A new uncertainty-aware similarity for user-based collaborative filtering

summary:User-based Collaborative Filtering (UBCF) is a common approach in Recommender Systems (RS). Essentially, UBCF predicts unprovided entries for the target user by selecting similar neighbors. The effectiveness of UBCF greatly depends on the selected similarity measure and the subsequent choice of neighbors. This paper presents a new Uncertainty-Aware Similarity measure "UASim" which enhances CF by accurately calculating how similar, dissimilar, and uncertain users' preferences are. Uncertainty is a key factor of "UASim" that is managed in the neighborhood selection step of CF. Extensive experimental evaluation, conducted on Flixter, Movielens-100K, and Movielens-1M datasets, indicates that "UASim" shows better performance compared to many representative predefined similarity measures. The proposed measure demonstrates enhancements across various performance indicators, namely: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), coverage, and the F-score

Effet d’incorporation des feuilles de persil frais et séché sur la qualité organoleptique et microbiologique du fromage fondu Soutenu

Le présent travail consiste à enrichir le fromage fondu par différentes concentrations de persil frais et sèche afin d’obtenir un aliment fonctionnel. L’analyse de la plante a montré que le taux d’humidité du persil frais était de 79%, la teneur en cendres était de 3,175% et celle de persil séché était de 16,725%, ayant aussi des teneurs appréciables en polyphénols, flavonoïdes, et en caroténoïdes. Cette étude conduit a évalué l’influence de l’incorporation de persil sur les paramètres physicochimiques, microbiologiques, et organoleptiques de fromage fondu au fil du temps. D’après les résultats d’analyses physico-chimiques ; le PH et la teneur en sel du fromage tendent à se diminuer avec le temps en revanche, l’humidité du fromage a augmenté. Les résultats d’analyses microbiologiques montrent que le fromage enrichi présente une qualité hygiénique acceptable. L’analyse sensorielle a révélé que l’échantillon 4 (fs 0,1 g) est le plus apprécié par les dégustateurs

Activity recognition in smart homes with self verification of assignments

Activity recognition in smart homes provides valuable benefits in the field of health and elderly care by remote monitoring of patients. In health care, capabilities of both performing the correct recognition and reducing the wrong assignments are of high importance. The novelty of the proposed activity recognition approach lies in being able to assign a category to the incoming activity, while measuring the confidence score of the assigned category that reduces the false positives in the assignments. Multiple sensors deployed at different locations of a smart home are used for activity observations. For multi-class activity classification, we propose a binary solution using support vector machines, which simplifies the problem to correct/incorrect assignments. We obtain the confidence score of each assignment by estimating the activity distribution within each class such that the assignments with low confidence are separated for further investigation by a human operator. The proposed approach is evaluated using a comprehensive performance evaluation metrics. Experimental results obtained from nine publicly available smart home datasets demonstrate a better performance of the proposed approach compared to the state of the art

Evidential-Link-based Approach for Re-ranking XML Retrieval Results

In this paper, we propose a new evidential link-based approach for re-ranking XML retrieval results. The approach, based on Dempster-Shafer theory of evidence, combines, for each retrieved XML element, content relevance evidence, and computed link evidence (score and rank). The use of the Dempster–Shafer theory is motivated by the need to improve retrieval accuracy by incorporating the uncertain nature of both bodies of evidence (content and link relevance). The link score is computed according to a new link analysis algorithm based on weighted links, where relevance is propagated through the two types of links, i.e., hierarchical and navigational. The propagation, i.e. the amount of relevance score received by each retrieved XML element, depends on link weight which is defined according to two parameters: link type and link length. To evaluate our proposal we carried out a set of experiments based on INEX data collectio

Integration of discriminative and generative models for activity recognition in smart homes

Activity recognition in smart homes enables the remote monitoring of elderly and patients. In healthcare systems, reliability of a recognition model is of high importance. Limited amount of training data and imbalanced number of activity instances result in over-fitting thus making recognition models inconsistent. In this paper, we propose an activity recognition approach that integrates the distance minimization (DM) and probability estimation (PE) approaches to improve the reliability of recognitions. DM uses distances of instances from the mean representation of each activity class for label assignment. DM is useful in avoiding decision biasing towards the activity class with majority instances; however, DM can result in over-fitting. PE on the other hand has good generalization abilities. PE measures the probability of correct assignments from the obtained distances, while it requires a large amount of data for training. We apply data oversampling to improve the representation of classes with less number of instances. Support vector machine (SVM) is applied to combine the outputs of both DM and PE, since SVM performs better with imbalanced data and further improves the generalization ability of the approach. The proposed approach is evaluated using five publicly available smart home datasets. The results demonstrate better performance of the proposed approach compared to the state-of-the-art activity recognition approaches

