The Footprint of Factorization Models and Their Applications in Collaborative Filtering

Factorization models have been successfully applied to the recommendation problems and have significant impact to both academia and industries in the field of Collaborative Filtering ( CF ). However, the intermediate data generated in factorization models’ decision making process (or training process , footprint ) have been overlooked even though they may provide rich information to further improve recommendations. In this article, we introduce the concept of Convergence Pattern, which records how ratings are learned step-by-step in factorization models in the field of CF. We show that the concept of Convergence Patternexists in both the model perspective (e.g., classical Matrix Factorization ( MF ) and deep-learning factorization) and the training (learning) perspective (e.g., stochastic gradient descent ( SGD ), alternating least squares ( ALS ), and Markov Chain Monte Carlo ( MCMC )). By utilizing the Convergence Pattern, we propose a prediction model to estimate the prediction reliability of missing ratings and then improve the quality of recommendations. Two applications have been investigated: (1) how to evaluate the reliability of predicted missing ratings and thus recommend those ratings with high reliability. (2) How to explore the estimated reliability to adjust the predicted ratings to further improve the predication accuracy. Extensive experiments have been conducted on several benchmark datasets on three recommendation tasks: decision-aware recommendation, rating predicted, and Top- N recommendation. The experiment results have verified the effectiveness of the proposed methods in various aspects.

A Deep Content-Based Model for Persian Rumor Verification

During the development of social media, there has been a transformation in social communication. Despite their positive applications in social interactions and news spread, it also provides an ideal platform for spreading rumors. Rumors can endanger the security of society in normal or critical situations. Therefore, it is important to detect and verify the rumors in the early stage of their spreading. Many research works have focused on social attributes in the social network to solve the problem of rumor detection and verification, while less attention has been paid to content features. The social and structural features of rumors develop over time and are not available in the early stage of rumor. Therefore, this study presented a content-based model to verify the Persian rumors on Twitter and Telegram early. The proposed model demonstrates the important role of content in spreading rumors and generates a better-integrated representation for each source rumor document by fusing its semantic, pragmatic, and syntactic information. First, contextual word embeddings of the source rumor are generated by a hybrid model based on ParsBERT and parallel CapsNets. Then, pragmatic and syntactic features of the rumor are extracted and concatenated with embeddings to capture the rich information for rumor verification. Experimental results on real-world datasets demonstrated that the proposed model significantly outperforms the state-of-the-art models in the early rumor verification task. Also, it can enhance the performance of the classifier from 2% to 11% on Twitter and from 5% to 23% on Telegram. These results validate the model's effectiveness when limited content information is available.

Observer-Based Control of Tilting-Pad Thrust Bearings

The active control of hydrodynamic bearings is beginning to receive more attention in the pursuit of lower power losses and reduced maintenance. This paper presents a method by which, from simple measurements, rich information can be deduced from a running bearing that can used to modify the operating parameters of the unit. The bearing is a line-pivot, unidirectional, steadily loaded, directly lubricated tilting pad thrust bearing. This control is achieved by designing an Observer whose inputs include the output measurement(s) from the bearing. The Observer is, in some ways, an inverse model of the bearing (or Plant) that runs in parallel to the bearing and estimates the states of the bearing, such as the applied load, pivot height, minimum film thickness, maximum temperature, effective temperature and power loss. These estimated parameters can then be used in a control algorithm to modify bearing parameters such as inlet temperature or pivot location. It is demonstrated that disturbances in the load on the bearing can be detected simply by measuring a representative temperature in the bearing or changes in pivot height. Appropriate corrective action can then be employed. Whilst only steady-state operation is considered, the method could be developed to study time-varying situations.

Sound Can Help Us See More Clearly

In the field of video action classification, existing network frameworks often only use video frames as input. When the object involved in the action does not appear in a prominent position in the video frame, the network cannot accurately classify it. We introduce a new neural network structure that uses sound to assist in processing such tasks. The original sound wave is converted into sound texture as the input of the network. Furthermore, in order to use the rich modal information (images and sound) in the video, we designed and used a two-stream frame. In this work, we assume that sound data can be used to solve motion recognition tasks. To demonstrate this, we designed a neural network based on sound texture to perform video action classification tasks. Then, we fuse this network with a deep neural network that uses continuous video frames to construct a two-stream network, which is called A-IN. Finally, in the kinetics dataset, we use our proposed A-IN to compare with the image-only network. The experimental results show that the recognition accuracy of the two-stream neural network model with uesed sound data features is increased by 7.6% compared with the network using video frames. This proves that the rational use of the rich information in the video can improve the classification effect.

