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关键词： Vehicles   Privacy   Roads   Collaboration   Data privacy   Computer science   Monitoring   Cloud-fog-edge collaborative driver-vehicle-road framework   relation graphs   privacy quality   analytic accuracy   self-adaptive evolution  

摘要： There are three key roles in Intelligent Transportation Systems (ITS): driver, vehicle and road. However, existing static interactions among Driver-Vehicle-Road (DVR) are too passive to reflect the change of driver preferences, vehicle conditions, road conditions, etc. Therefore, we provide a data-driven Cloud-Fog-Edge Collaborative Driver-Vehicle-Road (CFEC-DVR) framework. The framework could self-adaptively evolves through continuous iteration to provide better ITS services for humans. The collaboration among DVR creates a lot of relation data that construct our relation graphs. Cloud brings some privacy risks. Relation graphs have great analytic value. As DVR collaboration, privacy quality and analytic accuracy are three key issues in the framework, we propose a Relation Graph Privacy-Preserving scheme with High Accuracy in our framework, which is named as RGPP-HA. Based on machine learning, our method nearly maximizes the difficulty for attackers to know exactly how many other roles are connected to the attacked role, which enhances the privacy quality. Meanwhile, we find as much valuable information as possible from roles' encrypted relations for more accurately analytic performance. Based on the experiments, we compare the proposed scheme RGPP-HA with existing classic and relevant schemes. The experimental results show that our scheme has the best privacy quality and analytic accuracy. This further verifies the feasibility of CFEC-DVR framework.

关键词： Childhood maltreatment   Early life stress   Sensitivity   Alcohol   Familial risk   Alcohol expectancies  

摘要： Early life stress, specifically childhood maltreatment, and parental risk for mood and substance use disorders (SUDs) are associated with increased risk for alcohol use disorder (AUD). There is limited data on how these factors interact to contribute to alcohol-related outcomes. Prior work has suggested early life stress may increase sensitivity to psychostimulants and that subjective response to alcohol is heritable. It is unclear if early life stress alters sensitivity to alcohol and interacts with parental risk for mood/SUDs, which in turn may act as a risk factor for AUD. The current study uses within-subjects placebo-controlled alcohol administration methods to investigate the effects of childhood maltreatment on subjective response to alcohol in young adults with and without parental risk of mood/SUDs. Additionally, we explored interactions with drinking context (i.e., drinking in a bar vs. non-bar context). Within individuals with parental risk for mood/SUDs, there was a positive relation between total Childhood Trauma Questionnaire (CTQ) score and how drunk individuals reported feeling across both alcohol and placebo conditions (parental risk group-by-CTQ interaction p = .01;main effect of CTQ within individuals with parental risk for mood/SUDs p = .005). When exploring interactions with drinking context (bar vs. non-bar context), we observed a significant drinking context-by-parental risk-by-CTQ interaction (p = .03), with CTQ score positively associated with greater positive valence/positive arousal feelings in the parental risk group if they consumed their beverages in the bar context (p = .004) but not if they consumed their beverages in the non-bar context. Results suggest childhood maltreatment may contribute to variation in subjective response to the positive effects of alcohol-possibly mediated by alcohol cues and/or expectancies-in young adults with parental risk for mood/SUDs.

关键词： Knowledge engineering   Computer science   Machine learning algorithms   Systematics   Scholarships   Clustering algorithms   Symbols  

摘要： In this paper, a new Global k-modes (GKM) algorithm is proposed for clustering categorical data. The new method randomly selects a sufficiently large number of initial modes to account for the global distribution of the data set, and then progressively eliminates the redundant modes using an iterative optimization process with an elimination criterion function. Systematic experiments were carried out with data from the UCI Machine learning repository. The results and a comparative evaluation show a high performance and consistency of the proposed method, which achieves significant improvement compared to other well-known k-modes-type algorithms in terms of clustering accuracy.

关键词： Computer science   Information technology   Machine learning   Society  

摘要： Despite their wide use, large language models are still mysterious. Revealing their true nature is urgent and important.

关键词： Computer science   Mathematics and computing   Technology   Society  

摘要： Large language models mimic human chatter, but scientists disagree on their ability to reason.

