Fast simulation of the energy depositions in high-granular detectors is needed for future collider experiments with ever increasing luminosities. Generative machine learning (ML) models have been shown to speed up and augment the traditional simulation chain in physics analysis. However, the majority of previous efforts were limited to models relying on fixed, regular detector readout geometries. A major advancement is the recently introduced CaloClouds model, a geometry-independent diffusion model, which generates calorimeter showers as point clouds for the electromagnetic calorimeter of the envisioned International Large Detector (ILD). In this work, we introduce CaloClouds II which features a number of key improvements. This includes continuous time score-based modelling, which allows for a 25 step sampling with comparable fidelity to CaloClouds while yielding a $6\times$ speed-up over Geant4 on a single CPU ($5\times$ over CaloClouds). We further distill the diffusion model into a consistency model allowing for accurate sampling in a single step and resulting in a $46\times$ ($37\times$) speed-up. This constitutes the first application of consistency distillation for the generation of calorimeter showers.

Cybersecurity attacks are becoming increasingly sophisticated and pose a growing threat to individuals, and private and public sectors. Distributed Denial of Service attacks are one of the most harmful of these threats in today's internet, disrupting the availability of essential services. This project presents a novel deep learning-based approach for detecting DDoS attacks in network traffic using the industry-recognized DDoS evaluation dataset from the University of New Brunswick, which contains packet captures from real-time DDoS attacks, creating a broader and more applicable model for the real world. The algorithm employed in this study exploits the properties of Convolutional Neural Networks (CNN) and common deep learning algorithms to build a novel mitigation technique that classifies benign and malicious traffic. The proposed model preprocesses the data by extracting packet flows and normalizing them to a fixed length which is fed into a custom architecture containing layers regulating node dropout, normalization, and a sigmoid activation function to out a binary classification. This allows for the model to process the flows effectively and look for the nodes that contribute to DDoS attacks while dropping the "noise" or the distractors. The results of this study demonstrate the effectiveness of the proposed algorithm in detecting DDOS attacks, achieving an accuracy of .9883 on 2000 unseen flows in network traffic, while being scalable for any network environment.

Whether and how data scientists, statisticians and modellers should be accountable for the AI systems they develop remains a controversial and highly debated topic, especially given the complexity of AI systems and the difficulties in comparing and synthesising competing claims arising from their deployment for data analysis. This paper proposes to address this issue by decreasing the opacity and heightening the accountability of decision making using AI systems, through the explicit acknowledgement of the statistical foundations that underpin their development and the ways in which these dictate how their results should be interpreted and acted upon by users. In turn, this enhances (1) the responsiveness of the models to feedback, (2) the quality and meaning of uncertainty on their outputs and (3) their transparency to evaluation. To exemplify this approach, we extend Posterior Belief Assessment to offer a route to belief ownership from complex and competing AI structures. We argue that this is a significant way to bring ethical considerations into mathematical reasoning, and to implement ethical AI in statistical practice. We demonstrate these ideas within the context of competing models used to advise the UK government on the spread of the Omicron variant of COVID-19 during December 2021.

In the last decade, UAVs have become a widely used technology. As they are used by both professionals and amateurs, there is a need to explore different control modalities to make control intuitive and easier, especially for new users. In this work, we compared the most widely used joystick control with a custom human pose control. We used human pose estimation and arm movements to send UAV commands in the same way that operators use their fingers to send joystick commands. Experiments were conducted in a simulation environment with first-person visual feedback. Participants had to traverse the same maze with joystick and human pose control. Participants' subjective experience was assessed using the raw NASA Task Load Index.

Peer review is the method employed by the scientific community for evaluating research advancements. In the field of cybersecurity, the practice of double-blind peer review is the de-facto standard. This paper touches on the holy grail of peer reviewing and aims to shed light on the performance of AI in reviewing for academic security conferences. Specifically, we investigate the predictability of reviewing outcomes by comparing the results obtained from human reviewers and machine-learning models. To facilitate our study, we construct a comprehensive dataset by collecting thousands of papers from renowned computer science conferences and the arXiv preprint website. Based on the collected data, we evaluate the prediction capabilities of ChatGPT and a two-stage classification approach based on the Doc2Vec model with various classifiers. Our experimental evaluation of review outcome prediction using the Doc2Vec-based approach performs significantly better than the ChatGPT and achieves an accuracy of over 90%. While analyzing the experimental results, we identify the potential advantages and limitations of the tested ML models. We explore areas within the paper-reviewing process that can benefit from automated support approaches, while also recognizing the irreplaceable role of human intellect in certain aspects that cannot be matched by state-of-the-art AI techniques.

