Positive Labels Research Articles

Abstract 12691: Comparison of Deep Learning and Radiomic Features to Differentiate Non-Ischemic and Ischemic Cardiomyopathy Using Cardiac MRI Cine Imaging

Introduction: Cardiac magnetic resonance (CMR) is frequently utilized to characterize etiology of cardiomyopathy (CM), but there is need for improved disease classification, standardization in the interpretation of findings, and throughput in analysis. Radiomics has been shown to classify disease in a semi-automated manner. Alternatively, deep learning (DL) provides the ability to identify unknown features in image data. Therefore, we sought to compare DL and radiomic approaches to differentiate ischemic vs non-ischemic cardiomyopathy (ICM vs NICM), using cardiac magnetic resonance (CMR) short axis cine images. Methods: We selected 291 patients with cardiomyopathy (CM) who underwent a CMR exam at Cleveland Clinic between 2008 and 2018, of which 249 had NICM (positive label) based on expert review of the CMR exam and electronic medical record documentation. We compared a radiomic and end-to-end DL approach to identify CM etiology from short axis cine images. Automatically generated radiomic features describing myocardial shape, texture, thickness, and motion in the cine images were used to train several machine learning classifiers. In the DL approach, we directly used the cine images to train several DL classifiers, without extracting radiomic features. We evaluated the classifiers through 5-fold cross validation using the area under the curve (AUC), F1-score, and accuracy metrics. Statistical significance was evaluated using paired 2-tailed t-test at 0.05 level. Results: Support vector machine (SVM) and DenseNet121 achieved the best metrics for radiomic and DL approaches respectively. The radiomic and DL approach achieved similar AUCs of 0.852 and 0.858 respectively, but DL approach achieved statistically significant higher F1-score of 0.758 vs 0.585 of the radiomic approach. Conclusions: An end-to-end DL approach more accurately identified NICM vs ICM compared to a radiomics approach, using only cine CMR images.

A Completely Digital Workflow for Differentials in Bone Marrow Cytomorphology Supported By Machine Learning Provides Promising Results in Object Detection

Background: Cytomorphology is an essential method to assess disease phenotypes. Recently, promising results of automation, digitalization and machine learning (ML) for this gold standard have been demonstrated. We reported on successful integration of such workflows into our lab routine, including automated scanning of peripheral blood smears and ML-based classification of blood cell images (ASH 2020). Following this pilot project, we are focusing on an equivalent approach for bone marrow.Aim: To establish a multistep-approach including scan of bone marrow smears and detection/classification of all kinds of bone marrow cell types in healthy individuals and leukemia patients.Methods: The method includes a pre-scan at 10x magnification for detecting suitable “areas of interest” (AOI) for cytomorphological analysis, a high resolution capture of a predefinable number of AOI at 40x magnification (always using oil) and an automated object detection and classification. For all scanning tasks, a Metafer Scanning System (Zeiss Axio Imager.Z2 microscope, automatic slide feeder SFx80 and automated oil disperser) from MetaSystems (Altlussheim, GER) was used.To generate training data for AOI detection, 37 bone marrow smears were scanned at 10x magnification. 6 different quality classes of regions (based on number and distribution of cells) were annotated by hem experts using polygons. In total, 185,000 grid images were extracted from the annotated regions and used for training a deep neural network (DNN) to distinguish the 6 quality classes and to generate a position list for a high resolution scan (40x magnification).In addition, we scanned the labeled AOI of 68 smears at 40x magnification, acquiring colour images (2048x1496 pixels) of bone marrow cell layers. Each single cell was labeled by human investigators using rectangular bounding boxes (in total: 47,118 cells in 511 images). We set up a supervised ML model, using the labeled 40x images as an input. We fine-tuned the COCO dataset pre-trained YOLOv5 model with our dataset and evaluated using 5-fold cross valuation. To reduce overfitting, image augmentation algorithms were applied.Results: Our first DNN was able to detect (10x magnification) and capture (40x magnification) AOI in bone marrow smears, sorted by quality and in acceptable time spans. Average time for the 10x pre-scan was 6 min. From the resulting position list, the 50 positions with highest quality values were acquired at an average of 1:30 min.Our second, independent DNN was able to detect nucleated cells at 94% sensitivity and 75% precision in unlabeled bone marrow images (40x magnification). In this model, we overweighted recall over precision (5:1) to avoid missing any objects of interest, assuming that false positive labels could be corrected by human investigators when reviewing digital images.For the classification of single cells, a third independent DNN will be necessary. Actually, different approaches are being tested, including our existing blood cell classifier and a former collaborative bone marrow classification model based on a training set of 100,000 annotated bone marrow cells. Depending on these results, new training data for generation of a completely new model could be assessed.The two existing models enable a fully automated digital workflow including scan of bone marrow smears and delivery of single cell image galleries for human classification already now.Conclusion: We here present solutions for multiple-DNN-based tools for bone marrow cytomorphology. They allow working digitally and remotely in routine diagnostics. Final solutions will offer single cell classifications and galleries for human review and include real time training of respective classifier models with dynamic datasets. [Display omitted] DisclosuresHaferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Kern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.

