The skeleton of a baby has been found beneath the floorboards of a house. The discovery was made by contractors renovating the property in Bishop Auckland, County Durham, on Monday. Forensic analysts, including an expert anthropologist, have been brought in to help determine its age and how long it has been in the current location. A post-mortem examination and CT scan are scheduled for later this week to determine the cause of death. Durham Police said it had also begun tracing previous residents of the address in Fore Bondgate.

Covertly intercepting video signals is a very old-fashioned way to go about electronic spying, but a new method discovered by researchers puts a frightening spin on it. A research team out of Uruguay has found that it's possible to intercept the wireless electromagnetic radiation coming from an HDMI cable and interpret the video by processing it with AI. Three scientists from the University of the Republic in Montevideo published their findings on Cornell's ArXiv service, spotted by Techspot. According to the paper, it's possible to train an AI model to interpret the tiny fluctuations in electromagnetic energy from the wired HDMI signal. Even though it's a wired standard and it's usually encrypted digitally, there's enough electromagnetic signal coming off of these cables to detect without direct access. Detecting and decoding are two different things, of course.

Different approaches have been adopted in addressing the challenges of Artificial Intelligence (AI), some centred on personal data and others on ethics, respectively narrowing and broadening the scope of AI regulation. This contribution aims to demonstrate that a third way is possible, starting from the acknowledgement of the role that human rights can play in regulating the impact of data-intensive systems. The focus on human rights is neither a paradigm shift nor a mere theoretical exercise. Through the analysis of more than 700 decisions and documents of the data protection authorities of six countries, we show that human rights already underpin the decisions in the field of data use. Based on empirical analysis of this evidence, this work presents a methodology and a model for a Human Rights Impact Assessment (HRIA). The methodology and related assessment model are focused on AI applications, whose nature and scale require a proper contextualisation of HRIA methodology. Moreover, the proposed models provide a more measurable approach to risk assessment which is consistent with the regulatory proposals centred on risk thresholds. The proposed methodology is tested in concrete case-studies to prove its feasibility and effectiveness. The overall goal is to respond to the growing interest in HRIA, moving from a mere theoretical debate to a concrete and context-specific implementation in the field of data-intensive applications based on AI.

The demand for social robots in fields like healthcare, education, and entertainment increases due to their emotional adaptation features. These robots leverage multimodal communication, incorporating speech, facial expressions, and gestures to enhance user engagement and emotional support. The understanding of design paradigms of social robots is obstructed by the complexity of the system and the necessity to tune it to a specific task. This article provides a structured review of social robot design paradigms, categorizing them into cognitive architectures, role design models, linguistic models, communication flow, activity system models, and integrated design models. By breaking down the articles on social robot design and application based on these paradigms, we highlight the strengths and areas for improvement in current approaches. We further propose our original integrated design model that combines the most important aspects of the design of social robots. Our approach shows the importance of integrating operational, communicational, and emotional dimensions to create more adaptive and empathetic interactions between robots and humans.

We introduce a novel self-supervised deep clustering approach tailored for unstructured data without requiring prior knowledge of the number of clusters, termed Adaptive Self-supervised Robust Clustering (ASRC). In particular, ASRC adaptively learns the graph structure and edge weights to capture both local and global structural information. The obtained graph enables us to learn clustering-friendly feature representations by an enhanced graph auto-encoder with contrastive learning technique. It further leverages the clustering results adaptively obtained by robust continuous clustering (RCC) to generate prototypes for negative sampling, which can further contribute to promoting consistency among positive pairs and enlarging the gap between positive and negative samples. ASRC obtains the final clustering results by applying RCC to the learned feature representations with their consistent graph structure and edge weights. Extensive experiments conducted on seven benchmark datasets demonstrate the efficacy of ASRC, demonstrating its superior performance over other popular clustering models. Notably, ASRC even outperforms methods that rely on prior knowledge of the number of clusters, highlighting its effectiveness in addressing the challenges of clustering unstructured data.

We contrast high effectiveness of state of the art deep learning architectures designed for general audio classification tasks, refined for respiratory insufficiency (RI) detection and blood oxygen saturation (SpO$_2$) estimation and classification through automated audio analysis. Recently, multiple deep learning architectures have been proposed to detect RI in COVID patients through audio analysis, achieving accuracy above 95% and F1-score above 0.93. RI is a condition associated with low SpO$_2$ levels, commonly defined as the threshold SpO$_2$ <92%. While SpO$_2$ serves as a crucial determinant of RI, a medical doctor's diagnosis typically relies on multiple factors. These include respiratory frequency, heart rate, SpO$_2$ levels, among others. Here we study pretrained audio neural networks (CNN6, CNN10 and CNN14) and the Masked Autoencoder (Audio-MAE) for RI detection, where these models achieve near perfect accuracy, surpassing previous results. Yet, for the regression task of estimating SpO$_2$ levels, the models achieve root mean square error values exceeding the accepted clinical range of 3.5% for finger oximeters. Additionally, Pearson correlation coefficients fail to surpass 0.3. As deep learning models perform better in classification than regression, we transform SpO$_2$-regression into a SpO$_2$-threshold binary classification problem, with a threshold of 92%. However, this task still yields an F1-score below 0.65. Thus, audio analysis offers valuable insights into a patient's RI status, but does not provide accurate information about actual SpO$_2$ levels, indicating a separation of domains in which voice and speech biomarkers may and may not be useful in medical diagnostics under current technologies.

