AIGC In China: Current Developments And Future Outlook

The increasing attention given to AI Generated Content (AIGC) has brought a profound impact on various aspects of daily life, industrial manufacturing, and the academic sector. Recognizing the global trends and competitiveness in AIGC development, this study aims to analyze China's current status in the field. The investigation begins with an overview of the foundational technologies and current applications of AIGC. Subsequently, the study delves into the market status, policy landscape, and development trajectory of AIGC in China, utilizing keyword searches to identify relevant scholarly papers. Furthermore, the paper provides a comprehensive examination of AIGC products and their corresponding ecosystem, emphasizing the ecological construction of AIGC. Finally, this paper discusses the challenges and risks faced by the AIGC industry while presenting a forward-looking perspective on the industry's future based on competitive insights in AIGC

Multiscale modeling of surfactant phase behavior in the remediation of DNAPL contamination

The brine barrier remediation technique (BBRT) has been proposed as a novel Brine barrier remediation techniques (BBRT) that use surfactants have been proposed for remediating subsurface environments contaminated by dense non-aqueous phase liquids (DNAPLs). Their successful implementation requires an understanding of surfactant phase behavior including surfactant accumulation at the water/DNAPL interface and surfactant precipitation due to the presence of high aqueous-phase concentrations of brine. Multiscale modeling based upon thermodynamics and molecular dynamics (MD) was performed to investigate surfactant precipitation and molecular details at the surfactant-modified water/DNAPL interface. While these modeling results advance the understanding of surfactant behavior, a few open issues must be addressed before these new methods can be considered reliable and mature

Influences of Serendipity on Consumer Medical Information Personalization

Serendipity is an important concept in the field of information science. It has a potential of enhancing information seeking process by unexpected discovery. Serendipitous recommendation has been incorporated into the design of personalized systems to minimize blind spots in information delivery. Little evidence has been found to identify how serendipity influences personalization of consumer medical information delivery. This dissertation attempts to examine what roles serendipity plays in filtering consumer medical information and to understand how to incorporate serendipity in an effective manner. In addition, the study seeks to clarify user attitudes on unexpected discoveries of medical content in filtering settings as well as users' interest changes during this process. To empirically analyze the influence of serendipity, a medical news filtering system named MedSDFilter was developed. The system can personalize the delivery of news articles based on users' interest profiles. In MedSDFilter, serendipitous recommendation was integrated into personalized filtering through one of three serendipity models (randomness-based, knowledge-based and learning-based). Using Medical News Today site as information source, three different system modalities were compared by conducting user experiments. Thirty staff members were recruited to read and rate medical news delivered by one of three system modalities. The results of user study indicate that serendipity has an important role in medical news content delivery. As for how to incorporate serendipity, it is shown that using physician knowledge effectively enhanced serendipitous recommendation. In addition, the results suggest that the performance of serendipitous recommendation was further improved after learning algorithms were adopted. This study also provide some evidence to show user satisfaction on unexpected discovery and user interest change associated with this type of discovery. Finally, the study demonstrated the individual difference in seeking consumer medical information. The results of this study provide the system designers implications and suggestions to avoid potential drawbacks related to over-personalization in information delivery. This study enhances the understanding of users' behavior regarding the consumption of medical information and generates new guidelines which can be used in developing information systems in medical area.Doctor of Philosoph

Scene-Driven Exploration and GUI Modeling for Android Apps

Due to the competitive environment, mobile apps are usually produced under pressure with lots of complicated functionality and UI pages. Therefore, it is challenging for various roles to design, understand, test, and maintain these apps. The extracted transition graphs for apps such as ATG, WTG, and STG have a low transition coverage and coarse-grained granularity, which limits the existing methods of graphical user interface (GUI) modeling by UI exploration. To solve these problems, in this paper, we propose SceneDroid, a scene-driven exploration approach to extracting the GUI scenes dynamically by integrating a series of novel techniques including smart exploration, state fuzzing, and indirect launching strategies. We present the GUI scenes as a scene transition graph (SceneTG) to model the GUI of apps with high transition coverage and fine? grained granularity. Compared with the existing GUI modeling tools, SceneDroid has improved by 168.74% in the coverage of transition pairs and 162.42% in scene extraction. Apart from the effectiveness evaluation of SceneDroid, we also illustrate the future potential of SceneDroid as a fundamental capability to support app development, reverse engineering, and GUI regression testing

Generation-Guided Multi-Level Unified Network for Video Grounding

Video grounding aims to locate the timestamps best matching the query description within an untrimmed video. Prevalent methods can be divided into moment-level and clip-level frameworks. Moment-level approaches directly predict the probability of each transient moment to be the boundary in a global perspective, and they usually perform better in coarse grounding. On the other hand, clip-level ones aggregate the moments in different time windows into proposals and then deduce the most similar one, leading to its advantage in fine-grained grounding. In this paper, we propose a multi-level unified framework to enhance performance by leveraging the merits of both moment-level and clip-level methods. Moreover, a novel generation-guided paradigm in both levels is adopted. It introduces a multi-modal generator to produce the implicit boundary feature and clip feature, later regarded as queries to calculate the boundary scores by a discriminator. The generation-guided solution enhances video grounding from a two-unique-modals' match task to a cross-modal attention task, which steps out of the previous framework and obtains notable gains. The proposed Generation-guided Multi-level Unified network (GMU) surpasses previous methods and reaches State-Of-The-Art on various benchmarks with disparate features, e.g., Charades-STA, ActivityNet captions