Location-based social networks (LBSNs) such as Foursquare o er a platform for users to share and be aware of each others physical movements. As a result of such a sharing of check-in information with each other, users can be influenced to visit (or check-in) at the locations visited by their friends. Quantifying such influences in these LBSNs is useful in various settings such as location promotion, personalized recommendations, mobility pattern prediction etc. In this paper, we develop a model to quantify the influence specific to a location between a pair of users. Specifically, we develop a model called LoCaTe, that combines (a) a user mobility model based on kernel density estimates; (b) a model of the semantics of the location using topic models; and (c) a model of inter-check-in time using exponential distribution. We show the applicability of LoCaTe for location promotion and location recommendation tasks using LBSNs. Our model is validated using a long-term crawl of Foursquare data collected between Jan 2015 aAS' Feb 2016, as well as other publicly available LBSN datasets. Our experiments demonstrate the efficacy of LoCaTe in capturing location-specific influence between users. We also show that LoCaTe improves over state-of-the-art models for the coarse-grained task of location promotion.
The proliferation of fake news on social media has opened up new directions of research for timely identifi- cation and containment of fake news, and mitigation of its widespread impact on public opinion. While much of the earlier research was focused on identification of fake news using content based solutions, that deter- mine the truthfulness of a piece of news based on its text contents only, or using feedback based solutions that exploit users? activities towards the news on social media, such as propagation patterns or comments, there has been a rising interest in active intervention strategies to counter the spread of misinformation and its impact on society. In this survey, we describe the problem of fake news and the technical challenges associated with identification and mitigation of fake news. We present an overview of existing methods and techniques applicable to fake news identification and mitigation, along with insights and details of the sig- nificant advances in various methods, and the practical advantages and limitations of each. Further, we enumerate a list of challenges and open problems that outline new directions of research, and provide a comprehensive list of available datasets with a summarization of their characteristic features, in order to facilitate future research and enable the development of solutions that are interdisciplinary and effective in practice.
Today's AI still faces two major challenges. One is that in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated learning framework, which includes horizontal federated learning, vertical federated learning and federated transfer learning. We provide definitions, architectures and applications for the federated learning framework, and provide a comprehensive survey of existing works on this subject. In addition, we propose building data networks among organizations based on federated mechanisms as an effective solution to allows knowledge to be shared without compromising user privacy.
In this paper, we study the problem of online heterogeneous transfer learning, where the objective is to make predictions for a target data sequence arriving in an online fashion, and some offline labeled instances from a heterogeneous source domain are provided as auxiliary data. The feature spaces of the source and target domains are completely different, thus the source data cannot be used directly to assist the learning task in the target domain. To address this issue, we take advantage of unlabeled co-occurrence instances as intermediate supplementary data to connect the source and target domains, and perform knowledge transition from the source domain into the target domain. We propose a novel online heterogeneous transfer learning algorithm called Online Heterogeneous Knowledge Transition (OHKT) for this purpose. In OHKT, we first seek to generate pseudo labels for the co-occurrence data based on the labeled source data, and then develop an online learning algorithm to classify the target sequence by leveraging the co-occurrence data with pseudo labels. Experimental results on real-world data sets demonstrate the effectiveness and efficiency of the proposed algorithm.
With the increasing demand in using 3D mesh data over networks, supporting effective compression and efficient transmission of meshes have caught lots of attention in recent years. This paper introduces a novel compression method for 3D mesh animation sequences, supporting user-defined and progressive transmissions over networks. Our motion-aware approach starts with clustering animation frames based on their motion similarities, dividing a mesh animation sequence into fragments of varying lengths. This is done by a novel temporal clustering algorithm, which measures motion similarity based on the curvature and torsion of a space curve formed by corresponding vertices along a series of animation frames. We further segment each cluster based on mesh vertex coherence, representing topological proximity within an object under certain motion. To produce a compact representation, we perform intra-cluster compression based on Graph Fourier Transform (GFT) and Set Partitioning In Hierarchical Trees (SPIHT) coding. Optimized compression results can be achieved by applying GFT due to the proximity in vertex position and motion. We adapt SPIHT to support progressive transmission and design a mechanism to transmit mesh animation sequences with user-defined quality. Experimental results show that our method can obtain a high compression ratio while maintaining a low reconstruction error.
Detecting abnormal behaviors of students in time and providing personalized intervention and guidance at the early stage is important in educational management. Academic performance prediction is an important building block to enabling this pre-intervention and guidance. Most of the previous studies are based on questionnaire surveys and self-reports, which suffer from a small sample size and social desirability bias. In this paper, we collect longitudinal behavioral data from 6,597 students' smart cards and propose three major types of discriminative behavioral factors, diligence, orderliness, and sleep patterns. Empirical analysis demonstrates these behavioral factors are strongly correlated with academic performance. Furthermore, motivated by social influence theory, we analyze the correlation of each student's academic performance with his/her behaviorally similar students'. Statistical tests indicate this correlation is significant. Based on these factors, we further build a multi-task predictive framework based on a learning-to-rank algorithm for academic performance prediction. This framework captures inter-semester correlation, inter-major correlation and integrates student similarity to predict students' academic performance. The experiments on a large-scale real-world dataset show the effectiveness of our methods for predicting academic performance and the effectiveness of proposed behavioral factors.
As JPEG is the most widely used image format, the importance of tampering detection for JPEG images in blind forensics is self-evident. In this area, extracting effective statistical characteristics from a JPEG image for classification remains a challenge. Effective features are designed manually in traditional methods, sug- gesting that extensive labor-consuming research and derivation is required. In this paper, we propose a nov- el image tampering detection method based on deep multi-scale discriminative networks (MSD-Nets). The multi-scale module is designed to automatically extract multiple features from the discrete cosine transform (DCT) coefficient histograms of the JPEG image. This module can capture the characteristic information in different scale spaces. In addition, a discriminative module is also utilized to improve the detection effect of the networks in those difficult situations when the first compression quality (QF1) is higher than the second one (QF 2). A special network in this module is designed to distinguish the small statistical differ- ence between authentic and tampered regions in these cases. Finally, a probability map can be obtained and the specific tampering area located using the last classification results. Extensive experiments demonstrate the superiority of our proposed method in both quantitative and qualitative metrics when compared with state-of-the-art approaches.