaptation that he finds useful. We go into more detail in section 5.1.2.

Phase 1: Discovering users' clusters

The first phase of our approach aims to train the model and discover the different clusters of users' behaviors as depicted in Figure 2. Our attention so far, in this phase, is oriented towards achieving the following steps: 1) Gathering data from different participants, 2) Processing the collected data, 3) Building the model by selecting the right ML algorithms, 4) Gaining insights from the model's results, and 5) Transforming results into visual graphs (i.e., data visualization). This phase has row-collected data as input and the newly identified user groups are the output. We extract the features of each of the discovered groups. Subsequently, the distinctive characteristics of each group are identified and utilized as input for the second phase, in order to deliver tailored adaptations to each user group. We go through this section with the different steps mentioned above. We present an updated toolkit for collecting user log files, as well as the clustering methods that were used. We provide an overview and analysis of the collected data. We present an overview of the performance of the model and visualize the results obtained.

Data Collection

We focus on the Android OS due to its dominance among mobile operating systems and the availability of all essential UI elements found on competing mobile platforms, including app notifications, lock screens, and home screens. Collecting data from diverse mobile users is the first step in building our model. To accomplish this task, we use an open-source Android toolkit called Automate proposed by Holzmann et al. [START_REF] Holzmann | An android toolkit for supporting field studies on mobile devices[END_REF]. The toolkit allows for flexible use, such as pausing/resuming data collection and modifying settings by selecting which data to track. The collected data will be stored locally and can be exported as a ZIP archive of CSV files.

We chose this tool because it meets our needs for collecting large numbers of log files from volunteers. However, this tool contains flaws that could hinder our research. The participants have to manually send the generated CSV every 24 hours. This causes the individual to get bored and possibly give up on the experience. To automatically and regularly send the log files to a predefined mailbox, we had to update the app. Figure 3 depicts the log file-sending process after revision. The source code is available on Github a .

Accordingly, we took the following steps:

• Updating of the dependencies (SDK, libraries): the last activity on the Automate code dates back to August 2017. Thus, it was necessary to make an update to cope with Android's latest versions.

• Adding UserActivityBroadcastReciever component: It starts the tracking process when the user activates his phone. So the collected data will be sent, this component operates when two conditions are checked: 1) the logs collected are substantial enough (i.e., 8 hours from the last send) 2) the phone's connectivity to send these logs. If two conditions are satisfied, this component will request MailSenderService to send the logs.

• Incorporating the MailSenderService service:It is called each time the UserActivi-tyBroadcastReciever estimates that an email carrying logs should be sent. A Java application (Web or Android) is not able to manage mail sending and receiving unless it is integrated with a mail server. This server has to be always accessible and use standard mail-sending protocols. The MailSenderService allows the connection to an SMTP server via the SMTP protocol using an URL (server URL), a username, and a password. Afterward, this service builds a Message type Object that includes: 1) subject of the message, 2) body of the message, 3) the recipients, and 4) the attachment (i.e., a zip file containing the CSV files).

a https://github.com/mint-hagenberg.

author

The mail server will return a positive response if the mail is correctly sent to the recipient. If a problem occurs, it will return a negative response and assume that the logs were not sent, and consequently, it will try to send them again until this operation is completed successfully.

The processing of the data is handled by managers, where each one collects a specific type of data and transforms it into various types of structured data (CSV). In our case, we studied the file generated by the manager App Sequence Manager.

Ethical Considerations

We are fully aware of the privacy ramifications of using the acquired data for research purposes. We have taken all necessary precautions to protect the privacy of the involved participants. Additionally, the toolkit is an open-source project and maintained on GitHub b , allowing users to examine it easily. To allay user concerns about privacy, participants are informed of the data collection prior to downloading Automate. They provide their consent via their devices.

Participants were recruited through online postings and social media. After installing the APK file, the user has to enable the accessibility service when the app is started for the first time. After the permissions are granted, the tracking starts automatically and can be modified in the tracking settings. Hence, no action is taken unless the user gives his or her approval.

The conducted experiment is anonymous. As the user installs the toolkit, a generated user-specific identifier ID is assigned to that user. We only have access to his ID. Thus we can not correlate the user's ID with physical individuals.

