dataesr/scientific-paragraphs-categorization huggingface dataset:
In Workshop 2A, pupils engaged with GTM, while in Workshop 2B, they engaged with ML4Kids. After classifying the students into co-design teams based on their interests, we gave them a design template to record the functions of the app, the kind of data collected (image, sound, or pose), and different categories the model should recognize. The students then collected data and trained their models on GTM and ML4Kids while we provided support when needed. To conclude the session, the participants were asked to present their developing thoughts and sketch the design interface for their own apps. Finally, Workshop 3 focused on testing the prototype apps designed before the session. The students were also briefed on the app development process and given feedback regarding their design plans. We concluded the session by interviewing each student team to gather their perspectives and reflections on the design process, including how their understanding of ML concepts had evolved.
C. DATA ANALYSIS Our data analysis relied on a qualitative content analysis  [72] , and we specifically adopted the approach of Vartiainen et al. [23]. We started with the students' drawings and descriptions of their thoughts about ML (through the white paper task), analyzing the kinds of design ideas initiated and the rationales behind the ideas. We carefully examined students' accounts of their experiences with the main stages of the co-design along with their results. In addition, we observed how the student ideas improved throughout the developing process of the design phases. We examined the design idea descriptions, the use or basis of the application in question, and the model type to be adopted (sound, image, or poses). We further examined the improvements that students identified as necessary while testing their applications.
With the support of the researchers, students were able to explore how image recognition, voice recognition, and pose recognition work by training their ML models. During this stage, we emphasized playful learning and exploration without support. Students were able to explore the image project, audio project, and pose project on GTM. For example, some students trained a model to recognize various emotions and moods through their facial gestures, while others taught the machine to recognize various sign language alphabets, as seen in Figure  4. We concluded the workshop with an individual assignment to search for everyday problems and generate ML ideas to solve these problems. We identified some instances that indicated students'have begun to recognize the basic principles of ML algorithm such as classification and its application in real world.
C. IDEAS TO EXPLICATED DATASETS AND DESIGN INTERFACE After thorough analysis and consideration, we selected four student ideas (the currency converter application, the gate pass application, the noisemaker detector application, and the musical instrument identifier application) to prototype as web-based ML apps. We assisted the students in reengineering some ideas to make them feasible, considering their level of expertise, the ML technologies available, and the timeframe. The four ideas were implemented on both platforms -GTM and ML4KIDS. The collection of data and model training were completed during Workshop 2, which was conducted in two phases, Workshop 2A and Workshop 2B. This workshop achieved Learning Objective 2 (LO2). During each of the two phases of Workshop 2, students were asked to discuss the operation of the application, the nature of data, and how to collect data in a guided environment to better articulate the idea for the ML application.
The students trained various models according to their group's design idea. The currency converter and gate pass were based on the image tools in GTM and ML4Kids. The currency converter recognized dollar notes and converted them to their Nigerian naira equivalent. In comparison, the gate pass automated gate access to the school through facial recognition. The data for training the currency converter was collected from the internet through the Google search engine, while the students' faces were used for training the gate pass. Finally, the noisemaker detector and musical instrument identifier were based on the sound tools in GTM and ML4Kids. The students recorded their voices to train the noisemaker detector model and downloaded sounds of different instruments from the internet to train the musical instrument identifier model.
Also, the GTM and ML4KIDS platforms did not work as the students expected, and on some occasions, navigating the platforms required assistance from the experts and facilitators in the classroom. We asked the students if they requested help at any point during the workshops. The excerpt below is from the conversation between the interviewer and Bolanle: Interviewer: Was there any situation where you needed help in the course of modeling this application? Bolanle: Yes Interviewer: In what situation was that and why? Bolanle: When we were putting the data into classes. We needed clarification as to how much data we needed and number of classes we should use to train our model (Excerpt from the group creating the currency converter using GTM) The clarification that was sought about the amount of data required to train a model provided us the opportunity to educate the students that the more the data, the better the model will perform optimally.
D. APPLICATIONS TESTING AND REFLECTION ON THE PROCESS We started the third and final workshop by detailing the app development process. Alongside the models trained by the students, we designed and integrated a graphical user inter-face using the Angular Material JavaScript library. We also gave feedback on the performance of the models trained by the students, explained what guided decisions on the application development, discussed the constraints, and later gave students a 1  URL for the site of their application. On the URL, as indicated below, we bundled three models into one website: the currency converter model, the gate pass model, and the musical instrument identifier model. The navigation bar has three tabs where students can navigate through each of the applications. The first tab hosts the currency converter application, which uses the device camera as an input source.
