dataesr/scientific-paragraphs-categorization huggingface dataset:
Then you work with them at the table, so that they understand why you are asking them to transform in the data what they know very well, because otherwise they will tell you 'But why are you asking me all these questions?'. They understand why they have to tell you the information, they understand what you are trying to get at, and that you are trying to get there together. It seems like a detail, but it is the fundamental thing (to increase their trust)" (CG13). Other actions are gathering subjective and experiential knowledge through voice-based instruments, extracting patterns from AI model training datasets to identify key variables with significant impact while eliminating non-impactful variables, and involving front-end employees and customers in the loop to interactively validate AI model outcomes and verify the model's continued effectiveness following human interventions, as stated by interviewee CF7.
Manage complexity and connectivity This is a major challenge, as AI-powered CRM systems often integrate a variety of different technologies, data sources, and operational processes. This can make it difficult to ensure that all the systems are working together seamlessly. Companies undergoing AI-CRM transformation projects often grapple with legacy systems as they migrate towards cloud-based solutions. These organizations also contend with a multitude of existing integrations, databases, tools, and various touchpoints, resulting in a complex landscape of dialogue between windows and dashboards. Consequently, integrating AI solutions within an already intricate IT environment generally presents a significant challenge. Additionally, introducing another tool for users can sometimes pose difficulties in terms of adoption.
000-50. 000 2019 Chief Data Officer CI16 Company I Retail 50-200 2018 CRM Manager CJ20 Company J Service > 50. 000 2018 Chief Data Officer CK21 Company K Retail 50-200 2020 Chief Digital Officer CL22 Company L Service > 50. 000 2018 Data scientist PA8 Provider/Consultancy Tech, provider of software and technologies for 10. 000-50. 000 2005 CX Specialist Company A databases PB9 Provider/Consultancy Tech, AI development consultancy 1-50 2016 Founder and Independent advisor Company B PC10 Provider/Consultancy Tech, AI provider of AI software dedicated to the 50-200 2016 Head of Marketing and Communication Company C insurance industry PD11 Provider/Consultancy Tech, AI/ML solutions provider 1-50 2019 Chief Executive Officer Company D PE12 Provider/Consultancy Tech, AI transformation consultancy 1-50 2018 Associate Partner Company E PF17 Provider/Consultancy Tech, AI and NLA consultancy 200-1000 2008 Chief Technical Officer Company F EA18 Expert A - - - HDR of Digital and Artificial Intelligence in Marketing EB19 Expert B - - - Head of several AI Business and start-ups.
2 ). After inclusion in the study, RDT was performed depending on the technical capacity of each inclusion centre. All patients reported to have a positive thick film were included in the study regardless of the result of RDT, including some patients with positive microscopy result but without RDT result (due to limited technical capacity of some inclusion centres) because a large majority of Djiboutian P. falciparum strains were reported to be undetectable by HRP2-based RDT  [19]. After obtaining informed consent from the adult patient or parents (or legal guardians) of children, several drops (approximately 200 µL) of finger-pricked capillary blood were imbibed onto Whatman 3MM filter paper, dried, and stored at -20 °C in a sealed plastic bag with a dessicant for molecular analysis. Socio-demographic, epidemiological (including the use of bed nets), and mobility data were collected from individual adult patients or parents (or legal guardians) of febrile children.
Malaria prevalence Despite the limitations of the diagnostic tools available in Djibouti, 71% of suspected symptomatic malaria cases were confirmed by PCR in the present study. This prevalence is higher than that reported by the country's NMCP in 2019  [8]  (i. e. 24% of confirmed suspected malaria cases) or previous study conducted in Ethiopia  [31]. Variations between years were observed in the current study. This may be partly due to the sampling period, the extension of the transmission period due to climate change, the occurrence of other epidemics during the study period, or the change of RDT in 2021. As a point of comparison, if the available data in Djibouti city between January and September 2019 provided by the NMCP are considered, 26. 8% of suspected malaria cases were confirmed by RDT or microscopy  [8].
