SAIN: Self-Attentive Integration Network for Recommendation

With the growing importance of personalized recommendation, numerous recommendation models have been proposed recently. Among them, Matrix Factorization (MF) based models are the most widely used in the recommendation field due to their high performance. However, MF based models suffer from cold start problems where user-item interactions are sparse. To deal with this problem, content based recommendation models which use the auxiliary attributes of users and items have been proposed. Since these models use auxiliary attributes, they are effective in cold start settings. However, most of the proposed models are either unable to capture complex feature interactions or not properly designed to combine user-item feedback information with content information. In this paper, we propose Self-Attentive Integration Network (SAIN) which is a model that effectively combines user-item feedback information and auxiliary information for recommendation task. In SAIN, a self-attention mechanism is used in the feature-level interaction layer to effectively consider interactions between multiple features, while the information integration layer adaptively combines content and feedback information. The experimental results on two public datasets show that our model outperforms the state-of-the-art models by 2.13%Comment: SIGIR 201

Look at the First Sentence: Position Bias in Question Answering

Many extractive question answering models are trained to predict start and end positions of answers. The choice of predicting answers as positions is mainly due to its simplicity and effectiveness. In this study, we hypothesize that when the distribution of the answer positions is highly skewed in the training set (e.g., answers lie only in the k-th sentence of each passage), QA models predicting answers as positions can learn spurious positional cues and fail to give answers in different positions. We first illustrate this position bias in popular extractive QA models such as BiDAF and BERT and thoroughly examine how position bias propagates through each layer of BERT. To safely deliver position information without position bias, we train models with various de-biasing methods including entropy regularization and bias ensembling. Among them, we found that using the prior distribution of answer positions as a bias model is very effective at reducing position bias, recovering the performance of BERT from 37.48% to 81.64% when trained on a biased SQuAD dataset.Comment: 13 pages, EMNLP 202

Formal Verification of Security Model Using SPR Tool

In this paper, formal verification methodologies and the SPR (Safety Problem Resolver) model checking tool are used for verifying a security model's safety. The SPR tool makes it possible to analyze security issues on security systems based on the access control model. To illustrate this approach, a case study of the Simple Access Control Model (SACM) is used and specific safety problems of the security model are analyzed using the SPR tool

Double-Layer Buffer Template to Grow Commensurate Epitaxial BaBiO3 Thin Films

We propose a BaCeO3/BaZrO3 double-layer buffer template, grown on a SrTiO3 substrate, for epitaxial growth of a target oxide film with large lattice constants of over 4.1 . Lattice mismatch from the substrate was mostly accommodated for by a BaZrO3 arbitrating layer. Having an ideal in-plane lattice structure, BaCeO3 served as the main-buffer to grow the target material. We demonstrated commensurate epitaxy of BaBiO3 (BBO,a = 4.371 ) utilizing the new buffer template. Our results can be applied to heteroepitaxy and strain engineering of novel oxide materials of sizable lattice constants. © Author(s) 20161421sciescopu

Correlates associated with participation in physical activity among adults: a systematic review of reviews and update

Background Understanding which factors influence participation in physical activity is important to improve the public health. The aim of the present review of reviews was to summarize and present updated evidence on personal and environmental factors associated with physical activity. Methods MEDLINE and EMBASE were searched for reviews published up to 31 Jan. 2017 reporting on potential factors of physical activity in adults aged over 18 years. The quality of each review was appraised with the Assessing the Methodological Quality of Systematic Reviews (AMSTAR) checklist. The corrected covered area (CCA) was calculated as a measure of overlap for the primary publications in each review. Results Twenty-five articles met the inclusion criteria which reviewed 90 personal and 27 environmental factors. The average quality of the studies was moderate, and the CCA ranged from 0 to 4.3%. For personal factors, self-efficacy was shown as the strongest factor for participation in physical activity (7 out of 9). Intention to exercise, outcome expectation, perceived behavioral control and perceived fitness were positively associated with physical activity in more than 3 reviews, while age and bad status of health or fitness were negatively associated with participation in physical activity in more than 3 reviews. For environmental factors, accessibility to facilities, presence of sidewalks, and aesthetics were positively associated with participation in physical activity. Conclusions The findings of this review of reviews suggest that some personal and environmental factors were related with participation in physical activity. However, an association of various factors with physical activity could not be established because of the lack of primary studies to build up the organized evidence. More studies with a prospective design should be conducted to understand the potential causes for physical activity