Topic-controllable summarization is an emerging research area with a wide range of potential applications. However, existing approaches suffer from significant limitations. For example, the majority of existing methods built upon recurrent architectures, which can significantly limit their performance compared to more recent Transformer-based architectures, while they also require modifications to the model's architecture for controlling the topic. At the same time, there is currently no established evaluation metric designed specifically for topic-controllable summarization. This work proposes a new topic-oriented evaluation measure to automatically evaluate the generated summaries based on the topic affinity between the generated summary and the desired topic. The reliability of the proposed measure is demonstrated through appropriately designed human evaluation. In addition, we adapt topic embeddings to work with powerful Transformer architectures and propose a novel and efficient approach for guiding the summary generation through control tokens. Experimental results reveal that control tokens can achieve better performance compared to more complicated embedding-based approaches while also being significantly faster.

Unmanned boats, while navigating at sea, utilize active compensation systems to mitigate wave disturbances experienced by onboard instruments and equipment. However, there exists a lag in the measurement of unmanned boat attitudes, thus introducing unmanned boat motion attitude prediction to compensate for the lag in the signal acquisition process. This paper, based on the basic principles of waves, derives the disturbance patterns of waves on unmanned boats from the wave energy spectrum. Through simulation analysis of unmanned boat motion attitudes, motion attitude data is obtained, providing experimental data for subsequent work. A combined prediction model based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Particle Swarm Optimization (PSO), and Support Vector Machine (SVM) is designed to predict the motion attitude of unmanned boats. Simulation results validate its superior prediction accuracy compared to traditional prediction models. For example, in terms of mean absolute error, it improves by 17% compared to the EMD-PSO-SVM model.

This survey reviews the AIS 2024 Event-Based Eye Tracking (EET) Challenge. The task of the challenge focuses on processing eye movement recorded with event cameras and predicting the pupil center of the eye. The challenge emphasizes efficient eye tracking with event cameras to achieve good task accuracy and efficiency trade-off. During the challenge period, 38 participants registered for the Kaggle competition, and 8 teams submitted a challenge factsheet. The novel and diverse methods from the submitted factsheets are reviewed and analyzed in this survey to advance future event-based eye tracking research.

Aligning large language models (LLMs) with human expectations requires high-quality instructional dialogues, which can be achieved by raising diverse, in-depth, and insightful instructions that deepen interactions. Existing methods target instructions from real instruction dialogues as a learning goal and fine-tune a user simulator for posing instructions. However, the user simulator struggles to implicitly model complex dialogue flows and pose high-quality instructions. In this paper, we take inspiration from the cognitive abilities inherent in human learning and propose the explicit modeling of complex dialogue flows through instructional strategy reuse. Specifically, we first induce high-level strategies from various real instruction dialogues. These strategies are applied to new dialogue scenarios deductively, where the instructional strategies facilitate high-quality instructions. Experimental results show that our method can generate diverse, in-depth, and insightful instructions for a given dialogue history. The constructed multi-turn instructional dialogues can outperform competitive baselines on the downstream chat model.

Autonomous vessels have emerged as a prominent and accepted solution, particularly in the naval defence sector. However, achieving full autonomy for marine vessels demands the development of robust and reliable control and guidance systems that can handle various encounters with manned and unmanned vessels while operating effectively under diverse weather and sea conditions. A significant challenge in this pursuit is ensuring the autonomous vessels' compliance with the International Regulations for Preventing Collisions at Sea (COLREGs). These regulations present a formidable hurdle for the human-level understanding by autonomous systems as they were originally designed from common navigation practices created since the mid-19th century. Their ambiguous language assumes experienced sailors' interpretation and execution, and therefore demands a high-level (cognitive) understanding of language and agent intentions. These capabilities surpass the current state-of-the-art in intelligent systems. This position paper highlights the critical requirements for a trustworthy control and guidance system, exploring the complexity of adapting COLREGs for safe vessel-on-vessel encounters considering autonomous maritime technology competing and/or cooperating with manned vessels.

