With the advent and popularity of social network, more and more users like to share their experiences, such as ratings, reviews, and blogs. The new factors of social network like interpersonal influence and interest based on circles of friends bring opportunities and challenges for recommender system (RS) to solve the cold start and sparsity problem of datasets. Some of the social factors have been used in RS, but have not been fully considered. In this paper, three social factors, personal interest, interpersonal interest similarity, and interpersonal influence, fuse into a unified personalized recommendation model based on probabilistic matrix factorization. The factor of personal interest can make the RS recommend items to meet users' individualities, especially for experienced users. Moreover, for cold start users, the interpersonal interest similarity and interpersonal influence can enhance the intrinsic link among features in the latent space. We conduct a series of experiments on three rating datasets: Yelp, MovieLens, and Douban Movie. Experimental results show the proposed approach outperforms the existing RS approaches. Index Terms—Interpersonal influence, personal interest, recommender system, social networks —————————— ——————————

Personalized Recommendation Combining User Interest and Social Circle

From social media has emerged continuous needs for automatic travel recommendations. Collaborative filtering (CF) is the most well-known approach. However, existing approaches generally suffer from various weaknesses. For example , sparsity can significantly degrade the performance of traditional CF. If a user only visits very few locations, accurate similar user identification becomes very challenging due to lack of sufficient information for effective inference. Moreover, existing recommendation approaches often ignore rich user information like textual descriptions of photos which can reflect users’ travel preferences. The topic model (TM) method is an effective way to solve the “sparsity problem,” but is still far from satisfactory. In this paper, an author topic model-based collaborative filtering (ATCF) method is proposed to facilitate comprehensive points of interest (POIs) recommendations for social users. In our approach, user preference topics, such as cultural, cityscape, or landmark, are extracted from the geo-tag constrained textual description of photos via the author topic model instead of only from the geo-tags (GPS locations). Advantages and superior performance of our approach are demonstrated by extensive experiments on a large collection of data.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=4199&context=sis_research

Author Topic Model-Based Collaborative Filtering for Personalized POI Recommendations

Hyperspectral image superresolution is a highly attractive topic in computer vision and has attracted many researchers’ attention. However, nearly all the existing methods assume that multiple observations of the same scene are required with the observed low-resolution hyperspectral image. This limits the application of superresolution. In this paper, we propose a new framework to enhance the resolution of hyperspectral images by exploiting the knowledge from natural images: The relationship between low/high-resolution images is the same as that between low/high-resolution hyperspectral images. In the proposed framework, the mapping between low- and high-resolution images can be learned by deep convolutional neural network and be transferred to hyperspectral image by borrowing the idea of transfer learning. In addition, to study the spectral characteristic between low- and high-resolution hyperspectral image, collaborative nonnegative matrix factorization (CNMF) is proposed to enforce collaborations between the low- and high-resolution hyperspectral images, which encourages the estimated solution to extract the same endmembers with low-resolution hyperspectral image. The experimental results on ground based and remote sensing data suggest that the proposed method achieves comparable performance without requiring any auxiliary images of the same scene.

Hyperspectral Image Superresolution by Transfer Learning

There has been an exponential growth in the application of AI in health and in pathology. This is resulting in the innovation of deep learning technologies that are specifically aimed at cellular imaging and practical applications that could transform diagnostic pathology. This paper reviews the different approaches to deep learning in pathology, the public grand challenges that have driven this innovation and a range of emerging applications in pathology. The translation of AI into clinical practice will require applications to be embedded seamlessly within digital pathology workflows, driving an integrated approach to diagnostics and providing pathologists with new tools that accelerate workflow and improve diagnostic consistency and reduce errors. The clearance of digital pathology for primary diagnosis in the US by some manufacturers provides the platform on which to deliver practical AI. AI and computational pathology will continue to mature as researchers, clinicians, industry, regulatory organizations and patient advocacy groups work together to innovate and deliver new technologies to health care providers: technologies which are better, faster, cheaper, more precise, and safe.

/pdf/translational-ai-and-deep-learning-in-diagnostic-pathology-4gxgwy8rd6.pdf

Translational AI and Deep Learning in Diagnostic Pathology

Big data increasingly benefit both research and industrial area such as health care, finance service and commercial recommendation. This paper presents a personalized travel sequence recommendation from both travelogues and community-contributed photos and the heterogeneous metadata (e.g., tags, geo-location, and date taken) associated with these photos. Unlike most existing travel recommendation approaches, our approach is not only personalized to user's travel interest but also able to recommend a travel sequence rather than individual Points of Interest (POIs). Topical package space including representative tags, the distributions of cost, visiting time and visiting season of each topic, is mined to bridge the vocabulary gap between user travel preference and travel routes. We take advantage of the complementary of two kinds of social media: travelogue and community-contributed photos. We map both user's and routes’ textual descriptions to the topical package space to get user topical package model and route topical package model (i.e., topical interest, cost, time and season). To recommend personalized POI sequence, first, famous routes are ranked according to the similarity between user package and route package. Then top ranked routes are further optimized by social similar users’ travel records. Representative images with viewpoint and seasonal diversity of POIs are shown to offer a more comprehensive impression. We evaluate our recommendation system on a collection of 7 million Flickr images uploaded by 7,387 users and 24,008 travelogues covering 864 travel POIs in nine famous cities, and show its effectiveness. We also contribute a new dataset with more than 200 K photos with heterogeneous metadata in nine famous cities.

