dataesr/scientific-paragraphs-categorization huggingface dataset:
Annotations of Presentations' Quality One of the goals related to the development of the 3MT French dataset is to propose a novel annotation scheme useful to assess the quality of a presentation. Several annotation schemes, described in Section 2, were used in previous work. Similarly, the jury prizes assigned to the participants of the French 3MT competition follow an evaluation grid (see Section 4. 1). Each of them (previous schemes and 3MT grid) focuses on different criteria, but we can identify some common dimensions, related to non-verbal behaviours, verbal behaviour (i. e. , the content of the speech), and the perception of the speaker. The latter includes criteria beyond the actual behaviours, such as the level of persuasiveness and perceived self-confidence of the speaker. Most of the previous annotation schemes also include the assessment of the overall performance.
Keywords In addition to the annotation scheme related to the perception of the presentation quality, we also asked the raters to provide one or more keywords related to the topic of the presentation. The original reason for it was to check if the raters watched the videos carefully and to double-check their French comprehension (in addition to the control question, see above). The keywords have been manually validated by the authors of this work. This brings additional content to the 3MT French dataset, that could be exploited for research purposes. For example, it could be investigated whether the agreement between the raters in the choice of the keywords is reflected in their agreement about the presentation quality.
40$ and 1$ for each 1-minute and 3-minute full slices, respectively. The average duration to annotate a 1-minute slice was of around 3 minutes, and around 5 minutes for the 3-minute full slice. Each rater was free to annotate as many slices as they wanted, but only annotated each presentation once. That means, if a rater annotated the beginning slice of ALS01 presentation, they could not rate anymore the middle, end nor full slice of ALS01. The answers from raters who did not watch the videos entirely were discarded and replaced by new annotations. The same procedure was applied to annotations whose keywords were off-topic or not in French. The set of the final annotations provided in the 3MT French dataset contains, for each slice of each presentation, the root mean scores (RMS) of persuasiveness, perceived self-confidence, audience engagement and global evaluation, as well as the set of the keywords provided by the three raters.
Descriptive Analyses In this section, we report some descriptive analyses related to the annotations collected through the Amazon Mechanical Turk platform. As stated above, the new dataset and the novel annotation scheme presented in the paper are proposed as a novel perspective for the analysis of a public speaking judgement, relying on first impressions and primacy and recency theories. Accordingly, the analyses presented in this section focus on the correlations between the measured variables (i. e. , persuasiveness, perceived self-confidence, global evaluation), the observed slices (i. e. , beginning, middle, end or full) and the judgements given during the competition (i. e. , jury and audience prizes).
In particular, we computed a oneway random, average score ICC  (McGraw & Wong, 1996)  for each variable and slice. The ICC values are reported in Table  1. Each line corresponds to one slice (the 1-minute slices beginning, middle and end, and the 3-minute full video). Each column corresponds to the annotated dimensions described in Section 5. 1. The low values of agreement between the raters are not surprising, indeed it is a common issue when performing subjective annotations in the context of social computing studies  (Salminen, Al-Merekhi, Dey, & Jansen, 2018)  or when rating emotion databases  (Siegert, Böck, & Wendemuth, 2014) , especially when using crowdsourcing  (Karpinska, Akoury, & Iyyer, 2021). Inspired from suggestions in  (Siegert et al. , 2014)  and  (Karpinska et al. , 2021) , we limited the risk of high variance by providing context information and carefully checking the French-speaking requirement and the time spent to complete the annotation task.
Anyway, the sparsely reactions of the audience make audience engagement not fully exploitable for the 3MT French dataset, but we believe that it would be an interesting variable to annotate for other corpora containing audience's videos. This variable is not further analysed here. In order to handle the general low agreement between annotators, the consensus between the annotators is then built by computing the root mean square (RMS), as made by  Dinkar, Colombo, Labeau, and Clavel (2020).
Conclusion We presented the 3MT dataset, a new corpus for the analysis of public speaking quality. It contains the presentations of PhD students participating in the French edition of 3-minute Thesis competition. The particularity of the dataset is that the information about the jury and audience prizes awarded during the competition has been integrated with a set of ratings collected online through a novel annotation scheme and protocol. Global evaluation, persuasiveness, perceived self-confidence and audience engagement have been annotated at different time windows (i. e. , the beginning, middle or end of the presentation, or the full video). Keywords related to the topic of each video are also available. This new resource would interest several researchers working on public speaking assessment and training, as well as it will allow for perceptive studies, both under a behavioural and linguistic point of view.
