Frederic Raber
email: frederic.raber@dfki.de

Antonio Krueger
email: krueger@dfki.de

Towards Understanding the Influence of Personality on Mobile App Permission Settings

Keywords: usable privacy, mobile privacy, app privacy, machine learning

In this paper we investigate the question whether users' personalities are good predictors for privacy-related permissions they would grant to apps installed on their mobile devices. We report on results of a large online study (n=100) which reveals a significant correlation between the user's personality according to the big five personality scores, or the IUIPC questionnaire, and the app permission settings they have chosen. We used machine learning techniques to predict user privacy settings based on their personalities and consequently introduce a novel strategy that simplifies the process of granting permissions to apps.

Introduction

In earlier days of smartphones, users had no ability to choose the permissions each app received. Every app had a fixed set of permissions, which the user had to accept prior to installing the app. If she did not agree with even one of the requested permissions, she had no other choice than to not install the app.

This changed with Android 4.3 ("Jellybean"), where a hidden permission manager called AppOps for the first time gave the users the ability to change the individual permissions of an app. Since Android OS 6.0, the permission manager is integrated and visible to the user by default. For each of the permissions an app requests, the user is given a switch to either allow or deny the permission. On average, an average user has 95 apps [START_REF] Olmstead | The next web. android users have an average of 95 apps installed on their phones, according to yahoo aviate data[END_REF] with five permissions on average [START_REF] Olmstead | Pew research center. an analysis of android app permissions[END_REF] for each of them, resulting in a massive amount of 475 permission settings in total. According to earlier work, many users are unaware of, or at least uncomfortable with, permissions they granted to their apps [START_REF] Felt | Android permissions: User attention, comprehension, and behavior[END_REF][START_REF] Felt | Android permissions demystified[END_REF][START_REF] Harbach | Using personal examples to improve risk communication for security & privacy decisions[END_REF][START_REF] Kelley | Privacy as part of the app decision-making process[END_REF].

Even worse, apps request more permissions than they need for operation [START_REF] Chia | Is this app safe?: A large scale study on application permissions and risk signals[END_REF]. Despite this fact, the average smartphone user is not aware of the risks that remain in relation to this. App stores, and also the community ratings, which are the basis for app decisions of most users, do not indicate these privacy risks at all [START_REF] Chia | Is this app safe?: A large scale study on application permissions and risk signals[END_REF]. Felt et al. [START_REF] Felt | The effectiveness of application permissions[END_REF][START_REF] Felt | Android permissions: User attention, comprehension, and behavior[END_REF] and Kelley et al. [START_REF] Kelley | A conundrum of permissions: Installing applications on an android smartphone[END_REF] have shown that the current way of displaying permissions is not clear to users and ineffective in informing them about potential risks.

Apart from better visualizing permissions and the associated privacy risks and still letting the user decide on each permission, other researchers explored the possibilities of automatically predicting and setting the app permissions using different techniques. Liu et al. succesfully trained a machine learning prediction using the settings of the four million users of the LBE Privacy Guard app [START_REF] Liu | Reconciling mobile app privacy and usability on smartphones: Could user privacy profiles help?[END_REF], or in another approach the purpose of each permission [START_REF] Liu | Follow my recommendations: A personalized privacy assistant for mobile app permissions[END_REF], to derive a set of user profiles and assign each user the permission profile she needs.

There is evidence that a user's personality, captured by the big five personality measures [START_REF] Costa | Psychological Assessment Resources, I.: Revised NEO Personality Inventory (NEO PI-R) and NEO Five-Factor Inventory (NEO-FFI). Psychological Assessment Resources[END_REF], correlates with privacy and posting behavior, for example, on Facebook [START_REF] Bachrach | Personality and patterns of facebook usage[END_REF]: Extraverted users have more friends, and post more statuses and likes on Facebook. Similar results could be observed for openness. In contrast, more conscentious subjects are less likely to "like" a post or be a member in a large number of groups. There is also a correlation of the personality and mobile apps that are chosen by the users, and vice versa it is possible to derive the personality of a user given the installed apps on her smartphone [START_REF] Xu | Towards understanding the impact of personality traits on mobile app adoption -a scalable approach[END_REF]. Although it is known that personality corresponds to the usage pattern and privacy behavior on online social networks, there has not been a deeper look into the effect of personality and privacy attitudes on the choice of permission settings, and how such a correlation can be facilitated to generate an individual permission settings profile for each user.

In this paper, we try to shed light on this question and explore the influence of personality and privacy on the choice of permission settings on mobile apps, using the big five personal inventory and the IUIPC1 questionnaire. This paper does not advance machine learning or AI, nor does it test a fully working prototype, but it bridges the gap between those two. The core contribution is the feasability analysis of the application of these techniques to mobile app privacy/security; and the derivation of design guidelines for future user interfaces in that research area. In detail we try to solve the following research questions:

1. Is there a correlation between the general personality measures (e.g. Big Five) and the app permission choice? 2. Is there a correlation between the privacy attitude (e.g. IUIPC) and the app permission choice? 3. Are there correlations inside the permission settings ? 4. Are the correlations big enough to be facilitated within a machine learning prediction? 5. How could a system look like that uses machine-learning based prediction to support the user during his privacy setting process?

We conducted a user study to capture the privacy attitudes of 100 users, together with their desired app permission settings. The results have been used as training data for a privacy wizard, that automatically sets the individual app permissions based on machine learning. In addition to this static approach, we examined a Dynamic Permission Settings Prediction, which observes the user as she adapts the Permission Settings of an app, and proposes additional changes based on the user's input on the fly. The results show that both the static as well as the dynamic setting prediction perform better than the current standard. These two approaches allow us to support two different use-cases that capture the typical privacy setting behavior of most smartphone users: The static approach supports a use-case where a user just bought a new smartphone, and wants to set all permissions togehter. In contrast to this the dynamic approach targets for the group of users that already own a smartphone, and supports them in adapting the settings of the apps from time to time.

Related Work

Privacy and Personality Questionnaires There exist several approaches for measuring a person's privacy attitude within different domains. One of the earliest publications in this field is Alan Westin's work on consumer privacy indices, which was later summarized by Kumaraguru and Cranor [START_REF] Kumaraguru | Privacy indexes: A survey of westin's studies[END_REF]. Westin proposed three different categories of users to express their privacy attitudes: The Unconcerned hardly care about their privacy and tend to publish all information to the entire audience of a network. Fundamentalists in contrast try to disclose as little information as possible in order to preserve their privacy. The third group of persons, the Pragmatists, attempt to keep a balance between privacy and usability: Pragmatists believe that privacy is an important aspect, but on the other hand accept the necessity to share information in order to benefit, for example, from an additional app feature.

Although the Westin Categories have been widely used in research, the concept has several design flaws, as Woodruff and Pihur discovered recently [START_REF] Woodruff | Would a privacy fundamentalist sell their dna for $1000... if nothing bad happened thereafter? a study of the westin categories, behavior intentions, and consequences[END_REF]. The members of the different categories do not behave significantly differently in their actions regarding privacy. Especially the coarse-grained categorization into three categories makes it hard to predict the user's reactions to hypothetical scenarios or permission settings. The authors critisize the questionnaire as too unspecific to capture any significant effects.

The PCS 2 questionnaire [START_REF] Buchanan | Development of measures of online privacy concern and protection for use on the internet[END_REF] is more detailed and consists of 28 questions in four categories: General Caution, Technical Protection and Privacy Concern. Although more detailed, the questionnaire still adresses the general privacy attitude of a person, and not the specific context of app privacy and privacy in the context of online companies.

In contrast, the CFIP 3 [START_REF] Smith | Information privacy: Measuring individuals' concerns about organizational practices[END_REF] and the IUIPC [START_REF] Malhotra | Internet users' information privacy concerns (iuipc): The construct, the scale, and a causal model[END_REF] questionnaire based on it, were designed explicitely to measure the privacy of internet users, especially in the context of online shopping companies and their data collection. The authors found that the privacy attitude regarding online companies can be well expressed using three privacy measures: The control measure, which determines how far a subject desires to have control over the disclosure and transfer of her personal information, the desired awareness on how and to whom the personal information is disclosed, and collection describing how important it is for the subject to 2 Privacy Concern Scale 3 Scale of Concern For Information Privacy know which personal data is collected. As the IUIPC is the privacy questionnaire which best fits the goals of our paper, we used it in the survey of our main study.