During the same period, data analysed in the present study have shown that 56. 3% of suspected malaria cases were confirmed by PCR. This difference can be partly explained by the more stringent inclusion criteria used in the present study, in which the initial diagnosis of suspected malaria cases was based on clinical examination with additional onsite confirmation by microscopy or RDT, whereas only clinical examination was considered in previous studies  [8]. Furthermore, PCR performed in the present study is more sensitive than RDT, as previously shown in other studies  [20]. A higher confirmation rate of malaria diagnosis is, therefore, expected in the present study. In the present study, it was observed that malaria affected all age groups in Djibouti city. The age distribution of the patients included broadly corresponded to the Djiboutian population structure observed in the latest census (Additional file 2: Figure  S1 ).
Plasmodium species In line with earlier studies conducted in Djibouti  [6, 10, 11] , the present study showed that the majority of malaria infections in symptomatic patients are due to P. falciparum (83. 3%) in Djibouti city, followed by P. vivax (11. 2%). No other Plasmodium spp. was detected by PCR in the present study in concordance with the results of previous studies  [11]. This is the first study that assessed the prevalence of P. vivax in Djibouti over a period of 4 years using PCR. The results showed that P. vivax accounts for 16. 8% of malaria-confirmed cases in Djibouti city, of which 5. 6% are mixed P. falciparum-P. vivax infections. Previous monitoring based on RDT or microscopy reported higher percentages of P. vivax. The latest available data from the NMCP reported 13,373 P. vivax infections in 2019, which represents 27% of malaria infections  [8].
Limitations of study There are several limitations in this study. First, due to scarcity of qualified microscopists in the country and lack of compliance to the national diagnostic guidelines, parasitaemia was not available. Parasitaemia plays an important role in RDT detection. Parasite density below the RDT detection threshold or very high parasite density can influence the performance of RDT  [64]. Secondly, samples were collected only in Djibouti city. Therefore, the data generated in this study do not reflect malaria prevalence in the entire territory of Djibouti. An earlier seroprevalence study showed that the prevalence of P. falciparum was high in two other districts of Djibouti  [11]. Third, the study population represents part of the patient population randomly selected in a limited number of health centres and one referral hospital in Djibouti city.
DNA is a promising candidate for long-term data storage due to its high density and endurance. The key challenge in DNA storage today is the cost of synthesis. In this work, we propose composite motifs, a framework that uses a mixture of prefabricated motifs as building blocks to reduce synthesis cost by scaling logical density. To write data, we introduce Bridge Oligonucleotide Assembly, an enzymatic ligation technique for synthesizing oligos based on composite motifs. To sequence data, we introduce Direct Oligonucleotide Sequencing, a nanopore-based technique to sequence oligos without assembly and amplification. To decode data, we introduce Motif-Search, a novel consensus caller that provides accurate reconstruction despite synthesis and sequencing errors. Using the proposed methods, we present an end-to-end experiment where we store the text "HelloWorld" at a logical density of 84 bits/cycle (14-42× improvement over state-of-the-art.
However, the FP rate of Motif-Search is still lower than reverse alignment. While missing TP is an issue as it can lead to data loss, Fig. 6  The number of true positive and false positive oligos reconstructed by Motif-Search and Minimap2 for different sequence coverages with expanded motif sets. i) Motif-Search reconstructs more true positive oligos than reverse alignment even without the knowledge of reference oligos. ii) False positive rises for both approaches when the motif set size increases. extra FP is not a problem as it can easily be discarded by using auxiliary metadata and/or error-control coding. These results clearly demonstrate that (i) our motif-based, BOA method can successfully encode information in DNA, and (ii) with sufficient coverage, Motif-Search is capable of reconstructing all original oligos, and thereby ensuring successful decoding, despite errors introduced by enzymatic BOA and DOS.