Optimasi Parameter Support Vector Machine Berbasis Algoritma Firefly Pada Data Opini Film

The Support Vector Machine (SVM) method is a method that is widely used in the classification process. The success of the classification of the SVM method depends on the soft margin coefficient C, as well as the parameter  of the kernel function. The SVM parameters are usually obtained by trial and error, but this method takes a long time because they have to try every combination of SVM parameters, therefore the purpose of this study is to find the optimal SVM parameter value based on accuracy. This study uses the Firefly Algorithm (FA) as a method for optimizing SVM parameters. The data set used in this study is data on public opinion on several films. Class labels used in data classification are positive class labels and negative class labels. The amount of data used in this study is 2179 data, with the distribution of 436 data as test data and 1743 data as training data. Based on this data, an evaluation process was carried out on the Firefly Algorithm-Support Vector Machine (FA-SVM). The results of this study indicate that the Firefly Algorithm can obtain the optimal combination of SVM parameters based on accuracy, so there is no need for trial and error to get that value. This is evidenced by the results of the FA-SVM evaluation using a value range of C=1.0-3.0 and =0.1-1.0 resulting in the highest accuracy of 87.84%. The next evaluation using a range of values ​​C=1.0-3.0 and =1.0-2.0 resulted in the highest accuracy of 87.15%.

The impact of recency and adequacy of historical information on sepsis predictions using machine learning

Sepsis is a major public and global health concern. Every hour of delay in detecting sepsis significantly increases the risk of death, highlighting the importance of accurately predicting sepsis in a timely manner. A growing body of literature has examined developing new or improving the existing machine learning (ML) approaches for timely and accurate predictions of sepsis. This study contributes to this literature by providing clear insights regarding the role of the recency and adequacy of historical information in predicting sepsis using ML. To this end, we implemented a deep learning model using a bidirectional long short-term memory (BiLSTM) algorithm and compared it with six other ML algorithms based on numerous combinations of the prediction horizons (to capture information recency) and observation windows (to capture information adequacy) using different measures of predictive performance. Our results indicated that the BiLSTM algorithm outperforms all other ML algorithms and provides a great separability of the predicted risk of sepsis among septic versus non-septic patients. Moreover, decreasing the prediction horizon (in favor of information recency) always boosts the predictive performance; however, the impact of expanding the observation window (in favor of information adequacy) depends on the prediction horizon and the purpose of prediction. More specifically, when the prediction is responsive to the positive label (i.e., Sepsis), increasing historical data improves the predictive performance when the prediction horizon is short-moderate.

Supervised contrastive learning over prototype-label embeddings for network intrusion detection

Contrastive learning makes it possible to establish similarities between samples by comparing their distances in an intermediate representation space (embedding space) and using loss functions designed to attract/repel similar/dissimilar samples. The distance comparison is based exclusively on the sample features. We propose a novel contrastive learning scheme by including the labels in the same embedding space as the features and performing the distance comparison between features and labels in this shared embedding space. Following this idea, the sample features should be close to its ground-truth (positive) label and away from the other labels (negative labels). This scheme allows to implement a supervised classification based on contrastive learning. Each embedded label will assume the role of a class prototype in embedding space, with sample features that share the label gathering around it. The aim is to separate the label prototypes while minimizing the distance between each prototype and its same-class samples. A novel set of loss functions is proposed with this objective. Loss minimization will drive the allocation of sample features and labels in embedding space. Loss functions and their associated training and prediction architectures are analyzed in detail, along with different strategies for label separation. The proposed scheme drastically reduces the number of pair-wise comparisons, thus improving model performance. In order to further reduce the number of pair-wise comparisons, this initial scheme is extended by replacing the set of negative labels by its best single representative: either the negative label nearest to the sample features or the centroid of the cluster of negative labels. This idea creates a new subset of models which are analyzed in detail.The outputs of the proposed models are the distances (in embedding space) between each sample and the label prototypes. These distances can be used to perform classification (minimum distance label), features dimensionality reduction (using the distances and the embeddings instead of the original features) and data visualization (with 2 or 3D embeddings).Although the proposed models are generic, their application and performance evaluation is done here for network intrusion detection, characterized by noisy and unbalanced labels and a challenging classification of the various types of attacks. Empirical results of the model applied to intrusion detection are presented in detail for two well-known intrusion detection datasets, and a thorough set of classification and clustering performance evaluation metrics are included.