Personalized Travel Sequence Recommendation on Multi-Source Big Social Media

Nowadays, it is very convenient to capture photos by a smart phone. As using, the smart phone is a convenient way to share what users experienced anytime and anywhere through social networks, it is very possible that we capture multiple photos to make sure the content is well photographed. In this paper, an effective scalable mobile image retrieval approach is proposed by exploring contextual salient information for the input query image. Our goal is to explore the high-level semantic information of an image by finding the contextual saliency from multiple relevant photos rather than solely using the input image. Thus, the proposed mobile image retrieval approach first determines the relevant photos according to visual similarity, then mines salient features by exploring contextual saliency from multiple relevant images, and finally determines contributions of salient features for scalable retrieval. Compared with the existing mobile-based image retrieval approaches, our approach requires less bandwidth and has better retrieval performance. We can carry out retrieval with <200-B data, which is <5% of existing approaches. Most importantly, when the bandwidth is limited, we can rank the transmitted features according to their contributions to retrieval. Experimental results show the effectiveness of the proposed approach.

Scalable Mobile Image Retrieval by Exploring Contextual Saliency

The ability to accurately quantitate specific populations of cells is important for precision diagnostics in laboratory medicine. For example, the quantization of positive tumor cells can be used clinically to determine the need for chemotherapy in a cancer patient. In this paper, we describe a supervised learning framework with Convolutional Neural Network (CNN) and cast the cell counting task as a regression problem, where the global cell count is taken as the annotation to supervise training, instead of following the classification or detection framework. To further decrease the prediction error of counting, we tune several cutting-edge CNN architectures (e.g. Deep Residual Network) into the regression model. As the final output, not only the cell count is estimated for an image, but also its spatial density map is provided. The proposed method is evaluated with three state-of-the-art approaches on three cell image datasets and obtain superior performance.

/pdf/cell-counting-by-regression-using-convolutional-neural-ebvemftdqa.pdf

Cell Counting by Regression Using Convolutional Neural Network

Fine-grained food recognition is the detailed classification that provides more specialized and professional attribute information of food. It is the basic work to realize healthy diet recommendations and cooking instructions, nutrition intake management, and cafeteria self-checkout system. Chinese food lacks structured information, and ingredients composition is an important consideration. The current approaches mostly focus on global dish appearance without any analysis of ingredient composition and fully considering the attention of regional features. In this paper, we propose an Attention Fusion Network (AFN) and Food-Ingredient Joint Learning module for fine-grained food and ingredients recognition. The AFN first focuses on the food discrimination region against unstructured defeat and generates the feature embeddings jointly aware of the ingredients and food. The Food-Ingredient Joint Learning module aims at alleviating the issue of ingredients imbalance. Therefore, we propose a balance focal loss to optimize the feature expression ability of the network for ingredients. In experiments, the results of ingredients recognition show the state-of-the-art performances on fine-grained Chinese food dataset VIREO Food-172.

Food and Ingredient Joint Learning for Fine-Grained Recognition

The ability to automatically detect certain types of cells or cellular subunits in microscopy images is of significant interest to a wide range of biomedical research and clinical practices. Cell detection methods have evolved from employing hand-crafted features to deep learning-based techniques. The essential idea of these methods is that their cell classifiers or detectors are trained in the pixel space, where the locations of target cells are labeled. In this paper, we seek a different route and propose a convolutional neural network (CNN)-based cell detection method that uses encoding of the output pixel space. For the cell detection problem, the output space is the sparsely labeled pixel locations indicating cell centers. We employ random projections to encode the output space to a compressed vector of fixed dimension. Then, CNN regresses this compressed vector from the input pixels. Furthermore, it is possible to stably recover sparse cell locations on the output pixel space from the predicted compressed vector using $L_1$-norm optimization. In the past, output space encoding using compressed sensing (CS) has been used in conjunction with linear and non-linear predictors. To the best of our knowledge, this is the first successful use of CNN with CS-based output space encoding. We made substantial experiments on several benchmark datasets, where the proposed CNN + CS framework (referred to as CNNCS) achieved the highest or at least top-3 performance in terms of F1-score, compared with other state-of-the-art methods.

Cell Detection in Microscopy Images with Deep Convolutional Neural Network and Compressed Sensing

Landmark summarization with diverse viewpoints is very important in landmark retrieval, as it can create a comprehensive description of a landmark for users. In this paper we present an approach for summarizing a collection of landmark images from diverse viewpoints. First, we group landmark images with content overlap by viewpoint album (VA) generation. Second, we model the relative viewpoint of each image within the VA based on the spatial layout of distinctive descriptors of a landmark. Third, we express the relative viewpoint of an image with a 4-D viewpoint vector, including horizontal, vertical, scale, and rotation. Finally, we summarize the landmarks in terms of viewpoints. Experimental results show the effectiveness of the proposed landmark summarization approach.

Yao Xue

Papers

Scalable Mobile Image Retrieval by Exploring Contextual Saliency

Cell Counting by Regression Using Convolutional Neural Network

Food and Ingredient Joint Learning for Fine-Grained Recognition

Cell Detection in Microscopy Images with Deep Convolutional Neural Network and Compressed Sensing

Landmark Summarization With Diverse Viewpoints