dataesr/scientific-paragraphs-categorization huggingface dataset:
Coding was performed by two researchers, working independently. Coding decisions, as well as any relevant policy text annotations, were captured using custom software (see Additional file 1: Figure  AF5 ). All decisions were reviewed to reach a consensus agreement on policy coverage. The nature of information actually collected and transmitted by apps was then compared to specific commitments made in privacy policies. We also recorded the operating system permissions requested by each app at installation or during subsequent use, for example access to user contacts or geolocation service, as well as configuration options offered by each app to control the transmission of data to developer and third-party services.
Statistical analysis Data were compiled into a single dataset for analysis (supplied as Additional file 2). We used simple descriptive statistics to summarize aspects of data collection, mobiledevice storage and transmission. Unless otherwise stated, the unit of analysis is the platform-independent app. Expectations that apps available on both iOS and Android would substantially share privacy-related characteristics were confirmed. Therefore, to avoid double counting, we combined these apps for analysis. Because of the potential Fig. 1  A 'man-in-the-middle' attack. A man-in-the-middle attack is able to intercept network traffic sent by a mobile app in a way that is invisible to users and services risk to current users, we have chosen not to identify specific apps with confidentiality vulnerabilities.
However, in November 2014, the NHS Health Apps Library was provided with details of the vulnerabilities we identified. We hypothesized that the likelihood of an app having a privacy policy should not vary by platform or by the distribution model (free or paid). Although apps for iOS are subject to a quality control process prior to release, and although developers of paid-for apps may have greater resources to invest in quality assurance, an accreditation program should apply the same privacy evaluation rules to apps, and no significant differences should be found. We used Fisher's exact test to calculate the two-tailed probability of an association between the transmission of personal information and platform, distribution model and availability of a privacy policy. Statistical analysis was done in STATA, version 10.
Results of data entry and mobile-device storage assessment Most apps (89 %, n = 70/79) included some form of data entry mechanism. The majority of these (83 %, n = 58/ 70) collected generic user-generated content such as annotations (20 %, n = 14/70), appointments (  14  A small proportion of apps (7 %, n = 5/70) captured other sensitive information, including ethnicity (n = 3), employment status (n = 2) and sexuality (n = 1). The majority of apps (84 %, n = 59/70) also collected other types of user-generated content. This information was commonly related to aspects of health self-management, for example annotations in personal health records (20 %, n = 14/70), reminders (19 %, n = 13/70) or appointments (14 %, n = 10/70). Some apps collected information explicitly intended to be shared with others.
In addition to issues affecting information transmission, risks to confidentiality were identified in software application programming interfaces (APIs) provided by online services to communicate with apps. Of 27 apps with such services, 16 (59 %, n = 16/27) allowed unencrypted access. Two apps had critical vulnerabilities which permitted access to user information, including information belonging to other users, without authorization. The first did not implement any form of access control, and the second was susceptible to SQL injection, a type of attack that manipulates queries sent to a database  [33].
Operating system permissions The assessment approach used to evaluate permissions requested by apps to access operating system components, such as device cameras or contact databases, varied by platform. For apps available on Android (n = 42), a systematic assessment was performed against permissions explicitly declared at the time of app installation. Common permission requests for which justifiable uses existed in all cases were for network access by 93 % (n = 49/42), requesting device state by 71 % (n = 30/42) and accessing local data storage by 52 % (n = 22/42). Less common permissions for which justifiable purposes were also apparent were to enable a user to place a call by activating a number stored by an app (26 %, n = 11/42), provide notifications using vibration (17 %, n = 7/42), receive push notifications (5 %, n = 2/42) and, in one diary app, connect to a Bluetooth weighing scale.