Clustering method

Generally, the choice of algorithms is related to the size of the dataset. In our context, we selected algorithms that correspond best to small and medium-sized datasets.

There are two different clustering methods. The first requires that the number of clusters be fixed beforehand. We tested different algorithms, such as K-means and Spectral clustering with a different number of clusters. The second automatically determines the number of clusters. We tested Affinity Propagation and Mean Shift, and we compared the results in section 4.5.

We evaluate the performance of the chosen unsupervised ML algorithms by using Silhouette score, Calinski Harabasz score, and Davies Bouldin score.

Data Overview

In this section, we describe and analyze our dataset, and we present the exploited features. The basic analysis reveals app usage-related information and allows us to determine the features that could influence user behavior.

Dataset

The dataset we collected contains 37313 usage records in total (entries) resulting from 2212 log files collected from 71 volunteers over a 1-month period of time. The data was b http://mint-hagenberg.github.io/automate-documentation. collected and sent nearly every eight hours by the Automate Toolkit. The collected log files have been processed to extract the relevant information. Consequently, we needed to delete, add, and translate some attributes.

We were inspired by Zhao et al.'s work [START_REF] Zhao | Discovering different kinds of smartphone users through their application usage behaviors[END_REF] in segmenting the daytime and studying users' activity throughout the day: morning (6:01 am to 12:00 pm), afternoon (12:01 pm to 6:00 pm), evening (6:00 pm to 00:00 am), and night (00:01 am to 6:00 am).

In our context, app category (e.g., social media, photography, communication, etc.) is an important feature because it has greater significance than just using the application name. The use of the categories of mobile apps leads to a more general and less complex representation of the user's behavior.

In order to identify the type of app, we used Kaggle datasets c where the categories of mobile apps crawled from Google Play-Store d . We distinguish 23 groups of apps.We removed redundancy and added apps to existing categories (i.e., apps used across the country).

Dataset Analysis

The participants in our experiment are heterogeneous. It includes people of different genders, ages, backgrounds, different occupations, and different types of smartphones. After installing the app Automate successfully, the participant is invited to fill out a form to get more information about the demographic data. Table 3 Based on the remaining users' demographics, we see that our dataset has more female users than male users. Almost 69 (68.7%) of users are between the ages of 20 and 29, with the majority of them being students. We divided the dataset into two sub-datasets. The first contains all of the categories, while the second only considers the top 12 (less than 200 uses). The rest are put in a category that we call "Others".

The analysis carried out shows that users have the most activities in the afternoon (from 12:01 pm to 6:00 pm) and much less at night (from 12:01 am to 6:00 am), as illustrated in figure 4.

By analyzing both datasets, we noticed the following:

• The top five categories having the highest number of occurrences among the datasets are, respectively, Social Media, Communication, Productivity, Life Style, and Map and Navigation.

• Users tend to use their smartphones more in the afternoon than in the evening (with a slight difference in the morning). Analyzing the collected data is a necessary step to learning more about app usage behavior and deciding which features influence users' behavior the most.

Experiment Results

Our dataset includes features such as the "Frequency" and "Duration" of use of each category per day, and in each part of the day. We tried to merge "Frequency" and "Duration" as an added feature. We split our dataset into two sub-datasets. The first contains all categories (23 categories), while the second contains only categories below the threshold of 200 uses (12 categories). We combined similar categories like "Games" with "Entertainment", the "Calendar" with "Productivity". The rest (e.g., education, sports) are grouped under "Others".

We collected log files from 71 volunteers. However, some users' collection periods are as short as a few hours. We deleted these and then tested both datasets on 61 users.

We measure, in the following subsections, the performance of the applied algorithms on our two sub-datasets through the evaluation metrics mentioned in section 4.3. We examine user behavior clusters and report algorithm performance in each dataset with various features: duration, frequency, frequency & duration per day, as well as frequency, duration, and frequency & duration in each part of the day.

Dataset 1 results

We summarize the performance of the various algorithms in table 4.

The results show that the algorithms K-means and Spectral Clustering outperform the other algorithms based on Silhouette Score and Calinski Harabasz Score. The algorithm On the other hand, Mean Shift provides better results with the feature Duration according to Davies Bouldin' measure (0.48). However, the distribution of clusters (k = 9) is not balanced. The first cluster contains 47 users, and the other clusters each contain only one user.

