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TOC Alert for Publication# 6570650 2021July 26<![CDATA[Table of contents]]>83C1537219<![CDATA[IEEE Publication Information]]>83C2C2115<![CDATA[Federated Data: Toward New Generation of Credible and Trustable Artificial Intelligence]]>835385451180<![CDATA[Seed Investment Bounds for Viral Marketing Under Generalized Diffusion and Selection Guidance]]>$gamma $ percentage of the market share by some target time $t$ with the desired level of confidence. To do this, we first introduce a generalized diffusion model for social networks. A distance-dependent random graph is then considered as a model for the underlying social network, which we use to analyze the proposed diffusion model. Using the fact that vertices degrees have an almost Poisson distribution in distance-dependent random networks, we then provide a lower bound on the probability of the event that the time it takes for an idea (or a product, campaign, disease, and so on) to dominate a prespecified $gamma $ percentage of a social network (denoted by $R_gamma $ ) is smaller than some preselected target time $t > 0$ , i.e., we find a lower bound on the probability of the event ${R_gamma leq t}$ . Simulation results performed over a wide variety of networks, including random and real world, are then provided to verify that our bound indeed holds in practice. The Kullback–Leibler divergence measure is used to evaluate the performance of our lower bound over these groups of networks, and as expected, we note that for networks that deviate more from the Poisson degree distribution, our lower bound weakens. In the case where absolute/full domination of the market-share is desired, under the linear threshold diffusion model, a particular case of our generalized diffusi-
n model, an upper bound on the size of the seed set is derived, and a selection algorithm is developed to show its tightness. This is also extended to the partial market-share situation.]]>835465561005<![CDATA[An Ensemble of Heterogeneous Incremental Classifiers for Assisted Reproductive Technology Outcome Prediction]]>835575674024<![CDATA[The Pandemic Holiday Blip in New York City]]>835685771712<![CDATA[A Spatiotemporal Recurrent Neural Network for Prediction of Atmospheric PM2.5: A Case Study of Beijing]]>${R} ^{2}$ , mean relative error (MRE), and mean absolute error (MAE). The MAE, MRE, and ${R} ^{2}$ are 16.1, 0.79, and 0.35 at the Gucheng station and 19.53, 0.82, and 0.36 at the Dongsi station.]]>835785883583<![CDATA[A Hybrid Probabilistic Multiobjective Evolutionary Algorithm for Commercial Recommendation Systems]]>835895981960<![CDATA[Steganalysis of Digital Images Using Deep Fractal Network]]>835996061469<![CDATA[Identifying and Analyzing Cryptocurrency Manipulations in Social Media]]>836076173509<![CDATA[Smoothing Adversarial Training for GNN]]>836186292393<![CDATA[WiONE: One-Shot Learning for Environment-Robust Device-Free User Authentication via Commodity Wi-Fi in Man–Machine System]]>how you behave” rather than “who you are”. The key idea is to apply deep learning to user physical behavior captured by Wi-Fi channel state information (CSI) to identify legitimate users while rejecting spoofers. The design of WiONE faces two challenges, namely, how to capture the subtle behavior, such as a keystroke on CSI, and how to mitigate the heavy environment-specific training required by deep learning. For the former, we design a behavior enhancement model based on the Rician fading to highlight the behavior-induced information by suppressing the behavior-unrelated information on channel response. For the latter, we develop a behavior characterization method tailored for the prototypical networks to facilitate the extraction of the domain-independent behavioral features and enable one-shot recognition of a new user in a new environment. Numerous experiments are conducted in several real-world environments, and the results show that WiONE outperforms its state-of-the-art rivals in authentication performance with much less training effort.]]>836306424758<![CDATA[ReMEMBeR: Ranking Metric Embedding-Based Multicontextual Behavior Profiling for Online Banking Fraud Detection]]>836436542731<![CDATA[Predicting Stance Polarity and Intensity in Cyber Argumentation With Deep Bidirectional Transformers]]>836556672278<![CDATA[Solving Last-Mile Logistics Problem in Spatiotemporal Crowdsourcing via Role Awareness With Adaptive Clustering]]>836686812704<![CDATA[Financial Advisor Recruitment: A Smart Crowdsourcing-Assisted Approach]]>836826881254<![CDATA[Bottom–Up Modeling of Design Knowledge Evolution: Application to Circuit Design Community Characterization]]>$Delta Sigma $ ADCs). Opportunities to improve the communities were discussed.]]>836897032451<![CDATA[Community Hiding by Link Perturbation in Social Networks]]>837047152378<![CDATA[A Theoretically Guaranteed Approach to Efficiently Block the Influence of Misinformation in Social Networks]]>$k$ nodes in a social graph to minimize the spread of rumor source at the end of a propagation process. In this article, we propose a two-step method called influence blocking maximization using martingale (IBMM) to solve IBM problem under competitive independent cascade model (ICM) with both $(1-1/e-varepsilon)$ -approximation guarantee and practical runtime efficiency. In the proposed method, first we calculate the number of required samples using a set of estimation techniques based on martingale; and then we generate the samples and find top-$k$ savior nodes. We perform extensive experiments on three real-world data sets and three rumor sets with different behaviors. We both experimentally and theoretically show that the effectiveness of IBMM is close to greedy. The results also show that IBMM is very fast, in particular, for a network with 265 214 nodes, 420 045 edges, and a set of 50 high influential nodes as rumor, when $k = 50$ , $l=1$ , and $varepsilon = 0.5$ IBMM returns the solution within 3.5 s.]]>837167272363<![CDATA[A Nonlinear Feature Fusion-Based Rating Prediction Algorithm in Heterogeneous Network]]>837287364742<![CDATA[Social Signal-Driven Knowledge Automation: A Focus on Social Transportation]]>837377534969<![CDATA[Complicating the Social Networks for Better Storytelling: An Empirical Study of Chinese Historical Text and Novel]]>Records of the Three Kingdoms (Records), and a historical novel of the same story, Romance of the Three Kingdoms (Romance). We employ deep-learning-based natural language processing (NLP) techniques to extract characters and their relationships. The adopted NLP approach can extract 93% and 91% characters that appeared in the two books, respectively. Then, we characterize the social networks and sentiments of the main characters in the historical text and the historical novel. We find that the social network in Romance is more complex and dynamic than that of Records, and the influence of the main characters differs. These findings shed light on the different styles of storytelling in the two literary genres and how the historical novel complicates the social networks of characters to enrich the literariness of the story.]]>837547673848<![CDATA[Continuous Profit Maximization: A Study of Unconstrained Dr-Submodular Maximization]]>837687791797<![CDATA[Detecting Framing Changes in Topical News]]>domains, in which earlier surveys have found framing changes. Finally, our work highlights the predictive utility of framing change detection, by identifying two domains in which framing changes foreshadowed substantial legislative activity, or preceded judicial interest.]]>837807913175<![CDATA[IEEE Transactions on Computational Social Systems society information]]>83C3C3137<![CDATA[IEEE Transactions on Computational Social Systems Information for Authors]]>83C4C4183