dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors thank  Dr Łukasz Mendyk  from the  University of Life Sciences in Poznań (Poland)  for his comments and critical reading of the manuscript. Szabolcs Balogh  ( University of Debrecen, Department for Landscape Protection and Environmental Geography, Hungary ),  Leila Benidire  ( Cadi Ayyad University, Faculty of Sciences and Techniques, Marrakech, Morocco ),  Cornie Van Huyssteen  ( University of the Free State in Bloemfontein, South Africa ) helped authors in compiling information from textbooks. In particular,  Dr. Ashim Datta  is grateful to  Pranay Das  ( ICSE, X Standard ),  Saikat Datta  ( ICSE, X Standard ),  Sheryl Rajput  ( CBSE, X Standard ),  Shaena Choudhary  ( CBSE, IX Standard ),  Pratap Choudhary  ( CBSE, VII Standard ) and the Director,  ICAR-CSSRI, Karnal  for providing the facilities during the study.
Analysis of the action proposals First, facilitators classified the action sheets according to the place where they were collected and the main topic of the proposal (e. g. , development of livestock activities, construction of water supply networks, soil conservation schemes, etc. ). A team of research assistants translated all action sheets from Arabic to French and entered them into a database using the KoBoCollect tool. Due to the very large number of action sheets, similar proposals coming from the same living territory were merged, and appeared in the database only once. For example, in El Ayoun, eleven action sheets proposing to drill a borehole to provide domestic water in douar Joueline were aggregated into a single record in the database. Conversely, when several actions were proposed on the same sheet, "new " sheets were generated and entered in the database as separated records.
Actions were coded 1 if they were innovative, 0 otherwise. • Whether the proposed actions aimed at benefitting individuals or collectives : for the purpose of this analysis, actions were considered as "individual " when they benefitted only one individual or one family, and as "collective ", when they benefitted at least two families or a small village. Due to the processing of the proposals (merging and separation) and limited resources for data entry, it was not possible to assess how many people contributed to the proposals. Indeed, for the actions proposed by only one person in each territory, and when the proponent participated to one of the diagnosis workshops, his/her participant ID number was recorded. This was not the case for the actions corresponding to "merged " proposals. We can only tell how many people participated in the diagnosis restitution workshops and, for only a share of the proposals, whether contributors participated to the workshops.
Acknowledgements This research was supported by the climate change adaptation program for vulnerable rural territories of Tunisia (PACTE), a program implemented by the  Tunisian Ministry of Agriculture  with funding from the  Agence Française de Développement  and the  Fonds Français pour l'Environnement Mondial. The support of  Raphaëlle DUCROT  with the literature review is warmly acknowledged. The authors also thank the observers of the participatory process:  Awatef AMRI ,  Dalel AMRI ,  Rabii ARFAOUI ,  Sahbi BACCOUCHI ,  Mariem JENDOUBI  and  Asma MEDIOUNI. The authors are also very grateful to all the persons who have contributed to the computerized data entry:  Abir KHALDI ,  Inès SAIDI ,  Hedia EZZEDINE ,  Amal BEN SALEM. Finally yet importantly , the authors would like to express their gratitude to all the people who have contributed to the participatory processes, as participants, facilitators, organizers, experts, coordinators, technicians and drivers.
Data and measures This study uses cross-section data from Australian organic food consumers. Respondents were recruited from a national research panel through an online survey. Prior to the main data collection stage, a pilot study was undertaken using a sample of 37 subjects, which resulted in several minor adjustments. To decrease response fatigue, the length of the questionnaire was decreased and the wording in several items was revised to achieve a greater clarity  (Burchell and Marsh, 1992). A prerequisite for participation was that respondents were at least 18 years and have purchased organic foods in the past six months. A total of 17,615 invites from the online panel survey were sent out, from which 1,011 completed responses from the Australian adult population were received  (Anisimova, 2016; Anisimova et al.