Dataset 2 results

The performance of algorithms is summarized in table 5. The K-means and Spectral Clustering algorithms are the most efficient using the Frequency feature according to the Silhouette Score (0.55) and Calinski Harabasz Score (14.45) metrics with k = 2. The first cluster has 46 users. while the second has 15. On one hand, the Agglomerative Clustering algorithm works well with the Duration feature according to Davies Bouldin Score. On the other hand, a good result is obtained by using the Mean Shift and Agglomerative Clustering algorithms, according to Davies Bouldin's measure.

Features & Metrics

Cluster analysis

We chose the best-performing features based on the assessment metrics to analyze users' behavior. We used the Calinski Harabasz Score and Silhouette Score scores to avoid the unbalanced distribution of Davies Bouldin scores.

Observing the results, the feature Frequency has the best results in both datasets for the assessment measure Calinski Harabasz Score respectively (12.314) and (14.45) with a balanced distribution. We chose to study the first dataset attribute, Frequency Part_of_day. author The use of apps during the day varies between the two clusters. Users in the first cluster, as shown on the left side of figure 5 are more active in the morning and in the afternoon, while those in the second cluster, on the right side, are more active in the afternoon and the evening. In addition, users in the second cluster are more active at night than those in the first one. • Compared to the second group of users, the first group of users uses the Productivity category more frequently in the morning, afternoon, and evening, with an average frequency of use equal to 65. They prefer the Entertainment category in the morning and evening, and the Games category in the afternoon. Users of this group use the Maps and navigation category much more than the second group's users in the morning and afternoon.

• Users of the first group use the Weather and Music and Audio category less than those of the second group. On the other hand, the users of the second cluster are less communicative and more active during the night hours.

• The users of the second group use the Lifestyle and Shopping apps much more than the users of the first group and this is during the morning, afternoon, and evening with an average frequency of use equal to 98. They also use the Weather, Video Players and Editors categories in the evening, and Music and audio more than the first group.

Phase 2: Interface adaptation

Our research goal is to adapt the mobile UI based on how users interact with their mobile apps. We introduce in this section the adaptation phases, and we present the adaptation rules that contribute to the delivery of an adapted user interface. We also present some case studies.

Adaptation phases

We suggest two stages of adaptation: the primary adaptation, which is carried out automatically once the user's cluster is detected, and the advanced adaptation, which requires the user's approval.

Primary adaptation

This type of adaptation is based on the cluster's features on the one hand and on the UI state on the other (i.e., an overloaded interface). Each cluster has its own adaptation according to its characteristics, such as the most and least used categories, the most and least used apps, and the most frequently used apps in each part of the day. Hence, it is necessary to first identify the user's cluster.

(A) Adaptation method: A smartphone typically consists of multiple screens, including the home screen and other screens. Thus, it is crucial to identify "which adaptation should be applied to which screen?". As an answer to this question, we propose the following:

-Adaptation performed on home screen:

We presumed that mobile users make the most use of the home screen. For accessibility purposes, we assume that the user moves to the home screen apps that he or she deems essential. Therefore, we propose: i) relocating apps where the most frequently used apps are moved to the home screen (if they are currently on another screen and are among the TOP 5 most frequently used apps), ii) zooming-in the icons of the most utilized apps at each time of day.

-Adaptation performed on other Screen: Similarly, we assume that the other screens are used less frequently than the home screen. We propose: i) relocating apps so that the apps with the lowest usage are moved to other screens, leaving the home screen with only the apps the user requires, ii) ungrouping a frequently used app from an existing group and facilitating access to the app, iii) grouping the five apps with the lowest frequency of use and labeling the group "least used apps". The user may have several screens. We decided to apply these changes to the screen that appears after the home screen.

We would like to point out that both of the adaptations mentioned above are realized simultaneously.

(B) Adaptation algorithm:

author

The input for our algorithm includes the user's home screen and the screen they use most frequently, and information about their app usage patterns compared to their cluster. The output of the algorithm is either one or two adapted interfaces, with the latter being generated only if the user has a secondary screen beside their home screen.

As cluster's features and the interface overcrowdness are considered in primary adaptation, we set as an input the state of the screens to be adopted.