Here I sit, making men in my own image: how learning disorder labels affect teacher student’s expectancies

Teacher’s evaluation of students is sometimes negatively affected by diagnostic labels. We explored such negative effects caused by the labels “dyscalculia”, “dyslexia”, and “ADHD” in teacher students. In Experiment 1, we varied the presence of the dyscalculia and dyslexia label in vignettes between participants. The dyslexia but not the dyscalculia label had a negative effect on participant’s academic expectations. In our preregistered Experiment 2, the presence of the ADHD label in vignettes was manipulated within participants. To understand the cognitive mechanisms driving label effects, we explored participants’ attributions regarding the students’ problems. Furthermore, a short dissonance-based intervention for counteracting negative label effects was implemented. Unexpectedly, we found both negative and positive label effects. The label led to more positive performance expectations and to more negative ratings of problem stability and problem control. The dissonance-based intervention led to more positive evaluations regardless of whether the ADHD label was mentioned or not. Overall, our findings suggest that learning-disorder labels affect teachers’ expectations in different ways.

Identifying COVID-19 by using spectral analysis of cough recordings: a distinctive classification study.

Sound signals from the respiratory system are largely taken as tokens of human health. Early diagnosis of respiratory tract diseases is of great importance because, if delayed, it exerts irreversible effects on human health. The Coronavirus pandemic, which is deeply shaking the world, has revealed the importance of this diagnosis even more. During the pandemic, it has become the focus of researchers to differentiate symptoms from similar diseases such as influenza. Among these symptoms, the difference in cough sound played a distinctive role in research. Clinical data collected under the supervision of doctors in a reliable environment were used as the dataset consisting of 16 subjects suspected of COVID-19 with a specific patient demographic. Using the polymerase chain reaction test, the suspected subjects were divided into two groups as negative and positive. The negative and positive labels represent the patients with non-COVID and with a COVID-19 cough, respectively. Using the 3D plot or waterfall representation of the signal frequency spectrum, the salient features of the cough data are revealed. In this way, COVID-19 can be differentiated from other coughs by applying effective feature extraction and classification techniques. Power spectral density based on short-time Fourier transform and mel-frequency cepstral coefficients (MFCC) were chosen as the efficient feature extraction method. From among the classification techniques, the support vector machine (SVM) algorithm was applied to the processed signals in order to identify and classify COVID-19 cough. In terms of results evaluation, the cough of subjects with COVID-19 was detected with 95.86% classification accuracy thanks to the radial basis function (RBF) kernel function of SVM and the MFCC method. The diagnosis of COVID-19 coughs was performed with 98.6% and 91.7% sensitivity and specificity, respectively.

Spectrum bias in algorithms derived by artificial intelligence: a case study in detecting aortic stenosis using electrocardiograms.

Spectrum bias can arise when a diagnostic test is derived from study populations with different disease spectra than the target population, resulting in poor generalizability. We used a real-world artificial intelligence (AI)-derived algorithm to detect severe aortic stenosis (AS) to experimentally assess the effect of spectrum bias on test performance. All adult patients at the Mayo Clinic between 1 January 1989 and 30 September 2019 with transthoracic echocardiograms within 180 days after electrocardiogram (ECG) were identified. Two models were developed from two distinct patient cohorts: a whole-spectrum cohort comparing severe AS to any non-severe AS and an extreme-spectrum cohort comparing severe AS to no AS at all. Model performance was assessed. Overall, 258 607 patients had valid ECG and echocardiograms pairs. The area under the receiver operator curve was 0.87 and 0.91 for the whole-spectrum and extreme-spectrum models, respectively. Sensitivity and specificity for the whole-spectrum model was 80% and 81%, respectively, while for the extreme-spectrum model it was 84% and 84%, respectively. When applying the AI-ECG derived from the extreme-spectrum cohort to patients in the whole-spectrum cohort, the sensitivity, specificity, and area under the curve dropped to 83%, 73%, and 0.86, respectively. While the algorithm performed robustly in identifying severe AS, this study shows that limiting datasets to clearly positive or negative labels leads to overestimation of test performance when testing an AI algorithm in the setting of classifying severe AS using ECG data. While the effect of the bias may be modest in this example, clinicians should be aware of the existence of such a bias in AI-derived algorithms.