The big five personal inventory, first created by Costa and McCrae [START_REF] Costa | Psychological Assessment Resources, I.: Revised NEO Personality Inventory (NEO PI-R) and NEO Five-Factor Inventory (NEO-FFI). Psychological Assessment Resources[END_REF], is currently the most widely accepted questionnaire to capture a person's personality. Although most reviews are very positive [START_REF] John | The Big Five Trait Taxonomy: History, Measurement, and Theoretical Perspectives[END_REF][START_REF] Costa | Psychological Assessment Resources, I.: Revised NEO Personality Inventory (NEO PI-R) and NEO Five-Factor Inventory (NEO-FFI). Psychological Assessment Resources[END_REF], there are also some critical voices [START_REF] Block | A Contrarian View of the Five-Factor Approach to Personality Description[END_REF]. Nevertheless, it is established as the standard personal inventory questionnaire. The big five is a questionnaire (also called the NEO-PI-R) in its newer form consisting of 240 items, resulting in five personality measures: Openness to experience, denoting general appreciation for art, emotion, adventure etc.; Conscientiousness, meaning the tendency to show self-discipline; Extraversion meaning higher or lower social engagement; Agreeableness in terms of cooperation with other people and Neuroticism as the tendency to experience negative emotions. The questionnaire in its original version is very long and requires up to 30 to 40 minutes for completion, making it unsuitable in most scenarios. Our scenario also requires a shorter solution, as we cannot prompt a user to fill in a 40-minute questionnaire before the first use of a permission recommendation app. Gosling et al. developed a shorter version to capture the big five personality traits, consisting of only ten questions [START_REF] Gosling | A Very Brief Measure of the Big-Five Personality Domains[END_REF]. Although the precision of this so-called Ten Item Personality Measure (TIPI) is not as good as with the NEO-PI-R, the results can still precisely describe the personality of a subject. The "big five" of personality can also be extracted out of written text, e.g. blog or social network entries [START_REF] Chen | Making use of derived personality: The case of social media ad targeting[END_REF]. The user burden for gathering the big five personality measures can therefore be reduced to a minimum. As stated in the introduction, there is evidence that personality correlates with the Facebook sharing behavior. We also expect some effects on the permission settings of mobile apps, and therefore included the TIPI questionnaire in our study.

Permission prediction techniques Privacy settings prediction has been a popular topic in several domains, among others online social networks. Fang and LeFevre [START_REF] Fang | Privacy wizards for social networking sites[END_REF] proposed a semi-supervised machine learning technique to infer privacy settings of a user's social network (SN) friends: The user is asked to label several of her friends on the SN with privacy privileges. The decision on how many and which friends have to be labeled is made by their algorithm. After this annotation phase, the software predicts the privacy privileges for the remaining, unlabeled friends.

Ravichandran et al. [START_REF] Ravichandran | Capturing social networking privacy preferences: Can default policies help alleviate tradeoffs between expressiveness and user burden?[END_REF] propose the use of privacy templates for each user, in the context of location sharing with mobile apps. They observed 30 users using a mobile phone app and asked them to annotate their privacy desires towards location sharing (share location/do not share location) whenever they changed their context, e.g. when they came home from work. The app recorded the time when a context change appeared, as well as the corresponding privacy desires. Using decision trees and clustering techniques, they created several privacy profile templates. Their experiment has shown that with only three templates, the preferences of a user are matched with 90% accuracy.

There are several publications describing the prediction of mobile application settings using different data sources for the prediction. Other approaches use machine learning to predict the settings [START_REF] Liu | Reconciling mobile app privacy and usability on smartphones: Could user privacy profiles help?[END_REF][START_REF] Liu | Follow my recommendations: A personalized privacy assistant for mobile app permissions[END_REF][START_REF] Lin | Modeling users' mobile app privacy preferences: Restoring usability in a sea of permission settings[END_REF]. Ismail et al. [START_REF] Ismail | Crowdsourced exploration of security configurations[END_REF] describe an approach which facilitates crowdsourcing in order to find an optimal tradeoff between denied permissions and usability of the app, tailored to an individual user. Liu et al. [START_REF] Liu | Reconciling mobile app privacy and usability on smartphones: Could user privacy profiles help?[END_REF] use a large online database of the LBE Privacy Guard app, containing the app settings of 4.8 million users, as training data for their prediction using a linear support vector machine. 90% of the user records are used for training, 10% for testing the accuracy of the prediction. When it comes to prediction, the system uses 20% of the app settings of a user to predict the remaining 80% of settings. They used only permissions, the user and the app id for the prediction to achieve a precision score of 64.28% to 87.8%, depending on the features used. Privacy or personality attitudes were not taken into account. A similar work [START_REF] Liu | Follow my recommendations: A personalized privacy assistant for mobile app permissions[END_REF] used feedback to suggest the permission settings: For each critical permission, the system gives the user an overview on other apps and their usage frequency of the questionable permission. The user is then asked whether she feels comfortable with the previous usage or not. Based on this feedback, permission settings are recommended for the app. In total, 78.7% of the recommendations were accepted. Nevertheless, the approach needs the knowledge about the permission usage frequency of the already installed apps, and can therefore not be applied if a user just started using a new smartphone (known as the cold start problem). The last related work to be mentioned here [START_REF] Lin | Modeling users' mobile app privacy preferences: Restoring usability in a sea of permission settings[END_REF] uses static code analysis to reconstruct the purpose of each app permission. Privacy preferences that reflect people's comfort with a permission's purpose are used to cluster the settings and to gather a finite set of privacy profiles. These profiles can later be used to assign the appropriate set of permissions for each individual user.

To conclude the related work on permission prediction, there have been several approaches using crowdsourcing or machine learning techniques like clustering, based on the permission settings themselves or using comfort with the purpose of a permission. The effect of personality on the choice of permissions has to the best of our knowledge not been explored so far. In the next sections, we will describe a system which predicts the app permission settings using the privacy attitude or personality of a user. Unlike other related work [START_REF] Liu | Follow my recommendations: A personalized privacy assistant for mobile app permissions[END_REF], our approach does not need any knowledge about previous smartphone usage behavior, and can therefore be seen as a first step towards solving the cold start problem in this scenario.

User Study

We conducted an online user study to discover correlations between the personality or privacy attitudes of a person, and her app permission settings. The focus was hereby on discovering whether there are correlations that can be used for a prediction, rather than measuring how strong the correlation could be with a large training set. To avoid side-effects, we decided to record the personality and privacy measures directly using a questionnaire rather than trying to infer the data from online social network behavior. As discussed in the last section, broader questionnaires like Westin's categories or the CFIP lead to suboptimal results. Therefore we used the more specific IUIPC questionnaire in order to capture the privacy wishes of the subjects. The personality was captured using the big five personality measure [START_REF] Costa | Psychological Assessment Resources, I.: Revised NEO Personality Inventory (NEO PI-R) and NEO Five-Factor Inventory (NEO-FFI). Psychological Assessment Resources[END_REF], more specifically the abbreviated Ten Item Personality score (TIPI) [START_REF] Gosling | A Very Brief Measure of the Big-Five Personality Domains[END_REF], which is a compressed version of the big five scale using only ten questions in total. Although possible, we did not extract the personality measures but used the TIPI for this study, to reduce any possible side-effects caused by the derivation of the measures.

In addition to these two questionnaires, we posed two additional questions regarding privacy and privacy invasion (see Table 1). In detail, we asked the subjects how recently they have been a target of a privacy invasion (five point ordinal scale from very frequently to never), and how often they enter wrong information on purpose on online websites (percentage as a numeric scale). The next subchapter describes the participants, procedure and parts of the survey in greater detail.

Label Question

Falsify Some websites ask you for personal information. When asked for such information, what percent of the time would you falsify the information?

Invasion

Have you ever been the target of a privacy invasion (e.g. your data was misused or shared without your knowledge)? Table 1. Question text and label of the additional question set.

Methodology

The study was conducted as an online survey using the software LimeSurvey4 . 100 participants were recruited using Prolific Academic, 5 which allows us to select only active Android smartphone users with at least three of their own apps installed. Users had to conduct the study at a PC or Laptop at home. Studies in the past have shown that participants who are recruited via online services, like in our case, lead to a similar quality of the results, like participants recruited at a university [START_REF] Buhrmester | Amazon's mechanical turk: A new source of inexpensive, yet high-quality, data?[END_REF]. The participants were paid a compensation of 2£ upon successful participation. To motivate the subjects to fill out the questionnaire honestly, the compensation was only paid after the submitted data was checked for plausibility by us. If the result of a subject was rejected, for example if she failed to answer the control questions correctly, a new participant has been recruited to fill in the gap. Therefore we have exactly 100 viable results.

The age of the participants ranged from 18 to 61 years (average 30.13, SD 8.53). The recruited audience was very diverse: We recruited students, selfemployed workers, employees, and also homemakers.

The survey can be divided into two parts: In the first part, we asked the subjects to fill out the above described privacy and personality questionnaires. In the second phase, we asked them to look up the permissions of their up to ten most frequently used apps, and let them enter them into the survey form (see upper right of Figure 1). Next to each of the permissions, just like in the Andoid OS 6.0 interface where permissions are allowed per default, we asked them to state whether they would reject the permission if they could. The second column ("I would revoke the permission") is only active for permissions that are marked as "owned by the app" in the first column. According to previous work [START_REF] Harbach | Using personal examples to improve risk communication for security & privacy decisions[END_REF], users hardly know which permissions are requested and how they can determine which ones are used. Therefore the subjects were given simple step-by-step instructions including screenshots of every step (see lower right of Figure 1), in order to make sure they are able to retrieve the permissions for their apps correctly. To make sure users can conduct the task correctly, the questionnaire asked to enter the permissions of a specific app (namely google maps). Only if the task was done correctly, participants were allowed to continue. Different app versions or different android os versions can request a different set of permissions, therefore we only checked whether different subjects entered a different permission set for the same application, if the version was the same. In our study, this was not the case.

The survey ended with a short feedback question in free-text style.