According to the user's cluster, we start extracting clusters' features, such as the top 5 most used categories (C i T op5cat[]), the top 5 most used apps (C i T op5apps[]), as well as the least used apps (C i Least5apps[]) and the top 5 most used apps in each part of the day (T op5appsByP oD[]).

author

(N ew_C i T op5cat[]); otherwise, if (C i T op5cat[])
remains the same as when we started the adaptation, change remains f alse and the adaptation continues.

The user might not like the changes that have been made. He might cancel some of the provided adaptations. Consequently, these changes won't be made for this user during the next phase of adaptation. This implicit feedback may serve as a launching point for advanced adaptation.

Advanced adaptation

The user may not appreciate the primary adaptations, which are based on the cluster's features and the interface state (i.e., overcrowded). The advanced adaptation takes the user's preferences into account when making the adaptation. We think that involving the user in the process of UI adaptation could help increase the smartphone's usefulness. He selects the adaptation based on what he desires or what he considers useful. A user profile is created based on the gathered data, which includes:

-User characteristics: It includes details, namely the user's age (needs associated with the elderly), physical disabilities (such as visual or auditory impairment, difficulty in selection), and expertise level (beginner, expert).

-User choices: It includes the choices made by the user. The user can choose the interface on which to implement the adaptation (the home screen or other screens). The user has the option to accept or decline the various adaptations. If the user declines, the subsequent adaptation suggestion is displayed. The suggested adaptations are listed below.

• Grouping apps: It entails grouping the most and least used apps into one group, each separately.

• Ungrouping apps: It consists of dissociating one or more of the frequently used apps from an existing group. It is crucial to note that we take into account whether or not the target screen is overloaded. We also ensure that the group was not recently formed.

• Relocating apps: It consists of transferring one or more apps from one screen to another. The user can choose to:

* Relocate the most used apps from an existing screen to the home screen.If it is overloaded, the adaptation will be canceled.

* Relocate the least used apps from the home screen to another screen that the user chooses. If the target screen is overloaded, the adaptation will be canceled.

* Relocate both the most used apps and the least used apps. The same conditions will be applied.

• Rearranging screen: It consists of reorganizing the apps on the user-selected screen. For the frequently used apps, the user can choose the area of the screen (top, middle, or bottom) and the order of realignment (from right to left or the other way around).

• Zooming-in apps: It consists of increasing the most-used apps' icon size according to each part of the day.

The purpose of this adaptation is to provide easy access to the apps that users use the most. In light of this, we set up adaptation rules to make sure that each user got the right adaptations.

Adaptation rules

Our main focus in this stage is the selection of consistent adaptation rules considering the user's choice and the current context information such as UI state. In artificial intelligence, various production systems have been found in literature, such as rule-based expert systems [START_REF] Nl Griffin | A rule-based inference engine which is optimal and VLSI implementable[END_REF]. The rule's syntax is composed of two parts:

IF(condition 1 * condition 2 * . . . * condition n )THENconclusion
The proposed production system is made up of four subsets of production rules: general rules, interface rules, user rules, and app category rules.

General rules

We have established rules regarding the battery level. The main goal is to save energy so that the user can interact with the system for as long as possible. If the battery level is below 20%, then offer to close apps operating in the background until the next recharge. If the battery level reaches 5%, then force the shutdown of apps running in the background, and this until the next recharge.

Interface rules

The interface rules consider the interface's state and the user's choices. It consists of the selected screen and the various adaptation types outlined in the preceding section 5.1.2. Depending on the time of the day, the provided adaptation is applied. The user can choose to apply the adaptation either on the main screen or on another screen. If the user's smartphone has multiple screens, the user will select the screen that best suits him.

User rules

It includes the user's features such as disability, age, and expertise level. User disability encompasses visual and auditory problems in addition to selection inability.

We thought about both the case where the user is blind and the case where the user has just trouble seeing. When the user is unable to see, he is offered many adaptations, like reciting the notifications aloud, vibrating when an app is selected, and pronouncing the app's name when selecting it.

When the user is visually impaired, various adaptations are provided, like increasing system volume (i.e., ringing phone, notification sound) and enhancing the font and notification size. Users should be able to visualize all the screen items, and the notifications should be easy to read. We provide further insight in the table 6.

