CAISA at SemEval-2023 Task 8: Counterfactual Data Augmentation for Mitigating Class Imbalance in Causal Claim Identification

The class imbalance problem can cause machine learning models to produce an undesirable performance on the minority class as well as the whole dataset. Using data augmentation techniques to increase the number of samples is one way to tackle this problem. We introduce a novel counterfactual data augmentation by verb replacement for the identification of medical claims. In addition, we investigate the impact of this method and compare it with 3 other data augmentation techniques, showing that the proposed method can result in a significant (relative) improvement in the minority class

A study on the effect of workaholism on human resource productivity: A case study of managers of East Azerbaijan Water and Waste Water Company

These days, work is considered as an integral part of the human life and many people spend significant amount of their time in different organizations and departments to earn income. Unlimited organizational pressures and demands facing people have made them allocate much of their time on working. Because of these pressures, people are becoming increasingly subject to workaholism. On the other hand, leaders and managers are trying to improve performance and activities of their respective organizations. Therefore, different concepts such as productivity are turned to the major subject of the management and organizational studies within the same organizations. Note that today changeable and competitive environment and the available limited resources and facilities have turned the concept of productivity into one the most important preoccupations of management within modern organizations. In view of the limited studies and information available in Iran on workaholism and its adverse consequences, the present research intends to investigate and identifies the impacts of workaholism components on human resource productivity. In the present, research the descriptive-survey research method is used and where statistical community includes 130 managers of the East Azerbaijan Water and Waste Company. Using the correlation coefficient and linear regression technique the research tries to investigate the relationships between the concepts of workaholism and human resource productivity and demonstrates how they are applied in above-mentioned community

Improving BERT Performance for Aspect-Based Sentiment Analysis

Aspect-Based Sentiment Analysis (ABSA) studies the consumer opinion on the market products. It involves examining the type of sentiments as well as sentiment targets expressed in product reviews. Analyzing the language used in a review is a difficult task that requires a deep understanding of the language. In recent years, deep language models, such as BERT \cite{devlin2019bert}, have shown great progress in this regard. In this work, we propose two simple modules called Parallel Aggregation and Hierarchical Aggregation to be utilized on top of BERT for two main ABSA tasks namely Aspect Extraction (AE) and Aspect Sentiment Classification (ASC) in order to improve the model's performance. We show that applying the proposed models eliminates the need for further training of the BERT model. The source code is available on the Web for further research and reproduction of the results

UniParma at SemEval-2021 Task 5: Toxic Spans Detection Using CharacterBERT and Bag-of-Words Model

With the ever-increasing availability of digital information, toxic content is also on the rise. Therefore, the detection of this type of language is of paramount importance. We tackle this problem utilizing a combination of a state-of-the-art pre-trained language model (CharacterBERT) and a traditional bag-of-words technique. Since the content is full of toxic words that have not been written according to their dictionary spelling, attendance to individual characters is crucial. Therefore, we use CharacterBERT to extract features based on the word characters. It consists of a CharacterCNN module that learns character embeddings from the context. These are, then, fed into the well-known BERT architecture. The bag-of-words method, on the other hand, further improves upon that by making sure that some frequently used toxic words get labeled accordingly. With a 4 percent difference from the first team, our system ranked 36th in the competition. The code is available for further re-search and reproduction of the results

A novel Region of Interest Extraction Layer for Instance Segmentation

Given the wide diffusion of deep neural network architectures for computer vision tasks, several new applications are nowadays more and more feasible. Among them, a particular attention has been recently given to instance segmentation, by exploiting the results achievable by two-stage networks (such as Mask R-CNN or Faster R-CNN), derived from R-CNN. In these complex architectures, a crucial role is played by the Region of Interest (RoI) extraction layer, devoted to extracting a coherent subset of features from a single Feature Pyramid Network (FPN) layer attached on top of a backbone. This paper is motivated by the need to overcome the limitations of existing RoI extractors which select only one (the best) layer from FPN. Our intuition is that all the layers of FPN retain useful information. Therefore, the proposed layer (called Generic RoI Extractor - GRoIE) introduces non-local building blocks and attention mechanisms to boost the performance. A comprehensive ablation study at component level is conducted to find the best set of algorithms and parameters for the GRoIE layer. Moreover, GRoIE can be integrated seamlessly with every two-stage architecture for both object detection and instance segmentation tasks. Therefore, the improvements brought about by the use of GRoIE in different state-of-the-art architectures are also evaluated. The proposed layer leads up to gain a 1.1% AP improvement on bounding box detection and 1.7% AP improvement on instance segmentation. The code is publicly available on GitHub repository at https://github.com/IMPLabUniPr/mmdetection/tree/groie_de