Experimental results show that the proposed LSDNN architecture offers lower complexity and a huge improvement in latency over the LMMSE. Next, we evaluate the performance, power, and area (PPA) of the LS and LSDNN on application-specific integrated circuit (ASIC) implementations in 45 nm technology. We demonstrate that the word-length optimization can substantially improve PPA for the proposed architecture in ASIC implementations. The AXI-compatible hardware IPs and PYNQ-based graphical user interface (GUI) demonstrating the real-time performance comparison on the ZMPSoC are physical deliverables of this work. Please refer to  [22]  for source codes, datasets, and hardware files used in this work. The work presented in this paper is a significant extension of the conference paper  [11]. Here, we improve the LSDNN algorithm as per the memory constraints of the SoC, two LSDNN models, and detailed performance analysis, including the effect of training SNR.
This has lead to numerous AI-MDL based approaches for various problems such as modulation classification  [30] ,  [31] , spectrum sensing  [15] ,  [32] , multipleinput multiple-output (MIMO) high-resolution channel feedback  [33] ,mobility and beam management  [34] , link adaptation  [35]. With the evolution of heterogeneous large-scale networks, existing frameworks suffer from scaling issues due to the high cost of exhaustive searching and iterative heuristic algorithms  [36]. Such scaling issues can be handled more efficiently using AI-MDL approaches  [37]. To bring DL-aided intelligent and reconfigurable wireless PHY into reality, issues such as potential use cases, achievable gain and complexity trade-off, evaluation methodologies, dataset availability, and standard compatibility impact need to be addressed  [6] ,  [16] ,  [18] ,  [19].
Its performance is sensitive to the SNR conditions, which demand SNR-specific models resulting in high reconfigurable time and large on-chip memory. This model is further enhanced  [21]  by replacing the two CNN models with a single deep residual neural network (ReEsNet) to reduce its complexity and improve performance. Interpolation-ResNet in  [41]  replaces the transposed convolution layer of ReEsNet with a bilinear interpolation block to reduce the complexity further and make the network flexible for any pilot pattern. The computational complexity and memory requirements of these models are high, even higher than the complexity of PHY. In this work, we focus on augmenting the conventional channel estimation with DL instead of DL-based channel estimation  [20] ,  [21] ,  [41]. The validation on synthetic datasets is another concern in DL-based approaches.
This has been addressed in some of the recent works, such as  [42]  where DL-based channel estimation is performed using real radio signals, demonstrating the feasibility in practical deployment. This works provides a detailed process and addresses the concerns regarding dataset creation from the RF environment and training and testing the models using real radio signals. The authors in  [39]  used the concept of transfer learning in  [43]  to devise a twophase training strategy to train a DL-based PHY using realworld signals and demonstrated over-the-air communication. It offered similar performance as conventional PHY validating the feasibility of DL-based approaches in a real-radio environment. However, limited efforts have been made on hardware realization of the DL-based wireless PHY  [44] -  [48].
The scheduler inserts the preamble OFDM symbols at the beginning of each frame, comprising a certain number of data symbols. Next, the complete OFDM frame is transmitted through the wireless channel. At the receiver, OFDM symbols containing the LTS preamble are detected and extracted, followed by OFDM demodulation. Then, channel estimation and equalization operations are performed. The frequency-domain input-output relation between the transmitted and the received OFDM frame at the receiver can be expressed as follows Y[k, i] = H[k, i]X[k, i] + V[k, i]. (1) Here, X[k, i], H[k, i], Y[k, i], and V[k, i] denote the transmitted OFDM frame, the frequency domain channel gain, the received OFDM frame, and the addictive white Gaussian noise (AWGN) with zero mean and variance, N 0. k and i denote the sub-carrier and OFDM symbol indices within the received frame, respectively.
• ĤLS ∈ R K on×1 is converted from complex to real-valued domain by stacking the real and imaginary values in one vector such that Ĥ (R) LS ∈ R 2K on×1. • Ĥ(R) LS ∈ R 2K on×1 is fed as an input to the employed DNN. • Finally, the processed LS complex-valued channel estimates Ĥ(DNN) LS ∈ R K on×1 is obtained by converting the realvalued output vector of DNN back to the complex-valued vector. A training dataset of 30,000 samples is generated using MATLAB-based IEEE802. 11p PHY. The transmitter transmits the LTS over the channel for a wide range of SNR. The LS estimate of received LTS and the corresponding channel frequency response are used as the training data. The dataset is then divided into a training set of 24,000 samples and a validation set of 6,000 samples in an 80:20 ratio. During inference, the OFDM frame consisting of LTS and data symbols is transmitted over the channel.