More specifically, in order to imitate the human visual system during the PC quality evaluation that captures the geometric and color degradation, we render the PC into different 2D views using a perspective projection. Then, the projected 2D views are divided into patches that are fed to a Convolutional Neural Network (CNN) to learn sophisticated and discriminative visual quality features for evaluating the local quality of each patch. Finally, the overall quality score of the PC is obtained by pooling the quality score patches. We conduct extensive experiments on three benchmark databases: ICIP2020, SJTU, and WPC, and we compare the proposed model to the existing Full-Reference, Reduced-Reference, and No-Reference state-of-the-art methods. Based on the experimental results, our proposed model achieves high correlations with the subjective quality scores and outperforms the state-of-the-art methods.
Therefore, it is  VOLUME 11, 2023  This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4. 0 License. For more information, see https://creativecommons. org/licenses/by-nc-nd/4. 0/ essential to develop effective methods that accurately assess the quality of PC and preserve the quality of the user experience. Two families of methods are usually adopted to evaluate PC degradation  [11] ,  [12] ,  [13] : subjective and objective metrics. The subjective ones are based on human judgment for the evaluation, which makes it cumbersome and expansive in practical real-world situations. However, this type of method is used to construct a PC annotated database. In this case, the annotation refers to the subjective quality scores (ground truth) of the PC, often called Mean Opinion Scores (MOS).
2) We conduct a large study on the impact of different model parameters, such as the number of rendering views, the type of the CNN feature extractor, and the spatial pooling technique used to aggregate the predicted quality scores of the views. This study is not taken into consideration in the projection-based NR state-of-the-art metrics. 3) We compare the proposed method with various Full-Reference, Reduced-Reference, and No-Reference state-of-the-art methods on three PCQA benchmark databases: ICIP2020, SJTU, and WPC. Extensive experiments show that the proposed method outperforms all the NR and RR methods and is competitive or even better than the FR methods. Moreover, our method shows better performance over the 3 databases, and on mixed and individual types of distortion. The remainder of this paper is organized as follows.
1) TRAINING PHASE In the training phase, we first construct an annotated database. Since the degraded PC samples from all state-of-the-art PC databases have homogeneous distortions, we affect the normalized Mean Opinion Score MOS (subjective quality score) as an annotation to all extracted patches from the same PC. After that, using the built database, we train the CNN model using three different networks for comparison: AlexNet  [52] , VGG  [53]  and ResNet  [54]. This comparison allows studying the effect of the architecture as well as the impact of depth on the performance of the proposed method. • AlexNet  [52] : is a CNN classification model that won the Imagenet Large-scale Visual Recognition Challenge (ILSVRC) in 2012. It is composed of 5 convolutional layers with max-pooling, followed by 3 fully connected layers.
We underline that we adjust all the baselines to make them suitable for the NR-PCQA task by modifying the size of their last three fully-connected layers into 512, 512, 1 neurons, respectively. Each fully-connected layer is followed by a dropout set to 0. 5 and a ReLu function, except for the output layer that has a Sigmoid function. Then, using the pre-trained CNN models, mentioned above, on ImageNet dataset  [52] , we fine-tune the networks on our dataset to adapt their weights to the quality prediction task by minimizing the following L 1 Norm loss function: Loss = 1 N N i=1 |PMOS i -MOS i | (2) where MOS i and PMOS i represent the subjective and estimated quality score of the patches, respectively. N is the total number of patches. To select the parameters of the trained model, we use a validation set that contains PC objects different from the training and the test sets.
A. POINT CLOUD DATABASES We evaluate our proposed PCQA method on two databases: • ICIP2020  [55] : is composed of 6 reference PC objects that represent human body models, including four watertight/full-coverage objects (Soldier, LongDress, Loot and RedandBlack) and two semi-coverage PC (Ricardo10 and Sarah9). Each original PC is degraded with 3 types of compression distortion caused by G-PCC Octree, G-PCC Trisoup, and V-PCC with a 5-level rating scale, 1 indicating the lowest quality and 5 the highest quality. Consequently, the number of degraded objects is 90. • SJTU  [39] : contains 9 reference PC and 378 distorted ones, which include both human body models and inanimate objects. Each original PC is degraded according to 7 types of distortion with 6 different levels caused by 4 independent distortions: Octree-based compression (OT), Color noise (CN), Geometry Gaussian noise (GGN) and Downsampling (DS), and 3 superimposed distortions: Downscaling and Color noise (DS+CN or D+C), Downscaling and Geometry Gaussian noise (DS+GGN or D+G), Color noise and Geometry Gaussian noise (CN+GGN or C+G).