Brain-Computer Interfaces connect the brain to external control devices, necessitating the accurate translation of brain signals such as from electroencephalography (EEG) into executable commands. Graph Neural Networks (GCN) have been increasingly applied for classifying EEG Motor Imagery signals, primarily because they incorporates the spatial relationships among EEG channels, resulting in improved accuracy over traditional convolutional methods. Recent advances by GCNs-Net in real-time EEG MI signal classification utilised Pearson Coefficient Correlation (PCC) for constructing adjacency matrices, yielding significant results on the PhysioNet dataset. Our paper introduces the EEG Graph Lottery Ticket (EEG_GLT) algorithm, an innovative technique for constructing adjacency matrices for EEG channels. It does not require pre-existing knowledge of inter-channel relationships, and it can be tailored to suit both individual subjects and GCN model architectures. Our findings demonstrated that the PCC method outperformed the Geodesic approach by 9.65% in mean accuracy, while our EEG_GLT matrix consistently exceeded the performance of the PCC method by a mean accuracy of 13.39%. Also, we found that the construction of the adjacency matrix significantly influenced accuracy, to a greater extent than GCN model configurations. A basic GCN configuration utilising our EEG_GLT matrix exceeded the performance of even the most complex GCN setup with a PCC matrix in average accuracy. Our EEG_GLT method also reduced MACs by up to 97% compared to the PCC method, while maintaining or enhancing accuracy. In conclusion, the EEG_GLT algorithm marks a breakthrough in the development of optimal adjacency matrices, effectively boosting both computational accuracy and efficiency, making it well-suited for real-time classification of EEG MI signals that demand intensive computational resources.

We present a unified framework called deep dependency networks (DDNs) that combines dependency networks and deep learning architectures for multi-label classification, with a particular emphasis on image and video data. The primary advantage of dependency networks is their ease of training, in contrast to other probabilistic graphical models like Markov networks. In particular, when combined with deep learning architectures, they provide an intuitive, easy-to-use loss function for multi-label classification. A drawback of DDNs compared to Markov networks is their lack of advanced inference schemes, necessitating the use of Gibbs sampling. To address this challenge, we propose novel inference schemes based on local search and integer linear programming for computing the most likely assignment to the labels given observations. We evaluate our novel methods on three video datasets (Charades, TACoS, Wetlab) and three image datasets (MS-COCO, PASCAL VOC, NUS-WIDE), comparing their performance with (a) basic neural architectures and (b) neural architectures combined with Markov networks equipped with advanced inference and learning techniques. Our results demonstrate the superiority of our new DDN methods over the two competing approaches.

Reviews are in for a tiny 700 wearable computer, less than 2 square-inches in size, made by two former Apple employees who promised a breakthrough'iPhone killer.' And they haven't been kind: Humane's AI Pin has been called'The Worst Product I've Ever Reviewed' garnering low 4-out-of-10 scored from major tech publications. The device -- which is worn on the user's lapel, answers spoken commands via AI, and projects a tiny screen onto their hand -- has been criticized for hardware that overheats in just'a couple of minutes,' AI that delivers'incorrect answers' and worse. Now, Humane's employees and engineers have admitted that the AI Pin, which also requires a 24 monthly subscription plan, is'frustrating sometimes' and that the harsh reviews have been'honest' and'solid.' It's yet to be seen if the public will prefer tapping an object on their chest as opposed to pulling their phone out of their pocket Some tech industry boosters lashed out at influential YouTuber reviewer Marques Brownlee, whose negative review of the AI Pin has 3.7 million views, accusing him of'carelessness' for'potentially killing someone else's nascent project' with his critique.

Amazon Music is joining Spotify in starting to offer a generative AI-powered playlist feature. For now, Maestro is available in beta to a small number of Amazon Music users in the US on iOS and Android. Folks who are included in the beta will see Maestro on the home screen after they update to the latest version of the app. They can also access the tool by tapping the plus button to create a new playlist. The idea is to use natural language prompts to create any kind of playlist imaginable.

Child sexual exploitation is on the rise online and taking new forms such as images and videos generated by artificial intelligence, according to an annual assessment released on Tuesday by the National Center for Missing & Exploited Children (NCMEC), a US-based clearinghouse for the reporting of child sexual abuse material. Reports to the NCMEC of child abuse online rose by more than 12% in 2023 compared with the previous year, surpassing 36.2m The majority of tips received were related to the circulation of child sexual abuse material (CSAM) such as photos and videos, but there was also an increase in reports of financial sexual extortion, when an online predator lures a child into sending nude images or videos and then demands money. Some children and families were extorted for financial gain by predators using AI-made CSAM, according to the NCMEC. The center received 4,700 reports of images or videos of the sexual exploitation of children made by generative AI, a category it only started tracking in 2023, a spokesperson said.