Fact sheet: Automatic Self-Reported Personality Recognition Track

We propose an informed baseline to help disentangle the various contextual factors of influence in this type of case studies. For this purpose, we analysed the correlation between the given metadata and the self-assigned personality trait scores and developed a model based solely on this information. Further, we compared the performance of this informed baseline with models based on state-of-the-art visual, linguistic and audio features. For the present dataset, a model trained solely on simple metadata features (age, gender and number of sessions) proved to have superior or similar performance when compared with simple audio, linguistic or visual features-based systems

Designing an optimised supply network for sustainable conversion of waste agricultural plastics into higher value products

Agricultural plastics are currently characterised by a predominantly linear take-make-dispose value chain, thus being a major stream of waste that contributes to significant environmental and economic issues. Therefore, policy makers have recently indicated the adoption of circular economy approaches as the way forward for plastics. This study addresses the problem of agricultural plastic waste as a major stream of landfilled waste by assessing the potential for recycling the plastic into higher value products through pyrolysis and by optimally designing the respective supply network to support this process. A Mixed Integer Linear Programming (MILP) model is developed to optimise the end-to-end supply network design, from the waste generation stage up to the end consumer of the produced material. The model is supported by experimental results on the pyrolysis performance for contaminated plastic samples. The model is applied in a case study of the Scottish agricultural sector to showcase its potential in assessing the feasibility and financial viability in addition to the positive environmental impact on agricultural plastic waste supply networks. The results demonstrate the potential of using the pyrolysis technology for agricultural plastic waste recycling as an example of a circular economy approach and the benefits of using the developed model for decision making purposes, as well as the potential for waste reduction and the implications for farmers’ operations

Impact of management polyphony in family business: A review

The link between family and family-owned enterprise has persisted for generations within the framework of family, ownership, management, and business. Family-owned companies are created in order to generate revenue and preserve the family\u27s tradition. With the proper management strategy, family businesses can survive and be passed down to future generations. In family businesses, the founders and family member managers are the only ones who carry out main management tasks including planning, organizing, coordinating, leading, supervising, and controlling. Management polyphony can happen in a family-owned firm when more than one family member is designated as the manager. Although managerial polyphony may be viewed as a source of organizational transformation in well-managed circumstances, it may also be one of the primary causes of conflict in a family-owned business. This paper discussed theoretically the concept of management polyphony in family-owned firms

The more the better? The effect of domain-specific dataset on entity extraction from Dutch criminal records

The Dutch police force generates very high amounts of documents such as transcripts of interrogations, evidence findings, statements of people involved, all of which need to be read and processed by analysts. Automating the entity extraction in the documents would greatly help the police force. Neural network-based approaches using contextual word embeddings are considered the current state-of-the-art approach to tackle the named entity recognition (NER) problem in the Dutch. There are available domain-independent NER datasets in the literature well as pre-trained NER models. However, earlier studies show that domain-independent models do not work well for domain-specific tasks. As annotation is highly costly, in this study, we train a set of BERTje embeddings based NER models with the varying size of police dataset in addition to the domain-independent set to observe the effect of domain-specific dataset in the training. We follow a training, validation, and test split to ensure a proper experimental protocol. We observe that the slope of the performance increase is decreasing with the number of target domain documents in the training set and stabilizes on the validation set around 250-300 documents. The NER system has a better performance on the held-out test set (85\% macro-average F1 score over five entity categories) compared to the validation set, showing the generalization power of the investigated framework

Text-based Interpretable Depression Severity Modeling via Symptom Predictions

Mood disorders in general and depression in particular are common and their impact on individuals and society is high. Roughly 5% of adults worldwide suffer from depression. Commonly, depression diagnosis involves using questionnaires, either clinician-rated or self-reported. Due to the subjectivity in questionnaire methods and high human-related costs involved, there are ongoing efforts to find more objective and easily attainable depression markers. As is the case with recent audio, visual and linguistic applications, state-of-the-art approaches for automated depression severity prediction heavily depend on deep learning and black box modeling without explainability and interpretability considerations. However, for reasons ranging from regulations to understanding the extent and limitations of the model, the clinicians need to understand the decision making process of the model to confidently form their decisions. In this work, we focus on text-based depression severity level prediction on DAIC-WOZ corpus and benefit from PHQ-8 questionnaire items to predict the symptoms as interpretable high level features. We show that using a multi-task regression approach with state-of-the-art text-based features to predict the depression symptoms, it is possible to reach a viable test set Concordance Correlation Coefficient performance comparable to the state-of-the-art systems

Using Explainability for Bias Mitigation: A Case Study for Fair Recruitment Assessment

In this study, we propose a bias-mitigation algorithm, dubbed ProxyMute, that uses an explainability method to detect proxy features of a given sensitive attribute (e.g., gender) and reduces their effects on decisions by disabling them during prediction time. We evaluate our method for a job recruitment use-case, on two different multimodal datasets, namely, FairCVdb and ChaLearn LAP-FI. The exhaustive set of experiments shows that information regarding the proxy features that are provided by explainability methods is beneficial and can be successfully used for the problem of bias mitigation. Furthermore, when combined with a target label normalization method, the proposed approach shows a good performance by yielding one of the fairest results without deteriorating the performance significantly compared to previous works on both experimental datasets. The scripts to reproduce the results are available at: https://github.com/gizemsogancioglu/expl-bias-mitigation