Within recent years, Financial Credit Risk Assessment (FCRA) has become an increasingly important issue within the financial industry. Therefore, the search for features that can predict the credit risk of an organization has increased. Using multiple statistical techniques, a variance of features has been proposed. Applying a structured literature review, 258 papers have been selected. From the selected papers, 835 features have been identified. The features have been analyzed with respect to the type of feature, the information sources needed and the type of organization that applies the features. Based on the results of the analysis, the features have been plotted in the FCRA Model. The results show that most features focus on hard information from a transactional source, based on official information with a high latency. In this paper, we readdress and -present our earlier work [1]. We extended the previous research with more detailed descriptions of the related literature, findings, and results, which provides a grounded basis from which further research on FCRA can be conducted.
Industrial robot manipulators are widely used for repetitive applications that require high precision, like pick-and-place. In many cases, the movements of industrial robot manipulators are hard-coded or manually defined, and need to be adjusted if the objects being manipulated change position. To increase flexibility, an industrial robot should be able to adjust its configuration in order to grasp objects in variable/unknown positions. This can be achieved by off-the-shelf vision-based solutions, but most require prior knowledge about each object tobe manipulated. To address this issue, this work presents a ROS-based deep reinforcement learning solution to robotic grasping for a Collaborative Robot (Cobot) using a depth camera. The solution uses deep Q-learning to process the color and depth images and generate a greedy policy used to define the robot action. The Q-values are estimated using Convolutional Neural Network (CNN) based on pre-trained models for feature extraction. Experiments were carried out in a simulated environment to compare the performance of four different pre-trained CNNmodels (RexNext, MobileNet, MNASNet and DenseNet). Results showthat the best performance in our application was reached by MobileNet,with an average of 84 % accuracy after training in simulated environment.
In a recent official statement, Google highlighted the negative effects of fake reviews on review websites and specifically requested companies not to buy and users not to accept payments to provide fake reviews (Google, 2019). Also, governmental authorities started acting against organisations that show to have a high number of fake reviews on their apps (DigitalTrends, 2018; Gov UK, 2020; ACM, 2017). However, while the phenomenon of fake reviews is well-known in industries as online journalism and business and travel portals, it remains a difficult challenge in software engineering (Martens & Maalej, 2019). Fake reviews threaten the reputation of an organisation and lead to a disvalued source to determine the public opinion about brands. Negative fake reviews can lead to confusion for customers and a loss of sales. Positive fake reviews might also lead to wrong insights about real users’ needs and requirements. Although fake reviews have been studied for a while now, there are only a limited number of spam detection models available for companies to protect their corporate reputation. Especially in times with the coronavirus, organisations need to put extra focus on online presence and limit the amount of negative input that affects their competitive position which can even lead to business loss. Given state-of-the-art derived features that can be engineered from review texts, a spam detector based on supervised machine learning is derived in an experiment that performs quite well on the well-known Amazon Mechanical Turk dataset.
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Kumasi and RokitScience contribute to increasing the ownership and income of cocoa farmers, with an emphasis on women. Kumasi has a successful history of developing and marketing cocoa juice, which aims to keep as much income as possible with the farmer. RokitScience has been involved in the creation of the Rokbar: a "bean to bar" empowering chocolate bar that is marketed and made entirely by women. Kumasi and RokitScience started setting up a cocoa-fruit-lab at the cocoa-cooperative COVIMA in early 2021 in Ivory-Coast, in collaboration with Beyond Beans Foundation/ETG and Döhler and financially supported by the Sustainable-Trade-Initiative (IDH). The goal is to support the cooperative, which is led by women, with the establishment of circular cocoa juice and chocolate production and in this way increase the income of the members of the cooperative. The cocoa pod contains cocoa beans embedded in cocoa pulp. This pulp is sweet and juicy and partly needed for cocoa bean fermentation for flavor development. Residual pulp can be used for new products like drinks, marmalades and more. The collaboration in the cocoa fruit lab created momentum to try-out a more circular approach whereby the extraction of juice was linked to a shorter fermentation period of the beans, influencing quality features of both the beans and potentially the chocolate. However, to optimize the production of juicy beans further and find a market for this (and potentially other) products requires further testing and development of a value proposition and marketing strategy. The main question of Kumasi and RokitScience at Hanzehogeschool Groningen and NHLStenden Hogeschool Amsterdam is: What is the effect on the quality of beans and chocolate if fermented after the extraction of juice? How can this be optimized: comparing ‘cocoa of excellence’ fermentation and drying to traditional post-harvest practices and how can we tell the world?
Kumasi and RokitScience contribute to increasing the ownership and income of cocoa farmers, with an emphasis on women. Kumasi has a successful history of developing and marketing cocoa juice, which aims to keep as much income as possible with the farmer. RokitScience has been involved in the creation of the Rokbar: a "bean to bar" empowering chocolate bar that is marketed and made entirely by women. Kumasi and RokitScience started setting up a cocoa-fruit-lab at the cocoa-cooperative COVIMA in early 2021 in Ivory-Coast, in collaboration with Beyond Beans Foundation/ETG and Döhler and financially supported by the Sustainable-Trade-Initiative (IDH). The goal is to support the cooperative, which is led by women, with the establishment of circular cocoa juice and chocolate production and in this way increase the income of the members of the cooperative. The cocoa pod contains cocoa beans embedded in cocoa pulp. This pulp is sweet and juicy and partly needed for cocoa bean fermentation for flavor development. Residual pulp can be used for new products like drinks, marmalades and more. The collaboration in the cocoa fruit lab created momentum to try-out a more circular approach whereby the extraction of juice was linked to a shorter fermentation period of the beans, influencing quality features of both the beans and potentially the chocolate. However, to optimize the production of juicy beans further and find a market for this (and potentially other) products requires further testing and development of a value proposition and marketing strategy.The main question of Kumasi and RokitScience at Hanzeschool Groningen and Hogeschool Amsterdam is: What is the effect on the quality of beans and chocolate if fermented after the extraction of juice? How can this be optimized: comparing ‘cocoa of excellence’ fermentation and drying to traditional post-harvest practices and how can we tell the world?
