While a drone superhighway might sound better suited to a science-fiction blockbuster than the Midlands, it's set to become a reality this summer. The world's first drone superhighway will open in the UK between June and early July, allowing pilotless drones to make high-speed deliveries across the country. Developed by drone software provider Altitude Angel, the 165-mile-long Skyway network will connect Coventry in the Midlands to Milton Keynes in the Southeast. However, sceptics have warned that the drone highway'inevitably poses risk' for the privacy and safety of Britons living in its flight path. Speaking to MailOnline, Chris Cole, director of campaign group Drone Wars, said: 'While the drone industry are incredibly happy about this, for people who end up living under the drones it may well end up being super annoying and super intrusive.'

The group has run programming sessions and explored how to use AI to hone Tory campaign messages but, Mr Reeve says, it now "mostly focuses on speaking with MPs in more private and safe spaces to help coach politicians on what AI means and how it can be a positive force".

They say it's for all, but is it really?" So begins a 65-second, AI-generated animated video that touches on hot-button issues in the United States ranging from drug addiction and imprisonment rates to growing wealth inequality. As storm clouds gather over an urban landscape resembling New York City, the words "AMERICAN DREAM" hang in a darkening sky as the video ends. The message is clear: Despite its promises of a better life for all, the United States is in terminal decline. The video, titled American Dream or American Mirage, is one of a number of segments aired by Chinese state broadcaster CGTN – and shared far and wide on social media – as part of its A Fractured America animated series. Other videos in the series contain similar titles that invoke images of a dystopian society, such as American workers in tumult: A result of unbalanced politics and economy, and Unmasking the real threat: America's military-industrial complex. CGTN and the Chinese embassy in Washington, DC did not respond to requests for comment. The Fractured America series is just one example of how artificial intelligence (AI), with its ability to generate high-quality multimedia with minimal effort in seconds, is beginning to shape Beijing's propaganda efforts to undermine the United States' standing in the world. Henry Ajder, a UK-based expert in generative AI, said while the CGTN series does not attempt to pass itself off as genuine video, it is a clear example of how AI has made it far easier and cheaper to churn out content. "The reason that they've done it in this way is, you could hire an animator, and a voiceover artist to do this, but it would probably end up being more time-consuming.

The goal of field boundary delineation is to predict the polygonal boundaries and interiors of individual crop fields in overhead remotely sensed images (e.g., from satellites or drones). Automatic delineation of field boundaries is a necessary task for many real-world use cases in agriculture, such as estimating cultivated area in a region or predicting end-of-season yield in a field. Field boundary delineation can be framed as an instance segmentation problem, but presents unique research challenges compared to traditional computer vision datasets used for instance segmentation. The practical applicability of previous work is also limited by the assumption that a sufficiently-large labeled dataset is available where field boundary delineation models will be applied, which is not the reality for most regions (especially under-resourced regions such as Sub-Saharan Africa). We present an approach for segmentation of crop field boundaries in satellite images in regions lacking labeled data that uses multi-region transfer learning to adapt model weights for the target region. We show that our approach outperforms existing methods and that multi-region transfer learning substantially boosts performance for multiple model architectures. Our implementation and datasets are publicly available to enable use of the approach by end-users and serve as a benchmark for future work.

Internet of Things (IoT) sensor data or readings evince variations in timestamp range, sampling frequency, geographical location, unit of measurement, etc. Such presented sequence data heterogeneity makes it difficult for traditional time series classification algorithms to perform well. Therefore, addressing the heterogeneity challenge demands learning not only the sub-patterns (local features) but also the overall pattern (global feature). To address the challenge of classifying heterogeneous IoT sensor data (e.g., categorizing sensor data types like temperature and humidity), we propose a novel deep learning model that incorporates both Convolutional Neural Network and Bi-directional Gated Recurrent Unit to learn local and global features respectively, in an end-to-end manner. Through rigorous experimentation on heterogeneous IoT sensor datasets, we validate the effectiveness of our proposed model, which outperforms recent state-of-the-art classification methods as well as several machine learning and deep learning baselines. In particular, the model achieves an average absolute improvement of 3.37% in Accuracy and 2.85% in F1-Score across datasets