While the task of automatically detecting eating events has been examined in prior work using various wearable devices, the use of smartphones as standalone devices to infer eating events remains an open issue. This paper proposes a framework that infers eating vs. non-eating events from passive smartphone sensing and evaluates it on a dataset of 58 college students. First, we show that time of the day and features from modalities such as screen usage, accelerometer, app usage, and location are indicative of eating and non-eating events. Then, we show that eating events can be inferred with an AUROC (area under the receiver operating characteristics curve) of 0. 65 using subject-independent machine learning models, which can be further improved up to 0. 81 for subject-dependent and 0. 81 for hybrid models using personalization techniques.
As a case study, a dataset of over 12000 events provided by 58 college students in Mexico is used. We show that features from modalities such as application usage, accelerometer, location, and time of the day are informative of eating and non-eating events. Contribution 2: We define and evaluate the task of inferring eating and non-eating events using passive smartphone sensing data, obtaining an AUROC of 0. 65 with subjectindependent models, which can be increased to 0. 81 with subject-dependent models. Moreover, we show that feature selection plays a key role when training subject-dependent models. Each user might need models that use different features (compared to others) to achieve high performance. This shows the behavioral diversity of people around eating and the need to consider such diversity in building machine learning models that recognize eating events.
We also found that hybrid models (partially personalized) perform reasonably well and on par with subject-dependent models. The results illustrate the potential of using passive smartphone sensing for building context-aware and automated mobile food diaries. This paper is organized as follows. Section II, describes the background and discusses related work. In Section III, the study design, data collection procedure, and feature extraction techniques are provided. Section IV, presents a descriptive analysis and a statistical analysis of the dataset. The inference task is defined and evaluated in Section V. A number of important issues are discussed in Section VI. Finally, the paper is concluded in Section VII.
B. MOBILE FOOD DIARIES Food diaries are essential to understanding individual and population-level eating practices  [4]. While manual food logging using pencil-and-paper based techniques could be useful for self-reflection, mobile food diaries allow logging fine-grained details (weight and size of dishes, variety of dishes) about eating episodes more systematically by searching food types, varieties, and sizes in a database and logging them  [4]. Some recent mobile food diaries have also looked into easing the process of logging using speech and photos  [41]. Going a step further, some studies looked into estimating the calorie intake  [42]  and also calorie deficit by combining information from food diaries and passively detected physical activities  [43]. Some popular mHealth apps that provide food journaling functionalities include Samsung Health, Lose It!, MyFitnessPal, EasyDietDiary, and SparkPeople  [4] ,  [9].
III. DATA, SMARTPHONE FEATURES, AND DEFINITION OF EATING/NON-EATING EPISODES A. DATASET We used a dataset regarding the eating behavior of young adults from our previous work  [9]. This dataset consists of self-reports and smartphone sensing data regarding eating and non-eating events from college students (mean age 23. 4, 44% men) in Mexico. A mobile app called i-Log  [50]  was used to collect data from participants. An entry questionnaire was given to participants to collect demographic information. During the deployment, time diaries captured details regarding food intake similar to a mobile food diary and end-of-theday surveys that captured additional details regarding activity, sleep, and mood. The users were sent notifications three times a day to fill out a food intake questionnaire which asked about the number of eating episodes they had within the last four hours.
This report helps to determine an approximate eating time (T anc ) as an anchor for the last eating episode. For example, if a self-report was done at 8. 00 pm, and if the eating episode occurred 31-60 minutes ago, the approximate eating time was about 45 minutes ago (mean of 31 and 60), hence T anc = 7. 15 pm. Further, in Case 2, except for the time window corresponding to that eating episode, the rest of the times correspond to non-eating. Hence, a maximum of two non-eating events from such self-reports were randomly sampled. Furthermore, in Case 1, the last four hours would correspond to a non-eating period, and a maximum of three time windows were randomly sampled from the last four hours as T anc for non-eating events. Moreover, it is worth noting that all eating and non-eating events were chosen such that they are non-overlapping when sensor data are aggregated with a time window (described in the next sub section), hence avoiding any biases in the evaluation.