However, Incorporates strong personal identifiers, health-related information and other sensitive information while almost two-fifths (38 %, n = 16/42) requested geolocation permissions, a clear reason, such as a mapping function, was identified in only five cases. Similarly, justifiable reasons were only found in one out of three apps requesting device camera access and two of three apps requesting access to a local contacts database, and one app requested calendar permissions without a clear purpose. In no case where permissions were requested without a clear use apparent in the app was there evidence of data being collected via that route, suggesting that these findings represent inaccurate permission declarations rather than malicious intent. In contrast to Android, iOS apps (n = 37 not available on Android) used an on-demand approach to permission management, where an opportunity to grant or reject access was generated at the time when a particular feature Incorporates strong personal identifiers, health-related information and other sensitive information; b because these topics are only relevant for apps that transmit data, the denominator for calculated percentages is the number of apps with a privacy policy that also transmit data; c for these domains, policies were additionally examined to distinguish between rights afforded to individuals and those denied.
Certification and curated app 'whitelists' offer a route to allay these worries. However, if assurances are offered, they must be robust. Apps available through the NHS Health Apps Library exhibited substantial variation in compliance with data protection principles, demonstrated both by the availability and content of privacy policies, and adherence to recommended practices for confidentiality enforcement. Over half included functions in which personal details, health-related information, or both, were transferred to online services, but a fifth of such apps, and two-thirds of apps overall, did not have a privacy policy. In this respect, health apps, whether accredited or not  [10] , appear to be little better than non-medical apps available through general app stores [39], despite greater potential sensitivities surrounding health-related information.
A small number of apps transmitted both unsecured personal and health information, for example research data pairing device and personal identifiers with details of substance use. However, the bigger potential risk to privacy is probably identity-related. Half of apps transmitting user account details sent usernames and passwords unencrypted. Armed with such information, a malicious user might be able to access other resources, for example email or online bank accounts. We found examples of complete personal datasets, including name, date of birth and contact details, sent as plain text. No apps encrypted local data stores, despite the widespread use of PIN or password security within apps that might reasonably lead a user to believe their information was protected. While recent changes proposed by operating system manufacturers aim to ensure that information stored on devices are encrypted by default, responsibility for ensuing confidentiality during transmission will remain with developers.
Data Acquisition Module One of the challenging tasks in developing the proposed game apprentice is to interface little known sensors in the game industry such as ultrasound. To meet the requirement, a portable helmet based system has been used to capture motor behaviour during singing and other oral-motor functions in a nonlaboratory experimental environment  (Al Kork, et al. , 2014)   (Chawah, et al. , 2014)   (A Jaumard-Hakoun, et al. , 2013). The system, based on vocal tract sensing methods developed for speech production and recognition, consists of a lightweight "hyper-helmet" containing an ultrasonic (US) transducer to capture tongue movement, a video camera for the lips, and a microphone for audio capturing as seen in Figure  2. Moreover, facial data are captured using a Kinect v1 sensor.
Architecture of the Communication Module The communication module is responsible for providing bilateral data transfer between the sensors and the game. The application communicates with various external entities such as the hyper-helmet, audio comparison and evaluation server. Figure  3  illustrates the communication architecture. The module is capable of acquiring multiple video streams in real-time from the sensors of the hyperhelmet by using a third party sensor integration and controlling software called as RTMaps (S. A. -RTMaps, 2012). Thus the system actually communicates with RTMaps via Transmission Control Protocol. Besides video, the module also supports real-time transfer of the 3D Tongue Model's vertex positions. The module supports up to 60 fps data transfer either for video or 3D animation.
This server receives the audio file including the sound produced by the learner and compares it with the corresponding expert sound (which is part of the Human Beat Box experts' database). Following the completion of comparison, the software generates a detailed text file that contains the similarity ratio, sub-parts and corresponding similarities etc. This text file is send back to the game via web-service. This output is used to evaluate the performance of the learner. It should be noted that this remote evaluation takes very short time and it does not introduce any lag or latency. This evaluation approach was designed for the first version of the game However, in future versions there will be improvements regarding this approach and more information will be taken into account by the evaluation algorithm such as the position of the tongue, the opening of the mouth, etc.
