Organizing interesting webpages into hot topics is one of key steps to understand the trends of multimodal web data. A state-of-the-art solution is firstly to organize webpages into a large volume of multi-granularity topic candidates; hot topics are further identified by estimating their interestingness. However, these topic candidates contain a large number of fragments of hot topics due to both the inefficient feature representations and the unsupervised topic generation. This paper proposes a bundling-refining approach to mine more complete hot topics from fragments. Concretely, the bundling step organizes the fragment topics into coarse topics; next, the refining step proposes a submodular-based method to refine coarse topics in a scalable approach. The propose unconventional method is simple, yet powerful by leveraging submodular optimization, our approach outperforms the traditional ranking methods which involve the careful design and complex steps. Extensive experiments demonstrate that the proposed approach surpasses the state-of-the-art method (i.e., latent Poisson deconvolution Pang et al. (2016)) 20% accuracy and 10% one on two public data sets, respectively.

Future event prediction (FEP) is a long-standing and crucial task in the world, as understanding the evolution of events enables early risk identification, informed decision-making, and strategic planning. Existing work typically treats event prediction as classification tasks and confines the outcomes of future events to a fixed scope, such as yes/no questions, candidate set, and taxonomy, which is difficult to include all possible outcomes of future events. In this paper, we introduce OpenEP (an Open-Ended Future Event Prediction task), which generates flexible and diverse predictions aligned with real-world scenarios. This is mainly reflected in two aspects: firstly, the predictive questions are diverse, covering different stages of event development and perspectives; secondly, the outcomes are flexible, without constraints on scope or format. To facilitate the study of this task, we construct OpenEPBench, an open-ended future event prediction dataset. For question construction, we pose questions from seven perspectives, including location, time, event development, event outcome, event impact, event response, and other, to facilitate an in-depth analysis and understanding of the comprehensive evolution of events. For outcome construction, we collect free-form text containing the outcomes as ground truth to provide semantically complete and detail-enriched outcomes. Furthermore, we propose StkFEP, a stakeholder-enhanced future event prediction framework, that incorporates event characteristics for open-ended settings. Our method extracts stakeholders involved in events to extend questions to gather diverse information. We also collect historically events that are relevant and similar to the question to reveal potential evolutionary patterns. Experiment results indicate that accurately predicting future events in open-ended settings is challenging for existing LLMs.

Welcome to the special section highlighting cutting-edge research and innovation emerging from East Asia and Oceania. Our region encompasses Southeast Asia, Oceania, and Asia-Pacific countries, including Japan and Korea. The articles in this section--designated as "Hot Topics" and "Big Trends"--aim to not only showcase technological advancements from this region, but also to strengthen research collaboration and communication with regions worldwide. This special section brings together some of the most innovative research in computer science and technology from this flourishing region. The articles cover a wide range of topics, from state-of-the-art developments in learning analytics, AI and machine learning, education, Big Data, neuromorphic computing, and blockchain technology, to applications in disease prediction and assistive devices.

February may technically be the shortest month but it certainly can feel long… I think I sensed a slight brightening in the morning light but I may have been mistaken… Maybe time for a bit of distraction with a wrap up of data science developments in the last month. Don't miss out on more ChatGPT fun and games in the middle section! Following is the March edition of our Royal Statistical Society Data Science and AI Section newsletter. Hopefully some interesting topics and titbits to feed your data science curiosity. If you like these, do please send on to your friends- we are looking to build a strong community of data science practitioners.

With 1.3 billion users, Instagram (IG) has also become a business tool. IG influencer marketing, expected to generate $33.25 billion in 2022, encourages companies and influencers to create trending content. Various methods have been proposed for predicting a post's popularity, i.e., how much engagement (e.g., Likes) it will generate. However, these methods are limited: first, they focus on forecasting the likes, ignoring the number of comments, which became crucial in 2021. Secondly, studies often use biased or limited data. Third, researchers focused on Deep Learning models to increase predictive performance, which are difficult to interpret. As a result, end-users can only estimate engagement after a post is created, which is inefficient and expensive. A better approach is to generate a post based on what people and IG like, e.g., by following guidelines. In this work, we uncover part of the underlying mechanisms driving IG engagement. To achieve this goal, we rely on statistical analysis and interpretable models rather than Deep Learning (black-box) approaches. We conduct extensive experiments using a worldwide dataset of 10 million posts created by 34K global influencers in nine different categories. With our simple yet powerful algorithms, we can predict engagement up to 94% of F1-Score, making us comparable and even superior to Deep Learning-based method. Furthermore, we propose a novel unsupervised algorithm for finding highly engaging topics on IG. Thanks to our interpretable approaches, we conclude by outlining guidelines for creating successful posts.

The use of big data and algorithms to automate decision-making has been on the rise for many years. Data collection and "commercial surveillance" is a pervasive practice among social media companies and may other providers of online services. Join us as we consider the Federal Trade Commission's proposed rulemaking considering these issues as well as the "Blueprint for an AI Bill of Rights – Making Automated Systems Work for the American People" recently released by the White House Office of Science and Technology. CLE credit: CLE credit in CA, FL, IL, NJ (via reciprocity), NY, PA, TX, and VA is currently pending approval.

Deep learning is a quintessentially'hot topic'. It is surrounded by hype, often referenced and not discussed as thoroughly as it should be.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Generative AI and LLMs were two of the hottest topics at NeurIPS 2022, which brought the AI and ML community back in-person for the first time since 2019 and has offered "a lot of excitement," said Alice Oh, professor at the Korea Advanced Institute of Science and Technology and the conference's lead program chair. Some of that excitement may have been the sound of thousands of keyboards trying out OpenAI's ChatGPT demo, which was released on Wednesday and has been the talk of Twitter, if not NeurIPS, since then. But the Conference and Workshop on Neural Information Processing Systems, a machine learning and computational neuroscience conference held every December, this year in New Orleans, certainly had plenty of its own buzz going on. According to conference leaders, over 10,000 were in attendance in person, with another 3,000 tuning in online.

In the first part of the Autodidacts path to a MSc level in AI/Machine Learning, using UCL's MSc as a lighthouse to guide us through the rough waters of building a Machine Learning MSc curriculum, we had a look at some of the most established and helpful resources for a beginning ML engineer. Moving on to the second part of our attempt to build a curriculum for the autodidact enthusiast of Machine Learning, we will dive into one of the hot topics during the past decade. This is no other than Deep Learning. Although technically a sub-category of Machine Learning, Deep Learning has evolved into its own paradigm and has earned the title of'one of the pillars of ML' and for good reasons. The past decade has seen a huge number of successful applications and technological advancements that utilise Deep Learning.

Machine Learning is a hot topic! Machine Learning is a hot topic! Python Developers who understand how to work with Machine Learning are in high demand. But how do you get started? Maybe you tried to get started with Machine Learning, but couldn't find decent tutorials online to bring you up to speed, fast.