Results

The 100 participants entered in total the settings of 876 apps into the system. On average each user filled in the details of three to ten apps, 8.65 on average. Figure 2 shows a detailed graph on the number of settings with a specific amount of denied permissions. In most cases (447 out of the 876 settings), no permission was denied. The answers to the different items of the IUIPC and TIPI questionnaires have been reversed if needed, and combined to the according three (IUIPC) or five (TIPI) personality measures, as described in literature [START_REF] Malhotra | Internet users' information privacy concerns (iuipc): The construct, the scale, and a causal model[END_REF][START_REF] Gosling | A Very Brief Measure of the Big-Five Personality Domains[END_REF]. We only used these combined measures for the machine learning, as well as for the statistical analysis.

Table 2 shows how often each of the permissions was denied throughout the study.

For each participant and app permission, we computed a permission coefficient, that denotes how often the permission is denied by this user. The permission coefficient comb is computed as comb = |rejected| |used| , where |used| denotes how many of his four to ten apps used the permission, and |rejected| how often the permission has been rejected. For each participant, we thereby have 17 permission coefficients, one for each permission, that give us the normalized likelihood of a permission to be denied. These coefficient values range continuously from 0 (never denied) to 1 (always denied) and are independent between participants. In the next step, we wanted to find out whether the questionnaire answers correlate with the permission coefficients. According to the shape of our data (independent, mostly ordinal values, not necessarily normal-distributed), we decided to use a non-parametric test, and therefore performed a Spearman correlation ("Spearman's Rho") on the results of the questionnaire and the permission coefficients. The results are shown in Tables 3 and4. The measures of the privacy and personality questionnaires are on the rows, whereas the app permission coefficients are plotted as the columns of the table. Significant and highly significant correlations are marked with one or two asterisks, and colored in gray or dark gray, respectively. Note that for our purpose, the correlation coefficient is more important than the significance, as it denotes the ascent of the regression line between the data points. The higher the value, the easier it is to forecast a permission coefficient given the questionnaire answers. The measures of the specialized IUIPC privacy questionnaire (collection, control, awareness) received the best correlation scores, the collection measure yields high correlation coefficients which are mostly (highly) significant for most of the permissions. Control and awareness both correlate on some of the permissions.

The general privacy questionnaires (TIPI) received lower but still useful correlation scores. The open to experiences of the subjects correlates with two of the app permissions and is, together with conscientousness and emotional stability (one significant correlation each) the most expressive personality measure. Although only four personality -permission pair lead to significant correlations, the correlation coefficients still remain medium high for several other combinations, making it a promising candidate for machine-learning based prediction. The additional questions on the other hand, received only small correlation scores. We therefore dropped the additional questionnaires for the machine learning and the evaluation.

In addition to the correlations between privacy, personality and the permission settings, we also observed the pairwise correlations between the permission settings. In contrast to the correlation between questionnaire and permissions, not only some but the whole lot of pairwise correlations are highly significant with correlation coefficients reaching from 0.217 (Phone -Wifi) up to 0.859. The highest correlations can be achieved with the permission pairs cellular information -identity (r = 0.859), cellular information -purchase (r = 0.81) and cellular information -history (r = 0.778).

Permission Wizard

Based on the results of the user study, we decided to use the results as training data to predict the privacy settings of a user's apps, based on her personality and privacy attitudes. The current Android interface allows all permissions, therefore the interesting cases are the ones where at least one permission is denied. We followed the example of earlier work [START_REF] Liu | Follow my recommendations: A personalized privacy assistant for mobile app permissions[END_REF][START_REF] Liu | Reconciling mobile app privacy and usability on smartphones: Could user privacy profiles help?[END_REF] and concentrated on these harder cases for our evaluation, and took only them into account for the prediction and evaluation.

Similar publications [START_REF] Liu | Reconciling mobile app privacy and usability on smartphones: Could user privacy profiles help?[END_REF] used a simple SVM algorithm for their prediction. We also tried out SVM and several other classification methods, and achieved the best results with a KNeighbors implementation with two as the number of neighbors.

As we only have 2 9 = 512 combinations of input features, we were able to determine the optimum set of features by using a brute-force procedure of training and selecting the features according to the precision of each of the combinations one after another. We followed the usual way for training, adjusting parameters, and validating the prediction of a machine learning algorithm. We used a ten-fold cross validation to prevent a biasing of the data. In this validation method, the data is split into ten parts of the same size, and the validation procedure is performed ten times: In each of the ten runs, the data set is split into two basic parts: The first part is called the training set, and is composed of 90% of the data set. It is used to train, to calibrate the prediction algorithm and select the optimal features. The second and remaining part is called the test set, and is not used for training and fitting, it remains untouched. It is used for the evaluation of the results later. We used 80% of the training set (not the test set, as this remains untouched until the evaluation in the next chapter) to fit the algorithm, and 20% to find the optimal set of features using the above-mentioned brute-force method. After each run, another of the ten splits is used as the test set, and the remaining splits for the training set. After the ten distinct runs, we used the average precision of all runs for selecting the best set of features. Table 3 shows the features selected for the prediction using the IUIPC or the big five personality measures as input features.

Feature Set Selected features IUIPC

Collection, Control Personality Extraversion, OpenExperiences Table 3. Selected features for each of the two feature sets IUIPC and personality.

The selected features correspond to the measures with the highest correlation in Tables 3 and4, supporting the correctness of the selection method.

Evaluation We evaluated the results of the prediction following a similar approach as for the selection of input features using ten-fold cross validation: First the prediction is trained with the training set consisting of 90% of the data. This time, we used a static set of input features as described in Table 3, that was not changed throughout the evaluation procedure. Afterwards, the feature values from the test set are used to predict the permission settings, and compared with the actual permission settings of the test set. Again this procedure was repeated ten times, and the results were averaged.

In order to get an impression of the quality of the results, we implemented a naive approach to predict the settings, which will later be called the baseline or random condition. We started with a simple random method, which randomly predicts "allow" or "deny" for each of the permissions, giving a 50% accuracy. Since the percentage of allow and deny differs from permission to permission and is rarely at 50% for both (see Table 2), we enhanced the random approach by a probabilistic component: We first use the training set to calculate the probability of getting allowed or denied for each permission respectively. Based on these probabilities, we then predict the permission settings on the test set. If for example a setting for Contacts permission has to be chosen, the prediction will decide to "allow" with a probability of 63.7%, and to "deny" in 36.3% of all cases.

As before, ten runs have been conducted to evaluate the probabilistic random method, and the results have been averaged.

The percentage of correct predictions of this probabilistic Random approach, as well as the correctness using only the IUIPC or the personality metrics as features, is shown in Table 4. The columns denote the feature sets, whereas the rows contain the different app permissions. The topmost row ("all") denotes the average percentage over all permissions.

Although the probabilistic approach achieves significantly better results (M = 59, 64) than a pure random method, the machine learning-based prediction can still outperform it with both feature sets (M IU IP C = 70.92, M P ersonality = 69.37). Best results can be achieved for the bluetooth (M IU IP C = 96.66, M P ersonality = Overall, the machine learning approach outperformed the random probabilistic method by more than 10%.

Dynamic Setting Prediction

Besides the prediction of all settings at once using the personality and privacy measures, we discovered techniques for how the pairwise correlation between the permission settings can be used to actively support the smartphone user during her decision process, while setting the permission settings. Given that the permission settings are displayed as a scrollable list as on Android OS, we assume that most smartphone users traverse the list from the top to the the bottom, and change the permissions they want to set to "deny". As soon as a change is made, we can take this change as well as the permission settings above this entry, as an input to predict the remaining settings below. This technique will later be called dynamic setting prediction. In detail, we trained estimators for all possible compinations of selected permissions, and serialized them to a file which is loaded into a cache at startup of the application. As soon as the user interacts with the settings, the estimator corresponding to the selected settings is retrieved from the cache and used for the prediction. We used the study data to simulate the subject's behavior when setting the permission settings of all of her apps, either without support, or with the support of the dynamic setting prediction. We observed the prediction accuracy using only the already-set permissions, as well as the set permissions in addition to the IUIPC, personality, and all together. The distribution of study data to training and test set is the same as described in the last chapter: 90% for training, and 10% as the test set for validating the correctness of the prediction.

The procedure for the validation works as follows: Initially, the predicted setting allows all permissions. Then we traverse each permission of the setting to be evaluated, one after another, and check whether the prediction meets the actual permission setting. If not, the predicted setting is adjusted, and the remaining permissions below are predicted based on the permissions above. Whenever this occurs, a user interaction (later called a click ) is recorded. The validation in pseudo-code is described below. # t r a v e r s e a l l t h e u s e r s e t t i n g s f o r each u s e r s e t t g in t e s t s e t : # i n i t i a l l y , a l l s e t t i n g s a r e # s e t t o " a l l o w " pred=a l l o w a l l f o r each perm in u s e r s e t t g : We compared the number of clicks needed when using the dynamic setting prediction to a case where the user simply clicks on all permissions she wants to deny, without any support, as it is currently implemented on Android. As our prediction technique requires a user input, e.g. setting at least one permission to "deny", we used only app settings for the evaluation where one or more permissions were denied.

Table 5 shows the results of the evaluation procedure. For each of the input sets described above, we compared the average clicks needed for each app setting with ("Clicks (supported)") and without ("Clicks (unsupported)") the support of the Dynamic Setting Prediction. Columns one to three describe the percentage of cases where the user needed fewer clicks ("won"), the same amount of clicks ("draw"), or more clicks ("lost") with the prediction than without any support.