CONCLUSIONS A first version of an interactive game-like application to learn, perform and evaluate modern contemporary singing focusing on the Human Beat Box case study was developed in the context of the i-Treasures project. The game integrates several state-of-the-art sensors such as Kinect for facial data capture and a prototype multi-sensor hyper-helmet for vocal tract capture. It provides a user-friendly interface including an engaging 3D recording studio environment and singer avatars and visualizes a variety of sensor outputs on user demand. A virtual tutor guides the user through the game and provides real-time feedback for the evaluation of the user's performance in different singing activities. The developed game framework is capable of providing bilateral communication with external sensors and third party sensor control software such as RTMaps.
Introduction Longitudinal measures of brain volumetry are powerful tools to assess the anatomical changes underlying on-going neurodegenerative processes. In different neurological disorders, such as multiple sclerosis (MS), Alzheimer's disease (AD) and Parkinson's disease (PD), brain atrophy has been shown to be a good surrogate marker of disease progression  [1] [2] [3]. Magnetic resonance imaging (MRI) can provide reproducible 3D structural images of the brain, which can be used to assess its integrity. Furthermore, the emergence of freely available longitudinal MRI databases, (e. g. ,Alzheimer's Disease Neuroimaging Initiative (ADNI)  [4] , Open Access Series of Imaging Studies(OASIS)  [5]  and others) provide the necessary data to develop and test new methods and investigate the longitudinal structural changes of healthy and pathological brains.
Image processing in MRI-based neuro-anatomical studies is often performed in a cross-sectional manner where each time-point is evaluated independently. Typically, brain morphometry comparisons can be done by matching paired images (template-to-subject or subject-tosubject), where the deformation field is used to map atlas regions or to compute voxel-wise comparisons of anatomical changes as in deformation-based morphometry (DBM). However, in the context of longitudinal datasets, the robust estimation of anatomical changes is still challenging  [6]. Indeed, in the case of neurodegeneration occurring in a short period of time (2-3 years), if we assume that longitudinal changes are smoothly varying, spatially local, and temporally monotonic processes, considering individual time-points independently can generate unnecessarily noisy longitudinal measurements due to the intrinsic noise associated with each visit.
Methods The objective of the template creation algorithm is to find the non-linear transformations that minimize the anatomical shape differences between images to create the most representative average of the subject's anatomy. Processing is achieved in two steps. First, all data is processed cross-sectionally to bring each volume into stereotaxic space. Second, this data is used to build a subject-specific individual template. The method and notation is inspired from Fonov et al. [22]  and Aubert-Broche et al. [26] , and described in the following sections. The nomenclature is summarized in Table  1. The Montreal Neurological Institute research ethics committee gave approval for this study. Two neuroimaging datasets ("scan-rescan" and "ADNI") were used anonymously to evaluate the proposed algorithm and all subjects gave informed consent.
Experiments Data. Two neuroimaging datasets were used anonymously to evaluate the proposed algorithm: Scan-rescan and ADNI. First, to evaluate stability and potential bias, a scan-rescan database of 20 healthy subjects scanned 4 times within the same week (twice during a first session and twice again over 2 different days) was used. Each subject was taken out from the scanner before getting back in for each rescan session. No volume change is expected for the subjects in this database. The T1-weighted MRI images were acquired on a 1. 5T SIEMENS MRI scanner with a 3D spoiled gradient echo (GRE) sequence (TR = 22ms, TE = 9. 2ms, flip angle = 30°, 1mm isotropic voxels). Second, to evaluate the performance of the algorithm when changes over time are expected, we used data obtained from the publically available ADNI database (adni.
