Noflag
各位大家好，我叫沈宜婷，可以叫我noflag，專攻資訊安全，體制外學生，跳脫現今教育制度，是一位致力在資安圈打拼的女性
研究項目：車聯網資安、應用程式安全、機器學習
github : https://github.com/Trinity-SYT-SECURITY
☞中華資安國際 SOC team，實習生
☞NCKU 金融資安實驗室，行動應用程式資安檢測人員
☞Google Developer Group (GDG) Taichung Organizer
Sessions
Flutter 是一個由 Google 開發的跨平台應用程式開發框架，讓開發者可以使用一套程式碼開發 iOS、Android、Web 和桌面應用程式。SonarQube 是著名開源程式碼檢測工具。隨著應用程式規模的不斷擴大，程式碼品質和安全性的重要性也日益凸顯，程式碼靜態掃描是安全程式開發工作第一步．本演講將介紹如何使用SonarQube來進行Flutter程式的靜態程式分析，以確保程式品質和可維護性。我們將深入探討SonarQube的工作原理以及如何配置SonarQube來掃描Flutter程式。此外，我們還會講解SonarQube如何提供即時反饋，並生成報告和指示，幫助開發團隊識別和解決潛在的程式問題。
Flutter is a cross-platform application development framework developed by Google, allowing developers to use a single codebase to develop applications for iOS, Android, Web, and desktop. SonarQube is a well-known open-source code analysis tool. As the scale of applications continues to expand, the importance of code quality and security becomes increasingly prominent, and static code scanning is the first step in secure software development. This talk will introduce how to use SonarQube for static code analysis of Flutter applications to ensure code quality and maintainability. We will delve into the working principles of SonarQube and how to configure it for scanning Flutter code. Additionally, we will discuss how SonarQube provides real-time feedback and generates reports and guidelines to help development teams identify and address potential code issues.
《英雄聯盟》的大型賽事吸引了眾多觀眾，但小型比賽的曝光度較低。為了提高小型比賽的知名度，提出了「LAIC- League AI Commentator」系統，使用人工智慧技術濃縮比賽內容成摘要，讓觀眾在短時間內了解比賽的重點和轉折。系統包含用戶介面、資訊擷取、生成式人工智慧和語音合成四大部分，各自負責互動、資訊收集、分析和回答生成。團隊計劃將系統與比賽畫面結合，提升觀眾觀看體驗，增加小型比賽的曝光和關注。
The large-scale tournaments of League of Legends have attracted a lot of viewers, but smaller competitions have lower exposure. To increase the visibility of these smaller events, the "LAIC - League AI Commentator" system has been proposed. It utilizes artificial intelligence technology to condense the content of the matches into summaries, allowing viewers to quickly understand the key moments and turning points. The system consists of four main components: user interface, information retrieval, generative AI, and voice synthesis. Each component is responsible for interaction, data gathering, analysis, and generating responses respectively. The team plans to integrate the system with the game footage to enhance the viewing experience for the audience and increase exposure and attention for the smaller competitions.