Nutri-Score: The Most Efficient Front-of-Pack Nutrition Label to Inform Portuguese Consumers on the Nutritional Quality of Foods and Help Them Identify Healthier Options in Purchasing Situations

Several studies have identified Front-of-Pack Nutrition Labels (FoPLs) as a promising strategy to improve the nutritional quality of consumers’ food choices and encourage manufacturers to offer healthier products. This study aims to fill the evidence gap regarding the most effective FoPL among the Portuguese population. In total, 1059 Portuguese participants were recruited through a web panel provider and asked to declare their intended food choices and to rank three sets of products (pizza, cakes and breakfast cereals) according to their nutritional quality, first in the absence of any labelling, and then with a FoPL displayed on-pack (five FoPLs tested). Finally, participants were asked to answer nine statements related to perceptions of FoPLs. Results showed that participants improved their food choices, depending on the FoPL and the food category. All FoPLs led to a higher percentage of correct responses on the ranking task compared to the no label condition. The Nutri-Score was among the FoPLs producing the greatest improvement across all food categories compared to the reference intakes (OR = 6.45 [4.43–9.39], p-value < 0.0001) and facilitating the highest percentage to correctly rank products according to nutritional quality. This study suggests that, among the available options, Nutri-Score is the most efficient FoPL to inform Portuguese consumers of the nutritional quality of foods and help them identify healthier options in mock purchasing situations.

L’incidence d’un enseignement centré sur la forme sur la performance orale

The present study examines the timing effect of form-focused instruction within a task on language performance. One hundred and ten university-level, French as a second language students of B1 and B2 proficiency performed a ranking task. Two groups received instruction on the subjunctive prior to completing the task, two groups received instruction during the task, and two groups received instruction after the task. Performance was analyzed along the lines of structural complexity, accuracy, fluidity, and lexical complexity. The group receiving instruction prior to task completion displayed the most structural complexity, overall accuracy, and fluidity. Instruction during the task resulted in the greatest degree of lexical complexity and accuracy with respect to the use of the subjunctive. The post-task instruction group did not stand out in any respect. The results of the study demonstrate that form-focus instruction given prior to task completion does not necessarily yield negative effects on performance, contrary to some theoretical predictions or pedagogical recommendations regarding Task-Based Language Teaching.

A Students’ Preferences-Based Approach to Select Methods for Detecting and Handling Free-Riding

Free-riding is a serious challenge in group projects. While there are various methods to reduce free-riding, marketing educators still face a difficult task when selecting an appropriate method for their course. In this study, we propose a students’ preferences-based approach that supports marketing educators with the selection of methods to detect and handle free-riding. To measure these preferences, students completed an online survey based on a choice task about two methods to detect free-riding and a ranking task about four methods to handle free-riding ( n = 254). Their answers were analyzed using chi-squared tests, Borda scores, and rank-ordered logit models. The results show that (a) neither Dutch nor international students have a clear preference for one of the two detection methods (the reporting system vs. the process evaluation system), (b) grade discussion (a possible reduction of the free-rider’s grade based on a conversation with the course coordinator about each student’s contribution) is the most preferred method to handle free-riding, and (c) international students have a stronger preference for stricter handling methods. Marketing educators can apply the proposed approach, or use our specific findings, for designing methods to reduce free-riding in their courses.

Honeybees solve a multi-comparison ranking task by probability matching

Honeybees forage on diverse flowers which vary in the amount and type of rewards they offer, and bees are challenged with maximizing the resources they gather for their colony. That bees are effective foragers is clear, but how bees solve this type of complex multi-choice task is unknown. Here, we set bees a five-comparison choice task in which five colours differed in their probability of offering reward and punishment. The colours were ranked such that high ranked colours were more likely to offer reward, and the ranking was unambiguous. Bees' choices in unrewarded tests matched their individual experiences of reward and punishment of each colour, indicating bees solved this test not by comparing or ranking colours but by basing their colour choices on their history of reinforcement for each colour. Computational modelling suggests a structure like the honeybee mushroom body with reinforcement-related plasticity at both input and output can be sufficient for this cognitive strategy. We discuss how probability matching enables effective choices to be made without a need to compare any stimuli directly, and the use and limitations of this simple cognitive strategy for foraging animals.

An Approach to Knowledge Base Completion by a Committee-Based Knowledge Graph Embedding

Knowledge bases such as Freebase, YAGO, DBPedia, and Nell contain a number of facts with various entities and relations. Since they store many facts, they are regarded as core resources for many natural language processing tasks. Nevertheless, they are not normally complete and have many missing facts. Such missing facts keep them from being used in diverse applications in spite of their usefulness. Therefore, it is significant to complete knowledge bases. Knowledge graph embedding is one of the promising approaches to completing a knowledge base and thus many variants of knowledge graph embedding have been proposed. It maps all entities and relations in knowledge base onto a low dimensional vector space. Then, candidate facts that are plausible in the space are determined as missing facts. However, any single knowledge graph embedding is insufficient to complete a knowledge base. As a solution to this problem, this paper defines knowledge base completion as a ranking task and proposes a committee-based knowledge graph embedding model for improving the performance of knowledge base completion. Since each knowledge graph embedding has its own idiosyncrasy, we make up a committee of various knowledge graph embeddings to reflect various perspectives. After ranking all candidate facts according to their plausibility computed by the committee, the top-k facts are chosen as missing facts. Our experimental results on two data sets show that the proposed model achieves higher performance than any single knowledge graph embedding and shows robust performances regardless of k. These results prove that the proposed model considers various perspectives in measuring the plausibility of candidate facts.

Deep Match to Rank Model for Personalized Click-Through Rate Prediction

Click-through rate (CTR) prediction is a core task in the field of recommender system and many other applications. For CTR prediction model, personalization is the key to improve the performance and enhance the user experience. Recently, several models are proposed to extract user interest from user behavior data which reflects user's personalized preference implicitly. However, existing works in the field of CTR prediction mainly focus on user representation and pay less attention on representing the relevance between user and item, which directly measures the intensity of user's preference on target item. Motivated by this, we propose a novel model named Deep Match to Rank (DMR) which combines the thought of collaborative filtering in matching methods for the ranking task in CTR prediction. In DMR, we design User-to-Item Network and Item-to-Item Network to represent the relevance in two forms. In User-to-Item Network, we represent the relevance between user and item by inner product of the corresponding representation in the embedding space. Meanwhile, an auxiliary match network is presented to supervise the training and push larger inner product to represent higher relevance. In Item-to-Item Network, we first calculate the item-to-item similarities between user interacted items and target item by attention mechanism, and then sum up the similarities to obtain another form of user-to-item relevance. We conduct extensive experiments on both public and industrial datasets to validate the effectiveness of our model, which outperforms the state-of-art models significantly.