It will allow for investigating whether a speaker's behaviours have a different impact on the observers' perception of their performance according to when these behaviours are realised during the speech. The automatic assessment of a speaker's performance could benefit from this information by assigning different weights to segments of behaviour according to their relative position in the speech. In addition, a training system could be more efficient by focusing on improving the speaker's behaviour during the most important moments of their performance. The second contribution of this paper is the development of a new annotation scheme that could be used on other public speaking datasets in addition to the 3MT French one. Its purpose is to provide a quicker and reliable alternative to the large amount of existing schemes, by focusing on the perception of the performance and considering the common dimensions previously used by other authors.
Ethical Impact Statement The videos annotated in the 3TM French dataset are publicly available on YouTube and their rights belong to CNRS and CPU, who gave us their approval to analyse the videos for research purposes. One potential ethical concern related to the dataset would be to use the data to automatically judge the quality of a speech. This is not the purpose of our work, since we are interested in understanding how people form their judgements and to develop training tools to improve public speaking skills. As in all research activities involving human beings, gender differences may exist. Potential gender bias need to be addressed by the researchers interested in using the 3MT French dataset as an integral part of their analyses to ensure the highest level of scientific quality. The gender of the speaker is provided, while no information about the gender of the annotators was collected through the Amazon Mechanical Turk platform.
Billions smartphones and smart objects running on batteries use Android, i. e. on the Linux kernel. To save energy, the main kernel leverage is to put processors in a low power state as soon as they are idle. It predicts the next event to estimate the sleep duration and choose a sleep state accordingly. Several wake-up sources (interrupts, events. ) impact this prediction which is usually done considering them as a single source. The resulting signal is nearly random and difficult to predict. Over the years, CPUs increasingly supported deeper idle states but the prediction paradigm was never put into question. We propose to predict the next event by splitting the wake-up source signal into smaller and simpler event patterns. We describe a fast and efficient algorithm along with a kernel-level performance evaluation.
INTRODUCTION E NERGY efficiency of mobile platforms has a direct link to their autonomy. As Android platform runs on billions of devices  [23] , its operating system powered by Linux is of the utmost importance energy-wise. Linux also runs on most servers (96% of web servers for example 1 ) and most supercomputers (100% of the Top500 of June 2022) These embedded systems, like smartphones, chromebooks or tablets, have an architecture close to a desktop system: they are multi-cores; they run up to multi-GHz frequency; and they have several GB of RAM. As they are running on batteries, the power management for energy efficiency is no longer an option but a critical aspect of the hardware support. Hardware provides several features to reduce the energy consumption and the operating system (O/S) uses them with different techniques to achieve the best trade-off between performance and energy savings.
If the break even is not reached, the CPU ends up consuming more energy with a latency penalty. On the other hand, if a shallower (less profound) state is used with a processor idle for a longer time, a deeper sleep mode would be more adequate to save energy  [28]. Classically, the Linux scheduler is performance focused, thus most of the decisions are using a performance based policy  [6]. The standard way to take into account energy efficiency in the Linux kernel is to race to idle  [16, 1]. The Linux scheduler 2 implements some basic mechanisms to estimate the idle duration. The estimation of this idle duration is obtained using a sliding average. However, this idle duration does not imply the CPU will be idle during all the time, as it might have to process some interrupt handlers. There can be up to several hundreds of busy-idle transitions per second, and wrong idle state selections can significantly impact the user experience and battery life.
The main contributions presented in this article are the following: • New low overhead interrupt prediction algorithm with high correctness (up to 20% improvement) and energy improvement (up to 5%) on several workloads on ARM and x86 along with its implementation in the Linux kernel; • Experimental performance evaluation of idle state selection (correctness and energy) on ARM and x86 boards; • Comparison of multiple idle state selection algorithms including the one included in the Android O/S (menu governor) and one currently worked on (TEO governor) on actual Android boards. The next section will cover the state of the art. Section 3 will describe the links between interrupt prediction and processor idle modes present in modern processors. Then section 4 will show our proposed interrupt prediction approach.
STATE OF THE ART Heterogeneous processor architectures like big. LITTLE  [27]  or dynamIQ  [17]  running at very high frequencies received all the attention to apply dynamic voltage and frequency techniques driven by an energy aware scheduler. This keen interest has put the idle path energy saving in the background, resulting in a paradigm that has never been questioned in more than a decade. In 2007, the Linux kernel had the power management framework enriched with a new framework dedicated to handling CPU idle states  [18]. The implementation has since been split into different components including the governor. This component is there to select the CPU idle state. It includes all the prediction logic, based on idle -busy transitions and has been tuned over time with the x86 hardware evolution.