The LS estimate of LTS is passed as input to the trained DNN, and the estimates thus obtained are used to equalize the data symbols for subsequent BER analysis. Please refer to  [22]  for source codes, datasets, and hardware files used in this work. The DNN is trained to reduce the loss function J Ω,b ( H, y L ĤLS ) over one long training symbol, where H is the original channel frequency response and ĤLS is the channel estimated using the LS approach. Training is an iterative process where the model parameters are updated using optimization functions to minimize the loss over time. Various optimization functions such as stochastic gradient descent, root mean square prop, and adaptive moment estimation (ADAM) can be used  [50]. Hyper-parameters for the DNN architecture is chosen manually. Multiple iterations were performed with different hyperparameters until the one with the best accuracy and low latency was found.
Even though LSDNN1 has less complexity, it offers superior performance than LSDNN2. The NMSE is also a function of the number of training samples; hence, the dataset should be sufficiently large. Similar results are also observed for different wireless channels and preamble types. Corresponding plots are not included to avoid the repetition of results. The improved performance of the LSDNN can be attributed to the capability of the DNN to extract channel features and learn a meaningful representation of the given dataset. Specifically, it accurately learns to distinguish between the AWGN noise and the channel gain. The LS estimation does not consider noise, resulting in noisy estimates at low SNRs. As SNR increases, the noise content in the LS estimation reduces, resulting in improved performance.
A. LSDNN Architecture At the receiver, the LTS samples are extracted from the data obtained after the OFDM demodulation, as shown in Fig. 2. LSDNN uses these symbols for channel estimation, and it involves five tasks: 1) Data extraction, 2) LS-based channel estimation, 2) Pre-processing, 3) DNN, and 4) Post-processing, as shown in Fig. 6. In this section, we present the architectures to accomplish these tasks. The serial data with complex samples received after OFDM demodulation are extracted with independent real and imaginary parts. For the chosen system model with an OFDM symbol comprising of 52 sub-carriers, the input to LS estimation is a vector with 104 real samples. Another input to LS estimation is a reference LTS sequence of the same size. The LS estimation involves the complex division operation of the received (y p ) and reference (x p ) versions of the LTS.
For instance, the LS estimation of QPSK modulated LTS sequence needs only one complex multiplication operation. In the case of IEEE 802. 11p, the LTS and pilot symbols are BPSK modulated. The LS estimation is reduced to choosing either the received complex value or its two's compliment based on whether the LTS is +1 or -1, respectively. The output of the LS channel estimation is further processed using DNN architecture. Before DNN, pre-processing is needed to normalize the DNN input to have zero mean and unit standard deviation. The estimation of mean and standard deviation during inference is not feasible due to limited onchip memory and latency constraints. Hence, we use the precalculated values of the mean and standard deviation using the training dataset. Similarly, the DNN output is de-normalized so that transmitted data can be demodulated for BER-based performance analysis of the end-to-end transceiver.
C. Hardware Software Co-design and Demonstration In Fig. 9 , various building blocks of the channel estimation tasks such as LS, LMMSE, LSDNN, scheduler, DMA, interrupt, and GUI controller is shown. For illustration, we have shown all channel estimation approaches on the FPGA. To enable this, we have developed AXI-stream compatible hardware IPs and interconnected them with PS via directmemory access (DMA) in the scatter-gather mode for efficient data transfers. Later in Section VI, we considered various architectures via hardware-software co-design by moving the blocks between ARM Processor and FPGA. We have deployed PYNQ based operating system on the ARM processor of the ZCU111 platform, which takes care of various scheduling and controlling operations. It also enables graphical user interface (GUI) development for real-time demonstration.
A. Word Length Impact on Channel Estimation Performance In Section IV-B, we have analyzed the performance of various channel estimation approaches on floating-point arithmetic in Matlab. Conventionally, floating-point architectures using doubleprecision floating point (DPFP) and single-precision floating point (SPFP) WLs offer excellent functional accuracy. However, they require high resources, power, and execution time. Since the extremely-large dynamic range by point arithmetic may be needed due to the presence of fixed-width analog-to-digital converters (ADCs), fixed-point WL architectures are preferred. The FPGA offers complete flexibility in the WL selection; detailed analysis is needed to choose the appropriate WL. Here, we use W bits to represent each variable in fixed point WL. Out of W bits, we use I and (W -I) bits to represent the integer and fractional parts, respectively.
To identify appropriate values of W and I for the given architecture, we first use sufficient large W and analyze the functional accuracy for different values of I for all datasets and choose the smallest I, that offers the same performance as that of SPFL architecture. For the selected I, we find the lowest W using the same process. In Fig. 10 (a) and (b), we compare the NMSE and BER performance of the various LSDNN architectures, respectively, for six different WLs along with the SPFP architecture of the LS and DPFP architecture of the LMMSE. The architectures with the half-precision floating point (HPFL) and fixed-point architectures with WL below 24 and integer length of 8 (FP  (24, 8) ) lead to significant degradation in the NMSE. However, degradation in NMSE may not correspond to degradation in the BER due to data modulation in the wireless transceiver.