Studies on Management of Emergency Service Systems

RÉSUMÉ: Forts des outils de la théorie des files d’attente, de la géométrie stochastique et des extensions développées en cours de route, nous présentons des modèles descriptifs de systèmes de services d’urgence organisés en fonction du potentiel de limitation explicite des distances de dispatching avec une fidélité accrue du modèle et une stratégie de dispatching pour atteindre des performances maximales avec des ressources limitées. En utilisant le terme «sauvegardes partielles» pour faire référence à des règles d’expédition avec des limites explicites sur les distances d’expédition, nous étendons d’abord le modèle classique de mise en file d’attente hypercube pour inclure des sauvegardes partielles avec des priorités. La procédure étendue pourra représenter les systèmes de services d’urgence dans lesquels le sous-ensemble de serveurs pouvant être envoyés à une demande d’intervention d’urgence dépend de l’origine et du niveau de service demandé. Cela permet de développer des modèles d’optimisation dans lesquels le concepteur du système laisse le choix des unités de réponse pouvant être envoyées dans chaque zone de demande et peut être intégré à l’espace de la solution avec d’autres variables de décision d’emplacement ou d’allocation. La nouvelle méthode descriptive et les modèles d’optimisation sur lesquels reposent les plans de répartition et de répartition optimaux correspondants devraient indiscutablement améliorer les performances et mieux refléter le comportement réel des répartiteurs lorsque la configuration instantanée du système constitue un facteur majeur dans la prise de décision. Par la suite, nous étendons notre analyse. des déploiements statiques couverts par le premier modèle vers des systèmes à relocalisation dynamique. En faisant des hypothèses d’uniformité sur les origines des demandes de service et les emplacements des unités d’intervention, nous développons un cadre théorique pour une évaluation rapide et aléatoire de la performance du système avec une politique de sauvegarde partielle donnée et des résultats donnés spécifiés en fonction du temps de réponse. Le modèle général permet de révéler tout potentiel théorique d’amélioration des performances du système en utilisant des stratégies de dispatching de secours partielles aux stratégies tactiques ou opérationnelles, sans connaître les détails de la méthode de relocalisation dynamique utilisée ni même de la distribution de la demande au-delà du taux total d’arrivée et de la densité. Nous présentons des résultats auxiliaires et des outils à l’appui de notre traitement des systèmes de service d’urgence avec sauvegardes partielles, notamment des notes sur les distributions de distance avec des effets liés et quelques lois de conservation du débit liées aux situations de file d’attente rencontrées dans le cadre de ce travail.----------ABSTRACT: Armed with tools in queuing theory, stochastic geometry, and extensions developed along the way, we present descriptive models of emergency service systems organized around and emphasizing the potential of explicitly limiting dispatch distances in increasing model fidelity and as a dispatching strategy to achieve maximal performance with limited resources. Borrowing the term ”partial backups” to refer to dispatch policies with explicit limits on the dispatch distances, we first extend the classic hypercube queuing model to incorporate partial backups with priorities. The extended procedure will be able to represent emergency service systems where the subset of servers that can be dispatched to a request for emergency intervention depend on the origin and level of service requested. This allows for development of optimization models where the choice of response units eligible for dispatch to each demand zone is left to the system designer and can be integrated into the solution space along with other location or allocation decision variables. The new descriptive method and thus the optimization models built upon and the corresponding optimal location and dispatch plans, should arguably lead to better performance and better reflect the actual dispatchers’ behavior where the instantaneous system configuration constitutes a major factor in making assignment decisions. We next extend our analysis of static deployments covered by the first model to systems with dynamic relocation. Making uniformity assumptions on the origins of service requests and locations of the response units, we develop a theoretical framework for quick and dirty evaluation of the system performance with a given partial backup policy and a given outcome specified as a function of response time. The general model, makes it possible to reveal any theoretical potential to improve system performance by employing partial backup dispatching strategies at tactical or operational, without knowing the details of the dynamic relocation method used or even the demand distribution beyond the total arrival rate and the density per area. Finally, auxiliary results and tools supporting our treatment of emergency service systems with partial backups are presented, which include notes on distance distributions with boundary effects and a few rate conservation laws related to the queuing situations we encountered in this work

Evaluation of Iron Status in 9-Month to 5-Year-Old Children with Febrile Seizures: A Case-Control Study in the South West of Iran

ObjectiveFebrile convulsions are prevalent in children aged between 9 months and 5 years, with an incidence of 2-5%. On the other hand, iron deficiency anemia is the most common hematologic disease of infancy and childhood with a period of incidence that coincides with the time of developing febrile convulsions.Therefore, it is hypothesized that there is a possible association between these conditions. This study was designed to elucidate this association.Materials & MethodsTwo sex and age matched groups (n=50 in each) of 9-month to 5-year-old febrile children who were admitted to Abuzar Hospital between September 2003 and October 2004 were selected. The first group, or the case group, included children with the first attack of febrile seizure and the second group, or the control group, included febrile children without seizure. Blood samples were collected for measuring complete blood count (CBC) indices, serum Iron,ferritin and total iron binding capacity (TIBC) levels.ResultsBoth groups were comparable for age, sex, and the type of febrile illness at admission, except for seizure. There was no significant difference in CBC, Iron and TIBC between two groups but a signicant difference was seen in MCV (Mean Corpuscular Volume), especially in females (P= 0.017). The ferritin level in the case group was significantly lower (30.3 ±16.5 µg/dl) than the control group (84.2 ±28.5 µg /dl) (P= 0.000).ConclusionThe findings of this study suggested a positive association between iron deficiency and the first febrile seizure in children. Supplemental iron may prevent the recurrence of febrile seizure. Prudently, further studies with larger sample sizes and longer follow-up periods need to be undertaken to substantiate this hypothesis.

A Framework for Modelling Probabilistic Uncertainty in Rainfall Scenario Analysis

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv