The emergence of federated learning (FL) presents a promising approach to leverage decentralized data while preserving privacy. Furthermore, the combination of FL and anomaly detection is particularly compelling because it allows for detecting rare and critical anomalies (usually also rare in locally gathered data) in sensitive data from multiple sources, such as cybersecurity and healthcare. However, benchmarking the performance of anomaly detection methods in FL environments remains an underexplored area. This paper introduces FedAD-Bench, a unified benchmark for evaluating unsupervised anomaly detection algorithms within the context of FL. We systematically analyze and compare the performance of recent deep learning anomaly detection models under federated settings, which were typically assessed solely in centralized settings. FedAD-Bench encompasses diverse datasets and metrics to provide a holistic evaluation. Through extensive experiments, we identify key challenges such as model aggregation inefficiencies and metric unreliability. We present insights into FL's regularization effects, revealing scenarios in which it outperforms centralized approaches due to its inherent ability to mitigate overfitting. Our work aims to establish a standardized benchmark to guide future research and development in federated anomaly detection, promoting reproducibility and fair comparison across studies.

We propose using natural language outlines as a novel modality and interaction surface for providing AI assistance to developers throughout the software development process. An NL outline for a code function comprises multiple statements written in concise prose, which partition the code and summarize its main ideas in the style of literate programming. Crucially, we find that modern LLMs can generate accurate and high-quality NL outlines in practice. Moreover, NL outlines enable a bidirectional sync between code and NL, allowing changes in one to be automatically reflected in the other. We discuss many use cases for NL outlines: they can accelerate understanding and navigation of code and diffs, simplify code maintenance, augment code search, steer code generation, and more. We then propose and compare multiple LLM prompting techniques for generating outlines and ask professional developers to judge outline quality. Finally, we present two case studies applying NL outlines toward code review and the difficult task of malware detection.

Path loss prediction for wireless communications is highly dependent on the local environment. Propagation models including clutter information have been shown to significantly increase model accuracy. This paper explores the application of deep learning to satellite imagery to identify environmental clutter types automatically. Recognizing these clutter types has numerous uses, but our main application is to use clutter information to enhance propagation prediction models. Knowing the type of obstruction (tree, building, and further classifications) can improve the prediction accuracy of key propagation metrics such as path loss.

The discourse on risks from advanced AI systems ("AIs") typically focuses on misuse, accidents and loss of control, but the question of AIs' moral status could have negative impacts which are of comparable significance and could be realised within similar timeframes. Our paper evaluates these impacts by investigating (1) the factual question of whether future advanced AI systems will be conscious, together with (2) the epistemic question of whether future human society will broadly believe advanced AI systems to be conscious. Assuming binary responses to (1) and (2) gives rise to four possibilities: in the true positive scenario, society predominantly correctly believes that AIs are conscious; in the false positive scenario, that belief is incorrect; in the true negative scenario, society correctly believes that AIs are not conscious; and lastly, in the false negative scenario, society incorrectly believes that AIs are not conscious. The paper offers vivid vignettes of the different futures to ground the two-dimensional framework. Critically, we identify four major risks: AI suffering, human disempowerment, geopolitical instability, and human depravity. We evaluate each risk across the different scenarios and provide an overall qualitative risk assessment for each scenario. Our analysis suggests that the worst possibility is the wrong belief that AI is non-conscious, followed by the wrong belief that AI is conscious. The paper concludes with the main recommendations to avoid research aimed at intentionally creating conscious AI and instead focus efforts on reducing our current uncertainties on both the factual and epistemic questions on AI consciousness.

The rise of smart factories has heightened the demand for automated maintenance, and normal-data-based anomaly detection has proved particularly effective in environments where anomaly data are scarce. This method, which does not require anomaly data during training, has prompted researchers to focus not only on detecting anomalies but also on classifying severity levels by using anomaly scores. However, the existing performance metrics, such as the area under the receiver operating characteristic curve (AUROC), do not effectively reflect the performance of models in classifying severity levels based on anomaly scores. To address this limitation, we propose the weighted sum of the area under the receiver operating characteristic curve (WS-AUROC), which combines AUROC with a penalty for severity level differences. We conducted various experiments using different penalty assignment methods: uniform penalty regardless of severity level differences, penalty based on severity level index differences, and penalty based on actual physical quantities that cause anomalies. The latter method was the most sensitive. Additionally, we propose an anomaly detector that achieves clear separation of distributions and outperforms the ablation models on the WS-AUROC and AUROC metrics.

