Skipping-Based Collaborative Recommendations inspired from Statistical Language Modeling

Due to the almost unlimited resource space on the Web, efficient search engines and recommender systems have become a key element for users to find resources corresponding to their needs. Recommender systems aims at helping users in this task by providing them some pertinent resources according to their context and their profiles, by applying various techniques such as statistical and knowledge discovery algorithms. One of the most successful approaches is Collaborative Filtering, which consists in considering user ratings to provide recommendations, without considering the content of the resources; however the ratings are the only criterion taken into account to provide the recommendations, although including some other criterion should enhance their accuracy. One such criterion is the context, which can be geographical, meteorological, social, etc. In this chapter we focus on the temporal context, more specifically on the order in which the resources were consulted. The appropriateness of considering the order is domain dependent: for instance, it seems of little help in domains such as online moviestores, in which user transactions are barely sequential; however it is especially appropriate for domains such as Web navigation, which has a sequential structure. We propose to follow this direction for this domain, the challenge being to find a low enough complexity sequential model while providing a better accuracy. We first put forward similarities between Web navigation and natural language, and propose to adapt statistical language models to Web navigation to compute recommendations. Second, we propose a new model inspired from the n-gram skipping model. This model has several advantages: (1) It has both a low time and a low space complexity while providing a full coverage, (2) it is able to handle parallel navigations and noise, (3) it is able to perform recommendations in an anytime framework, (4) weighting schemes are used to alleviate the importance of distant resources. Third, we provide a comparison of this SLM inspired model to the state of the art in terms of features, complexity, accuracy and robustness and present experimental results. Tests are performed on a browsing dataset extracted from Intranet logs provided by a French bank. Results show that the use of exponential decay weighting schemes when taking into account non contiguous resources highly improves the accuracy, and that the anytime configuration is able to provide a satisfying trade-off between an even lower computation time and a good accuracy while conserving a good coverage

Modelling students' effort using behavioral data

International audienceStudents' effort is often considered a key factor for students' success. It has several related definitions, none of which is widely adopted. In this paper, we define students' effort as the experienced cognitive load, which is the total amount of cognitive resources used during the execution of a given task. We propose an effort model to quantify students' effort based on this construct. Our approach uses behavioral measures (i.e., interaction and eye gaze data). Our preliminary results show that the eye gaze measures have an intermediary relationship with effort, while the interaction measures have a weak relationship with effort and seem slightly complementary to eye gaze measures

Apport des Learning Analytics

National audienceLes Learning Analytics – ou analyse de l’apprentissage – constituent une discipline émergente à la confluence de l’informatique, des sciences de l’éducation et des mathématiques. Leur objet d’étude est la collecte, l’analyse et l’utilisation intelligentes de données produites par l’apprenant. Si les Learning Analytics puisent leurs techniques dans plusieurs communautés, elles constituent en tant que telles un phénomène assez récent apparu avec la généralisation du numérique éducatif et la disponibilité de données massives sur l’apprentissage. Cet article présente brièvement leurs objectifs et quelques exemples illustratifs de leurs apports potentiels à l’amélioration ou à la meilleure compréhension de l’apprentissage

Collaborative Filtering inspired from Language Modeling

International audienceRecommender systems filter resources for a given user by predicting the most pertinent item given a specific context. This paper describes a new approach of generating suitable recommendations based on the active user's navigation stream. The underlying hypothesis is that the items order in the stream results from the intrinsic logic of the user's behavior. We show similarities between natural language and Internet navigation and put forward navigation specificities. We then design a new model that integrates advantages of statistical language models such as n-grams and triggers to compute recommendations. The resulting Sequence Based Recommender has been tested on Internet navigation artificial corpora

Building a student effort dataset: what can we learn from behavioral and physiological data

International audienceDecades of studies have shown that student's success is strongly dependent on their effort. Recently, this concept made its way into the domain of Learning Analytics. One of the major difficulties of these works is to correctly define the effort and to find relevant means of measuring it. Our approach is based on the Cognitive Load Theory, which provides a theoretical background issued from Learning Sciences, desired by the Learning Analytics domain. The cognitive load is a multidimensional construct that represents the load that performing a given task imposes on the cognitive system, and is often considered by researchers as being equivalent to mental effort. The cognitive load has long been studied in educational sciences, and several types of measures have been proposed that can be classified into four categories: (1) subjective measures, i.e., students' perceived effort, (2) performance measures, e.g., the outcome of student work assessments, (3) physiological measures, such as pupil dilation and heart rate, and (4) behavioral measures, such as points of fixations, and keyboard and mouse usage. In an exploratory work, we proposed a new cognitive load measurement model based on behavioral data. Our data consisted in keyboard and mouse usage, as well as page views and fixation points from an eye tracker, and were collected in the context of an online Esperanto course. Our results showed that eye tracking data provided a better indication of effort than keyboard, mouse and page view data, and that a slight complementarity exists between these two types of information. In the same spirit, Larmuseau et al. (2019) investigated the correlation between the cognitive load and two physiological measures from smart watches: skin conductance and skin temperature. The participants were future school teachers taking a course as part of their training. One of their main findings is a moderate correlation between effort and skin conductance. However, both these last approaches are preliminary and only focused on small samples (less than 20 participants)

Analysis of the interaction products in U(Mo,X)/Al and U(Mo,X)/Al(Si) diffusion couples, with X = Cr, Ti, Zr

