LRMM: Learning to Recommend with Missing Modalities

Multimodal learning has shown promising performance in content-based recommendation due to the auxiliary user and item information of multiple modalities such as text and images. However, the problem of incomplete and missing modality is rarely explored and most existing methods fail in learning a recommendation model with missing or corrupted modalities. In this paper, we propose LRMM, a novel framework that mitigates not only the problem of missing modalities but also more generally the cold-start problem of recommender systems. We propose modality dropout (m-drop) and a multimodal sequential autoencoder (m-auto) to learn multimodal representations for complementing and imputing missing modalities. Extensive experiments on real-world Amazon data show that LRMM achieves state-of-the-art performance on rating prediction tasks. More importantly, LRMM is more robust to previous methods in alleviating data-sparsity and the cold-start problem.Comment: 11 pages, EMNLP 201

New Negentropy Optimization Schemes for Blind Signal Extraction of Complex Valued Sources

Blind signal extraction, a hot issue in the field of communication signal processing, aims to retrieve the sources through the optimization of contrast functions. Many contrasts based on higher-order statistics such as kurtosis, usually behave sensitive to outliers. Thus, to achieve robust results, nonlinear functions are utilized as contrasts to approximate the negentropy criterion, which is also a classical metric for non-Gaussianity. However, existing methods generally have a high computational cost, hence leading us to address the problem of efficient optimization of contrast function. More precisely, we design a novel “reference-based” contrast function based on negentropy approximations, and then propose a new family of algorithms (Alg.1 and Alg.2) to maximize it. Simulations confirm the convergence of our method to a separating solution, which is also analyzed in theory. We also validate the theoretic complexity analysis that Alg.2 has a much lower computational cost than Alg.1 and existing optimization methods based on negentropy criterion. Finally, experiments for the separation of single sideband signals illustrate that our method has good prospects in real-world applications

Phase Difference Index: A Frequency-domain Analysis Tool for Structural Mode Identification

A new structural mode-identification approach, called the Phase Difference Index plot (PDI plot), is used to identify the natural modes in a building from its acceleration records response to earthquake ground motion. The phase difference index can be calculated by the Hilbert transform of displacement response curves obtained from acceleration records at two different floors in the building. The torsional mode can also be identified if two accelerometers are installed on opposite sides in a floor. To demonstrate the power of the new approach, earthquake acceleration records from ten buildings in Taiwan and the U.S. are used. After applying narrow band-pass filter (frequency bandwidth of 0.05-0.1 Hz) to the acceleration records, and then using Hilbert transform on pairs of displacement records on chosen floors (obtained from the acceleration records), the phase difference between the corresponding pair of designated floors can be calculated. The cosine of the phase difference angle of the two displacement curves is defined as the phase difference index (PDI). The phase difference index versus frequency graph forms a PDI plot. Structural behavior at chosen floors and in various frequency bands can be found by studying the PDI plot. From comparison of PDI plot and typical building mode shapes obtained based on estimated stiffness and mass distribution, the natural modes can be identified. The method has been tested for accuracy and limitations by applying it to data obtained from numerical simulations. Response of one full scale four-story model in Japan, and ten actual buildings in U.S. and Taiwan have been studied using the proposed method. It is shown that using the PDI plot method more modes of a building can be identified confidently compared to traditional frequency-domain and time-domain based methods

Establishing Business Collaboration between Contractors and Developers Based on Management Information System

The relationship between developers and contractors can change to a strategic business collaboration based on Management Information System (MIS). The purpose of this paper is to show how to establish this strategic collaboration through an efficient information circulating mode based on MIS. The paper interprets the extent and the importance of information in projects, and puts forward a collaboration mode of information circulating. This mode is feasible taking into consideration both the technological and economic aspects. The strategic business collaboration could help contractors and developers reduce project costs and increase competence. As a result of the study, the conceptual model of the collaboration mode through Internet and MIS is presented. Key words: business collaboration, construction industry, Management Information System (MIS) Résumé: La relation entre les exploitants et entrepreneurs peut se changer en collaboration commerciale stratégique basée sur le Système d’Information de Management( SIM). Cet essai vise à démontrer comment établir cette collaboration stratégique par un mode de circulation efficace des informations fondé sur le SIM. L’article interprère l’ampleur et l’importance des informations dans les projets, et met en avance un mode de collaboration de la circulation des informations. Prenant en considération les aspects technologique et économique, ce mode est faisable. La collaboration commerciale stratégique pourrait aider les entrepreneurs et exploitants à réduire le coût du projet et élever la compétence. Comme le résultat de l’étude, le modèle de conception du mode de collaboration par Internet et SIM est présenté. Mots-Clés: collaboration commerciale, industrie de construction, Système d’Information de Management (SIM