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Event popularity prediction over social media is crucial for estimating information propagation scope, decision making, and emergency prevention. However, existing approaches only focus on predicting the occurrences of single attribute such as a message, a hashtag or an image, which are not comprehensive enough for representing complex social event propagation. In this paper, we predict the event popularity, where an event is described as a set of messages containing multiple hashtags. We propose a novel hashtag-influence-based event popularity prediction by mining the impact of an influential hashtag set on the event propagation. Specifically, we first propose a hashtag-influence-based cascade model to select the influential hashtags over an event hashtag graph built by the pairwise hashtag similarity and the topic distribution of event-related hashtags. A novel measurement is proposed to identify the hashtag influence of an event over its content and social impacts. A hashtag correlation-based algorithm is proposed to optimize the seed selection in a greedy manner. Then, we propose an event-fitting boosting model to predict the event popularity by embedding the feature importance over events into the XGBOOST model. Moreover, we propose an event-structure-based method, which incrementally updates the prediction model over social streams. We have conducted extensive experiments to prove the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Group recommendation has become highly demanded when users communicate in the forms of group activities in online sharing communities. These group activities include student group study, family TV program watching, friends travel decision, etc. Existing group recommendation techniques mainly focus on the small user groups. However, online sharing communities have enabled group activities among thousands of users. Accordingly, recommendation over large groups has become urgent. In this paper, we propose a new framework to accomplish this goal by exploring the group interests and the connections between group users. We first divide a big group into different interest subgroups, each of which contains users closely connected with each other and sharing the similar interests. Then, for each interest subgroup, our framework exploits the connections between group users to collect a comparably compact potential candidate set of media-user pairs, on which the collaborative filtering is performed to generate an interest subgroup-based recommendation list. After that, a novel aggregation function is proposed to integrate the recommended media lists of all interest subgroups as the final group recommendation results. Extensive experiments have been conducted on two real social media datasets to demonstrate the effectiveness and efficiency of our proposed approach. [ABSTRACT FROM AUTHOR]

Abstract Event detection over microblogs has attracted great research interest due to its wide application in crisis management and decision making etc. In natural disasters, complex events are reported in real time on social media sites, but these reports are invisible to crisis coordinators. Detecting these crisis events helps watchers to make right decisions rapidly, reducing injuries, deaths and economic loss. In sporting activities, detecting events helps audiences make better and more timely game viewing plans. However, existing event detection techniques are not effective at handling complex social events that evolve over time. In this paper, we propose an event detection method that takes advantage of retweeting behavior for handling the events evolution. Specifically, we first propose a topic model called RL-LDA to capture the social media information over hashtag, location, textual and retweeting behavior. Using RL-LDA, a complex event can be well handled by exploring the correlation between retweeting behavior and the event. Then to maintain the RL-LDA in a dynamic environment, we propose a dynamic update algorithm, which incrementally updates events over real time streams. Experiments over real-world datasets show that RL-LDA detects the temporal evolution of complex events effectively and efficiently. [ABSTRACT FROM AUTHOR]

This paper investigates how social images and change detection techniques can be applied to identify the damage caused by natural disasters for disaster assessment. We propose a multi-step image matching-based framework (MSIM), which takes advantages of fast clustering, near duplicate image detection and robust boundary matching for the change detection in disasters. We first model the social images by exploiting their tags and locations. After that, we propose a recursive 2-means algorithm over the new data model, and refine the near duplicate detection by local interest point-based matching over image pairs in neighboring clusters. Finally, we propose a novel boundary representation model called relative position annulus (RPA), which is robust to boundary rotation, location shift and editing operations. A new RPA matching method is proposed by extending dynamic time wrapping (DTW) from time to position annulus. We have done extensive experiments to evaluate the high effectiveness and efficiency of our approach. [ABSTRACT FROM AUTHOR]