C. WHAT IS AN EATING EVENT? A 2X time window around T anc was used to aggregate sensor data to match a self-report as given in Table  2. According to Figure  2 , 2X time window means that the inference can be done X minutes after the T anc. Using a larger time window like one hour (X = 30 minutes) is common for ubicomp studies regarding eating and drinking behavior that considers the context and behavior of participants in addition to the actual eating/drinking episode  [9] ,  [20] ,  [33] ,  [40]. This does not mean that the time of inference should be 30 minutes after the end of eating. In reality, it could be much lesser than that because eating episodes can span around 20-30 minutes. Moreover, even though most of the analysis in the next sections focuses on a one-hour time window, the time window can be changed depending on the dataset and application requirements.
In Section VI this is further discussed. With the said time window, for the previous example where T anc = 7. 15pm, sensor data would be aggregated from 6. 45pm (T anc -30) to 7. 45pm (T anc +30). In case participants reported that they did not have food during the last four hours, and if the self-report time is T sr , T anc was chosen carefully to make sure that a half-an-hour window was present on either side of the T anc , within the non-eating time window (T sr -30 >= T anc and T anc >= T sr -210). This is to ensure that there are sufficient sensor data (one-hour) in a non-eating time window to match the self-report. Again, it is worth noting that all the eating and non-eating events in the dataset contain non-overlapping sensor data. Hence, each event is mutually exclusive, and there is no data leakage.
User B has also used Chrome only while not eating. These individual app usage differences are in line with prior work in mobile sensing that has shown that app usage behavior could be used to identify users  [58]. Furthermore, app usage behavior has shown good performance for other eating and drinking behavior-related tasks  [1]. In addition, prior work has proposed that app usage can be used to gain an understanding of user context  [59]. Hence, when combined with findings in prior work, the descriptive results indicate why person-level models could be useful over population-level models to capture these fine-grained behavioral differences among people, especially when considering app usage behavior. In summary, descriptive analysis of the dataset shows that features from passive sensing modalities could indicate eating and non-eating events, especially when combined with the time of the day.
V. DETECTING EATING EVENTS AND IMPORTANT FEATURES (RQ2) A. TWO-CLASS EATING EVENT INFERENCE In this section, we used different feature group combinations to infer eating vs. non-eating events using smartphone sensing data. Scikitlearn framework  [65]  and python is used to conduct experiments in three phases using different model types: (1) Random Forest (RF)  [66] , (2) Naive Bayes (NB)  [67] , (3) Gradient Boosting (GB)  [68] , and (4) AdaBoost (AB)  [69]. These models were chosen by considering the tabular nature of the dataset, interpretability of results (e. g. , getting feature importance values), and the small size of the dataset. Further, similar to recent ubicomp work  [70] , Synthetic Minority Over-sampling Technique (SMOTE)  [71]  was used to prepare training sets for each inference task.
B. FEATURE IMPORTANCE FOR EATING EVENT DETECTION (RQ2) In order to study individual differences further, Figure  7  gives BASE feature importance values (Figure  7a ) and PERS2 feature importance values for three randomly selected users (Figure  7b , Figure  7c , and Figure  7d ) based on Gini importance of RF classifiers. These figures illustrate how datasets from different users have varying features that could discern between eating and non-eating. For example, BASE (0. 081, 0. 055) and user A (0. 076, 0. 048) had high values for time of the day (minutes_elapsed, hours_elapsed). However, for user C (0. 027, 0. 019) the value for those features are low. Moreover, BASE (0. 059) and user B (0. 053) had high values for battery_level, whereas for user A (0. 037) and user C (0. 031), had lower values compared to other features.