C. Effect of Training SNR For the results presented till now, the LSDNN model is obtained using the training SNR of 10 dB. Training a DNN on a single SNR allows it to converge faster and model the channel better as the DNN does not have to encounter multiple noise distributions during the training phase, thus reducing training complexity. However, faster training should not compromise the functional performance of the LSDNN. For in-depth performance analysis, we analyze the effect of training SNR on the performance of the LSDNN model. We initially consider various architectures of the LSDNN model trained using the dataset comprising all SNR samples ranging from -50 dB to 50 dB. As shown in Fig. 12 , the NMSE and BER performance is poor due to the reasons mentioned before. However, the selection of training SNR is not trivial since optimal training SNR may vary depending on the testing SNR, i.
D. Adaptable Architecture Though the LSDNN approach offers better performance than LMMSE without the need of prior channel knowledge, LSDNN performance depends on the efficacy of the training dataset with respect to the validation environment. For instance, the performance of LSDNN may degrade if the parameters such as testing SNR, preamble type, pilot type, etc. , of the training and validation environment do not match. For instance, the performance of the LSDNN trained on model M1 degrades significantly on two different models, M2 and M3, as shown in Fig. 15. This is expected because for a DNN to perform satisfactorily, the training and testing conditions should remain the same. To address this challenge, we need an adaptable architecture for LSDNN to enable an on-the-fly switch between various LSDNN models.
(a) and (b), single training SNR affects the performance of LSDNN considerably, especially at high testing SNRs. To address this issue, we consider the selection of training SNR based on the testing SNR range, and corresponding results are shown in Fig. 14. It shows the NMSE for different testing SNRs plotted against the selected training SNR. For each testing SNR, the NMSE first decreases as the training dataset SNR is increased till it reaches its minimum value and then starts increasing. The best training SNR is where the NMSE is minimum, e. g. , for the testing SNR range of 0 dB -20dB, the lowest NMSE corresponds to a training SNR of 10dB. Similarly, for testing SNR of -10 dB to 10 dB, the optimal training SNR is 0 dB. Note that training SNR is irrelevant at low SNRs due to the significant impact of noise on the received signal, and DNN is unable to learn the channel properties, resulting in high NMSE and BER.
However, its routine use remains limited. Some of the barriers to adoption are technical or connectivity issues experienced by patients, uncertainty in the language used, patient adherence to these telemonitoring platforms, especially for older patients, and the correct use of the data in current practice, in particular choosing a questionnaire corresponding to the actual patient's needs  (22, 23). In a previous study, we evaluated the usefulness of the digital platform Cureety for the remote monitoring of older cancer patients, and showed that compliance with the use of the digital tool was high in that subset of patients  (24). However, to our knowledge, there is no study focused on the impact of remote patient monitoring on prostate cancer. Remote monitoring could help address several of the challenges specific to the current management of prostate cancer patients, such as the monitoring of new hormonal therapies, ensuring compliance, and having a better view of adverse events experienced by the patients.
Patients The study was conducted from July 1, 2020 to May 30, 2022. All prostate cancer patients treated at the Bégin Military Hospital were eligible to participate in the study. There were two exclusion criteria: minors (17 years or younger) and patients who did not consent to the use of a digital remote monitoring tool. Patients were included during hospital visits while receiving anticancer therapy. Patients with internet access via smartphone or computer were included in the "app monitoring" cohort. Patients with no internet access or low digital autonomy were included in a "phone monitoring" cohort and were regularly contacted by phone to answer a personalized questionnaire assigned to them. All patients included in the PROTECTY study signed informed consent. We worked in part from patients from a previous study  (24) , but we focused here on prostate cancer patients, included new patients and were able to analyse the data over a longer monitoring period for all patients, compared to our previous work.
Data collection and measurements Population statistics (age at inclusion, gender, comorbidities, performans status, weight, height), disease characteristics (primitive, histology, stage at platform's inclusion, molecular biology), therapeutic characteristics (type, duration), enrollment in clinical trials were collected. Individual AEs, grades reported by patients, and global health status were recorded using the digital remote monitoring tool Cureety. The digital platform Cureety allows remote patient monitoring. It is a CE-marked medical device that was designed and tested for its use by cancer patients, including older patients  (24). The platform has been used as part of routine care at the Bégin hospital since July 2020, as described previously  (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24).
Statistical analysis Baseline demographics and digital background were summarised with descriptive statistics. Survival probabilities were estimated by the Kaplan-Meier method. Progression-free survival (PFS) was defined as the time from inclusion to progression; and overall survival (OS) was defined as the time from inclusion to allcause death. All statistical analyses were carried out with Statview software (SAS Institute, Cary, NC). All tests were two-tailed, and p values lower than 0. 05 were considered significant (Supplementary Material).