With the development of graph neural network, researchers begin to use bipartite graph to model user-item interactions for recommendation. It is worth noting that most of graph recommendation models represent users and items in the real-valued space, which ignore the rich representational capacity of the non-real space. Besides, the simplicity and symmetry of the inner product make it ineffectively capture the intricate antisymmetric relations between users and items in interaction modeling. In this paper, based on the framework of graph neural network, we propose Graph Collaborative Filtering for recommendation in Complex and Quaternion space (GCFC and GCFQ respectively). Specifically, we first use complex embeddings or quaternion embeddings to initialize users and items. Then, the Hermitian product (for GCFC) or Hamilton product (for GCFQ) and embedding propagation layers are used to further enrich the embeddings of users and items. As such, we can obtain both latent inter-dependencies and intra-dependencies between components of users and items. Finally, we aggregate the embeddings of different propagation layers and use the Hermitian or Hamilton product with inner product to obtain the intricate antisymmetric relations between users and items. We have carried out extensive experiments on four real-world datasets to verify the effectiveness of GCFC and GCFQ. [ABSTRACT FROM AUTHOR]

• S100A11 is highly expressed in chronic rhinitis and sinusitis with nasal polyps (CRSwNPs). • Upregulation of S100A11 expression inhibits proliferation and promotes apoptosis and inflammation of nasal epithelial cells. • S100A11 can activate the AMPK signaling pathway and inhibit the STAT3 signaling pathway in a RAGE-mediated manner. • S100A11 plays an important role in nasal mucosal epithelial remodeling and inflammation of CRSwNPs. Abnormal remodeling of the nasal mucosal epithelium and persistent chronic inflammation are important pathological features of chronic sinusitis with nasal polyps (CRSwNPs). In order to explore the molecular regulation mechanism of CRSwNPs, we performed iTRAQ protein profile analysis on 18 clinical samples collected (9 patients with nasal polyps and 9 healthy patients) and found that S100A11, a Ca2+-binding protein, was significantly higher in CRSwNPs. Subsequently, we demonstrated that S100A11 was mainly located in nasal mucosal epithelial cells and is up-regulated in human nasal epithelial stem/progenitor cells (hNESPCs) from CRSwNPs patients and CRSwNPs epithelial cell model established with S. aureus. To determine the functional role of S100A11 and the signal pathways in epithelial cells, we constructed S100A11 overexpression vector, small interfering RNA, recombinant protein-S100A11 (rh-S100A11) and RAGE inhibitor (sRAGE). Results showed that upregulation of S100A11 inhibited epithelial cell viability and promoted apoptosis and inflammation, in addition, S100A11 can regulate the signal homeostasis of AMPK-STAT3 via RAGE mediation in epithelial cells. Our findings suggest that S100A11 is involved in CRSwNPs epithelial tissue remodeling and inflammatory response regulation and may be a useful target for CRSwNPs therapy. [ABSTRACT FROM AUTHOR]

Mucus secretion and its composition are vital in the maintenance of airway health, among which hypoxia-inducible factors (HIFs) are thought to be involved in the regulation of mucin synthesis and regulation. Nasal mucus composition difference between healthy individuals and chronic rhinosinusitis (CRS) patients may contribute to the pathology of chronic nasal diseases, but so far, their role has yet to be completely understood. Nasal biopsy specimens were obtained from 24 healthy subjects and 99 patients with CRS without (CRSsNP, n=36) or with (CRSwNP, n=63) nasal polyps. Immunohistochemical (IHC) and immunofluorescent (IF) staining, quantitative real-time PCR, and western blot were performed to compare the nasal mucus composition between the subjects. Areas of the serous gland and mucous gland were both significantly increased in CRSsNP patients. In CRSwNP patients, a decrease in submucosal gland density and a marked increase in goblet cells were observed. The major gel-forming mucins in the sinonasal mucosa of CRSsNP and CRSwNP are MUC5B and MUC5AC respectively. Mucous cells are found in a higher proportion in both CRSsNP and CRSwNP. The proportion of MUC5AC-positive goblet cells was increased in CRSwNP. The mRNA level of HIF-2α was significantly increased in CRS, and both HIF-1α and HIF-2α were expressed in serous cell but not mucous cell. Over secretion and altered composition of mucus are observed in sinonasal mucosa of CRS, which was mainly associated with glandular hyperplasia in CRSsNP and goblet cell hyperplasia in CRSwNP. Mucus abnormality compromised both non-specific and specific antimicrobial capabilities in the sinonasal mucosa. HIF expression may contribute to differences in mucin synthesis and serous gland regulation, which needs further investigation to understand the pathology of CRS. [ABSTRACT FROM AUTHOR]

