Financial Analys king hours 0.5 Fig.8.Histogram of people's working duration in a week. 00 making relationships inference based on one-day observation (a)Working behavior-based (b)Shopping and home may sometimes be opportunistic.For instance,students in occupation inference results. behavior-based gender inference. the same school may be regarded as strangers or classmates Fig.9.Illustration of behavior-based occupation and gender inference results. depending on whether a face-to-face interaction is detected in one day.In order to reduce the opportunistic inferences,we of working hours.Working time STD is the average standard propose to infer the relationships in a relative long time period deviation of the start and ending time of working across (e.g.,multiple days,one week or several weeks)and utilize a multiple days and WH Distribution Kurtosis is a descriptor majority-vote approach to make the final decision. of the distribution shape,which represents how concentrate B.Behavior-based Demographics Inference the working duration is distributed.Figure 9(a)illustrates that the three working behaviors can well separate different Next,we discuss how to utilize the activity features to further capture people's behavior characteristics at various types of occupations,which suggests that we can utilize a threshold-based approach to determine people's occupations daily places and infer people's demographics (e.g.,occupation, by using these features.We note that different occupations may gender,religion and marriage). have similar working behaviors,such as financial analyst and 1)Behavior Derivation at Daily routine-based Places:In software engineer,we can further narrow the choices for the this work,we define the behavior as the mannerisms made by an individual in the daily routine-based place during a period occupation inference by leveraging the supplementary place contexts from Geo-information and user associated AP SSIDs of time (e.g,several days).A behavior usually consists of a series of activities,and thus can be described by the temporal as in Section V-A3. and spatial statistics of the activity features extracted from 3)Gender Inference:The information of user gender is the staying segments across different days.In particular,we more implicit compared with occupation,because there is no define three kinds of behaviors:1)home behavior,2)working information from surrounding APs,which directly links to behavior,and 3)leisure behavior based on three daily routine- this biological characteristic.However,we find that males and based place categories.We utilize the activity features of the females usually behave differently in some specific scenarios. same daily routine-based place across multiple days to derive For example,females tend to spend more time on housework the features that can characterize the three behaviors.We note and in-store shopping,while males tend to work for longer that the leisure behavior can be further specified according to hours [32].Such behavior difference shows the trend of the the fine-grained daily routine-based places in Section V-A3. majority people and exists in many countries according to the 2)Occupation Inference:Occupation is the job or profes- survey.Thus our basic idea is to examine a person's behavior sion of the user,which is related to the working behavior.The characteristics at home or in shops.From activity features, inference approach is based on the fact that people of different we derive three behavior features for gender inference:shop- occupations have different working time slots and duration ping duration,shopping frequency and home duration,which at Workplace(may include single or multiple nearby places), mainly capture the behavior patterns at home and leisure which reveals different working behaviors in temporal and spa- behavior at shops.Figure 9(b)illustrates that the three devised cial.Figure 8 illustrates the intuition by showing the working behavior features can well capture the differences between duration histogram of 4 users with different occupations in males and females in their behaviors at home and in shops a week.We find that office staff has the most concentrate Additionally.we also check the user's associated AP SSIDs at working duration,followed by Researchers,Faculties and leisure places,if any,to look for the particular leisure places Students,because company office uses more regular timetable that can differentiate gender,such as nail spa and beauty salon compared with school.Meanwhile,Faculties need to leave 4)Religion Inference:We further demonstrate that it is office for teaching and faculty meeting,which leads to wider possible to infer people's religion status (i.e.Christian or working duration distribution compared with Researchers.On Non-Christian)from surrounding APs.The intuition is that the other hand,Students have the most scattered working Christian usually goes to church every Sunday and shows a durations because they have different number of classes for regular pattern of leisure behavior around the church.There- each day and flexible hours at library for study. fore,we extract three religion behavior features:church atten- We derive three specific working behavior features to differ-dance days,church attendance duration and church attendance entiate working behaviors for multiple days at working place.frequency,and apply a threshold-based method to decide Working hour(WH Distribution range describes the range of Christian.We note that,by including more religion activities, the working duration histogram,which shows the flexibility we can also cover other religions or religious sects.Working hours 0 2 4 6 8 10 Percentage 0 0.5 1 Financial Analyst Working hours 0 2 4 6 8 10 Percentage 0 0.5 1 Researcher Working hours 0 2 4 6 8 10 Percentage 0 0.5 1 Faculty Working hours 0 2 4 6 8 10 Percentage 0 0.5 1 Student Fig. 8. Histogram of people’s working duration in a week. making relationships inference based on one-day observation may sometimes be opportunistic. For instance, students in the same school may be regarded as strangers or classmates depending on whether a face-to-face interaction is detected in one day. In order to reduce the opportunistic inferences, we propose to infer the relationships in a relative long time period (e.g., multiple days, one week or several weeks) and utilize a majority-vote approach to make the final decision. B. Behavior-based Demographics Inference Next, we discuss how to utilize the activity features to further capture people’s behavior characteristics at various daily places and infer people’s demographics (e.g., occupation, gender, religion and marriage). 