B. VALIDATION PROTOCOL AND EVALUATION METRICS To evaluate the performance and the effectiveness of our proposed method for the point cloud quality assessment, we use the 4-fold cross-validation protocol for ICIP2020, 7-fold for SJTU and 20-fold for WPC database. For all datasets, the different folds are organized as follows: one fold as a test set, another fold as the validation set, and the remaining folds as the training set. In this way, we assure that we do not have an overlapping between the three. Besides, we adopt the following metrics that are commonly used in the field of quality assessment: 1) Spearman Rank Order Coefficient (SROCC): it measures the monotonicity of the model estimation. The SROCC metric can be defined as follows: SROCC = 1 - J i=1 (rank(MS i ) -rank(MP i )) 2 J (J 2 -1) (8) where MP i and MS i are the estimated and the ground truth quality score while J indicates the total number of the objects.
3) IMPACT OF THE FINE-TUNED CNN NETWORKS The extracted patches are used to fine-tune the pre-trained CNN architecture. To study the influence of this later on the quality performance, we conduct experiments with three different CNN architectures pre-trained on ImageNet dataset: AlexNet, VGG, and ResNet. As recorded in Table  4 , the performance of the proposed model varies from a pre-trained model to another. The use of residual networks (ResNet18 and ResNet50) and shallow network (AlexNet) decrease the performance since the difference is statistically significant between them and the VGG based models, P-value < 0. 5. These results might be explained by the fact that AlexNet is a shallow model and thus cannot extract enough relevant features. For ResNet based models, it seems that the skip connections introduce a kind of redundancy that is not relevant for the quality task  [56] ,  [57].
However, it is giving competitive results compared to the state-of-the-art methods on ICIP2020 database. Moreover, we test the impact of the depth for VGG and ResNet based models. As we can observe, the depth did not affect the performance results since the p-value > 0. 5, which indicates that the difference is not statistically significant. From the previous remarks, we conclude that the network architecture has more influence on the model performance compared to the effect of the depth. Based on all the obtained results, we use VGG16 as our pre-trained CNN model to compare the performance of our method with the state-of-the-art metrics.
1) COMPARISON WITH FR AND RR METHODS In Table  6 , 7 and 8, we list the correlation coefficient results on ICIP2020, SJTU, and WPC databases of our method in comparison with FR and RR methods. In ICIP2020 database (Table  6 ), considering all the PC objects distortions and regarding the FR methods, the Pointto-Point MSE (i. e. P2P MSE)  [21] , Point-to-Point Hausdorff (i. e. P2P Hausdorff)  [21] , Point-to-Plane MSE (i. e. P2Pl MSE)  [22] , Point-to-Plane Hausdorff (i. e. P2Pl Hausdorff), Plane-to-Plane (i. e. Pl2Pl)  [22]  and Point-to-Distribution-Geometry MMD (i. e. P2D-G MMD)  [24]  metrics are based on the geometry structure and are given the lower performance. One reason is that these methods do not consider the color information and are based only on a simple geometric distance to compute the quality of the PC.
This is proved by the better performance in the Point-to-Distribution-Geometry-Joint-Geometry-and-Color MMD (i. e. P2D-JGC MMD)  [24] , PCQM  [28] , BitDance  [31] , and GraphSIM  [33]  metrics that include the color information for the PC evaluation. We denote that the FR projection-based methods including, JGC-ProjQM DISTS, JGC-ProjQM LPIPS methods  [41] , achieve good correlations on each degradation type and on all the database. However, the quality evaluation of these methods depends on the reference information that is not available in the majority of practical applications. For the RR methods, the PCM metric  [43] , which is based on the comparison between the original and degraded PC in the receiver side to evaluate the transmitted contents, archives a PLCC and a SROCC of 0. 882 and 0.