The k Nearest Neighbor (k-NN) query has been gaining more importance in extensive applications involving information retrieval, data mining, and databases. Specifically, in order to trade off accuracy for efficiency, approximate solutions for the k-NN query are extensively explored. However, the precision is usually order-insensitive, which is defined on the result set instead of the result sequence. In many situations, it cannot reasonably reflect the query result quality. In this paper, we focus on the approximate k-NN query problem with the order-sensitive precision requirement and propose a novel scheme based on the projection-filter-refinement framework. Basically, we adopt PCA to project the high-dimensional data objects into the low-dimensional space. Then, a filter condition is inferred to execute efficient pruning over the projected data. In addition, an index strategy named OR-tree is proposed to reduce the I/O cost. The extensive experiments based on several real-world data sets and a synthetic data set are conducted to verify the effectiveness and efficiency of the proposed solution. Compared to the state-of-the-art methods, our method can support order-sensitive k -NN queries with higher result precision while retaining satisfactory CPU and I/O efficiency. [ABSTRACT FROM AUTHOR]

GPS trajectory dataset with high sampling-rates is usually in large volume that challenges the processing efficiency. Most of the data points on trajectories are useless. This paper summarizes trajectories using stop points. We define a new concept of stay stability (i.e., time dividing distance or reciprocal of speed) between any two GPS points to detect stop points on individual trajectories. We propose a novel Mining Repeat Travel Behaviors Using Stop Regions (MRTBUSR) method. In MRTBUSR, a stop region is a popular region containing a certain number of close stop points that can be grouped into a cluster. We then retrieve common sequences of stop regions to denote repeat route patterns and further analyze the stop durations on a stop region to find repeat travel behaviors. The experiments on 20 labeled trajectories selected from GeoLife demonstrated the semantic effect, accuracy and near linear efficiency of our proposed method. [ABSTRACT FROM AUTHOR]

Hierarchical clustering is of great importance in data analytics especially because of the exponential growth of real-world data. Often these data are unlabelled and there is little prior domain knowledge available. One challenge in handling these huge data collections is the computational cost. In this paper, we aim to improve the efficiency by introducing a set of methods of agglomerative hierarchical clustering. Instead of building cluster hierarchies based on raw data points, our approach builds a hierarchy based on a group of centroids. These centroids represent a group of adjacent points in the data space. By this approach, feature extraction or dimensionality reduction is not required. To evaluate our approach, we have conducted a comprehensive experimental study. We tested the approach with different clustering methods (i.e., UPGMA and SLINK), data distributions, (i.e., normal and uniform), and distance measures (i.e., Euclidean and Canberra). The experimental results indicate that, using the centroid based approach, computational cost can be significantly reduced without compromising the clustering performance. The performance of this approach is relatively consistent regardless the variation of the settings, i.e., clustering methods, data distributions, and distance measures. [ABSTRACT FROM AUTHOR]

• Clustering is used to find groups of similar instances. • Multi-view feature can further benefit the instance selection process. • Results show the effectiveness and efficiency of the proposed model. Multi-instance learning (MIL) is a special kind of classification problem where samples (called "instances") are grouped into bags and labels are given only on bag level instead of instance level. From the expert and intelligent system perspective, MIL does not require full ground-truth labels which helps to reduce the cost of data labeling in real tasks. Many inviting problems such as image classification, video annotation and object detection can be formulated in MIL frameworks. In this paper, we propose a similarity-based method with clustering in a multi-view feature manner to solve MIL problems efficiently. The clustering is introduced as a novel strategy for instance selection. Considering unlabeled data, clustering methods fit well for obtaining the hidden structure information in feature space, and thus we utilize a clustering-based strategy to exploit such information on discovering positive instances. To fully use the original input data, we further develop our method in multi-view feature space, in order to make our model capture information from different feature space and provide support for the whole system. Experiments on benchmark datasets for classification have been conducted, and we also perform image retrieval to examine the extended multi-view model on a large image dataset, MIL-NUS-WIDE. The promising results demonstrate the effectiveness of our clustering-based MIL (CMIL) model. [ABSTRACT FROM AUTHOR]