The prediction works best when using all features, e.g. the previously set permissions, IUIPC, Personality and Additional questions as described in Table 3. In that case, the rate of needed clicks for the dynamic setting prediction drops 5. Results of the dynamic settings prediction, using only the previously selected permissions, or the permissions in addition to the IUIPC questionnaire, the Big Five Personality Score, our additional questionnaire or all previously mentioned questionnaires together.

to an average of 1.58 per setting, compared to 2.00 for the unsupported case. 91.89% of the settings require at maximum the same amount of clicks, 24.66% even less clicks than the unsupported version. Only in 8.11% of the cases did the user need more interactions with the prediction enabled. The ratio of needed clicks and the win/lose ratio slightly decreases with a decreasing feature set. Using only the previous permissions as the prediction input, the dynamic setting prediction needed on average 1.91 clicks for each setting, compared to 2.22 clicks without the prediction. In 82.89% of the cases, the prediction needed the same or fewer clicks, whereas in 17.15% of the settings, the unsupported version required less user interaction.

Discussion and Limitations

We were able to prove significant correlations both between the general personality (captured by the TIPI questionnaire) as well as the privacy attitude and the app permission choice. Furthermore, the correlations are powerful enough to train a machine learning algorithm, that is able to predict these settings based on the personality, with a precision of more than 70 %. The machine learning can be used in two different use-cases, first in a traditional privacy wizard, and second in a dynamic approach that supports the user on the fly while she is doing her settings, as described below. Nevertheless, there are still some points which can be improved, as discussed in the following subsections.

Possible implementations of the approaches

The two techniques presented in the former chapters can be used to implement two different use-cases: In the first use-case, a smartphone user buys a new smartphone, and has to enter his app permission settings for the first time. This is also the case, if the older smartphone was running an Adnroid version below 6.0, where app permissions are not supported. As mentioned in the related work section, users are either overchallenged by the technicality and complexity of the app permissions, or fear the burden of setting every single permission for each app they use. A privacy wizard based our machine learning estimators reduce this problem: An implementation of our approach offers the user to either answer the twelve questions of the IUIPC questionnaire, or to connect to facebook / twitter to read in the user's posts and to extract the big five personality measures out of her written text [START_REF] Chen | Making use of derived personality: The case of social media ad targeting[END_REF]. Afterwards the wizard suggests a complete set of permission settings, that can be reviewed by the user. Especially lay users profit from the questionnaire, as it contains only non-technical questions that are easy to answer. Furthermore, both user groups save a lot of time by answering only twelve IUIPC questions (or by just connecting to facebook) instead of on average 475 distinct app permission settings.

The second use-case considers a user which has a running smartphone, who wants to individually set the permission settings rather than to trust a permission wizard. In this case, the dynamic settings prediction could be integrated into Android's permission setting dialogue. When the users traverses the list of app permissions from top to the bottom (which can sometimes take a while), Android recognizes when a permission is set to deny, and denies also other permissions further below, which might also be denied according to our dynamic privacy prediction. Changes are marked in orange, so that the user can see which changes have been made automatically, and review them. According to the study results, this technique should save the user a significant amount of interaction (clicks) and save time which should also lead to a smaller frustration.

Different precision for different questionnaires

Both the IUIPC, as well as the personality questonnaire performed well in the validation study, giving an average precision of 70% and 69% of correct predictions. Although the personality measures can be automatically extracted in contrast to the IUIPC, we do not recommend to stick only to that questionnaire: Having a closer look at Table 4 reveals that both the IUIPC and the personality questionnaire complement each other: Permissions which are hard to predict in the additional questionnaire (like Contacts, Location, SMS, History) can be better predicted using the personality measures, and vice versa. If the best questionnaire is selected for each permission, a precision of 72.80% can be achieved within our test data. Whether that assumption holds for larger data sets has to be proven in future work.

Limited size of the training data set and precision of the prediction

The results in Figures 3 and4 indicate there is a strong correlation between the answers to the personality and privacy questionnaires, and the permission settings of the apps. We were also able to predict these settings using machine learning. The proposed approach predicted about ten percent more of the permission settings correctly compared to the naive approach. Compared to related work like Liu et al. [START_REF] Liu | Reconciling mobile app privacy and usability on smartphones: Could user privacy profiles help?[END_REF][START_REF] Lin | Modeling users' mobile app privacy preferences: Restoring usability in a sea of permission settings[END_REF], we did not have the possibility to draw on a large online database, containing millions of datasets. As we need the personality measures in addition to the permission settings, we had to gather the training data in an online survey, and therefore have, compared to the mentioned work, a relatively small dataset. Thus the performance of our personality-based prediction cannot directly be compared to these approaches. As usual with a machine learning approach, we expect the prediction precision to increase with an increasing data set size. We would like to explore how our approach performs with a large training database in the future, and compare it to other systems that base their prediction on the permissions of similar users [START_REF] Lin | Modeling users' mobile app privacy preferences: Restoring usability in a sea of permission settings[END_REF], or the purpose of the permission [START_REF] Liu | Reconciling mobile app privacy and usability on smartphones: Could user privacy profiles help?[END_REF].

Control of context factors

Each experiment faces the problem of contextual factors that cannot be controlled, we designed the experiment in a way to decrease these factors to a minimum. By using prescreening, our survey could only be edited using a laptop or computer, and not on the go using a mobile phone. We further required the participants to be at home. We were therefore able to assure the same location/occasion for each participant. There are still some context factors left, like general distrust towards app producers, that cannot be controlled. Similar work also discovered that the purpose of the permission has an impact on the decision [START_REF] Lin | Modeling users' mobile app privacy preferences: Restoring usability in a sea of permission settings[END_REF], which we would also like to add in a future version. To avoid personality biasing, we compared the results of the personality questionnaire with the mean values that have been recorded in earlier research. With our final data set, we could not prove any significant difference with one of the five personality traits.

Number of denied permissions per setting and dynamic prediction accuracy

The dynamic prediction of permission settings often has the same amount of clicks as the unsupported permission setting procedure, ranging from 59.4% without additional features up to 67.23% with all features enabled. The dynamic prediction clearly profits from user interaction; the more permissions the user sets to deny, the more input features are available for the prediction. Having a look at Figure 2, we can see that about one third of the permissions that have at least one denied permission, have exactly one denied permission. In these 33% of all cases, the dynamic prediction does not give any advantage, as it starts predicting only after the first user interaction. To the contrary, it is even possible that the algorithm predicts one of the following permissions as "deny", leading to more clicks than without the prediction. An additional 20% have only two denied permissions, which are also hard for the approach to predict. Despite these difficulties, the prediction still needs fewer clicks on average than the unsupported approach.

Prediction precision for the different permissions

The prediction accuracy greatly differs between the different permissions that Android offers to the apps. Comparing Tables 2 and4, we can see that these differences negatively correlate with the percentage of denial of a permission. When a permission is often allowed, the prediction accuracy, especially for the probabilistic model, is also high. Therefore the location information, as well as the SMS and contacts permissions are hardest to predict with the probabilistic model. On the other hand, these are the categories where the prediction outperforms the random probabilistic model the most.

Future work

We took a first step towards an automatic permission settings prediction using personality and privacy attitude as input features. We could prove a significant correlation between those two, and were able to design static, as well as an onthe-fly prediction for the permissions. Nevertheless, the evaluation was done only theoretically, using the study data. In future work, we would like to implement the described approach as a mobile app, and conduct a lab study on the effects of the prediction support. We are especially interested in how far the dynamic prediction will be accepted. Although in our theoretical study, the approach works as well as or better than a standard android permission manager in more than 91% of the cases, we would like to confirm our results in an applied scenario, where people interact with concrete examples rather than hypothetical ones. In a second step, an in-the wild study would be desirable where a prototypical app is released to the app store. Having a larger user base and additional training data would hopefully help to improve the prediction accuracy, and make it possible to compare the prediction to related work with a similar user base [START_REF] Liu | Reconciling mobile app privacy and usability on smartphones: Could user privacy profiles help?[END_REF][START_REF] Lin | Modeling users' mobile app privacy preferences: Restoring usability in a sea of permission settings[END_REF].

The decision for location privacy settings in Social Networks depends also on context factors like the current location of the user, the occasion, or the purpose for a location retrieval [START_REF] Patil | My Privacy Policy: Exploring End-user Specification of Free-form Location Access Rules[END_REF][START_REF] Lugano | To Share or Not to Share: Supporting the User Decision in Mobile Social Software Applications[END_REF]. The context a permission is given might also have an effect on the choice of mobile app permission settings, for example if a user wants to grant the web browser access to the internal storage only to store e-ticket receipts. The domain of app permissions further has several contexts which require a temporary or one-time permission grant, for instance when an app needs access to the SMS permission for sending a registration SMS once [START_REF] Jung | Short paper: Enhancing mobile application permissions with runtime feedback and constraints[END_REF]. Context information could be easily integrated into our prediction system as an additional feature.