People from different social and demographic groups express diverse perspectives and conflicting opinions on a broad set of topics such as product reviews, healthcare, law, and politics. A fair summary should provide a comprehensive coverage of diverse perspectives without underrepresenting certain groups. However, current work in summarization metrics and Large Language Models (LLMs) evaluation has not explored fair abstractive summarization. In this paper, we systematically investigate fair abstractive summarization for user-generated data. We first formally define fairness in abstractive summarization as not underrepresenting perspectives of any groups of people, and we propose four reference-free automatic metrics by measuring the differences between target and source perspectives. We evaluate nine LLMs, including three GPT models, four LLaMA models, PaLM 2, and Claude, on six datasets collected from social media, online reviews, and recorded transcripts. Experiments show that both the model-generated and the human-written reference summaries suffer from low fairness. We conduct a comprehensive analysis of the common factors influencing fairness and propose three simple but effective methods to alleviate unfair summarization. Our dataset and code are available at https://github.com/psunlpgroup/FairSumm.

The rapid development of large language models (LLMs) enables them to convey factual knowledge in a more human-like fashion. Extensive efforts have been made to reduce factual hallucinations by modifying LLMs with factuality decoding. However, they also pose risks of hindering knowledge updates, as they make models overly confident in known facts. In this work, we first revisite the current factuality decoding methods and verified their effectiveness in enhancing factual accuracy. Subsequently, we conduct further evaluation of several strong factuality decoding methods on the knowledge editing benchmark. All these decoding methods significantly diminish the performance of llama2 models compared to their original decoding, with the largest decrease being a staggering 81.3\%. This further indicates that the current existing decoding methods still cannot perfectly address the factual hallucinations, as they overlook the importance of preserving the flexibility for knowledge editing. Therefore, our work suggests that research into factual alignment should simultaneously focus on the effectiveness of knowledge editing.

We study the generalization capability of Unsupervised Learning in solving the Travelling Salesman Problem (TSP). We use a Graph Neural Network (GNN) trained with a surrogate loss function to generate an embedding for each node. We use these embeddings to construct a heat map that indicates the likelihood of each edge being part of the optimal route. We then apply local search to generate our final predictions. Our investigation explores how different training instance sizes, embedding dimensions, and distributions influence the outcomes of Unsupervised Learning methods. Our results show that training with larger instance sizes and increasing embedding dimensions can build a more effective representation, enhancing the model's ability to solve TSP. Furthermore, in evaluating generalization across different distributions, we first determine the hardness of various distributions and explore how different hardnesses affect the final results. Our findings suggest that models trained on harder instances exhibit better generalization capabilities, highlighting the importance of selecting appropriate training instances in solving TSP using Unsupervised Learning.

Counter narratives - informed responses to hate speech contexts designed to refute hateful claims and de-escalate encounters - have emerged as an effective hate speech intervention strategy. While previous work has proposed automatic counter narrative generation methods to aid manual interventions, the evaluation of these approaches remains underdeveloped. Previous automatic metrics for counter narrative evaluation lack alignment with human judgment as they rely on superficial reference comparisons instead of incorporating key aspects of counter narrative quality as evaluation criteria. To address prior evaluation limitations, we propose a novel evaluation framework prompting LLMs to provide scores and feedback for generated counter narrative candidates using 5 defined aspects derived from guidelines from counter narrative specialized NGOs. We found that LLM evaluators achieve strong alignment to human-annotated scores and feedback and outperform alternative metrics, indicating their potential as multi-aspect, reference-free and interpretable evaluators for counter narrative evaluation.

Online discussions frequently involve conspiracy theories, which can contribute to the proliferation of belief in them. However, not all discussions surrounding conspiracy theories promote them, as some are intended to debunk them. Existing research has relied on simple proxies or focused on a constrained set of signals to identify conspiracy theories, which limits our understanding of conspiratorial discussions across different topics and online communities. This work establishes a general scheme for classifying discussions related to conspiracy theories based on authors' perspectives on the conspiracy belief, which can be expressed explicitly through narrative elements, such as the agent, action, or objective, or implicitly through references to known theories, such as chemtrails or the New World Order. We leverage human-labeled ground truth to train a BERT-based model for classifying online CTs, which we then compared to the Generative Pre-trained Transformer machine (GPT) for detecting online conspiratorial content. Despite GPT's known strengths in its expressiveness and contextual understanding, our study revealed significant flaws in its logical reasoning, while also demonstrating comparable strengths from our classifiers. We present the first large-scale classification study using posts from the most active conspiracy-related Reddit forums and find that only one-third of the posts are classified as positive. This research sheds light on the potential applications of large language models in tasks demanding nuanced contextual comprehension.