Current metadata creation for web archives is time consuming and costly due to reliance on human effort. This paper explores the use of gpt-4o for metadata generation within the Web Archive Singapore, focusing on scalability, efficiency, and cost effectiveness. We processed 112 Web ARChive (WARC) files using data reduction techniques, achieving a notable 99.9% reduction in metadata generation costs. By prompt engineering, we generated titles and abstracts, which were evaluated both intrinsically using Levenshtein Distance and BERTScore, and extrinsically with human cataloguers using McNemar's test. Results indicate that while our method offers significant cost savings and efficiency gains, human curated metadata maintains an edge in quality. The study identifies key challenges including content inaccuracies, hallucinations, and translation issues, suggesting that Large Language Models (LLMs) should serve as complements rather than replacements for human cataloguers. Future work will focus on refining prompts, improving content filtering, and addressing privacy concerns through experimentation with smaller models. This research advances the integration of LLMs in web archiving, offering valuable insights into their current capabilities and outlining directions for future enhancements. The code is available at https://github.com/masamune-prog/warc2summary for further development and use by institutions facing similar challenges.

Image captioning strives to generate pertinent captions for specified images, situating itself at the crossroads of Computer Vision (CV) and Natural Language Processing (NLP). This endeavor is of paramount importance with far-reaching applications in recommendation systems, news outlets, social media, and beyond. Particularly within the realm of news reporting, captions are expected to encompass detailed information, such as the identities of celebrities captured in the images. However, much of the existing body of work primarily centers around understanding scenes and actions. In this paper, we explore the realm of image captioning specifically tailored for celebrity photographs, illustrating its broad potential for enhancing news industry practices. This exploration aims to augment automated news content generation, thereby facilitating a more nuanced dissemination of information. Our endeavor shows a broader horizon, enriching the narrative in news reporting through a more intuitive image captioning framework.

The wide spread of rumors on social media has caused a negative impact on people's daily life, leading to potential panic, fear, and mental health problems for the public. How to debunk rumors as early as possible remains a challenging problem. Existing studies mainly leverage information propagation structure to detect rumors, while very few works focus on correlation among users that they may coordinate to spread rumors in order to gain large popularity. In this paper, we propose a new detection model, that jointly learns both the representations of user correlation and information propagation to detect rumors on social media. Specifically, we leverage graph neural networks to learn the representations of user correlation from a bipartite graph that describes the correlations between users and source tweets, and the representations of information propagation with a tree structure. Then we combine the learned representations from these two modules to classify the rumors. Since malicious users intend to subvert our model after deployment, we further develop a greedy attack scheme to analyze the cost of three adversarial attacks: graph attack, comment attack, and joint attack. Evaluation results on two public datasets illustrate that the proposed MODEL outperforms the state-of-the-art rumor detection models. We also demonstrate our method performs well for early rumor detection. Moreover, the proposed detection method is more robust to adversarial attacks compared to the best existing method. Importantly, we show that it requires a high cost for attackers to subvert user correlation pattern, demonstrating the importance of considering user correlation for rumor detection.

Personalized news recommendation is important for users to find interesting news from massive information. As a heated topic that has wide applications in the industry, it has been extensively studied over decades and has made huge progress. In this paper, we discuss the topic of news recommendation. We implement an attention based model and demonstrate the great performance-boosting modern deep learning techniques bring in. The following chapter will be structured as follow: in chapter 2, we will briefly introduce the scenario of news recommendation. In Chapter 3, we discuss the details of the implementation of our model. In Chapter 4, we introduce the dataset we use. Chapter 5 will be a recap and conclusion Figure.1.

Simultaneous Localization and Mapping (SLAM) presents a formidable challenge in robotics, involving the dynamic construction of a map while concurrently determining the precise location of the robotic agent within an unfamiliar environment. This intricate task is further compounded by the inherent "chicken-and-egg" dilemma, where accurate mapping relies on a dependable estimation of the robot's location, and vice versa. Moreover, the computational intensity of SLAM adds an additional layer of complexity, making it a crucial yet demanding topic in the field. In our research, we address the challenges of SLAM by adopting the Particle Filter SLAM method. Our approach leverages encoded data and fiber optic gyro (FOG) information to enable precise estimation of vehicle motion, while lidar technology contributes to environmental perception by providing detailed insights into surrounding obstacles. The integration of these data streams culminates in the establishment of a Particle Filter SLAM framework, representing a key endeavor in this paper to effectively navigate and overcome the complexities associated with simultaneous localization and mapping in robotic systems.

We study "learning-based" sketching approaches for diverse tasks in numerical linear algebra: least-squares regression, $\ell_p$ regression, Huber regression, low-rank approximation (LRA), and $k$-means clustering. Sketching methods are used to quickly and approximately compute properties of large matrices. Linear maps called "sketches" are applied to compress data, and these concise representations are used to compute the desired properties. Specifically, we consider sparse sketches (such as CountSketch). Recent works have dealt with optimizing sketches for data distributions to perform better than their random counterparts. We extend this theme to several important and ubiquitous tasks, each of which requires a new analysis and novel practical methods. Specifically, our contributions are: 1) For all tasks, we introduce fast algorithms using learned sketches with worst-case guarantees. We give a simple task-agnostic method for retaining the worst-case guarantees of randomized sketching, which yields time-optimal algorithms for LRA and least-squares regression. Also, for $k$-means clustering, we give a faster alternative for retaining worst-case guarantees. 2) We show empirically that learned sketches are reliable in improving approximation accuracy, with comparison against "non-learned" sketching baselines. 3) We introduce a greedy algorithm for optimizing the location of the nonzero entries of a sparse sketch and prove guarantees for certain distributions on the LRA task. Previous work only looked at optimizing the values rather than the locations. Also, we show empirically that it further improves learned sketch performance.