Conclusion

The Android permission system is powerful, but the maintenance of each of the apps' permissions is very cumbersome and time-consuming. Related work already elaborated on the prediction of these settings using large online permission setting databases, crowdsourcing approaches or privacy profiles based on the permission purpose. We conducted an online user study with 100 participants to discover the effects of personality and privacy attitude on the permission settings. We found strong correlations between personality, privacy attitudes and the settings. We evaluated two approaches for supporting the user during her decision process. The first uses the personality and privacy measures to directly predict all app settings, whereas the second supports the user while doing the permission settings, taking the user's interaction as an input to predict the settings not done so far. Although the training set is very small compared to related work, we were able to outperform the current standard with both approaches.

Fig. 1 .

 1 Fig. 1.Step-by-step instructions for permission retrieval given to the subjects (lower right) and one of the ten questionnaire pages for capturing the app permissions and settings preference (allow/reject) in the upper left.

Fig. 2 .

 2 Fig. 2. Number of settings with a specific number of denied permissions.

Fig. 3 .

 3 Fig. 3. Correlations between the privacy/personality questions and app permission settings.

Fig. 4 .

 4 Fig. 4. Correlations between the privacy/personality questions and app permission settings continued.

93. 33 )

 33 and cellular info permissions (M IU IP C = 92.5, M P ersonality = 91.25). The location permission was hardest to predict (M IU IP C = 53.33, M P ersonality = 58.48).

  i f u s e r s e t t g [ perm ] ! = pred [ perm ] : # p r e d i c t i o n was wrong , # u s e r had t o change t h e s e t t i n g # -> p r e d i c t remai ning s e t t i n g s pred [ perm]= u s e r s e t t g [ perm ] p r e d i c t s e t t i n g s b e l o w ( )

Table 2 .

 2 Percentages of denies for each app permission.

	IX

Table 4 .

 4 Prediction accuracy (in percent of correct predictions) for the prediction with the Random Probabilistic Model (Random), and prediction using the IUIPC questionnaire or the Big Five Personality test.

	Permission Random IUIPC Personality
	All	59.64 70.92	69.37
	Purchase	59.37 78.13	67.50
	History	65.88 72.94	78.82
	Cellular	78.75 92.50	91.25
	Identity	51.87 68.44	60.62
	Contacts	48.88 55.18	64.44
	Calendar	70.00 80.00	81.11
	Location	45.15 53.33	58.48
	SMS	54.37 50.00	57.50
	Phone	53.33 67.33	58.66
	Photos	47.31 63.65	62.44
	Camera	53.92 60.00	61.07
	Microphone	52.50 74.00	69.00
	Wifi	68.82 86.47	78.82
	Bluetooth	84.44 96.66	93.33
	ID	56.08 64.78	58.70
	Other	63.55 71.33	68.22

Internet Users' Information Privacy Concerns

https://www.limesurvey.org, last accessed 09-05-2016

https://www.prolific.ac/, last accessed 09-05-2016


Deepak Akkil
email: deepak.akkil@uta.fi

Nitendra Rajput
email: nitendra@acm.org

Little Bear -A Gaze Aware Learning Companion for Early Childhood Learners

Keywords: Gaze, Touch, Pedagogical Agent, Early-childhood learning, Vocabulary building, Games, Mobile devices, Engagement

Computing devices such as mobile phones and tablet computers are increasingly used to support early childhood learning. Currently, touching the screen is the most common interaction technique on such devices. To augment the current interaction experience, overcome posture-related issues with tablet usage and promote novel ways of engagement, we propose gaze as an input modality in educational applications for early learners. In this demonstration, we present the Little Bear, a gaze aware pedagogical agent that tailors its verbal and non-verbal behaviour based on the visual attention of the child. We built an application using the Little Bear, to teach the names of everyday fruits and vegetables to young children. Our demonstration system shows the potential of gaze-based learning applications and the novel engagement possibility provided by gaze-aware pedagogical agents.

Introduction

Touchscreen devices such as Apple iPad and Microsoft Surface tablets are increasingly applied in early childhood pedagogical environments, such as preschools and kindergartens. Growing popularity and access to mobile devices provide exciting opportunities to design innovative, ubiquitous, and constructive learning experiences for young children. While the immense potential of such devices for early childhood learning is well accepted, there are also many concerns regarding touch-based interaction on such devices in an early childhood learning environment.

There are several challenges in designing engaging and intuitive touch-based applications for young children. Plowman and Mcpake [START_REF] Plowman | Seven myths about young children and technology[END_REF] note that if children do not understand what they need to do, the interactivity offered by such devices may be * This work was done when Nitendra Rajput was working for IBM Research. counter-productive to learning. This requires careful design of the stimuli and prompts, to make the interaction intuitive [START_REF] Hiniker | Touchscreen prompts for preschoolers: designing developmentally appropriate techniques for teaching young children to perform gestures[END_REF]. In addition, the underdeveloped fine motor skills and finger dexterity in children [START_REF] Hourcade | Interaction design and children[END_REF] lead to difficulty performing certain touch gestures [START_REF] Nacher | Multi-touch gestures for prekindergarten children[END_REF], lead to slower and error-prone interactions [START_REF] Vatavu | Touch interaction for children aged 3 to 6 years: Experimental findings and relationship to motor skills[END_REF] and cause accidental or unintended touch [START_REF] Mcknight | Touch-screen technology for children: giving the right instructions and getting the right responses[END_REF], affecting the overall interaction.

Another concern regarding the use of a mobile device is to balance the placement of the device for optimal touch interaction and considerations of neutral posture of the child. Straker et al. [START_REF] Straker | A comparison of posture and muscle activity during tablet computer, desktop computer and paper use by young children[END_REF] studied posture and muscle activity of children while using desktop computers and tablets and found that touch-based interaction on a tablet is linked with asymmetric spinal and strained neck postures. Similar concerns regarding posture and prolonged tablet use are also raised by teachers and parents [START_REF] Fawcett | Tablets in Schools : How Useful Are They ?[END_REF]. Researchers have proposed several recommendations to overcome the problem, by promoting task variations [START_REF] Straker | A comparison of posture and muscle activity during tablet computer, desktop computer and paper use by young children[END_REF] and encouraging elevated placement of the device which promote neutral viewing postures [START_REF] Young | Touch-screen tablet user configurations and case-supported tilt affect head and neck flexion angles[END_REF]. In turn, elevated placement of the device may make the touch interaction difficult.

A third challenge in designing learning applications for children is that children have very limited attention spans and easily get distracted by environmental factors (e.g. noise from the hallway, or a colourful object in the tablet screen of a peer) [START_REF] Bruckman | HCI for kids. Handbook of Human-Computer Interaction[END_REF]. Luna [START_REF] Luna | Developmental changes in cognitive control through adolescence[END_REF] note that the younger the children, the more easily they get distracted. It is hence important that educational applications designed for early learners are aware of children's attention and that they employ ways to reorient the attention when the child is distracted, to facilitate learning.

We propose gaze as a viable and potentially beneficial input modality in learning applications for children. Unlike explicit touch-based interaction that inherently requires the device to be placed close to the child, by using gaze, the child can interact with the device at a distance, enabling the device to be optimally placed to promote better posture.

Applications that are aware of the visual attention of the child could implicitly adapt themselves, by integrating learning with their curious visual exploration providing a rich and embodied experience. In addition, gaze has a strong association with attention and gaze-aware learning applications can also keep track of the attention of the child and employ means to reorient the attention when the child is distracted.

There are two distinct ways of using gaze information in learning applications. First, by using gaze as the only interaction modality, which could be useful in simple interaction tasks. Second, by using gaze in combination with conventional touch-based interaction, which could be suitable when more complex interactions are required. In this demonstration, we will focus on applications that use gaze as the only input modality. To showcase the interaction and engagement possibility offered by the modality, we designed Little Bear, an animated pedagogical agent capable of oral communication that is also aware of the visual attention of the learner.

Animated pedagogical agents or virtual characters designed to teach or guide users, provide engagement and motivational benefits in learning applications. However, Kramer and Bente [START_REF] Krämer | Personalizing e-learning. The social effects of pedagogical agents[END_REF] note that current generation of agents do not exhibit sophisticated non-verbal communication nor do they exert a social influence. They envision that agents that are more aware of the emotional and cognitive state of the user and show capacities for non-verbal communication, may have more pedagogical value. Gazeaware agents have been studied in previous research for children with special needs [START_REF] Lahiri | Design of a gaze-sensitive virtual social interactive system for children with autism[END_REF][START_REF] Ramloll | A gaze contingent environment for fostering social attention in autistic children[END_REF] and for adult users [START_REF] Mello | Gaze tutor: A gaze-reactive intelligent tutoring system[END_REF]. The novelty of our system is that the Little Bear, uses the gaze information to adapt its verbal as well as non-verbal behavior and exhibit emotional states as a mean to reorient attention of the child, when distracted from the learning activity. We designed an application with the "little-bear", a bear-like animated pedagogical agent. The application was set in a garden-like 3D environment, where the agent would take the child for a walk, and the was designed to teach children the names of some everyday fruits and vegetables. Different fruits and vegetables would appear on screen at pre-defined locations during the walk, which the child could interact with by using gaze. When the child glanced at a specific fruit, the bear spoke an interesting detail about the fruit. The speech was powered by IBM Watson text to speech service, and further customized by choosing the parameters for the speed of speech, pitch and pauses between words, to make the speech feel natural, fitting to the character and easy to understand. The agent also exhibited realistic lip movement and blink behavior to complement the speech.