This research is in line with Bisogni's contextual framework  [27] , which not only considers eating/drinking events but the whole context around them. Similarly, the models discussed in this paper could also be run with different time intervals, depending on the use case. For example, with a one-hour time window, if the inference task was performed for T anc = 2. 15pm at 2. 45pm, and run again for T anc = 2. 17pm at 2. 47pm, the granularity would be higher, compared to running the second inference after another hour for T anc = 3. 15pm at 3. 35pm. In addition, as shown in the previous section, this inference could be run for shorter time windows, obtaining reasonable inference accuracies. Even though the one-hour time window performed better for this dataset, shorter time windows might perform better for other datasets.
Coming back to the previous example, with a shorter twenty-minute time window, for T anc = 2. 15 pm, the inference could be done at 2. 25 pm, hence reducing the time between T anc and inference time. Hence, the time window and inference frequency should be chosen depending on the use case and the available data. Future work could explore how these aspects affect inference performance in more depth. Furthermore, we produced results for different time windows ranging from ten to ninety minutes (not included here due to space limitations). The best average results were obtained for the sixty minute case, closely followed by fifty minutes and seventy minutes. Hence, only the results for the sixty minute case were included throughout the paper. However, as mentioned earlier, different time windows could be used in future work depending on the dataset and features.
In the results section, we showed that models performed well for ALL, interaction sensing modalities (INTSEN -SCR, APP), and continuous sensing modalities (CONSEN -LOC, BAT, ACC) for both PERS1 and PERS2. While CONSEN would consume far more battery life as it uses accelerometer and location-related features, INTSEN only uses app usage and screen usage, which are just phone usage logs. This makes INTSEN inferences computationally cheaper than those using CONSEN or ALL. However, the set of features available in the dataset for APP and SCR are not rich, and future data collection efforts could look into collecting richer datasets that could further increase performance. Overall, INTSEN could be a low-cost alternative to phone-based eating event tracking, compared to CONSEN and wearable-based tracking.
g. , hand motion in wristbased sensing, chewing in earable-based sensing, or jaw bone movement in necklace-based sensing). In addition, some studies have modeled eating event detection as a time-series data analysis problem. In contrast, the approach discussed in our paper did not consider the time-series nature of the data, and we extracted eating and non-eating events using shortterm retrospective self-reports to model inferences using a tabular dataset (Figure  1 ). Due to these differences in sensing and modeling techniques, there is no direct comparison possible with previous work. It is worth noting that there exist subtle differences in how eating events/episodes are defined in different studies, and results should be interpreted with caution by understanding the essence of each individual work.
E. OTHER INFORMATIVE FEATURES It is important to acknowledge that it was challenging to generate interpretable and meaningful features from the dataset. First, it was clear from the results that ACC features were informative of eating and non-eating events. This means that activity levels are helpful in discerning between the two classes. However, the only available features were statistical features generated from the accelerometer axes, which are hard to interpret. More easily interpretable features such as activity level (i. e. , step count) and activity type (i. e. , walking, sitting, driving, running, cycling, etc. ), which can be captured using activity recognition engines in modern smartphones, were not available in the dataset. Future work should look into capturing such interpretable features, which can be generated with low-power consumption on a smartphone.
It was also impossible to determine app usage times for features in the APP group because such features were not available in the dataset. This is another aspect that could be improved when creating mobile sensing applications for future studies. Another challenge in the data filtering and processing phase was missing data, especially from the location sensor. As the location sensor consumes a high amount of power, there is a tendency for participants to turn off this sensor. Finally, researchers could examine other modalities such as touch and typing events, notification clicking behaviors, and continuous sensing modalities such as ambient light sensors, which are typically available in modern smartphones and have shown promise in other smartphone sensing based behavioral modeling tasks  [24].