We have included the user's age in the list of user characteristics in order to meet the requirements specific to the elderly. We offer less crowded UIs (i.e., informational density). We assist seniors by providing them with graphical support and guidance.

For users who have trouble selecting items, we recommend making the selection fields larger so that they can press the appropriate spot on the screen. We also considered the user's level of expertise, particularly for novices where permanent assistance is offered.

author User 1 prefers the advanced adaptation over the primary adaptation, according to a 1-5 scale (1: not satisfied to 5: very satisfied). He gave the primary adaptation a score of 2 and the advanced adaptation a score of 3.

• User 2: is a 24-year-old male user. He belongs to the second cluster. He has no particular organization for the screen, and he gets used to the initial configuration. The user prefers that the changes be made on the home screen. He chooses the option of relocating apps from one screen to another. He agreed to only mute but display the upcoming notifications. The left side of the figure 7 depicts the primary adaptation, where the top 5 most-used categories are listed on the home screen. The middle figure shows the second-mostused screen primary adaptation, where the top 5 least-used apps are grouped and labeled. The right side of the figure depicts the advanced adaptation, where the apps "Flashscore", "Twitter", "Facebook", and "Messanger" are moved to the home screen as they are among the 5 most-used applications. User 2 is neutral. He rated both the primary adaptation and the advanced adaptation as 3.

• User 3: is a 21-year-old female. She belongs to the first cluster. She modified the initial configuration to accommodate her app usage and places the most important apps on the home screen. The user prefers to adapt the second-most-used screen. She chooses the option of ungrouping a frequently used app from an existing group and grouping the least used apps. She agreed on hiding notifications from other apps and switch to silent mode. On the right side of the figure, the advanced adaptation is shown, in which the app "Gmail" is separated from the group called "Google". The third user seems to not like the adaptations since she gave both the primary adaptation and the advanced adaptation a 1 rating.

We presented a series of case studies involving 15 participants, including individuals with disabilities such as visual impairment and hearing difficulties. Our experiment revealed that users have varied approaches to organizing their interface. Some made minimal changes to their screens, while others rearranged apps for improved accessibility and personalized ease of use.

The feedback we received showed a diverse range of preferences. Some participants had no preference, some preferred the advanced adaptation over the primary adaptation, and others chose to keep the original adaptation provided. These findings have important implications for the design of smartphone interfaces. They suggest that there is a need for more customization options in interfaces to meet the unique needs and preferences of different users.

Through the received feedback, we noticed the importance of considering accessibility requirements, particularly for individuals with disabilities, when designing interfaces. Interfaces that are easy to use and accessible to all users, including those with disabilities, can lead to a better overall UX.

author 6 Discussion

Understanding users through smartphone app usage analysis is considered an encouraging approach. Even still, some challenges remain. In this section, we discuss the outcomes of our work as well as our research's shortcomings and challenges.

Benefits

Most studies that analyze smartphone usage data to understand users use an existing dataset based on a recent task list [START_REF] Huang | On the understanding of interdependency of mobile app usage[END_REF][START_REF] Li | Characterizing smartphone usage patterns from millions of android users[END_REF][START_REF] Liu | Understanding diverse usage patterns from large-scale appstore-service profiles[END_REF][START_REF] Li | Mining device-specific apps usage patterns from large-scale android users[END_REF][START_REF] Zhao | Discovering different kinds of smartphone users through their application usage behaviors[END_REF]. Its low sampling rate (data is collected once every hour) causes limitations. In fact, tracking app usage duration, frequency, and order of task list changes is not feasible. The logs we gathered reveal much about user behavior, including how long each app is used, how often it is used, and the order of the most utilized apps, either by the time of day or throughout the day.

We discovered two user groups. Users in the first group are more active in the afternoon and morning and are more communicative, with social media and communication being the most popular categories. This cluster's users utilize more productivity-related apps than the other cluster's users. Aside from that, users in the second group are more active in the afternoon and evening. Lifestyle apps are the most popular in this group, and users check the weather more frequently than users in the first cluster.

Identifying more than one group shows that treating all smartphone users the same is erroneous. Whoever has influence over the types of apps installed on phones and how users choose which apps to use can no longer treat users as a homogeneous group.