The development of highly fluent large language models (LLMs) has prompted increased interest in assessing their reasoning and problem-solving capabilities. We investigate whether several LLMs can solve a classic type of deductive reasoning problem from the cognitive science literature. The tested LLMs have limited abilities to solve these problems in their conventional form. We performed follow up experiments to investigate if changes to the presentation format and content improve model performance. We do find performance differences between conditions; however, they do not improve overall performance. Moreover, we find that performance interacts with presentation format and content in unexpected ways that differ from human performance. Overall, our results suggest that LLMs have unique reasoning biases that are only partially predicted from human reasoning performance.

In recent years, great advances in pre-trained language models (PLMs) have sparked considerable research focus and achieved promising performance on the approach of dense passage retrieval, which aims at retrieving relative passages from massive corpus with given questions. However, most of existing datasets mainly benchmark the models with factoid queries of general commonsense, while specialised fields such as finance and economics remain unexplored due to the deficiency of large-scale and high-quality datasets with expert annotations. In this work, we propose a new task, policy retrieval, by introducing the Chinese Stock Policy Retrieval Dataset (CSPRD), which provides 700+ prospectus passages labeled by experienced experts with relevant articles from 10k+ entries in our collected Chinese policy corpus. Experiments on lexical, embedding and fine-tuned bi-encoder models show the effectiveness of our proposed CSPRD yet also suggests ample potential for improvement. Our best performing baseline achieves 56.1% MRR@10, 28.5% NDCG@10, 37.5% Recall@10 and 80.6% Precision@10 on dev set.

This report describes trade-offs in the design of international governance arrangements for civilian artificial intelligence (AI) and presents one approach in detail. This approach represents the extension of a standards, licensing, and liability regime to the global level. We propose that states establish an International AI Organization (IAIO) to certify state jurisdictions (not firms or AI projects) for compliance with international oversight standards. States can give force to these international standards by adopting regulations prohibiting the import of goods whose supply chains embody AI from non-IAIO-certified jurisdictions. This borrows attributes from models of existing international organizations, such as the International Civilian Aviation Organization (ICAO), the International Maritime Organization (IMO), and the Financial Action Task Force (FATF). States can also adopt multilateral controls on the export of AI product inputs, such as specialized hardware, to non-certified jurisdictions. Indeed, both the import and export standards could be required for certification. As international actors reach consensus on risks of and minimum standards for advanced AI, a jurisdictional certification regime could mitigate a broad range of potential harms, including threats to public safety.

Large language models (LLM), based on generative pre-trained transformers (GPT), such as ChatGPT show high efficiency in the analysis of complex texts. These days, we can observe the emerging of many new smaller open source LLMs, e.g. Llama, Falcon, GPT4All, GPT-J, etc. Open source LLMs can be fine-tuned for specific custom problems and deployed on custom servers, e.g. in cloud computing services such as AWS, GCP. LLMs have some new features as compared to conventional language models based on transformers. One of them is zero-shot and few-shot learning, which consists in good performance of the model when we show it only few training examples or even no examples at all, but only the instructions describing what should be done. Another important feature is the reasoning when a model can generate new patterns and conclusions which are based on an input prompt and facts known by the model and which were not included into it directly during a training process. So, the model can generate analytical texts with unexpected but useful chains of thoughts. One of the approaches of using LLMs is based on retrieval augmented generation (RAG), which uses the results from other services e.g.

We compare different methods for extracting The multimodal affective computing field has seen audio features as well as different fusion significant advances in feature extraction and multimodal network methods to combine audio and text signals fusion methodologies in recent years. By to identify the best-performing procedures. We find combining audio, text and visual signals, these that the addition of audio signals consistently improves models offer a more comprehensive, nuanced understanding performance and also that our transformer of human emotions. However, there fusion network further enhances results for most are still limitations: hand-crafted feature extraction metrics and achieves state-of-the-art results across algorithms often lack flexibility and generalization all datasets, indicating its efficacy in enhancing across diverse tasks. To overcome these limitations, cross-modality modeling and its potential for multimodal recent studies have proposed fully end-to-end models emotion detection. From multi-loss training, that optimize both feature extraction and learning we also observe that 1) using distinct labels for processes jointly (Dai et al., 2021). Our work each modality in multi-loss training significantly extracts feature representations from pre-trained benefits the models' performance, and 2) training models for different modalities and combines them on multimodal features improves not only the overall in an end-to-end manner, which provides a comprehensive model performance but also the model's accuracy and adaptable solution for multimodal on the single-modality subnet.