In today's contemporary digital landscape, chatbots have become indispensable tools across various sectors, streamlining customer service, providing personal assistance, automating routine tasks, and offering health advice. However, their potential remains underexplored in the realm of network security, particularly for intrusion detection. To bridge this gap, we propose an architecture chatbot specifically designed to enhance security within edge networks specifically for intrusion detection. Leveraging advanced machine learning algorithms, this chatbot will monitor network traffic to identify and mitigate potential intrusions. By securing the network environment using an edge network managed by a Raspberry Pi module and ensuring ethical user consent promoting transparency and trust, this innovative solution aims to safeguard sensitive data and maintain a secure workplace, thereby addressing the growing need for robust network security measures in the digital age.

Federated Learning (FL) has emerged as a promising solution to address the limitations of centralised machine learning (ML) in oncology, particularly in overcoming privacy concerns and harnessing the power of diverse, multi-center data. This systematic review synthesises current knowledge on the state-of-the-art FL in oncology, focusing on breast, lung, and prostate cancer. Distinct from previous surveys, our comprehensive review critically evaluates the real-world implementation and impact of FL on cancer care, demonstrating its effectiveness in enhancing ML generalisability, performance and data privacy in clinical settings and data. We evaluated state-of-the-art advances in FL, demonstrating its growing adoption amid tightening data privacy regulations. FL outperformed centralised ML in 15 out of the 25 studies reviewed, spanning diverse ML models and clinical applications, and facilitating integration of multi-modal information for precision medicine. Despite the current challenges identified in reproducibility, standardisation and methodology across studies, the demonstrable benefits of FL in harnessing real-world data and addressing clinical needs highlight its significant potential for advancing cancer research. We propose that future research should focus on addressing these limitations and investigating further advanced FL methods, to fully harness data diversity and realise the transformative power of cutting-edge FL in cancer care.

The mental health assessment of middle school students has always been one of the focuses in the field of education. This paper introduces a new ensemble learning network based on BERT, employing the concept of enhancing model performance by integrating multiple classifiers. We trained a range of BERT-based learners, which combined using the majority voting method. We collect social network text data of middle school students through China's Weibo and apply the method to the task of classifying emotional tendencies in middle school students' social network texts. Experimental results suggest that the ensemble learning network has a better performance than the base model and the performance of the ensemble learning model, consisting of three single-layer BERT models, is barely the same as a three-layer BERT model but requires 11.58% more training time. Therefore, in terms of balancing prediction effect and efficiency, the deeper BERT network should be preferred for training. However, for interpretability, network ensembles can provide acceptable solutions.

But questions of Large language models (LLMs) have demonstrated human values and the answerability of the query generalization capabilities across NLP tasks such itself are difficult to model in terms of model confidence as question answering (QA) (Wei et al., 2022; (Yang et al., 2023). Chowdhery et al., 2022), abstractive summarization (Zhang et al., 2023a), and dialogue generation While prior work demonstrates the potential of (Yi et al., 2024). But these models are also unreliable, abstention in enhancing model safety and reliability having a tendency to "hallucinate" false information (Varshney et al., 2023; Wang et al., 2024c; in their responses (Ji et al., 2023b), generate Zhang et al., 2024a), the study of abstention has overly certain or authoritative responses (Zhou also been constrained to specific QA tasks. This et al., 2024b), answer with incomplete information task-specific approach limits the broader applicability (Zhou et al., 2023b), or produce harmful or of abstention strategies across the diverse dangerous responses (Anwar et al., 2024). In these range of scenarios encountered by general-purpose situations, the model should ideally abstain: to chatbots engaging in open-domain interactions.

We investigate methods for determining if a planar surface contains geometric deviations (e.g., protrusions, objects, divots, or cliffs) using only an instantaneous measurement from a miniature optical time-of-flight sensor. The key to our method is to utilize the entirety of information encoded in raw time-of-flight data captured by off-the-shelf distance sensors. We provide an analysis of the problem in which we identify the key ambiguity between geometry and surface photometrics. To overcome this challenging ambiguity, we fit a Gaussian mixture model to a small dataset of planar surface measurements. This model implicitly captures the expected geometry and distribution of photometrics of the planar surface and is used to identify measurements that are likely to contain deviations. We characterize our method on a variety of surfaces and planar deviations across a range of scenarios. We find that our method utilizing raw time-of-flight data outperforms baselines which use only derived distance estimates. We build an example application in which our method enables mobile robot obstacle and cliff avoidance over a wide field-of-view.