To counter this assumption, numerous parties may be involved, such as smartphone manufacturers and mobile app developers. Involving these parts can be advantageous, as each can contribute to improving smartphone user interaction.

This paper demonstrates the following advantages of the proposed approach when compared to the existing works: (1) adapting the smartphone's user interface based on how the user interacts with apps, as opposed to previous works that adapted the UI for a specific app; (2) adapting the UI by taking into account human factors such as user characteristics (i.e., difficulty in selection, visual or auditory impairment, etc.) and other factors including the app category and interface overcrowding; (3) Unlike other research works that propose approaches for pre-conception interface adaptation, our approach can be applied to postinterface conception.

Limitations

Although user behavior is used to identify distinct categories of users from collected log data, this study has drawbacks. Many studies stress that demographic information has a significant impact on app usage behavior. However, the conducted experiment is anonymous. Hence, we did not benefit from users' gender, age, or profession.

During data processing, we observed that the data collection period is not uniform (normally 1 month). Some users' collection periods are as short as a few hours. Others range from 11 to 28 days. This could be due to the user uninstalling or deactivating the app, or the OS shutting it down because it runs in the background.

Users of the two clusters have many app usage behaviors in common, making it difficult to label each cluster. As a consequence, we notice that the model requires minor adjustments to improve its performance.

Our research intends to design an adaptive mobile UI for distinct user groups depending on their app usage behaviors. We proposed, accordingly, a two-phased approach. The first phase involves discovering the different clusters of users by exploring the potential of Machine Learning (ML) algorithms, while the second involves adapting the UI.

We offer two levels of adaptation. The primary adaptation is based on the cluster's features. It is applied automatically once the user's cluster is detected. The Advanced type no longer concerns all users of a given cluster. The user is engaged in the process, and some adaptation requires his approval. We provided a variety of adaptations, including visual, audio, sensory, hybrid, and system adaptations. We established adaptation rules to provide each user with the appropriate adaptations.

Through the obtained result, we prove that the simplified hypothesis that all smartphone users are similar should not be applied and that mobile users can no longer be treated as if they are all part of one big group. We discussed that many parties could be involved and benefit from identifying the different groups of users.

We demonstrate through the result that the existence of different interface preferences, including the choice between the home screen or another screen, refutes the previously held belief that all smartphone users have a uniform preference for maintaining an unchanged home screen. This proves the importance of considering adapting the overall interface of the mobile device itself, rather than adapting the interface of a particular mobile app as most of the research work does. The observation that some participants chose to maintain the provided adaptation indicates that our hypothesis holds promise and can be further developed by incorporating certain relevant variables.

As a perspective, we plan to collect data from a targeted user group to test our approach and ensure that our experiments and results can be reproduced. We intend to enhance our model's performance and add a third phase that uses fine-tuning for continuous improvement and sends the noticed changes to the model so it can cope with them. We intend to investigate the frequency and periodicity of the adaptation in order to deliver the appropriate amount of adaptation at the appropriate time.

Figure 1

 1 Figure1The proposed approach for the adaptation process

Figure 2

 2 Figure 2 Process of discovering users' clusters

Figure 3

 3 Figure 3 After-revision log file sending process

Figure 4

 4 Figure 4 Frequency of use in each part of the day

Figure 5

 5 Figure 5 Period of day usage statistics: (a) Frequency of use in each part of the day for cluster 1; (b) Frequency of use in each part of the day for cluster 2

Figure 7

 7 Figure 7 The adaptation made to the second user's interface: (a) Home screen primary adaptation; (b) Second used screen primary adaptation; (c) Home screen advanced adaptation

Figure 8

 8 Figure 8 The adaptation made to the third user's interface: (a) Home screen primary adaptation; (b) Second used screen primary adaptation; (c) Second used screen advanced adaptation