International audienceIn the framework of the development of a low 235U enriched nuclear fuel for material testing reactors, γ-U(Mo)/Al based materials are considered as the most interesting prospect. In the process to optimize their composition, addition to both γ-U(Mo) and Al have been proposed. In this paper, the crystallographic composition of Interaction Layers (ILs) in γ-U(Mo,X)/Al and γ-U(Mo,X)/AlSi7 diffusion couples, with X = Cr, Ti, Zr, heat-treated at 600 °C for 2 h, were studied by micro-X-ray diffraction (μ-XRD). When compared to the U(Mo)/Al and U(Mo)/Al(Si) reference systems, all investigated systems involving either Al or Al(Si) as counterparts show interaction products composed of similar phases and related sequences of phase formation. Only relative thicknesses of sub-layers and relative fractions of intermediate phases are correlated with the nature of the X element in the γ-U(Mo,X) alloy. More generally this work shows that γ-U(Mo)/Al and γ-U(Mo)/Al(Si) ILs are now robustly described down to the micrometer scale

Sample Preparation and Warping Accuracy for Correlative Multimodal Imaging in the Mouse Olfactory Bulb Using 2-Photon, Synchrotron X-Ray and Volume Electron Microscopy

Integrating physiology with structural insights of the same neuronal circuit provides a unique approach to understanding how the mammalian brain computes information. However, combining the techniques that provide both streams of data represents an experimental challenge. When studying glomerular column circuits in the mouse olfactory bulb, this approach involves e.g., recording the neuronal activity with in vivo 2-photon (2P) calcium imaging, retrieving the circuit structure with synchrotron X-ray computed tomography with propagation-based phase contrast (SXRT) and/or serial block-face scanning electron microscopy (SBEM) and correlating these datasets. Sample preparation and dataset correlation are two key bottlenecks in this correlative workflow. Here, we first quantify the occurrence of different artefacts when staining tissue slices with heavy metals to generate X-ray or electron contrast. We report improvements in the staining procedure, ultimately achieving perfect staining in ∼67% of the 0.6 mm thick olfactory bulb slices that were previously imaged in vivo with 2P. Secondly, we characterise the accuracy of the spatial correlation between functional and structural datasets. We demonstrate that direct, single-cell precise correlation between in vivo 2P and SXRT tissue volumes is possible and as reliable as correlating between 2P and SBEM. Altogether, these results pave the way for experiments that require retrieving physiology, circuit structure and synaptic signatures in targeted regions. These correlative function-structure studies will bring a more complete understanding of mammalian olfactory processing across spatial scales and time

Characterizing bone density pattern and porosity in the human ossicular chain using synchrotron microtomography.

The auditory ossicles amplify and transmit sound from the environment to the inner ear. The distribution of bone mineral density is crucial for the proper functioning of sound transmission as the ossicles are suspended in an air-filled chamber. However, little is known about the distribution of bone mineral density along the human ossicular chain and within individual ossicles. To investigate this, we analyzed fresh-frozen human specimens using synchrotron-based phase-contrast microtomography. In addition, we analyzed the volume and porosity of the ossicles. The porosity for the auditory ossicles lies, on average, between 1.92% and 9.85%. The average volume for the mallei is 13.85 ± 2.15 mm3, for the incudes 17.62 ± 4.05 mm3 and 1.24 ± 0.29 mm3 for the stapedes. The bone density distribution showed a similar pattern through all samples. In particular, we found high bone mineralization spots on the anterior crus of the stapes, its footplate, and along areas that are crucial for the transmission of sound. We could also see a correlation between low bone mineral density and holey areas where the bone is only very thin or missing. Our study identified a similar pattern of bone density distribution within all samples: regions exposed to lower forces generally show higher bone density. Further, we observed that the stapes shows high bone mineral density along the anterior crus and its footplate, which may indicate its importance in transmitting sound waves to the inner ear

The C-terminal domain of the MERS coronavirus M protein contains a trans -Golgi network localization signal

International audienceCoronavirus M proteins represent the major protein component of the viral envelope. They play an essential role during viral assembly by interacting with all the other structural proteins. Coronaviruses bud into the endoplasmic reticulum (ER)-Golgi intermediate compartment (ERGIC), but the mechanisms by which M proteins are transported from their site of synthesis, the ER, to the budding site remain poorly understood. Here, we investigated the intracellular trafficking of the Middle East respiratory syndrome coronavirus (MERS-CoV) M protein. Subcellular localization analyses revealed that the MERS-CoV M protein is retained intracellularly in the trans-Golgi network (TGN), and we identified two motifs in the distal part of the C-terminal domain as being important for this specific localization. We identified the first motif as a functional diacidic DxE ER export signal, since substituting Asp-211 and Glu-213 with alanine induced retention of the MERS-CoV M in the ER. The second motif, 199 KxGxYR 204 , was responsible for retaining the M protein in the TGN. Substitution of this motif resulted in MERS-CoV M leakage toward the plasma membrane. We further confirmed the role of 199 KxGxYR 204 as a TGN retention signal by using chimeras between MERS-CoV M and the M protein of infectious bronchitis virus (IBV). Our results indicated that the C-terminal domains of both proteins determine their specific localization, namely, TGN and ERGIC/cis-Golgi for MERS-M and IBV-M, respectively. Our findings indicate that MERS-CoV M protein localizes to the TGN because of the combined presence of an ER export signal and a TGN retention motif