1) Behavior Derivation at Daily routine-based Places: In this work, we define the behavior as the mannerisms made by an individual in the daily routine-based place during a period of time (e.g, several days). A behavior usually consists of a series of activities, and thus can be described by the temporal and spatial statistics of the activity features extracted from the staying segments across different days. In particular, we define three kinds of behaviors: 1) home behavior, 2) working behavior, and 3) leisure behavior based on three daily routine￾based place categories. We utilize the activity features of the same daily routine-based place across multiple days to derive the features that can characterize the three behaviors. We note that the leisure behavior can be further specified according to the fine-grained daily routine-based places in Section V-A3. 2) Occupation Inference: Occupation is the job or profes￾sion of the user, which is related to the working behavior. The inference approach is based on the fact that people of different occupations have different working time slots and duration at Workplace (may include single or multiple nearby places), which reveals different working behaviors in temporal and spa￾cial. Figure 8 illustrates the intuition by showing the working duration histogram of 4 users with different occupations in a week. We find that office staff has the most concentrate working duration, followed by Researchers, Faculties and Students, because company office uses more regular timetable compared with school. Meanwhile, Faculties need to leave office for teaching and faculty meeting, which leads to wider working duration distribution compared with Researchers. On the other hand, Students have the most scattered working durations because they have different number of classes for each day and flexible hours at library for study. We derive three specific working behavior features to differ￾entiate working behaviors for multiple days at working place. Working hour(WH) Distribution range describes the range of the working duration histogram, which shows the flexibility 10 WH Distribution Range 5 0 0 WH Distribution Kurtosis 5 0 5 10 15 10 Working Time STD Researcher Professors Students Software Engineer Financial Analyst Shopping hours 1 0.5 0 0 Shopping frequency 0.5 1 5 10 15 Hours at home Female Male (a) Working behavior-based (b) Shopping and home occupation inference results. behavior-based gender inference. Fig. 9. Illustration of behavior-based occupation and gender inference results. of working hours. Working time STD is the average standard deviation of the start and ending time of working across multiple days and WH Distribution Kurtosis is a descriptor of the distribution shape, which represents how concentrate the working duration is distributed. Figure 9(a) illustrates that the three working behaviors can well separate different types of occupations, which suggests that we can utilize a threshold-based approach to determine people’s occupations by using these features. We note that different occupations may have similar working behaviors, such as financial analyst and software engineer, we can further narrow the choices for the occupation inference by leveraging the supplementary place contexts from Geo-information and user associated AP SSIDs as in Section V-A3. 3) Gender Inference: The information of user gender is more implicit compared with occupation, because there is no information from surrounding APs, which directly links to this biological characteristic. However, we find that males and females usually behave differently in some specific scenarios. For example, females tend to spend more time on housework and in-store shopping, while males tend to work for longer hours [32]. Such behavior difference shows the trend of the majority people and exists in many countries according to the survey. Thus our basic idea is to examine a person’s behavior characteristics at home or in shops. From activity features, we derive three behavior features for gender inference: shop￾ping duration, shopping frequency and home duration, which mainly capture the behavior patterns at home and leisure behavior at shops. Figure 9(b) illustrates that the three devised behavior features can well capture the differences between males and females in their behaviors at home and in shops. Additionally, we also check the user’s associated AP SSIDs at leisure places, if any, to look for the particular leisure places that can differentiate gender, such as nail spa and beauty salon. 4) Religion Inference: We further demonstrate that it is possible to infer people’s religion status (i.e. Christian or Non-Christian) from surrounding APs. The intuition is that Christian usually goes to church every Sunday and shows a regular pattern of leisure behavior around the church. There￾fore, we extract three religion behavior features: church atten￾dance days, church attendance duration and church attendance frequency, and apply a threshold-based method to decide Christian. We note that, by including more religion activities, we can also cover other religions or religious sects