This paper develops novel conformal prediction methods for classification tasks that can automatically adapt to random label contamination in the calibration sample, enabling more informative prediction sets with stronger coverage guarantees compared to state-of-the-art approaches. This is made possible by a precise theoretical characterization of the effective coverage inflation (or deflation) suffered by standard conformal inferences in the presence of label contamination, which is then made actionable through new calibration algorithms. Our solution is flexible and can leverage different modeling assumptions about the label contamination process, while requiring no knowledge about the data distribution or the inner workings of the machine-learning classifier. The advantages of the proposed methods are demonstrated through extensive simulations and an application to object classification with the CIFAR-10H image data set.

As satellites become smaller, the ability to maintain stable pointing decreases as external forces acting on the satellite come into play. At the same time, reaction wheels used in the attitude determination and control system (ADCS) introduce high frequency jitter which can disrupt pointing stability. For space domain awareness (SDA) tasks that track objects tens of thousands of kilometres away, the pointing accuracy offered by current nanosats, typically in the range of 10 to 100 arcseconds, is not sufficient. In this work, we develop a novel payload that utilises a neuromorphic event sensor (for high frequency and highly accurate relative attitude estimation) paired in a closed loop with a piezoelectric stage (for active attitude corrections) to provide highly stable sensor-specific pointing. Event sensors are especially suited for space applications due to their desirable characteristics of low power consumption, asynchronous operation, and high dynamic range. We use the event sensor to first estimate a reference background star field from which instantaneous relative attitude is estimated at high frequency. The piezoelectric stage works in a closed control loop with the event sensor to perform attitude corrections based on the discrepancy between the current and desired attitude. Results in a controlled setting show that we can achieve a pointing accuracy in the range of 1-5 arcseconds using our novel payload at an operating frequency of up to 50Hz using a prototype built from commercial-off-the-shelf components. Further details can be found at https://ylatif.github.io/ultrafinestabilisation

Wav2vec2 has achieved success in applying Transformer architecture and self-supervised learning to speech recognition. Recently, these have come to be used not only for speech recognition but also for the entire speech processing. This paper introduces an effective end-to-end speaker identification model applied Transformer-based contextual model. We explored the relationship between the hyper-parameters and the performance in order to discern the structure of an effective model. Furthermore, we propose a pooling method, Temporal Gate Pooling, with powerful learning ability for speaker identification. We applied Conformer as encoder and BEST-RQ for pre-training and conducted an evaluation utilizing the speaker identification of VoxCeleb1. The proposed method has achieved an accuracy of 87.1% with 28.5M parameters, demonstrating comparable precision to wav2vec2 with 317.7M parameters. Code is available at https://github.com/HarunoriKawano/speaker-identification-with-tgp.

How do you find an elusive animal that most people have never even seen dead in a fish market? Matthew McDavitt, above, knows how.Melody Robbins This story was originally published by Hakai Magazine and is reproduced here as part of the Climate Desk collaboration. Peter Kyne sits down at his desk to write a eulogy for a fish he's never met. No scientist has seen signs of the critically endangered Rhynchobatus cooki, or clown wedgefish, since a dead one turned up at a fish market in 1996. Kyne, a conservation biologist at Charles Darwin University in Australia who studies wedgefish, has worked only with preserved specimens of the spotted sea creature. "This thing's dust," Kyne thinks, feeling defeated as he writes the somber news in a draft assessment of the global conservation status of wedgefish species for the International Union for Conservation of Nature. Wedgefish are a type of ray.

Cloud Computing (CC) is revolutionizing the way IT resources are delivered to users, allowing them to access and manage their systems with increased cost-effectiveness and simplified infrastructure. However, with the growth of CC comes a host of security risks, including threats to availability, integrity, and confidentiality. To address these challenges, Machine Learning (ML) is increasingly being used by Cloud Service Providers (CSPs) to reduce the need for human intervention in identifying and resolving security issues. With the ability to analyze vast amounts of data, and make high-accuracy predictions, ML can transform the way CSPs approach security. In this paper, we will explore some of the most recent research in the field of ML-based security in Cloud Computing. We will examine the features and effectiveness of a range of ML algorithms, highlighting their unique strengths and potential limitations. Our goal is to provide a comprehensive overview of the current state of ML in cloud security and to shed light on the exciting possibilities that this emerging field has to offer.

Similar Narrative Retrieval is a crucial task since narratives are essential for explaining and understanding events, and multiple related narratives often help to create a holistic view of the event of interest. To accurately identify semantically similar narratives, this paper proposes a novel narrative similarity metric called Facet-based Narrative Similarity (FaNS), based on the classic 5W1H facets (Who, What, When, Where, Why, and How), which are extracted by leveraging the state-of-the-art Large Language Models (LLMs). Unlike existing similarity metrics that only focus on overall lexical/semantic match, FaNS provides a more granular matching along six different facets independently and then combines them. To evaluate FaNS, we created a comprehensive dataset by collecting narratives from AllSides, a third-party news portal. Experimental results demonstrate that the FaNS metric exhibits a higher correlation (37\% higher) than traditional text similarity metrics that directly measure the lexical/semantic match between narratives, demonstrating its effectiveness in comparing the finer details between a pair of narratives.

This article presents an open-source architecture for conveying robots' intentions to human teammates using Mixed Reality and Head-Mounted Displays. The architecture has been developed focusing on its modularity and re-usability aspects. Both binaries and source code are available, enabling researchers and companies to adopt the proposed architecture as a standalone solution or to integrate it in more comprehensive implementations. Due to its scalability, the proposed architecture can be easily employed to develop shared Mixed Reality experiences involving multiple robots and human teammates in complex collaborative scenarios.

The metaverse has gained significant attention from various industries due to its potential to create a fully immersive and interactive virtual world. However, the integration of deepfakes in the metaverse brings serious security implications, particularly with regard to impersonation. This paper examines the security implications of deepfakes in the metaverse, specifically in the context of gaming, online meetings, and virtual offices. The paper discusses how deepfakes can be used to impersonate in gaming scenarios, how online meetings in the metaverse open the door for impersonation, and how virtual offices in the metaverse lack physical authentication, making it easier for attackers to impersonate someone. The implications of these security concerns are discussed in relation to the confidentiality, integrity, and availability (CIA) triad. The paper further explores related issues such as the darkverse, and digital cloning, as well as regulatory and privacy concerns associated with addressing security threats in the virtual world.

As generative AI settles into the mainstream, growing numbers of courses and certifications are promising entry into the "hot job" of prompt engineering. Having skills in using natural language (such as English) to "prompt" useful content out of AI models such as ChatGPT and Midjourney seems like something many employers would value. But is it as simple as doing a short course and riding the wave to a six-figure salary? A Washington Post article published in February did a lot to seed the notion that prompt engineers are "AI whisperers" who "program in prose". It dropped some big salary numbers and quoted a job ad by Silicon Valley company Anthropic calling for people who have "a creative hacker spirit and love solving puzzles".

Australia has unveiled regulations requiring internet search engines to crack down on child sexual abuse material created by artificial intelligence. The online safety code announced on Friday will require services such as Google, Bing, DuckDuckGo and Yahoo to take "appropriate steps" to prevent the spread of child exploitation material, including "synthetic" images created by AI. The announcement comes after the eSafety commissioner delayed the implementation of an earlier version of the code in June after Microsoft and Google introduced AI functionality for their internet search engine services. "The use of generative AI has grown so quickly that I think it's caught the whole world off guard to a certain degree," eSafety Commissioner Julie Inman Grant said in a statement. Grant said the code was a "great example" of how regulators and tech firms could work together to make the internet more safe.