Table 1

 1 Research studies using log files for data collection

  collaborated with the end-users by applying user-

	Critics	Does not examine the	differences in app usage	between different types of users.	Did not explore how app usage	differs across users or user	groups.	Did not analyze the app usage	behavior of different kinds of	users.	Did not explore how app usage	varies across different kinds of	users.	Did not identify the differences	in usage behaviors among	various user groups.	Did not observe the variations in	usage behavior among different	user groups	Examined only a relatively	behavior: app simple	revisitation.
	Period Work Synthesis	7-28 Explored the mean number of interactions per day	Weeks for a user, the mean interaction length in seconds,	and the number of used apps.	1 week Showed how, where, and when smartphone apps	are used at an aggregate level		1 year Focused on characterizing the usage states that	exist between usage sessions + Analyzed how it	differ within and between the users.	14 Studied mobile device usage and engagement (the	months time period between unlocking and re-locking the	device).	4 Studied what apps the users used and how their	months usage changed over time, showing that the usage	is highly location-dependent.	1 Studied the length of usage sessions, the number	month of sessions per hour or per day, and the number of	apps per day at an aggregate level.	3 Identified users clusters based on app revisitation	months patterns (how often users return to a particular app).	4 Identified four types of users by analyzing users'	weeks
	Users	33 Android +	222 Windows	mobile	National level	(USA)		24 iPhone			34 iPhones			14 users			23 Android			165 Android		27 Android
	Used data	User	interactions +	app use	App usage	records		App usage	records		App usage +	Location +	DateTime	App usage	records +	Location	Sensory data	+ Mobile app	use	App usage	records	User	interactions
	Work	Falaki et	al. [25]		Xu et al.	[26]		Jesdabodi	et al. [6]		Yan et al.	[27]		Rahmati et	al. [28]		Shin et al.	[29]		Jones et al.	[30]	Banovic et	al. [31]

Table 2

 2 Synthesis of Mobile app usage behaviors research worksauthor centered design (abbr., UCD) principles, developed by Abras et al.[START_REF] Abras | User-centered design. Bainbridge, W. Encyclopedia of Human-Computer Interaction[END_REF], to create and evaluate engaging UIs. The authors developed new machine learning (abbr., ML) algorithms and techniques for detecting and classifying patients' behavior and symptoms. The method has been used to tailor the treatment to individual patient's needs and preferences.

Table 3

 3 depicts the percentages of users in the following demographic attributes: gender, age range, and function. Percentage of users in our dataset in demographic attribute

			Percentage of users with a specific demographic attribute
	Gender	Age range			Function
	Female	Male	20-29 30-39 40-49 50-59 Student Employee Employer Unemployed
	56.2% 43.8% 68.7% 22.9% 6.4%	2%	54.1%	38.7%	5%	2.2%

Table 4

 4 Dataset 1 summary table K-means has the best scoring with the attribute Frequency part_of_ day(0.53). We obtained 2 clusters, the first with 48 users and the second with 8 users. The best score of Calinski Harabasz measurement is derived with the attribute Frequency, where k = 2 and the distribution is 36 users and 20 users.

Table 5

 5 Dataset 2 summary table

Algorithm 1 Primary adaptation

Require: ∃( User j ∈ Cluster C i ), overloadedHS, overloadedScreen, distributed

Relocate the app to the other screen ; Relocate the app to the home screen ; end for 34: end while During this adaptation phase, we check the screen state to be adapted, where "overloadedHS" and "overloadedScreen" reports respectively whether the home screen and the other screen are overloaded with apps or not. The distributed variable shows if the Top 5 apps are spread out or if they are all on the same screen.

To cope with the cluster's changes, we made sure to update the cluster's features after adaptation. If the list of the top 5 most used categories changes, we update the list

App category rules

The category-specific adaptation is meant to make sure that the user stays focused on the task at hand and gives the app being used his or her full attention.

We chose to cover adaptation for 11 of the 23 categories, which are as follows: productivity, education, photography, health & fitness, communication, sport, games, music & audio, entertainment, maps & navigation, and video players and editors. Table 6 lists all of the possible adaptations that we propose.

After laying out the rules for UI adaptation, the next section shows some case studies for both basic and advanced adaptations.

Case studies

As stated previously, the user has the ability to personalize the UI as he deems appropriate.

We reviewed examples of users with distinct preferences here:

• User 1: is a 34-year-old male user with no physical disabilities. He belongs to the first cluster. He set up his screens to suit his needs. The user prefers that the changes be made on the home screen. As a form of adaptation, he chooses to remove a frequently used app from a group and move other apps from one screen to another. He agreed on 1) hiding notifications from other apps 2) increasing the currently operating app's volume and reducing the other apps' volume 3) muting the upcoming notifications (display notifications).