For CS, the structure segmentation was performed independently on the scan from each subject's time-point and the volume change was estimated by computing the volume difference between the visits. In the case of the IT and LIT longitudinal approaches, only the subject-specific template was segmented and the estimated non-linear transformations were used to transform the segmentation to each time-point and estimate the Jacobian determinant. The volume change was estimated by integrating the Jacobian determinant within the regional structure masks for whole brain, ventricles and hippocampi. • FreeSurfer (FS): The longitudinal stream of FSsoftware (version 5. 1) was chosen as it has shown better results for longitudinal analyses than the cross-sectional version, except for longitudinal whole brain measurement  [47].
KNBSI uses tissue specific normalization, k-means classifiers and specific parameters to account for large multi-site image intensity variability (in terms of SNR and tissue contrast differences). To account for the multiple tissue boundariesof the hippocampus, we used the double intensity windowing approach technique which estimates the boundary shift between CSF and grey matter as well as between grey and white matter  [54]. For the scan-rescan data, KNBSI was run locally for all structures after applying our in-house differential bias correction as recommended by the author. For the ADNI data, KNBSI data was downloaded from the ADNI site for whole brain and ventricles (Fox Lab, 2014-01-31), again to have optimally run values. We ran double window KNBSI locally for the hippocampi, as these values were not available on the ADNI website.
• Quantitative anatomical regional change (QUARC): QUARC  [37]  estimates the volume changes over a region defined in the baseline image where the deformation is estimated by combining pair-wise forward and backward non-linear transformations with intensity normalization. As QUARC is not publically available, we did not use it in the scan-rescan evaluation. However, for analysis of the ADNI data we downloaded QUARC results (UCSD, downloaded on 2014-06-01) from the ADNI web site. • Tensor-based morphometry (TBM): TBM method proposed by Hua et al. [18] , first estimates the statistical properties of the Jacobian determinant of non-linear deformations used to map training subjects to a population template. Second, a group of voxels with a significant rate of atrophy as measured by the Jacobian (p<0.
001) in the temporal lobes are defined as a region of interest ("stat-ROI"). Finally, a single measurement for each subject, of an independent testing set, is obtained by integrating the Jacobian determinant of the non-linear deformations to the identical population template within the stat-ROI. TBM is not publically available and was not evaluated with the scan-rescan data. TBM results for ADNI data were downloaded from the ADNI website (USC, 2013-11-17). Each image processing pipeline has a different level of robustness, and MRIs that do not pass quality control could adversely affect the estimation of statistical power. Instead of a headto-head comparison, we decided to keep only datasets that passed visual quality control. For the data downloaded from the ADNI website, quality control information was only available for FS, KNBSI, and QUARC data.
Subjects who passed quality control with the following arguments were kept for the power analyses: FS: QVERALLQC = "Pass" or "Partial"; for the ventricular KNBSI: BSI VENTACCEPT = 1, REGRATING 3, for KNBSI: KMNREGRATING 3; and QUARC QCPASS = 1. The final cohort number for each method is summarized in Table  3. Statistics. For the scan-rescan dataset, the percent volume change (VC) and the absolute percent volume change (aVC) were used respectively to evaluate bias and variability of structure volume (V). For each structure of each subject at time-point t of the n visitsand the structure average volume ( 1 n X n i¼1 V i ), VC and aVC were estimated with the following formulas: VC t ¼ 100 Ã 1 À V t 1 n X n i¼0 V i 0 B B B @ 1 C C C A and aVC t ¼ jVC t j ð 13Þ The significant differences between the match-paired segmentations were compared with a paired t-test for the VC comparison and a Wilcoxon sign-rank test for the aVC comparison.
