Extraction of Meta-Data for Recommendation Using Keyword Mapping

Expanding traditional video metadata and recommendation systems encompasses challenges that are difficult to address with conventional methodologies. Limitations in utilizing diverse information when extracting video metadata, along with persistent issues like bias, cold start problems, and the filter bubble effect in recommendation systems, are primary causes of performance degradation. Therefore, a new recommendation system that integrates high-quality video metadata extraction with existing recommendation systems is necessary. This research proposes the “Extraction of Meta-Data for Recommendation using keyword mapping,” which involves constructing contextualized data through object detection models and STT (Speech-to-Text) models, extracting keywords, mapping with the public dataset MovieLens, and applying a Hybrid recommendation system. The process of building contextualized data utilizes YOLO and Google’s Speech-to-Text API. Following this, keywords are extracted using the TextRank algorithm and mapped to the MovieLens dataset. Finally, it is applied to a Hybrid Recommendation System. This paper validates the superiority of this approach by comparing it with the performance of the MovieLens recommendation system that does not expand metadata. Additionally, the effectiveness of metadata expansion is demonstrated through performance comparisons with existing deep learning-based keyword extraction models. Ultimately, this research resolves the cold start and long-tail problems of existing recommendation systems through the construction of video metadata and keyword extraction.

View this article on IEEE Xplore

Reverse Engineering of Intel Microcode Update Structure

Microcode update mechanism have been widely used in modern processors. Due to the implementation details are not public, researchers are prevented from gaining any sort of further understanding currently. The microcode update binary which uploaded into Central Processing Unit (CPU) is the only accessible node in this update chain by researchers, but previous manual reverse analysis for a small amount of microcode updates has the disadvantages of incomplete coverage, slow speed, and low accuracy. Therefore, we first build a Sample Repository containing 504 Intel official microcode updates, then propose a semiautomatic analytical method named SJNW-MA to analyze samples. This work has the following merits: (1) automatic methods of similarity analysis and candidate feature mining improve the speed; (2) manual-assisted analysis based on expert knowledge can filter important features, to avoid redundant features or valuable common data blocks missing; (3) analysis for 504 microcode updates make the results of reverse engineering are more complete. Finally, we extract eleven structures of Intel microcode updates and group them into four categories. In addition, we also identify and describe some new metadata in microcode updates of the third and the fourth category, including a new 3072-bit RSA Modulus as well as corresponding RSA Exponent which indicates upgrade of security technology inside update mechanism.

View this article on IEEE Xplore