Intelligent connected vehicles (ICVs) are becoming the mainstream of the global automotive industry in the future, and its information security issues gradually emerge. The heterogeneity of Vehicle Ad hoc NETworks (VANETs) and the mobility of terminal nodes make the defense against the security attacks very complex, especially the internal attacks. In this article, toward defense against the internal attacks for ICVs in VANETs, we first propose a Gaussian-distribution-based and third-party-recommendation trust management model, which is called GTTMM, to solve the issue that the trust value increases too fast under continuous cooperation. And Bayesian-based trust decision scheme extending from GTTMM is named as BDTS, which incorporates the Bayesian Network, for defending against a variant of On-Off attack, called high trust value On-Off attack (HTV-On-Off attack). With the simulation study, it is demonstrated that, compared with reputation-based framework for high integrity sensor networks (RFSN), a traditional trust management model under the Beta distribution, GTTMM can better satisfy the principle of trust value, that is, "hard to get, easy to lose", while BDTS can defend against the HTV-On-Off attack effectively.
This paper presents a prototype of a chat room that detects offensive expressions in a video live streaming chat in real time. Focusing on Twitch, one of the most popular live streaming platforms, we created a dataset for the task of detecting offensive expressions. We collected 2,000 chat posts across four popular game titles with genre diversity (e.g., competitive, violent, peaceful). To make use of the similarity in offensive expressions among different social media platforms, we adopted state-of-the-art models trained on offensive expressions from Twitter for our Twitch data (i.e., transfer learning). We investigated two similarity measurements to predict the transferability, textual similarity, and game-genre similarity. Our results show that the transfer of features from social media to live streaming is effective. However, the two measurements show less correlation in the transferability prediction.
Background COVID-19 has disrupted lives and livelihoods and caused widespread panic worldwide. Emerging reports suggest that people living in rural areas in some countries are more susceptible to COVID-19. However, there is a lack of quantitative evidence that can shed light on whether residents of rural areas are more concerned about COVID-19 than residents of urban areas. Objective This infodemiology study investigated attitudes toward COVID-19 in different Japanese prefectures by aggregating and analyzing Yahoo! JAPAN search queries. Methods We measured COVID-19 concerns in each Japanese prefecture by aggregating search counts of COVID-19–related queries of Yahoo! JAPAN users and data related to COVID-19 cases. We then defined two indices—the localized concern index (LCI) and localized concern index by patient percentage (LCIPP)—to quantitatively represent the degree of concern. To investigate the impact of emergency declarations on people's concerns, we divided our study period into three phases according to the timing of the state of emergency in Japan: before, during, and after. In addition, we evaluated the relationship between the LCI and LCIPP in different prefectures by correlating them with prefecture-level indicators of urbanization. Results Our results demonstrated that the concerns about COVID-19 in the prefectures changed in accordance with the declaration of the state of emergency. The correlation analyses also indicated that the differentiated types of public concern measured by the LCI and LCIPP reflect the prefectures’ level of urbanization to a certain extent (ie, the LCI appears to be more suitable for quantifying COVID-19 concern in urban areas, while the LCIPP seems to be more appropriate for rural areas). Conclusions We quantitatively defined Japanese Yahoo users’ concerns about COVID-19 by using the search counts of COVID-19–related search queries. Our results also showed that the LCI and LCIPP have external validity.
Globally, 20% to 40% of medical resources are wasted, which could be avoided through professional audit of health insurance claims. The professional audit can pinpoint excessive use of unnecessary medicines and medical examinations. Taiwan’s National Health Insurance Bureau (TNHIB) deducts the weight that medical resources carry if regarded as unnecessary or abused when examining health insurance claims. The ratio of the deducted weight to the total weight claimed by a hospital is defined as the health insurance claim deduction rate (HICDR). A high HICDR increases the operating expenses of the hospital. In addition, it takes the hospital many resources to prepare and file appeals for the deduction. This study aims to: (1) minimize the weight deducted by the TNHIB for a hospital; and (2) facilitate efficient appeals to claim denials. It is expected that HICDR will be reduced through big data analytics. In this study, evidence-based medicine (EBM) is involved to clarify the debate, dilemmas, conflicts of interests in examining health insurance claims. A natural language method—latent Dirichlet allocation (LDA), was used to analyze patients’ medical records. The topics derived from the LDA are used as factors in the logistic regression model to estimate the probability of each claim to be deducted. The experimental results on various medical departments show that the proposed predictive model can produce accurate results, and lead to more than 41.7% reduction to the deduction of the health insurance claims. It is equivalent to more than a 750 thousand NT dollars saving per year. The efficiency of application is validated compared to the manual process that is time-consuming and labor intensive. Moreover, it is expected that this study will supplement the insufficiency of traditional methods and propose a new and effective solution to reduce the deduction rate.
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