In recent years, microblog systems such as Twitter and Sina Weibo have averaged multimillion active users. On the other hand, the microblog system has become a new means of rumor-spreading platform. In this paper, we investigate the machine-learning-based rumor identification approaches. We observed that feature design and selection has a stronger impact on the rumor identification accuracy than the selection of machine-learning algorithms. Meanwhile, the rumor publishers' behavior may diverge from normal users', and a rumor post may have different responses from a normal post. However, mass behavior on rumor posts has not been explored adequately. Hence, we investigate rumor identification schemes by applying five new features based on users' behaviors, and combine the new features with the existing well-proved effective user behaviorbased features, such as followers' comments and reposting, to predict whether a microblog post is a rumor. Experiment results on real-world data from Sina Weibo demonstrate the efficacy and efficiency of our proposed method and features. From the experiments, we conclude that the rumor detection based on mass behaviors is more effective than the detection based on microblogs' inherent features.

As nodes' characteristics that they are self-governed and resource-limited, wireless sensor networks (WSNs) face potential threats due to various attacks, among which the most threatening attack is wormhole attack. Wormhole attack severely imperils WSNs and is difficult to be detected, for it causes incorrect routing by private tunnels and damages to WSNs in terms of data leakage, data dropping, and delayed delivery. However, the existing solutions are based on additional hardware, incur high communication overhead, or fail to give consideration to all types of wormholes. In this paper, we propose CREDND, a protocol for creating a Credible Neighbor Discovery against wormholes in WSN, which can detect not only external wormholes through the hop difference between the own exclusive neighbors but also internal wormholes through enabling the common neighbor nodes as witnesses to monitor whether the authentication packets are forwarded by malicious nodes. CREDND is a simple, localized protocol and needs no special hardware, localization, or synchronization, but it improves the ability of wormhole defense. The simulation results are provided, showing that CREDND outperforms in wormhole detection than other same types of solutions. INDEX TERMS Secure neighborhood, neighbor discovery, network security, wireless sensor networks, wormhole attack.

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Limited biomarkers have been identified as prognostic predictors for stage III colon cancer. To combat this shortfall, we developed a computer-aided approach which combing convolutional neural network with machine classifier to predict the prognosis of stage III colon cancer from routinely haematoxylin and eosin (H&E) stained tissue slides. We trained the model by using 101 cancers from West China Hospital (WCH). The predictive effectivity of the model was validated by using 67 cancers from WCH and 47 cancers from The Cancer Genome Atlas Colon Adenocarcinoma database. The selected model (Gradient Boosting-Colon) provided a hazard ratio (HR) for high- vs. low-risk recurrence of 8.976 (95% confidence interval (CI), 2.824–28.528; P, 0.000), and 10.273 (95% CI, 2.177–48.472; P, 0.003) in the two test groups, from the multivariate Cox proportional hazards analysis. It gave a HR value of 10.687(95% CI, 2.908–39.272; P, 0.001) and 5.033 (95% CI,1.792–14.132; P, 0.002) for the poor vs. good prognosis groups. Gradient Boosting-Colon is an independent machine prognostic predictor which allows stratification of stage III colon cancer into high- and low-risk recurrence groups, and poor and good prognosis groups directly from the H&E tissue slides. Our findings could provide crucial information to aid treatment planning during stage III colon cancer.

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