Demonstration application

For accurate gaze tracking, we used Tobii EyeX, an off-the-shelf video-based gaze tracker. The agent used the gaze information to adapt its verbal and non-verbal behaviour. For example, when the child is distracted and does not look at the screen, the character becomes sad (see Figure 2a) and uses speech to attract attention by saying, "I become sad when you do not look at me."

A fixation of more than 500ms on a fruit resulted in its activation and the agent spoke an interesting detail about the fruit (e.g. you are looking at apple or apple is red in color). The fixation duration was selected, based on previous works that suggest that the normal median gaze fixation duration for children in an image viewing task is 300 ms and that children have difficulty fixating at a target for longer durations. The speech after the activation of a fruit lasted for roughly 3-6 seconds, during which the application did not respond to any other gaze fixations. Choosing a relatively short fixation duration for activation allowed our application to be implicitly reactive to the interest/visual attention of the child, without requiring an explicit gaze action. When the character was not speaking, the head of the character would orient towards the direction the user was looking at, giving an implicit feedback of gaze tracking and helping establish joint attention. When the character was speaking, the head of the character was oriented directly ahead, abiding by the established social conventions of eye contact during face-to-face conversation.

Summary

In this paper, we described the challenges of using touch-based interaction on mobile devices for children and how gaze input could be used as a beneficial input modality in educational applications for children. We further presented the little-bear, a gaze aware pedagogical agent that tailors its verbal and non-verbal behavior in response to visual attention of the user. Our demonstration system will allow others to experience the potential of gaze-based interaction. The demonstration and the related user study [START_REF] Akkil | Gaze Awareness in Agent-Based Early-Childhood Learning Application[END_REF] shows the novel engagement possibilities of a gaze aware pedagogical agent.

4

Requirements for the demonstration setup

The requirements for the demonstration setup are a desk, access to power sockets for the tablet, and preferably a demonstration area with no direct sunlight, or other intense sources of infrared light, immediately in front of, or behind, the user.

Fig 1 .

 1 Fig 1. Agent based learning application. The application can be interacted using gaze. The red boxes indicate the gaze reactive area.

Figure 2 .

 2 Figure 2. Agent non-verbal behaviour in response to visual attention of the child. (a) A frame from the sad animation. (b)-(e) head orientation of the bear changes based on the visual attention of the child (agent looks where the child is looking).


Arne Berger

Understanding The Informal Support Networks Of Older Adults in India

Keywords: older adults, cultural comparison, informal support, home

We proposes a field trip to understand how older adults in India construct and maintain informal support networks. The aim of the study is to get a nuanced view on older adults' practices of receiving from and providing support to peers, family, friends, and neighbors. Group discussions and collaborative photography will be applied to investigate. Findings will be interpreted to uderstand implications for how to design for support.

Project Description

There exists a plethora of research on smart objects and services, their implementation and evaluation. In contrast relatively little is collectively known about how older adults perceive, construct and maintain communication and support structures for help, security and comfort, within their home and the artifact ecologies at their disposal. Our goal is to understand how older adults socially shape, perceive and use informal support networks. How do they cope with the growing need for support? How do they reciprocate? To investigate we are conducting a comparative cultural study in Germany, Australia, China and Mumbai, India (this proposal).

Method: Group Discussion & Collaborative Photography

This field trip is building upon a study that we conducted in Germany. There, we utilized two methods. We conducted narrative interviews, and asked the helping person (the supporter) and the person being helped (the supported) to collaboratively take and discuss photographs of places and objects that are important for their support relation. This is a combination of "reflexive photography" and "collaborative photography". The first aims on "participants' interactions with their environment through their personal reflections on images" [1.] and arguably shares similarities with aspects of probes [2.]. With reflexive photography people are asked to take photographs of objects, places and topics within their daily life, followed by a discussing with the researchers. With collaborative photography informants and researcher take the photographs together. Still, participants remain the leading creative force in this endeavor: "Collaborating with informants to produce images need not involve the ethnographer taking the lead as photographer" [3.] A combination of both methods has proven to be insightful to understand the cooperation and communication between supporter and supported. Most importantly, to observe the cooperation and communication between the supporter and the supported while taking the photographs is prerequisite to get a nuanced view on the bond between them. In addition, older adults may not be acquainted with digital photography which makes the presence of the researchers helpful.

Plan

Our field trip is focusing on two local areas with different social geography. We look for people with at least two different socioeconomic backgrounds. We are aware that those different areas will call for different methodological approaches. With this proposal we can only give an estimate for the field trip plan. In the first phase we will explore general issues. We will conduct interviews with people reflecting on situations where they help older adults. In the second phase we aim to interact with individual pairs of supporter and supported. We will explore the neighborhood of the supporter and supported and shadow them. When possible we will interact directly with both supporter and supported and hold narrative interviews with both parties in order to gain insight into their support relations. Further, we will encourage the supporter and supported to take photographs of objects and places that are meaningful for their support relation and to comment on them. Also, we will document those objects and places via field photographs and field sketches. We plan to do this routine on both days of this field trip.

user group

We look for various pairs of supporter and supported. Within these pairs, one person is occasionally or habitually helping the other. Preferably both are older adults, but at least one of them should be 60 years or older. It is important that we can visit the supported person in or near their home together with the supporter.

location

We aim at two neighborhoods where people do not have servants to run their household. We rather look for neighborhoods where people are engaging in neighborhood help. As we are not acquainted with the local areas we trust the expertise of the local researchers to lead us to a suitable area for the field trip. We are open to all suggestions from the local researcher regarding a suitable location as well as socioeconomic background. And we need help from the local researchers in contacting people in these locations.

ethical considerations & financial aid

To engage the interviewees in the aim of our study, we trust that the interviewees in India can relate to the pictures we show and stories we tell from home. These vignettes will illustrate, how this study was done in cultural comparison. We trust that the local researchers will help us to organize financial compensation for the interviewees. We have funds available for gifts or a small honorarium to the interviewees or a donation for the benefit of the local area.

material

We will bring cameras, dictaphones, notebooks. We trust the local researchers to help with travel arrangements.

Expected Outcomes

The field trip is part of a comparative cultural study. Overall it will result in a description of how older adults design communication and support for help, security and comfort within the infrastructure of their domestic environment. Through the comparison we aim to understand these networks in areas that are shaped differently regarding their social geography and cultural background. This is an ethnographical study meant to inform the future design for support. The aim of this study is to help designers to align their work more closely with the needs and demands of older adults who are already engaged in informal network structures.

In reflection of previous studies we adopted a mixed method of interview and reflexive photography. We found it helpful to augment the interviews of supporter and supported with opportunities for them to directly reflect moments and places through collaborative photography. In observing and analyzing these acts of collaborate meaning making, issues of trust, values, and relevance structures will surface that may not emerge in interviews only. These issues, values, and relevance structure will in turn inform designers to better design for support.

3. 3

 3 schedule for day 1 and day 2 09 am -10 pm demographic profiling of the neighborhood, participant sampling 11 pm -12 pm group discussion / shadowing / exploring 01 pm -03 pm individual pairs of supporter and supported 03 pm -04 pm debriefing of the researchers 3.4 participating researchers Arne Berger Faculty for Computer Science, Chemnitz University of Technology Chemnitz, Germany Julia Weller Artist, Leipzig, Germany Dhaval Vyas Queensland University of Technology (QUT) Brisbane (QLD) Australia Alessandro Soro Queensland University of Technology (QUT) Brisbane (QLD) Australia Andreea Ioana Niculescu Human Language Technologies, Institute for Infocomm Research, Singapore Pascal Weidenmüller Designer, Plauen, Germany


Ojas Vyas
email: ojas.vyas@mindtree.com

Karan Rai Bahadur
email: karan.raibahadur@mindtree.com

A Digital Employability Marketplace

Keywords: ICT in social development -Interaction design for developing regions, User experience based approaches, User Interfaces for web applications, e-Government

The ecosystem of skilling & placement in India is faced with unique set of challenges. On one hand there are millions of unemployed youth looking for suitable opportunities in an apparently low demand market. While on the other, employers believe that there is a dearth of skilled talent. This problem persists despite numerous skill development programs being conducted by the government across the country. The prime reason for the lacuna is the absence of a scalable platform adopting a holistic approach towards solving these eco-system challenges, thereby unable to provide the right opportunity to all stakeholders. In order to address this employment paradox the social inclusion team at Mindtree, in collaboration with UNDP India, decided to develop a Digital Employability Marketplace based on the principle of platform economics. The idea was to develop a multi-sided cloud based platform leveraging information technology to enable meaningful interactions between stakeholders and processes.

Introduction

The problem of accessibility to employment in India is multifold. The International Labour Organization's (ILO) 2017 World Employment and Social Outlook report projects unemployment in India to increase in the coming years. While the numbers stood at 17.7 million in 2016, it is projected to increase to 17.8 million in 2017 and 18 million next year. There is an urgent need to foster the right innovations in the sector to boost both the economy by reducing the social inequality gap and meet the aspirations of millions of youth joining the working class each year.

