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关键词： Depression   Diseases   Social networking (online)   Libraries   Deep learning   Computer science   Videos   Deep learning (DL)   depression   machine learning (ML)   sentiment analysis   social networks (SNs)  

摘要： Users can interact with one another through social networks (SNs) by exchanging information, delivering comments, finding new information, and engaging in discussions that result in the production of vast volumes of data daily. These data, available in various forms, such as images, text, and videos, may be interpreted to reflect the user's activities, including their mental state regarding depression. For example, depression is a chronic disease from which the vast majority of users suffer, and it has emerged as a significant issue relating to mental health on a global scale. However, because these data are scant, unfinished, and sometimes given inaccurately, it is challenging to make an accurate automated diagnosis from them. Even though several procedures have been utilized over the past few decades to diagnose depression, machine learning (ML) and deep learning (DL) techniques supply superior insights. Thus, in this study, we review several state-of-the-art ML and DL techniques in terms of the systematic literature review (SLR) approach for depression detection. We also highlight some critical challenges from the existing literature that may help to explore for future study. Finally, we believe this survey will help readers and researchers in ML and DL to understand critical solutions in diagnosing depression.

关键词： Video games   Games   Education   Computer science education   Knowledge acquisition   Software   Programming profession   Active learning   computer-based instruction   educational technology   game-based learning (GBL)   online learning   serious games   teacher-authored games  

摘要： Contribution: This article analyzes the learning and motivational impact of teacher-authored educational video games on computer science education and compares its effectiveness in both face-to-face and online (remote) formats. This work presents comparative data and findings obtained from 217 students who played the game in a face-to-face format (control group) and 104 students who played the game in an online format (experimental group).Background: Serious video games have been proven effective at computer science education, however, it is still unknown whether the effectiveness of these games is the same regardless of their format, face-to-face or online. Moreover, the usage of games created through authoring tools has barely been *** Questions: Are teacher-authored educational video games effective in terms of learning and motivation for computer science students? Does the effectiveness of teacher-authored educational video games depend on whether they are used in a face-to-face or online format?Methodology: A quasi-experiment has been conducted by using three instruments (pre-test, post-test, and questionnaire) with the purpose of comparing the effectiveness of game-based learning in face-to-face and online formats. A total of 321 computer science students played a teacher-authored educational video game aimed to learn about software ***: The results reveal that teacher-authored educational video games are highly effective in terms of knowledge acquisition and motivation both in face-to-face and online formats. The results also show that some students' perceptions were more positive when a face-to-face format was used.

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： Health inequalities   Mental health  

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.