G. ADDITIONAL ASSUMPTIONS AND IMPLICATIONS In the first place, our work assumes that human behavior does not change significantly over time with respect to app usage, screen usage, and activities. However, this is not always the case as the lifestyle and behavior of people could indeed change over a period of time. Future work could look into using time windows (e. g. one month, two months, etc. ) when selecting data for training models. Unfortunately, the dataset used in our work did not allow to capture such behavioral changes because the data collection involved only a few weeks. Examining such temporal behavioral change was not a goal of this study and is out of the scope of our paper. In the second place, we assumed that there is no relation between eating and non-eating events of the same person, even within the same day.
However, this might not be the case because eating and not eating are temporally linked, e. g. , long periods of not eating could increase the possibility of eating. On the flip side, eating a meal right now would increase the possibility of non-eating in the next few hours. With the studied dataset, it was not possible to test such phenomena because only three self-reports were collected per day. Therefore, all eating and non-eating events might not be present. This topic is open for future investigation. In the third place, another limitation is that eating events could be under-reported for convenience or other reasons (i. e. , a person reporting non-eating in a case when an eating event indeed occurred). Even though this might add some noise to the dataset, prior work also suggests that it is difficult to capture and verify all self-reports during real-life experiments  [1] ,  [20] ,  [34] ,  [35].
Participant observations of weekly demonstrations and meetings were carried out between 24 November 2018 and March 2020, and between September 2021 and January 2022. They were supplemented by the viewing of participants' Facebook public accounts, thus bringing to light the diversity that the term "Yellow Vests" as a single unit tends to obscure. In addition to the irregular observations of the BeL list, a corpus of press articles was constituted from the Europress database and completed by manual research on local news sites. In order to trace the chronology of the campaign and the evolution of the mobilization of a small group of YV constituting a list for the municipal elections, a manual collection of the Facebook posts from the DB public page (353 members) and its administrator, from its foundation until September 2021 was undertaken.
e. in November and December 2018, at the height of the movement). The Bordeaux demonstrators are fairly similar to the respondents as a whole but differ from the YVs mobilized on roundabouts. They are slightly younger (only 18% are over 60, compared to 27%) and have a slightly higher level of education (65% have at least the baccalaureate, compared to 57%). Among the working population, there is a higher proportion of employees (35% vs. 28%) and an underrepresentation of blue-collar workers (21% vs. 29%). With regard to employment status and sector of activity, the first results based on the entire database (Collectif d'enquête sur les Gilets Jaunes et al. 2019) had highlighted the overrepresentation of the unemployed, but also of artisans, public service employees, industrial workers and the transport sector among men on roundabouts, and that of caregivers among women.
| Participants This study involved 183 French students (71 girls, 112 boys, aged 11 to 15; M age = 12. 8, SD = 1. 3) from eight classes in the same middle school. Among them, 31 (7 girls, 24 boys) were diagnosed with dyslexia by a speech therapist. Students with dyslexia were included in the mainstream classes and there was no age difference between the two groups of students with and without dyslexia (F [1, 181] <. 01, p =. 95). Prior to the study, the students and their parents received a letter informing them that participation was anonymous, and that data would remain confidential. Only students whose parents gave their informed consent for participation were included in the study and only when the students themselves also gave their consent. The protocol was approved by the University of [blinded for review]'s ethics committee (agreement reference: CER/2018-05).
| Statistical data analysis Our data analysis strategy was structured in two parts. First, self-judgement and performance individual scores were calculated using factor analysis. The following scores were calculated: Dimensions of self-judgement, Academic literacy, Non-academic literacy, and Academic mathematics. Second, we simultaneously investigated the relationship between dyslexia and the above-mentioned scores using mediation analyses. Dyslexia was used as a binary independent variable and the performance variables as continuous dependent variables. A mediator is a third variable that explains how or why two other variables are related  (Holland, Shore, & Cortina, 2017; Memona, Cheahb, Ramayahc, Ting, & Chuahe, 2018). In our case, self-judgement would mediate the relation between dyslexia and performance.