An overview of data fusion techniques for internet of things enabled physical activity recognition and measure

Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Measure (PARM) has been widely recognised as a key paradigm for a variety of smart healthcare applications. Traditional methods for PARM relies on designing and utilising Data fusion or machine learning techniques in processing ambient and wearable sensing data for classifying types of physical activity and removing their uncertainties. Yet they mostly focus on controlled environments with the aim of increasing types of identifiable activity subjects, improved recognition accuracy and measure robustness. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to an open and dynamic uncontrolled ecosystem by connecting heterogeneous cost-effective wearable devices and mobile apps and various groups of users. Little is currently known about whether traditional Data fusion techniques can tackle new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand potential use and opportunities of Data fusion techniques in IoT enabled PARM applications, this paper will give a systematic review, critically examining PARM studies from a perspective of a novel 3D dynamic IoT based physical activity collection and validation model. It summarized traditional state-of-the-art data fusion techniques from three plane domains in the 3D dynamic IoT model: devices, persons and timeline. The paper goes on to identify some new research trends and challenges of data fusion techniques in the IoT enabled PARM studies, and discusses some key enabling techniques for tackling them

A Numerical Simulation of the Effect of Buffer Layer Band Gap on the Performances of nc-Si : H Based Solar Cells

This paper describes an investigation, by using numerical simulation, into the impacts of i-nc-Si : H buffer layer band gap on the photovoltaic parameters of n-i-p hydrogenated nanocrystalline silicon (nc-Si : H) solar cells. The output external cell parameters, like, the short-circuit current (JSC), the open circuit voltage (VOC), the fill factor (FF) and efficiency (Eff) are simulated by varying the mobility band gap (Eg) of i-nc-Si : H buffer layer. Also, the band diagram of nc-Si : H n-i-p solar cell, the electric field and the traped hole density at i/p interface, and the external quantum efficiency, with different values of buffer layer band gap where optimized. The simulation result shows that in valence band and for both interfaces, the band offsets ΔEV1 at p-nc-Si : H (window layer) / i-nc-Si : H (buffer layer) and ΔEV2 at i-nc-Si : H (buffer layer) / i-nc-Si : H (absorber layer) can be affected by varying Eg. It is obtained that the values efficiency are 10.89 % and 11.33 % when the value of i-nc-Si : H buffer layer band gap are 1.4 eV and 1.55 eV, respectively. However, the i-nc-Si : H buffer layer band gap of 1.55 eV was optimized for obtaining a better efficiency for n-i-p solar cell based on hydrogenated nanocrystalline silicon

Sustaining Parental Loyalty in Repeat Participation of Youth Exchange Programs: A Study of Factors that Promote Repeat Participation in Sending Multiple Children with a Non-Profit Organization in Finland

The aim of this research is to investigate factors that contribute to parental loyalty in repeat participation in youth exchange programs. Through purposive sampling and semi-structured interviews, the study analyzed the experiences and perspectives of nine Finnish parents that sent a child or children on high school exchange with YFU Finland. The results indicate that trust, reputation, and service quality are significant triggers in the parental decision-making process, underlined by positive experiences. Parents who had been on YFU exchange themselves, trusted in their own positive experience in determining the exchange organization for their child. These parents perceived YFU experienced familiarity with YFU practices, i.e. trusted the YFU service quality. In contrary, other parents’ triggers could be divided in three stages: learning about YFU’s reputation through other’s positive exchange experiences, obtaining positive experiences of YFU’s service quality themselves, and finally, hearing about their child’s positive exchange experience. The research provides insights on how organizations can foster parental loyalty for rare, educational purchases’ by offering personalized and supportive services, leading to positive experiences and, in turn, repeat participation. The study offers significant value beyond YFU Finland in terms of enhancing customer retention in a highly competitive market

Simulation numérique du comportement non linéaire des structures en béton de fibre

77 f : ill. ; 30 cm. (+ CD-Rom)Dans le cadre de cette étude on s’intéresse au développement d’un nouvel outil permettant la simulation numérique du comportement non linéaire des structures en béton de fibre. Pour cela, nous nous sommes intéressés au comportement des poutres moyennant un modèle basé sur les théories de Bernoulli et Timochenko. Cette simulation est réalisée à l’aide d’un code écrit en langage Gibiane sur CASTEM qui est un logiciel de calcul des structures par élément fini. Enfin, plusieurs exemples extraits de la littérature ont été testés. La comparaison des résultats obtenus avec des résultats expérimentaux, est très satisfaisante