Harnessing Large-Scale Herbarium Image Datasets Through Representation Learning

The mobilization of large-scale datasets of specimen images and metadata through herbarium digitization provide a rich environment for the application and development of machine learning techniques. However, limited access to computational resources and uneven progress in digitization, especially for small herbaria, still present barriers to the wide adoption of these new technologies. Using deep learning to extract representations of herbarium specimens useful for a wide variety of applications, so-called “representation learning,” could help remove these barriers. Despite its recent popularity for camera trap and natural world images, representation learning is not yet as popular for herbarium specimen images. We investigated the potential of representation learning with specimen images by building three neural networks using a publicly available dataset of over 2 million specimen images spanning multiple continents and institutions. We compared the extracted representations and tested their performance in application tasks relevant to research carried out with herbarium specimens. We found a triplet network, a type of neural network that learns distances between images, produced representations that transferred the best across all applications investigated. Our results demonstrate that it is possible to learn representations of specimen images useful in different applications, and we identify some further steps that we believe are necessary for representation learning to harness the rich information held in the worlds’ herbaria.

TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels

As the world struggles with several compounded challenges caused by the COVID-19 pandemic in the health, economic, and social domains, timely access to disaggregated national and sub-national data are important to understand the emergent situation but it is difficult to obtain. The widespread usage of social networking sites, especially during mass convergence events, such as health emergencies, provides instant access to citizen-generated data offering rich information about public opinions, sentiments, and situational updates useful for authorities to gain insights. We offer a large-scale social sensing dataset comprising two billion multilingual tweets posted from 218 countries by 87 million users in 67 languages. We used state-of-the-art machine learning models to enrich the data with sentiment labels and named-entities. Additionally, a gender identification approach is proposed to segregate user gender. Furthermore, a geolocalization approach is devised to geotag tweets at country, state, county, and city granularities, enabling a myriad of data analysis tasks to understand real-world issues at national and sub-national levels. We believe this multilingual data with broader geographical and longer temporal coverage will be a cornerstone for researchers to study impacts of the ongoing global health catastrophe and to manage adverse consequences related to people’s health, livelihood, and social well-being.

Effect of Interlayer Coupling and Symmetry on High-Order Harmonic Generation from Monolayer and Bilayer Hexagonal Boron Nitride

High-order harmonic generation (HHG) is a fundamental process which can be simplified as the production of high energetic photons from a material subjected to a strong driving laser field. This highly nonlinear optical process contains rich information concerning the electron structure and dynamics of matter, for instance, gases, solids and liquids. Moreover, the HHG from solids has recently attracted the attention of both attosecond science and condensed matter physicists, since the HHG spectra can carry information of electron-hole dynamics in bands and inter- and intra-band current dynamics. In this paper, we study the effect of interlayer coupling and symmetry in two-dimensional (2D) material by analyzing high-order harmonic generation from monolayer and two differently stacked bilayer hexagonal boron nitrides (hBNs). These simulations reveal that high-order harmonic emission patterns strongly depend on crystal inversion symmetry (IS), rotation symmetry and interlayer coupling.

Classification of Assembly Operations Using Recurrent Neural Networks

Visual sensor data of manual assembly operations offers rich information that can be extracted in order to analyze and digitalize the assembly. The worker’s interaction with tools and objects, as well as the spatial–temporal nature of assembly operations, makes the recognition and classification of assembly operations a complex task. Therefore, classical methods of computer vision do not provide a sufficient solution. This paper presents a recurrent neural network for the classification of manual assembly operations using visual sensor data and addresses the question as to what extent such a solution is feasible in terms of robustness and reliability. Since complex assembly operations are a combination of basic movements, four main assembly operations of the Methods Time-Measurement base operations are classified using a machine learning approach. A dataset of these four assembly operations, reach, grasp, move and release, containing RGB-, infrared-, and depth-data is used. A Convolutional Neural Network—Long Short Term Memory architecture is investigated regarding its applicability due to the spatial–temporal nature of the data.

Wind speed inference from environmental flow–structure interactions. Part 2. Leveraging unsteady kinematics

This work explores the relationship between wind speed and time-dependent structural motion response as a means of leveraging the rich information visible in flow–structure interactions for anemometry. We build on recent work by Cardona, Bouman and Dabiri (Flow, vol. 1, 2021, E4), which presented an approach using mean structural bending. Here, we present the amplitude of the dynamic structural sway as an alternative signal that can be used when mean bending is small or inconvenient to measure. A force balance relating the instantaneous loading and instantaneous deflection yields a relationship between the incident wind speed and the amplitude of structural sway. This physical model is applied to two field datasets comprising 13 trees of 4 different species exposed to ambient wind conditions. Model generalization to the diverse test structures is achieved through normalization with respect to a reference condition. The model agrees well with experimental measurements of the local wind speed, suggesting that tree sway amplitude can be used as an indirect measurement of mean wind speed, and is applicable to a broad variety of diverse trees.

Can Information Reduce Ethnic Discrimination? Evidence from Airbnb

We use data from Airbnb to identify the mechanisms underlying discrimination against ethnic minority hosts. Within the same neighborhood, hosts from minority groups charge 3.2 percent less for comparable listings. Since ratings provide guests with increasingly rich information about a listing’s quality, we can measure the contribution of statistical discrimination, building upon Altonji and Pierret (2001). We find that statistical discrimination can account for the whole ethnic price gap: ethnic gaps would disappear if all unobservables were revealed. Also, three-quarters (2.5 points) of the initial ethnic gap can be attributed to inaccurate beliefs of potential guests about hosts’ average group quality. (JEL D83, J15, L84)