关键词： Aggregates   Data integration   Soft sensors   Data models   Computer science   Scalability   Fuses  

摘要： The huge values created by Big Data and the recent advances in cloud computing have been driving data from different sources into cloud repositories for comprehensive query services. However, cloud-based data fusion makes it challenging to verify if an untrusted server faithfully integrates data and executes queries or not. This is even harder for range-aggregate queries that apply aggregate operations on data within given ranges. In this article, we propose a query authentication scheme, named $\mathsf{MARS}$ , enabling a user to efficiently authenticate range-aggregate queries on multi-source data. Specifically, $\mathsf{MARS}$ creates a VG-tree by subtly integrating Expressive Set Accumulator into a multi-dimensional G-tree while signing the root digest with a multi-source aggregate signature scheme. Compared with previous solutions, $\mathsf{MARS}$ has the following merits: (1) Practicality. Instead of treating range and aggregate queries separately, the user can directly verify the statistical result of selected data. (2) Scalability. Instead of authenticating the individual result from each source, the user can perform an aggregative validation on the integrated result from multiple sources. The experimental results demonstrate the effectiveness of MARS. For large-scale data fusion, the user-side verification time increases by only 103 ms as the amount of data sources increases by five times.

摘要： Retraction: Cheng Y. Application of VR computer image technology in 3D virtualization of sports human science.J Concurrency Computat: Pract Exper. 2018;30-24. The above article, published online on 14 Sep 2018 Wiley Online Library (https://***/doi/10.1002/cpe.4934) has been retracted by agreement between the journal Editor, Professor David W. Walker, School of Computer Science and Informatics, Cardiff University, Cardiff CF24 3AA, UK and Wiley Periodicals, LLC. The retraction has been agreed following an investigation based on allegations raised by a third party. Several inconsistencies including general logical flaws and irrelevant citations were found, making the motivation and conclusions of the article untrustworthy. The editorial office did not receive a response from the authors, and so requested underlying experimental data was not available for evaluation. Accordingly, the editors consider the conclusions of this manuscript invalid.

关键词： Computational science   Computer science   Electrical and electronic engineering  

摘要： Synchronization of e-wearables can be challenging due to device performance variations. Here, the authors develop a general neural network-based solution that analyses and correct disparities between multiple virtual clocks and demonstrate it for a Bluetooth synchronized motion capture system at high frequency. Bluetooth-enabled wearables can be linked to form synchronized networks to provide insightful and representative data that is exceptionally beneficial in healthcare applications. However, synchronization can be affected by inevitable variations in the component's performance from their ideal behavior. Here, we report an application-level solution that embeds a Neural network to analyze and overcome these variations. The neural network examines the timing at each wearable node, recognizes time shifts, and fine-tunes a virtual clock to make them operate in unison and thus achieve synchronization. We demonstrate the integration of multiple Kinematics Detectors to provide synchronized motion capture at a high frequency (200 Hz) that could be used for performing spatial and temporal interpolation in movement assessments. The technique presented in this work is general and independent from the physical layer used, and it can be potentially applied to any wireless communication protocol.

关键词： Computational linguistics  

摘要： Large language models such as ChatGPT-3.5 and GPT-4.0 are ubiquitous and dominate the current discourse. Their trans-formative capabilities have led to a paradigm shift in how we interact with and utilize (text-based) information. Each day, new possibilities to leverage the capabilities of these models emerge. This paper presents findings on the performance of different large language models in a university of applied sciences' undergraduate computer science degree program. Our primary objective is to assess the effectiveness of these models within the curriculum by employing them as educational aids. By prompting the models with lecture material, exercise tasks, and past exams, we aim to evaluate their proficiency across different computer science domains. We showcase the strong performance of current large language models while highlighting limitations and constraints within the context of such a degree program. We found that ChatGPT-3.5 averaged 79.9% of the total score in 10 tested modules, BingAI achieved 68.4%, and LLaMa, in the 65 billion parameter variant, 20%. Despite these convincing results, even GPT-4.0 would not pass the degree program - due to limitations in mathematical calculations. Copyright © 2023, The Authors. All rights reserved.