By understanding how users decide on their commuting modes, it is possible to identify factors that can be influenced to change travel behavior and promote the adoption of more sustainable transportation modes. Agent-based modeling (ABM) is particularly valuable for this purpose, as it can represent complex systems like transportation and identify emerging collective behaviors resulting from the autonomous decisions of transport users interacting among them and with the environment (Kagho, Balac, and Axhausen 2020). These capabilities make ABM suitable for analyzing the impacts of transport policies (Wise, Crooks, and Batty 2017). However, the application of ABM in analyzing transport mode choices has been limited and studies have been conducted predominantly in developed countries (Cadavid and Salazar-Serna 2021; Salazar-Serna, Cadavid, Franco, and Carley 2023). The effectiveness of these findings may not extend seamlessly to developing regions due to different contextual policy needs and the distinct ways socioeconomic and cultural factors influence human behavior (Carley 1991; Salazar-Serna et al. 2023). Therefore, policies that have been successful in one setting might not achieve similar outcomes in another. Previous studies in transportation have identified various determinants affecting mode choice. These factors can be grouped into several categories: sociodemographic characteristics such as age, sex, occupation, and income level (Ashalatha et al. 2013); travel habits including distance traveled, travel time, origin-destination pairs, and trip purpose (Madhuwanthi et al. 2016); and attributes of the built environment like design, density, and capacity (Ewing and Cervero 2010). Additionally, attitudes and perceptions regarding transport modes, which cover aspects such as comfort, cost, security, safety, quality, and reliability, play a crucial role (Fu 2021).

As machine learning becomes increasingly integral to autonomous decision-making processes involving human interaction, the necessity of comprehending the model's outputs through conversational means increases. Most recently, foundation models are being explored for their potential as post hoc explainers, providing a pathway to elucidate the decision-making mechanisms of predictive models. In this work, we introduce traceable question-answering, leveraging an external knowledge repository to inform the responses of Large Language Models (LLMs) to user queries within a scene understanding task. This knowledge repository comprises contextual details regarding the model's output, containing high-level features, feature importance, and alternative probabilities. We employ subtractive counterfactual reasoning to compute feature importance, a method that entails analysing output variations resulting from decomposing semantic features. Furthermore, to maintain a seamless conversational flow, we integrate four key characteristics - social, causal, selective, and contrastive - drawn from social science research on human explanations into a single-shot prompt, guiding the response generation process. Our evaluation demonstrates that explanations generated by the LLMs encompassed these elements, indicating its potential to bridge the gap between complex model outputs and natural language expressions.

This chapter explores the essential role of Binding Corporate Rules (BCRs) in managing and facilitating secure health data transfers within corporate groups under the EU General Data Protection Regulation (GDPR). BCRs are tailored to ensure compliance with the GDPR and similar international data protection laws, presenting a flexible mechanism for transferring sensitive health and genomic data. The chapter situates BCRs within the broader spectrum of the GDPR international data transfer mechanisms, addressing the unique challenges posed by the sensitive nature of health data and the increased adoption of AI technologies. The European Data Protection Board (EDPB) Recommendations 1/2022 on BCRs, issued following the Schrems II decision, are critically analyzed, highlighting their stringent requirements and the need for a balanced approach that prioritizes data protection and an AI governance framework. The chapter outlines the BCR approval process, stressing the importance of streamlining this process to encourage broader adoption. It underscores the necessity of a multidisciplinary approach in developing BCRs, incorporating recently adopted international standards and frameworks, which offer valuable guidance for organizations to build trustworthy AI management systems. They guarantee the ethical development, deployment, and operation of AI, which is essential for its successful integration and the broader digital transformation. In conclusion, BCRs are positioned as essential tools for secure health data management, fostering transparency, accountability, and collaboration across international borders. The chapter calls for proactive measures to incentivize BCR adoption, streamline approval processes, and promote more innovative approaches, ensuring BCRs remain a robust mechanism for global data protection and compliance.

The neurons of Kolmogorov-Arnold Networks (KANs) perform a simple summation motivated by the Kolmogorov-Arnold representation theorem, which asserts that sum is the only fundamental multivariate function. In this work, we investigate the potential for identifying an alternative multivariate function for KAN neurons that may offer increased practical utility. Our empirical research involves testing various multivariate functions in KAN neurons across a range of benchmark Machine Learning tasks. Our findings indicate that substituting the sum with the average function in KAN neurons results in significant performance enhancements compared to traditional KANs. Our study demonstrates that this minor modification contributes to the stability of training by confining the input to the spline within the effective range of the activation function. Our implementation and experiments are available at: \url{https://github.com/Ghaith81/dropkan}