The Department of Skill Development, Entrepreneurship & Livelihood in the state of Karnataka wanted to tackle the challenge of unemployment at scale. To begin with, the objective was to meaningfully engage with more than 6, 00,000 youth registered on its official portal for employment and skilling. To analyze the situation on ground Mindtree's division for Social Inclusion collaborated with UNDP India to study the eco-system and understand the root cause. The team primarily identified 4 key issues:-1. There is a mismatch between the skills that young people acquire and what the market needs 2. Currently there are multiple skill development programs but most of the interventions are on the supply side rather than demand driven 3. The employers today lack access to quantitative information on how to convert nearlyemployable candidate to employable. Nor do they have the ability to influence training courses at micro-level / job-skill-gap level 4. There is no single eco-system where different stakeholders from the skilling and placement sector can co-create opportunities for the unemployed

Key Interventions

In the study, UNDP contributed with its experience and network of partners on field, and Mindtree through its experience in technology enabled grassroots livelihood interventions.

And after several rounds of stakeholder discussion and field engagements the teams at Mindtree and UNDP proposed a series of intervention. The proposed solution was to setup a digital marketplace for skilling & employment in Karnataka. The digital market place would enable intelligent match-making and codify the principals of skilling, apprenticeships and entrepreneurship on to a single platform, made accessible through various mediums.

System Level Interventions

From the skilling & placement perspective the solution calls for creating a digital platform which constitutes of multiple smaller systems freely interacting to find logical value through mutual interactions. This is a basic principal codified in Mindtree's technology platform. The platform enables interaction through "Phygital" interventions; i.e. interventions involving both physical as well as digital aspects. And all the different stakeholders in the eco-system are brought together onto a single platform. This provided with the seamless capability for different stakeholders to interact and find value makes platforms unique. Keeping in mind the various challenges and opportunities, the team proposed a 4-Fold Approach:

1. Enable candidates and Micro-entrepreneurs to upskill, and teach/enable access to job/self-employment market on the basis of their exact skills, gaps and strengths. 2. Enable employer to reach-out to candidates with the exact and specific needs in real time 3. Connect candidates/Micro-entrepreneurs, employers and supporting agencies like trainers, content creators, local skilling talent, hyper-local businesses onto a common digital marketplace 4. Create multiple channels of access so that youth from different sections and demography are able to find access to relevant opportunities with minimum or nobarriers.

Service level interventions

There solution makes three service level interventions. These are namely; 1. Counselling & Personalized handholding with unemployed candidates in order to help them make informed decisions 2. Facilitate collaboration and interaction between key market demand side players (employers, vocational training centers, apprenticeship centers) 3. Establish a process of continued engagement for candidates to access employment opportunities

Fig. 1. Flow representing a candidate's engagement for skilling and employment opportunities

Counselling and Personalized Engagement with Candidates: This would aim at providing a personalized handholding session for the candidate to help understand the various opportunities and future avenues she might have via counselling, skilling, upskilling (to cover a gap) and relevant job opportunities in the market. This would be carried out by the Candidate Engagement Unit which would be setup as a part of the program.

Engagement channels:

The candidate engagement would carry personalized engagement with prospective job candidates and micro-entrepreneurs through various mediums.

 The primary one being face to face counselling via tablets in counselling centers where youth can walk in and discuss various avenues pertaining to their skilling and job opportunities.  The counselling sessions will be sequenced after a pre-counselling phone call or self-service web/app connect with registered candidates.

Apart from Counselling at Taluka level, state sponsored employment exchange offices will be used to provide counselling to a larger set of candidate audience in order to increase the outreach leading to a wider solution adoption. The idea is to ensure inclusivity and greater coverage amongst those who might not have access to personal digital mediums.

These engagements will result in one of 4 specific recommendations for the candidate:

1. Upskilling 2. Apprenticeship 3. Candidate-Employer linking leading to employment 4. Micro-entrepreneurship

Employer Engagement: The primary role of this intervention team would be to gather skilling based insights from all stakeholders in the eco-system and incorporate these into the platform solution. The unit would look after job role curation, build assessment parameters based on the actual market requirements and develop insights for intelligent match making. These insights would be coded into the platform

The stakeholders on-boarded to the marketplace would include:

Conclusion

The proposed interventions aims to enable "Acute Personalization" for both the unemployed and the employer. The scope of the project is to cover more than 7, 00,000 unemployed in a structured manner over the course of the next 12 months. At the end of the program the following objectives would be achieved:

 Candidates connected to a market place  Employers connected to a market place  State level skill repository  Demand data on a real time basis  Demand-supply gap analysis by role & micro-skills

The platform approach has been adopted with the objective to build a digital eco-system which would be scalable with time. The objective in the long term is to make the platform accessible to a larger groups of stakeholders ensuring inclusivity. This would be achieved by reducing the barriers in technology adoption and making self-services option easily accessible.

  , and Upskilling Marketplace: The "I Got Skills" Platform would enable intelligent match making between candidates, employers, trainers and other institutions to create employment, apprenticeship and upskilling opportunities. All of these would get integrated over the digital platform to become a market place of opportunities. The Market Linkage Unit would create avenues to promote Short-term Internship and Micro-Entrepreneurship. A four member team of career coaches, would be stationed at the Model Career Center supported by Gov. of India. These coaches will receive incoming calls for assistance from candidates who have gone past the counselling stage.

Fig. 2 .

 2 Fig. 2. Representation of a registered candidate's touch points with the platform


Jaison Jacob
email: jaison.jacob01@sap.com

Santhosh Rao
email: santhosh.rao@sap.com

Design guidelines for exploring relationships in a connected big data environment

Keywords: Big-data, Network visualization framework, Visualizing connected data

Reimagining the 'SAP Investigative Case Management' frame-work from a log-based register of events to a direct interaction environment with the possibility to search, explore relationships between multiple enti-ties in one or more cases/incidents. This case study is about our approach in conceptualizing a generic network visualization method by deconstructing the existing data models. We devised a set of guidelines that can be employed to represent a large number of entities with the intention of examining their relationships.

Introduction

SAP Investigative Case Management is a SAP CRM1 solution used by an investigator, detective, or user from law enforcement agencies to report a crime or offence or to investigate or probe a crime or offence.

There are two types of incident reporting tools, viz. (1) systems for cases where there is a dispute between two people (2) systems for cases that require exploration of a network that has over 5000 entities. This type of data is dynamic in nature and grows over time. All of it can be thought of as "big data". Our secondary research [START_REF] Bach | Marco Winckler of Mobile applications for incident reporting systems in urban contexts: lessons learned from an empirical study[END_REF] says that there are 23 types of incident reporter tools that an investigator can use to report an incident, and there exists a common task pattern amongst them: (1) Users detect an incident. On the event of an incident, the user travels to the incident location to gather artefacts. (2) They create an incident report. The incident report contains a description, location, date and time of incident occurrence, and user identification. (3) Users follow up the incident to share information with other users. Depending on the condition of the report, there may be organizational restrictions on sharing the outcome of the report once it is registered.

State of art

There are several disadvantages in existing systems2 : ( SAP Investigative Case Management or ICM improved this existing system by providing a configurable entity model with more entity 'fields', to empower the reporter to enter detailed data about the incident. It has a search feature that revolutionizes "incident probing". Now the case worker can go through historical records, based on very specific entity type search. With this the system moved from a 'incident reporting system' to a 'probing system'

The improved list of entity types is as follows: case, lead, geo-location, objects associated with the incident, person and organizations, incidents and activity. Above all, ICM focused on relationships between cases. A reporter can now create associations between data entities using relationships and then rate the reliability of the linked data using a reliability matrix. Here the reliability of the information and source of the information are evaluated. This information can pertain to a relationship, or to a description, or to the profile of a person. For e.g., if a relationship exists between a suspect and a victim based on a witness account, you would be able to set a reliability level for the witness, and another for the relationship between suspect and victim.

Approach

But ICM had their own problems. It was difficult to define a new relationship in the system as the user had to search and find cases that are similar in nature, go through them and deduce who is related to what and then define relationships based on this knowledge.

Reduce the ICM framework to 'atoms'. The core principle is to connect an incident with people; both are always related by a time stamp and a geolocation. The incident can later be drilled further into an object and a location associated with an incident. To detail the above classification, we came up with a 'non-hierarchical' classification of all the entities, (a "node"). A 'node' can be a person, a location, an event or an object involved in the incident. Deconstruct the ICM framework by creating a network of atoms. A 'node' can have a 'relationship' with 'another node'. It is a representation of how different types of entities are related to each other from the perspective of an incident.

The network visualization framework. A network diagram is a set of entities exhibiting linear as well as non-linear relationships, graphically represented as nodes (entities) connected with lines (relationships). A network of nodes can exhibit a non-hierarchical distribution, to help the user simplify a complex relationship network and vice versa. A node can be related to one or more nodes. A node can contain sub-nodes and these sub-nodes can contain more nodes and so on.

Design Guidelines

Tell a story using data. Every selected node in the UI should answer three questions: 'When'(time), 'Where'(geolocation) and 'How' (relation with other nodes in network). To perform this task, the user should have the right set of actions at the right time -"contextual menu". The menu should accommodate the following actions: expand nodes, collapse nodes, show information about the node in focus, select multiple nodes (for comparison), delete (non-related) nodes.

Power of choice and importance of probing path. Often, multiple nodes surround the node in focus. Now the user has to make a decision on which node to select to explore further. Each choice made, can lead to more nodes and eventually builds a path. This path can tell how the investigation progressed over a period of time.