627, respectively. By studying the different types of distortions individually, we remark that the proposed method outperforms the stateof-the-art methods on the three compression distortion types (VPCC, G-PCC Trisoup, and G-PCC Octree). In SJTU database (Table  7 ), the proposed method provides the best correlation coefficient results on all the distortions and outperforms the state-of-the-art metrics in SROCC and PLCC correlation coefficients. It is worth noting that the correlation values of our method and all the state-of-the-art methods are less than the correlation values of ICIP2020 dataset. This could be justified by the type of distortions in the two databases. ICIP2020 database consists only of objects with compression types, while SJTU database has more challenging types of degradation such as acquisition noise and resampling, that can be applied individually (Octree-based compression (OT), Color noise (CN), Geometry Gaussian noise (GGN) and Downsampling (DS)) or superimposed (Downscaling and Color noise (D+C), Downscaling and Geometry Gaussian noise (D+G), Color noise and Geometry Gaussian noise (C+G)).
method  [39]  and our proposed method. We note that the PCM metric  [43]  provides poor results on all types of degradation, which could be explained by the large set of features used in their method that prevents its generalization across all types of degradation. In WPC database (Table  8 ), our approach outperforms all the compared methods for each distortion type and for the whole database. We note that most of the compared methods achieve lower performance on the WPC database compared to the other two databases. This can be explained by the fact that some WPC reference object samples are less sensitive to distortions in perceived visual quality. For example, Banana, Honeydew-melon, and Litchi (See Fig. 4 ), etc, samples show less complexity in geometry structure and in texture color, making the distortion difference less obvious and harder to measure.
2) COMPARISON WITH NR METHODS To make fair comparison of our method with the NR ones, we follow the protocol proposed in  [49]  and  [48]  on SJTU and WPC databases. We take 75% of the database for training and the remaining for testing. As shown in Table  9 , the proposed method achieves the best SROCC and PLCC results on both databases, largely outperforming the compared NR methods, except for ResSCNN which shows competitive results on SJTU database. This can be explained by the projection-based NR metrics, including PQA-Net and IT-PCQA methods, not taking into consideration the impact of the number of views and utilizing much unnecessary background information that could drop the model accuracy. In our proposed model, we address this issue by conducting a patch extraction in the central axis that contributes to removing the majority of the background information and focusing on evaluating the local degradation in the PC.
To better illustrate the accuracy of the compared state-ofthe-art metrics, we provide the scatter plots of the predicted quality values and the subjective scores of the 3 benchmark databases: ICIP2020, SJTU and WPC, in Fig. 6 , 7 and 8. Note that the blue line presented in each sub-plot is obtained by exploiting a nonlinear curve fitting process according to the suggestion of the Video Quality Experts Group (VQEG)  [58]  that is defined in Equation  10 , MS p = a 0 + a 1 * (MP) + a 2 * (MP) 2  + a 3 * (MP) 3  (10)  where MS p is the predicted subjective score of the proposed model, MP is the objective quality score and a 0 , a 1 , a 2 and a 3 are parameters to be estimated. The closer the points are to the blue line, the better the model correlation and vice versa. As expected, our proposed model shows a good fit with the nonlinear regression curve on the 3 databases while some of the methods based on the simple geometric distance and the PCM metric are away from the fitted curve specially on the WPC database.
E. CROSS-DATASET EVALUATION In this section, we investigate the generalization capability and the robustness of the proposed NR-PCQA model by evaluating the quality scores using a cross-database test. To do so, we first train the proposed model on SJTU database then we test it on the ICIP2020 and WPC databases. As presented in Table  10 , our model provides decent correlation results (SROCC = 0. 765, PLCC = 0. 725) on ICIP2020 database. We note that only a single distortion type (G-PCC Octree) is shared between the training and the test database while the other distortions are different. When tested on WPC, our model shows decreased results, which can be explained by the large difference between the distribution of the objects and the distortions in the training and the test database, being more challenging for PCQA models.