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Audio Retrieval Based on Milvus

Sound is an information-dense data type. Although it may feel antiquated in the era of video content, audio remains a primary information source for many people. Despite long-term decline in listeners, 83% of Americans ages 12 or older listened to terrestrial (AM/FM) radio in a given week in 2020 (down from 89% in 2019). Conversely, online audio has seen a steady rise in listeners over the past two decades, with 62% of Americans reportedly listening to some form of it on a weekly basis according to the same Pew Research Center study.

As a wave, sound includes four properties: frequency, amplitude, waveform, and duration. In musical terminology, these are called pitch, dynamics, tone, and duration. Sounds also help humans and other animals perceive and understand our environment, providing context clues for the location and movement of objects in our surroundings.

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Yolo-FastestV2: Faster and Lighter

Yolo-Fastest focuses on single-core, real-time inference performance and low CPU usage under real-time conditions. Not only can it realize real-time performance on mobile phones, but also on RK3399, Raspberry Pi 4, and various Cortex-A53 while remaining low-cost. The power consumption device meets certain real-time performance standards. After all, these embedded devices are much weaker than mobile phones, but they are more widely used and cheaper.

Let me talk about the original intention of Yolo-Fastest. Most people used Mobilenet-SSD in early lightweight object detection. In actual tests, it is difficult to achieve real-time on commonly used ARM devices; it can only be done on some high-end mobile phones. All big cores can barely reach real-time when booting, not to mention the "powerful" RK3399 and other commonly used ARM CPUs in the industry. Real-time is basically impossible.

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Detecting Viruses in Real-Time at Scale

Cybersecurity remains a persistent threat to both individuals and businesses, with data privacy concerns increasing for 86% of companies in 2020 and just 23% of consumers believing their personal data is very secure. As malware becomes steadily more omnipresent and sophisticated, a proactive approach to threat detection has become essential. Trend Micro is a global leader in hybrid cloud security, network defense, small business security, and endpoint security. To protect Android devices from viruses, the company built Trend Micro Mobile Security — a mobile app that compares APKs (Android Application Package) from the Google Play Store to a database of known malware. The virus detection system works as follows:

  • External APKs (Android application package) from the Google Play Store are crawled.
  • Known malware is converted into vectors and stored in Milvus.
  • New APKs are also converted into vectors, then compared to the malware database using similarity search.
  • If an APK vector is similar to any of the malware vectors, the app provides users with detailed information about the virus and its threat level.

To work, the system has to perform a highly efficient similarity search on massive vector datasets in real-time. Initially, Trend Micro used MySQL. However, as its business expanded so did the number of APKs with nefarious code stored in its database. The company’s algorithm team began searching for alternative vector similarity search solutions after quickly outgrowing MySQL.