Conclusions and future work

The network visualization framework can be applied on any set of data that exhibits linear as well as nonlinear relationships. E.g., Healthcare domain. A doctor can view the patient's medical history by way of a network, use a timeline to organize a series of medical incidents. The doctor can see how these incidents are related with each other with respect to time, the doctors involved, diagnosis and medical reports. Similarly, we can also employ such a network in a scenario to help a recruiter look for candidates to fill a specific role.

1) Limited pa- rameters to describe/report an incident. More

	parameters or entities
	= more detailed data. (2) Report keeping and lack of categorization.
	Storing a summary of incidents with no possibility to cross reference is
	of little or no use. (3) Not configurable based on context. If the re-
	porter wants to add a new field called "sexual assault" with "men" as
	victims w.r.t the case details, it's not possible, because according to the
	existing system, sexual assault victims can only be female.

  UI should scale to accommodate details of a growing network. The user should be able to simplify the view so that he can see only the necessary information: Natural Zoom: Zoom in and Zoom out, Semantic Zoom: When a user zooms out of a canvas with a number of selected nodes, only these selected nodes and the relationships should be highlighted. The rest can be hidden. Clustering for nodes in geolocation: when user zooms out in a map view, the nodes on the map should exhibit clustering. All similar nodes should be replaced with a single representative node. Divide and rule across data layers. Allow the user to choose visual filters: people, object, location and events. Possibility to view nodes on the canvas in an appropriate environment. Based on the context, show the nodes on a map, or as a network, or on a timeline.

	Direction of the probing path matters. The network exploration path
	is important to understand how nodes are related. Relationships change
	with user's perspective.
	Flexibility to zoom-in and out.

Non-biased UI and the proof for taking 'informed' decision. The system should capture the probing path since big data is dynamic in nature. Since it's an informed decision, the decision changes with the UI. The system should not bias the user while making his decisions. It should only aid him in deducing results.

SAP CRM is an integrated customer relationship management (CRM) software manufactured by SAP SE that targets business software requirements of midsize and large organizations in all industries and sectors.

Existing incident reporting systems. Incident information is reported, more or less, by employing the following entities: (1) Incident Description. Textual description of the incident or by choosing a description from a list of predefined incident types. In some cases, description is accompanied by a picture of the incident. (2) Time of incident occurrence. (3) Incident location. This includes a "geo location pin", where the user can pinpoint the incident or an address of the location supported by landmarks.[START_REF] Bach | Marco Winckler of Mobile applications for incident reporting systems in urban contexts: lessons learned from an empirical study[END_REF]


Elvin Kollie
email: elvinkollie@wlv.ac.uk

Fernando Loizides
email: fernando.loizides@wlv.ac.uk

Thomas Hartley
email: t.hartley2@wlv.ac.uk

Adam Worrallo
email: a.worrallo@wlv.ac.uk

TTracker: Using Finger Detection to Improve Touch Typing Training

Keywords: Finger Detection, Leap Motion, Touch Typing 1

Touch typing software teaches a user to use the correct finger combinations with the correct keyboard buttons. The ultimate goal is to teach the typist to type faster, more accurately and ergonomically correct. Our research presents the working prototype of a software and hardware setup that tracks not only the speed and accuracy of the correct buttons being pressed but also which fingers are used to press them; a dimension of training that has previously not been integrated into touch typing tutorials. We use novel technology (leap motion) to detect the accurate interaction between the user and the keyboard, giving precise feedback to the user in order for him or her to improve.

Introduction and Motivation

Touch-typing is a psychomotor skill involving combining a users' cognitive functions with physical movement. Touch-typing involves more than simply increasing typing speed, in that it enables typists to split the cognitive processing by offloading information from the visual channel. This means typists can designate more focus to interacting with the screen and therefore inadvertently increasing their typing speed in the process [START_REF] Feit | How We Type: Movement Strategies and Performance in Everyday Typing[END_REF]. This makes touch typing beneficial in note taking, programming, live communication and many other aspects of computer use.

Touch-typing is conventionally learned via websites that teach touch-typing concepts and finger mappings [START_REF] Poole | Touch typing instruction: Elementary teachers' beliefs and practices[END_REF]. The websites or stand-alone software identify which keys are pressed and the time taken to do so; thus giving feedback to the users in order to improve their performance. The positive influence that feedback has on typing proficiency has been proven multiple times on multiple devices [START_REF] Tittelbach | Effects of Performance Feedback on Typing Speed and Accuracy[END_REF][START_REF] Kim | A Study of Touch Typing Performance with Keyclick Feedback[END_REF][START_REF] Kim | Effect of Information Content in Sensory Feedback on Typing Performance using a Flat Keyboard[END_REF]. What the current feedback does not provide the user is the fingers used to press the keys, one of the areas in which the most mistakes may be made. In order to provide this type of feedback on touch-typing a method to track finger key associations needs to be in place. It would be impossible for a human to consistently detect finger-key associations over long periods of time.

Leveraging hand tracking technology, it is possible to identify if a user is touchtyping or not, providing a method to give feedback to people learning how to touchtype. The feedback provided could prove to be a more effective method of teaching people how to touch-type. In order to prove the concept of the new technology, a system is developed using the Leap Motion controller 1 to track hands actively typing on a keyboard.

In this paper we present the working prototype system and setup created to facilitate finger detection and feedback. We also present a pilot user test to identify initial interaction effectiveness of the prototype and to feed back in the development according to a user centered design approach. The next section presents the technology (software and hardware) while the subsequent section presents the pilot test with some of the main findings.

System Description

The prototype system consists of two parts, the software called TTracker and the hardware setup. The hardware part consists of a laptop or PC with a Leap Motion mounted on top of the keyboard area (See Figure 1). The Leap Motion controller is a USB peripheral device capable of tracking hands with a submillimeter accuracy. The leap motion uses infrared cameras to collect information at up to 200 frames per second. The effective range of the Leap Motion controller extends from approximately 25 to 600 millimeters above the device (1 inch to 2 feet). The field of view is 150° with roughly 8 cubic feet of interactive 3D space. If for some reason a finger were to be obstructed from the view of the Leap Motion it would use its predictive model to infer the location [START_REF] Colgan | How Does the Leap Motion Controller Work?[END_REF]. The Leap Motion then filters all of the unneeded information within the snapshot and expresses the results as a series of frames. These frames hold data such as fingertip location, palm location and finger direction. The TTracker software was written in Unity2 and, although not open source, can be provided to researchers upon request (See Figure 2). The software has two modules working together. The first module is the finger-key tracking module, which gathers information from the Leap Motions. The second module is the learning environment, which was put together with the first module to create the complete system. Specialized sentences and letters appear on the screen for the user to type. There are different levels depending on the skill level of the user. In order to teach touch mapping without breaking the user's focus from the screen, the application was set up so that the next finger to use on the to-be-typed screen flashes blue. After a key is pushed, the application checks if touch-typing was used, the finger used glows red for incorrect finger to key touch or green if the correct finger to key is used. The user gains 5 points for typing the correct letter but if they used touch-typing, they gain 10 points thereby incentivizing the use of touch-typing. If the user types the wrong letter, he / she loses 1 point. Sound was added to the application; a different audio file is played depending on touch-typing implementation.

Pilot User Testing

We wanted to test the prototype to improve the usability before releasing a beta version to the general public. To this end a pilot study took place with 10 participants aged 20-60 for qualitative and quantitative feedback. The test involved a pre-study interview in order to record the participants' profiles, a hands-on test with the prototype which lasted no more than 20 minutes and a semi-structured post-test interview.

The pre-study interview revealed some demographics about the participants and the level of touch typing experience that the participants had. 6 of the participants had knowledge of what touch typing was. 3 of the participants had previously tried (limited) to learn touch typing using an online resource. None of the participants were able to touch type in any formal way. All participants would use a computer at least 5 days a week and 7 would use it daily.

We want to find out how many errors users make while touch-typing and attempt to discover new ways to prevent these errors. Identifying how long it takes users to learn the touch-typing mapping is also imperative. During the prototype testing we were able to successfully record errors in using the correct finger to press specific keys (see Figure 3). With this method we can give individual feedback to each user on the areas they need to be improve specifically and also be able to feedback from all the users on the most common problematic keys and adjust the software training to cater for those.

Fig. 3. Errors in matching the correct finger to the correct key press for an individual user

In the post-study interview, the main questions revolved around the usability of the system. All participants agreed that the software' learnability was easy to grasp. The participants were unanimous in agreeing that highlighting the finger that is needed to press the next key on the screen was sometimes confusing. Visual attention is focused on the letters appearing on the screen and is therefore difficult to be shared with the image of the fingers. Longitudinal testing we hypothesize will gradually help the users learn the finger positions at which point they will not rely on the on screen finger animation. The general outcome was that the software would be better for registering and giving feedback after, rather than at, real time training. Only one participant reported the accuracy of the setup (finger to key detection) as less than 'extremely accurate' and we attribute this as an outlier due to calibration issues. The next version of the software was also suggested to include different auditory feedback for different types of presses.

Fig. 1 .

 1 Fig. 1. (left) the prototype architecture (right) the physical setup

Fig. 2 .

 2 Fig. 2. (left) the development process of TTracker (right) the prototype software showing the left index finger as the suitable finger for the next button press.

https://www.leapmotion.com/ (accessed December

2016)

https://unity3d.com/