Thus, if the shallowest state is selected, the tick will force a re-computation of the next event with a new idle state selection. Observation of idle periods is complex as any traditional monitoring tool might have an impact on the idle duration. In  [14] , authors propose a low level monitoring tool to help detect and characterize idle periods and the events related to exiting these idle periods. Compared to these approaches, we propose an integrated approach at the kernel level. It allows us to have more fine-grained control and low overhead. Also, as the goal is to manage the processor itself, errors in prediction cannot be corrected later on as the operating system itself can be suspended. Also as the proposed approach is integrated in the Linux kernel, its behavior is constrained by the existing capabilities of this operating system.
The wake up sources As we have seen, the choice of the best idle level is important to optimize energy consumption. Predicting the next waking event is needed to choose the best idle level. The current prediction paradigm in the Linux kernel is based on idle duration statistics. The idle duration is measured by computing the delta time between the enter and the exit times. In order to reduce the exit latency as much as possible, the statistics for the prediction are postponed to the next idle entering sequence. The idle duration can give a hint of the correctness of the state selection: if the duration is greater than the idle state's target residency but less than the next one, then the idle state selection is correct. The Linux kernel does this computation and exports the statistics to user-space via the debug file system.
Interrupt capture system The Linux kernel now provides a single hook where it delivers interrupts with their private data to the registered handlers. We merged a change in the Linux kernel 6 to capture the interrupt timings. Using this handler, this entry is added to a circular buffer array with 32 cells. The interrupt events history is thus recorded in a circular buffer array (Figure  4 ). The circular buffer contains the interrupt number and the timestamp information. This one is a 64bits size. With a separate definition, that is a 32bits integer for the interrupt number and a 64bits for the timestamp, at each interrupt we trash several cache lines when browsing the circular array. In order to optimize the size of the information and reduce the impact on the cache, we store an encoded 64bits value with the interrupt number on the first 16bits and the time-stamp in nanosecond for the last 48bits.
The set of values to predict is then limited to [0,20]. The reasoning behind using the log 2 to reduce the interval numbers is the following: the larger the interval is, the bigger the error can be which is considered acceptable. There is a reduced number of idle states and the maximum target residency is rarely greater than 5ms. So for a large interval, there is a high probability the value will be greater than the maximum target residency. At the opposite end of spectrum, if the intervals are small, a high precision is important to discriminate between nearby target residencies. The log 2 operation fits perfectly for this purpose. The Linux kernel low resolution timers use the same approach. The usage of log 2 in the algorithm mimics how timers are ordered in the timer wheel. The longer is the duration, the lower is the precision.
We merged a change in the Linux kernel to provide the array suffix data structure 8. The length of the suite is the period of the events. The array suffix is a data structure usually used for full text index search, data compression algorithm, bibliometric, combinatorics on words or bioinformatic. In order to build a set of suites, we use a simplified array suffix data structure, the suffixes are not sorted at the end because for our purpose we obviously want to keep the same order. As an example, let's consider the word 'banana', the derived suffixes will be 'anana', 'nana', 'ana', 'na' and 'a'. This applied to the log 2 interrupt events history will build from the end of the history a list of suffixes. However, we can not build an infinite number of suites of different periods. Our experiments show that for devices generating repeating patterns, the period of five numbers is usually the maximum observed before having a non deterministic repetition.
Workloads interrupt statistics This section gives an overview of the interrupt occurrences on the system while running the workload. It gives another indication of the interrupt activities in order to interpret the testing results. Figure  10  (using the same data as Table  1 ) shows the distribution of the interrupt events for several workloads. The glmark2 workload has half of the interrupts coming from the timer, a quarter from the GPU and another quarter from the i2c interrupts. The mp3 workload has most of the interrupts coming from the timers and from the USB. Actually the mp3 file is read from the network and the network card is a USB-ethernet adapter. The mp4 workload has the vast majority of the interrupt coming from the timers. Finally the wget workload is mostly characterized by the USB interrupts coming from the network usage.
Existing governors In the kernel, the cpuidle framework is responsible for selecting the right idle state. Several implementations exist in the kernel to provide this service. Currently, there are two governors in the Linux kernel. The first one is called ladder and the second one is the menu governor. The ladder governor is used on a system configured with periodic ticks, with small wake up latency constraint and does not apply to our purpose because it is focused on the server side where performance is more important than energy savings. It escalates the idle state when the last residency is greater than the idle state's residency. Its main limit is that the maximum sleep length is limited by the tick. It has not been used for more than a decade. The menu governor is used on tickless systems where the periodic tick is disabled at idle time in order to allow long idle period duration and to let the system enter deeper idle states (see Section 2).