Eye-tracking evidence for fixation asymmetries in verbal and numerical quantifier processing

AbstractWhen people are given quantified information (e.g., ‘there is a 60% chance of rain’), the format of quantifiers (i.e., numerical: ‘a 60% chance’ vs. verbal: ‘it is likely’) might affect their decisions. Previous studies with indirect cues of judgements and decisions (e.g., response times, decision outcomes) give inconsistent findings that could support either a more intuitive process for verbal than numerical quantifiers or a greater focus on the context (e.g., rain) for verbal than numerical quantifiers. We used two pre-registered eye-tracking experiments (n(1) = 148, n(2) = 133) to investigate decision-making processes with verbal and numerical quantifiers. Participants evaluated multiple verbally or numerically quantified nutrition labels (Experiment 1) and weather forecasts (Experiment 2) with different context valence (positive or negative), and quantities (‘low’, ‘medium’, or ‘high’ in Experiment 1 and ‘possible’, ‘likely’, or ‘very likely’ in Experiment 2) presented in a fully within-subjects design. Participants looked longer at verbal than numerical quantifiers, and longer at the contextual information with verbal quantifiers. Quantifier format also affected judgements and decisions: in Experiment 1, participants judged positive labels to be better in the verbal compared to the equivalent numerical condition (and to be worse for negative labels). In Experiment 2, participants decided on rain protection more for a verbal forecast of rain than the equivalent numerical forecast. The results fit the explanation that verbal quantifiers put more focus on the informational context than do numerical quantifiers, rather than prompting more intuitive decisions.

Deep Learning-Based COVID-19 Pneumonia Classification Using Chest CT Images: Model Generalizability.

Since the outbreak of the COVID-19 pandemic, worldwide research efforts have focused on using artificial intelligence (AI) technologies on various medical data of COVID-19–positive patients in order to identify or classify various aspects of the disease, with promising reported results. However, concerns have been raised over their generalizability, given the heterogeneous factors in training datasets. This study aims to examine the severity of this problem by evaluating deep learning (DL) classification models trained to identify COVID-19–positive patients on 3D computed tomography (CT) datasets from different countries. We collected one dataset at UT Southwestern (UTSW) and three external datasets from different countries: CC-CCII Dataset (China), COVID-CTset (Iran), and MosMedData (Russia). We divided the data into two classes: COVID-19–positive and COVID-19–negative patients. We trained nine identical DL-based classification models by using combinations of datasets with a 72% train, 8% validation, and 20% test data split. The models trained on a single dataset achieved accuracy/area under the receiver operating characteristic curve (AUC) values of 0.87/0.826 (UTSW), 0.97/0.988 (CC-CCCI), and 0.86/0.873 (COVID-CTset) when evaluated on their own dataset. The models trained on multiple datasets and evaluated on a test set from one of the datasets used for training performed better. However, the performance dropped close to an AUC of 0.5 (random guess) for all models when evaluated on a different dataset outside of its training datasets. Including MosMedData, which only contained positive labels, into the training datasets did not necessarily help the performance of other datasets. Multiple factors likely contributed to these results, such as patient demographics and differences in image acquisition or reconstruction, causing a data shift among different study cohorts.

Data Classification Using Decision Trees J48 Algorithm for Text Mining of Business Data

The business industry is generating a lot of data on daily business deals and financial transactions. These businesses are generating intensive-data like they need customer satisfaction on top priority, fulfilling their needs, etc. In every step, Data is being produced. This Data has a great value that is hidden from regular users. Data analytics is used to unhide those values. In our project, we are using a business-related dataset that contains strings and their class (0 or 1). 0 or 1 denotes the positive or negative string labels. To analyze this data, we are using a decision tree classification algorithm (J48 exceptionally) to perform text mining (classification) on our target dataset. Text mining comes under supervised learning (type). In-text mining, generally, we use two datasets. One is used to train the model, and the second dataset is used to predict the missing class labels in the second dataset based on this training model generated using the first dataset.