Method For the longitudinal dataset, the percent volume change measures atrophy or growth using the baseline volume (V 0 ) as a reference such as: longVC t ¼ 100 Ã 1 À V t V 0 ð14Þ For the longitudinal results, we use power analyses to estimate the required sample size to assess the interaction of treatment and time in a longitudinal study where smaller longitudinal variability will enable better detection of a potential treatment effect. Here, the volume change was estimated using a linear mixed-effect model (LME). Indeed, linear mixed-effect modeling has shown to be a powerful statistical technique to analyze longitudinal data  [55]. In this study, we used a simple LME of the volume changes (longVC) consisting of a temporal, time-point t interval (Interval It ) and group (Group) fixed-effects while subject (I) was chosen as random effects, such as: longVC It ¼ ðb 1 þ b 2 Â Group þ b I Þ Â Interval It þ ε It ð15Þ Power analyses, as described by Diggle et al.
Scan-rescan dataset The scan-rescan dataset should show no anatomical variability since the 4 MRI scans were acquired during a week. Fig  5  shows the brain, ventricle and hippocampi volume changes (VC and aVC) for the cross-sectional (CS) and the longitudinal techniques (IT, LIT, FS, SPM12 and KNBSI) for the repeated sessions. For VC and aVC, the smallest structures present the highest volume variability. The method variability ranking is similar across structures excepted for FS and KNBSI, which show more variability for the lateral ventricles and the hippocampi measures. No significant bias was found when comparing the mean VC of the different methods, as the mean VC values from all methods was centered on zero. When looking at the aVC, the longitudinal methods (IT and LIT) significantly reduce (p<0.
Longitudinal dataset The identical cohort of subjects from ADNI-1 was used to evaluate the different methods, however, subjects scans failing during pipeline processing or absent at the time of method result  publication were removed from the analyses and the final number of subjects analysed is summarized in Table  3  Smaller longitudinal variability should improve the statistical power to detect changes in an individual, and facilitate detection of group differences (treatment effects) and thus reduce the number of subjects required for analysis in a clinical trial. In Table  4 , we provide estimates of the different sample sizes required to detect a treatment effect that would reduce the annual AD atrophy rate by 25% for the different structures and methods. Table  4  also shows the annual atrophy rate for the different structures and methods.
Discussion In this article, we have presented a new approach for the estimation of individual longitudinal changes using individual subject-specific templates and spatio-temporal regularization. We also provide an unbiased framework for analysing longitudinal data where every time-point is processed with the same steps. A robust estimation of the deformations is obtained using an individual template approach, minimizing deformations between subject time-points. Meanwhile, a local spatio-temporal regularization is achieved with linear regression of the deformation field and a spatial regularization of its Jacobian matrices. The regression of the decomposition enables a temporal regularization at a local voxel level. Furthermore, we compared our technique with a traditional cross-sectional approach, as well as recent powerful methods, FS, SPM12, KNBSI, QUARC and TBM.
Longitudinal image analysis bias was assessed on a scan-rescan dataset, and power analysis to detect a potential treatment effect on an Alzheimer cohort was chosen. Longitudinal image analyses can be subject to bias in particular due to non-linear registration when an arbitrary reference image is chosen  [6]  or due to interpolation asymmetry  [57]. 4D Hammer  [58]  or non-linear registration such as Diffeomorphic Demons  [13, 59]  and ANTs  [60]  require a reference image to be defined, therefore introducing possible bias. Symmetric interpolation and registration might not be sufficient to correct for bias when there are more than 2 time-points. The use of an individual template, as suggested by Reuter et al. [16]   showed no bias and our approach exploits this strength and adds non-linear registration to obtain a more accurate anatomical correspondence between time-points.
Furthermore, the non-linear registration is performed towards a common target, producing unbiased deformation fields that can be combined to obtain robust and transitive non-linear deformations between time-points. The individual template estimation depends of the current set of the subject's time-points, thus the addition of new time-points will require a re-estimation of the individual template. This could limit its use in clinical settings where intermediate results are expected before the MRI acquisition of the final time-point. Future work should focus on the longitudinal measurement stability when an additional time-point is preprocessed and registered to the specific template previously estimated separately. In our experiments, we have demonstrated that the LIT method provides a more robust longitudinal measure on a scan-rescan dataset where no changes are expected.