Reference governors The menu governor is the default governor used for all mobile nowadays and will be our reference governor for our study. All results in the following will be provided as a ratio to this reference governor. The Teo governor is the current state of the art Linux governor and is expected to become the next generation default one. We propose to add some simple and baseline governors as reference and to help provide insight on the results: one targeting maximum performance; another one aiming to achieve a maximum power saving; and finally the last one always choosing random idle states. The governors used in the experimental section are: shallowest: The selected idle state is always the shallowest one deepest : The selected idle state is always the deepest one random : The selected idle state is selected randomly menu : The widely used governor in mobile systems teo : A recently introduced timer oriented based governor (see Section 2) mobile : The governor using our proposed next interrupt prediction system These different static decision governors added with the existing ones help to compare the result of a particular decision regarding a workload.
CONCLUSION AND PERSPECTIVES This article presents our proposal of a prediction based idle mode selector. We compare this algorithm with existing implementations in the Linux kernel along with several naive algorithms. The current paradigm for the idle state selection is to predict the idle duration based on the past idle periods. Unfortunately this approach can not differentiate the origin of the wake up and it results in attempts to predict the entire system behavior, including the scheduler. Under a medium activity involving all the wake up actors, the current menu governor does not perform better than a governor which randomly chooses an idle state. The menu governor has an acceptable number of correct predictions when the running application performs cyclic tasks like a frame decoding or packet receiving.
5. Low SNR typical of DAS recordings  (Lellouch et al. , 2020)  results in narrower frequency band to be used for the inversion, as compared to standard seismic instruments. Seismic moments and corner frequencies evaluated from the selected dataset exhibit selfsimilar behavior at almost constant stress drop (Figure  2   and the geometrical factor of 𝐶𝐶 𝑘𝑘 = 0. 26 from  Kaneko & Shearer (2014) , the averaged stress drop could be estimated to Δ𝜎𝜎 = (0. 7 ± 0. 4)MPa. We also compared moment magnitude estimations with moment magnitude obtained from the inversion of seismic records from on land CSN stations. We found great coherency between estimates (Figure  2b ). This indicates that the observed plateau level at low frequencies is representative of the event moment release and is not significantly affected by instrumental effects.
9 Irpinia earthquake in the Southern Apennines. The area is nowadays monitored by the Irpinia Near Fault Observatory (INFO),  (Chiaraluce et al. , 2022)  that detected several dozens of events during the deployment period manuscript submitted to Journal of Geophysical Research (Fig. 4a ). Figure  5  shows the recordings of a 𝑀𝑀 𝐿𝐿 2. 3 event occurred on 2021/09/20 at 13:07:55 and reveals the specific propagation pattern due to the shallow sedimentary layering, as described in  Trabattoni et al. (2022). As for the Chilean dataset, we considered events in the local bulletin (𝑀𝑀 𝐿𝐿 from 0. 3 to 2. 3 and epicentral distances up to ~60𝑘𝑘𝑘𝑘 from the interrogator unit) and extracted related DAS waveforms from the continuous data-stream. We selected the highest quality channels in the AC section of the cable following a procedure similar to what was described for the previous dataset.
For this dataset, we evaluated the source parameters for channels satisfying SNR>8 for 26 events that meet that criterion for at least at 40 channels. We selected 5s time windows around the S-pick (from 0. 5s before to 4. 5s after the S-pick) of a nearby station (COL3, distance from the fiber < 2𝑘𝑘𝑘𝑘, see Figure  4b ) to compute the amplitude spectra. By analyzing the spectra of the events along the fiber, we observed that the spectral decay following the plateau level starts around an apparent corner frequency of 5. 5𝑆𝑆𝑧𝑧 (Figure  7b ), irrespective of the event magnitude and location and of the channel along the fiber (Fig. 6a  and 7a ). This decay can be ascribed to local site effects that are dominant in the spectral behavior because spectral decays due to the source and to the anelastic attenuation are both expected to show a cut-off frequency dependent on magnitude (source contribution) and distance (regional anelastic attenuation).
Moreover, assuming an average values of the quality factor 𝑄𝑄 for the area (𝑄𝑄 = 230;  Zollo et al. , 2014; Amoroso et al. , 2017)  the spectral decay due to the anelastic attenuation is generally expected to start at frequencies higher than 5. 5𝑆𝑆𝑧𝑧 for the range of hypocentral distances associated with events recorded at the fiber. manuscript submitted to Journal of Geophysical Research We modelled the local site effects using the local attenuation coefficient 𝑘𝑘 𝑐𝑐 , as described in eq. (  10 )  (Anderson & Hough, 1984; Butcher et al. , 2020; Ktenidou et al. , 2014 Ktenidou et al. , , 2015)). To infer an average value for 𝑘𝑘 𝑐𝑐 , we selected small magnitude events in the dataset (𝑀𝑀 𝑤𝑤 < 1. 8) for which the source corner frequency is expected to be much larger than the observed cut-off frequency  (Zollo et al.