PERSEPSI MASYARAKAT TERHADAP PESANTREN : STUDI FENOMENOLOGI

This study aims to understand and analyze people's perceptions of the existence of Pondok Pesantren Tarbiyatul Muallimien al-Islamiyah (TMI) Al-Amien Prenduan, Sumenep, Madura. This research uses a qualitative approach to the type of case study. Data collection techniques are carried out through interviews, observation, and documentation. Data analysis was carried out through display data, data reduction, and concluding. The results showed that the community had a positive perception of the existence of the Pondok Pesantren Tarbiyatul Muallimien al-Islamiyah (TMI) Al-Amien Prenduan. The community provides a good and positive label and image for the boarding school with the attitude of the pesantren which has high disciplinary values, educators who care about their students, are ready for use in society, and students who have multi-competence and talent, so that the pesantren is in great demand by the surrounding community as the institution of choice for their sons and daughters.

New cryptographic hardness for learning intersections of halfspaces over boolean cubes with membership queries

We revisit the PAC learnability of the class of intersections of polynomially many halfspaces over boolean cubes in the membership query model. The previous works (Klivans-Sherstov 2009, Angluin-Kharitonov 1995) imply the unlearnability of this class based on cryptographic assumptions, which is established in the case that the learner cannot generate queries of positive labels. We investigate this issue, focusing on the case that the learner is given representations of input distributions (so it may generate queries of positive and negative labels).Our result is that assuming some new cryptographic primitives, the class is still unlearnable in the query model even if the learner has representations of the distributions. The result is established via a new argument. We show that if the class is learnable, we can come up with a differing-inputs obfuscator, which, however, does not exist given the cryptographic primitives. Thus the hardness result follows from the contradiction.

Local positive and negative label correlation analysis with label awareness for multi-label classification

Local positive and negative label correlation analysis with label awareness for multi-label classification

Semi-Supervised Learning Using Hierarchical Mixture Models: Gene Essentiality Case Study

Integrating gene-level data is useful for predicting the role of genes in biological processes. This problem has typically focused on supervised classification, which requires large training sets of positive and negative examples. However, training data sets that are too small for supervised approaches can still provide valuable information. We describe a hierarchical mixture model that uses limited positively labeled gene training data for semi-supervised learning. We focus on the problem of predicting essential genes, where a gene is required for the survival of an organism under particular conditions. We applied cross-validation and found that the inclusion of positively labeled samples in a semi-supervised learning framework with the hierarchical mixture model improves the detection of essential genes compared to unsupervised, supervised, and other semi-supervised approaches. There was also improved prediction performance when genes are incorrectly assumed to be non-essential. Our comparisons indicate that the incorporation of even small amounts of existing knowledge improves the accuracy of prediction and decreases variability in predictions. Although we focused on gene essentiality, the hierarchical mixture model and semi-supervised framework is standard for problems focused on prediction of genes or other features, with multiple data types characterizing the feature, and a small set of positive labels.

LightXML: Transformer with Dynamic Negative Sampling for High-Performance Extreme Multi-label Text Classification

Extreme multi-label text classification(XMC) is a task for finding the most relevant labels from a large label set. Nowadays deep learning-based methods have shown significant success in XMC. However, the existing methods (e.g., AttentionXML and X-Transformer etc) still suffer from 1) combining several models to train and predict for one dataset, and 2) sampling negative labels statically during the process of training label ranking model, which will harm the performance and accuracy of model. To address the above problems, we propose LightXML, which adopts end-to-end training and dynamical negative labels sampling. In LightXML, we use GAN like networks to recall and rank labels. The label recalling part will generate negative and positive labels, and the label ranking part will distinguish positive labels from these labels. Based on these networks, negative labels are sampled dynamically during label ranking part training. With feeding both label recalling and ranking parts with the same text representation, LightXML can reach high performance. Extensive experiments show that LightXML outperforms state-of-the-art methods in five extreme multi-label datasets with much smaller model size and lower computational complexity. In particular, on the Amazon dataset with 670K labels, LightXML can reduce the model size up to 72% compared to AttentionXML. Our code is available at http://github.com/kongds/LightXML.

Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling

Conversion rate (CVR) prediction is one of the most critical tasks for digital display advertising. Commercial systems often require to update models in an online learning manner to catch up with the evolving data distribution. However, conversions usually do not happen immediately after user clicks. This may result in inaccurate labeling, which is called delayed feedback problem. In previous studies, delayed feedback problem is handled either by waiting positive label for a long period of time, or by consuming the negative sample on its arrival and then insert a positive duplicate when conversion happens later. Indeed, there is a trade-off between waiting for more accurate labels and utilizing fresh data, which is not considered in existing works. To strike a balance in this trade-off, we propose Elapsed-Time Sampling Delayed Feedback Model (ES-DFM), which models the relationship between the observed conversion distribution and the true conversion distribution. Then we optimize the expectation of true conversion distribution via importance sampling under the elapsed-time sampling distribution. We further estimate the importance weight for each instance, which is used as the weight of loss function in CVR prediction. To demonstrate the effectiveness of ES-DFM, we conduct extensive experiments on a public data and a private industrial dataset. Experimental results confirm that our method consistently outperforms the previous state-of-the-art results.

An Unsupervised Multi-Shot Person Re-Identification Method via Mutual Normalized Sparse Representation and Stepwise Learning

Due to abundant prior information and widespread applications, multi-shot based person re-identification has drawn increasing attention in recent years. In this paper, the high labeling cost and huge unlabeled data motivate us to focus on the unsupervised scenario and a unified coarse-to-fine framework is proposed, named by Mutual Normalized Sparse Representation (MNSR). Our method is an iteration procedure and each iteration involves two key steps: label estimation and metric model learning. In the former, we present a MNSR model to infer the pairwise labels of cross-camera by endowing sparse representation coefficient with the probability property. MNSR explicitly takes the mutually correlation between cameras into consideration and thus produces more accurate results. Meanwhile, we propose a probability-guided positive pairwise label prediction method to mine hard positive samples. For the latter, we learn a metric model with the estimated pairwise labels as supervision. In this procedure, we select some reliable labels for training by configuring with a stepwise learning method, rather than use all the estimated pair samples. This procedure helps to prevent the noise samples damaging the learning of discriminative metric model, especially for the initial iterations. Extensive experiments are conducted on four publicly available datasets, including PRID 2011, iLIDS-VID, SAIVT-SoftBio and MARS, and the results demonstrate the superior performance of the MNSR method in comparison with state-of-the-art unsupervised multi-shot person re-identification methods.

Less Is Better: How Nutrition and Low-Carbon Labels Jointly Backfire on the Evaluation of Food Products.

(1) Background: Labeling is one of the significant strategies to guide sustainable consumption behaviors. Nowadays, multi labels being displayed on the front-of-pack of food products is a common phenomenon. However, labels seldom operate solo, and competition or complement effects may be exerted on different labels. Therefore, the research objective is to explore the interaction effect when nutrition and low-carbon labels appear simultaneously; (2) Methods: Across four scenario-based experiments, including ice cream, yogurt, steak, and toast, this study manipulated the separate and joint occurrences of low-carbon and nutrition labels, the interaction effect of joint labels was tested, and the serial mediation model, which includes resource allocation and anticipated enjoyment of food consumption, was verified; (3) Results: Results show that people have a positive preference for the nutrition label and the carbon label, respectively, while these two labels working simultaneously attenuate the positive effect of the single label. When facing nutrition and carbon labels simultaneously, people would infer partial resources are allocated to healthy and environmental aspects so they have a lower anticipated enjoyment from food consumption. Thus, these two labels working simultaneously attenuate the positive effect of the single label, and consumers have a lower evaluation of food products. In addition, the joint backfire on the effect is only exerted on people with a higher level of zero-sum bias and only when joint labels have a high consistency of labels; (4) Conclusions: This study solved the contradictory problem of the joint effect of positive labels. The findings in this research contribute to promote sustainable food consumption. We suggest that similar labels should be avoided in the same front-of-pack of food, and manufacturers need to use ads to bring down consumers’ zero-sum bias.

Automatic analysis of insurance reports through deep neural networks to identify severe claims

AbstractIn this paper, we develop a methodology to automatically classify claims using the information contained in text reports (redacted at their opening). From this automatic analysis, the aim is to predict if a claim is expected to be particularly severe or not. The difficulty is the rarity of such extreme claims in the database, and hence the difficulty, for classical prediction techniques like logistic regression to accurately predict the outcome. Since data is unbalanced (too few observations are associated with a positive label), we propose different rebalance algorithm to deal with this issue. We discuss the use of different embedding methodologies used to process text data, and the role of the architectures of the networks.