Email: bruno.adorno@manchester.ac.uk ABSTRACT This paper presents two approaches to obtain the dynamical equations of mobile manipulators using dual quaternion algebra. The first one is based on a general recursive Newton-Euler formulation and uses twists and wrenches, which are propagated through high-level algebraic operations and works for any type of joints and arbitrary parameterizations. The second approach is based on Gauss's Principle of Least Constraint (GPLC) and includes arbitrary equality constraints. In addition to showing the connections of GPLC with Gibbs-Appell and Kane's equations, we use it to model a nonholonomic mobile manipulator. Our current formulations are more general than their counterparts in the state of the art, although GPLC is more computationally expensive, and simulation results show that they are as accurate as the classic recursive Newton-Euler algorithm. Keywords: Mobile Manipulator Dynamics, Dual Quaternion Algebra, Newton-Euler Model, Gauss's Principle of Least Constraint, Euler-Lagrange Equations, Gibbs-Appell Equations, Kane's Equations. 1 INTRODUCTION In the last thirty years, there have been an expressive amount of papers dealing with different representations for robot modeling. Notorious examples can be found in the works of Feather-stone [1-3], McCarthy [4-6], Selig [7,8], and Bayro-Corrochano [9], among many others. One of the reasons for such investigations is that the complexity of a robotic system goes far beyond the complexity of the mechanism itself. A typical robotic system involves motion/force/impedance control, path planning, task planning, and many more higher-level layers. Therefore, representations that are very useful for robot modeling, such as homogeneous transformation matrices, not necessarily are easy to use when performing pose control or impedance control, for example [10].

The ACII Affective Vocal Bursts (A-VB) competition introduces a new topic in affective computing, which is understanding emotional expression using the non-verbal sound of humans. We are familiar with emotion recognition via verbal vocal or facial expression. However, the vocal bursts such as laughs, cries, and signs, are not exploited even though they are very informative for behavior analysis. The A-VB competition comprises four tasks that explore non-verbal information in different spaces. This technical report describes the method and the result of SclabCNU Team for the tasks of the challenge. We achieved promising results compared to the baseline model provided by the organizers.

Every other day, my WhatsApp bursts with messages from friends in Brazil and abroad expressing equal parts of excitement and apprehension as Sunday's Brazilian presidential elections approach. On Wednesday, my best friend who lives in the country's capital, Brasília, texted to say she was scared of wearing red clothes to go vote this weekend because red is the color associated with the Worker's Party of former President Luiz Inácio Lula da Silva. Lula, the current front-runner, has a real, if slim, chance to beat far-right incumbent President Jair Bolsonaro in the first round by getting more than 50 percent of valid votes. "The mood is terrible," she wrote, later adding that in the last 48 hours, four instances of political violence had been recorded across the country. My friend's worries are justified.

Location: Our offices are in London (Farringdon), with the ability to work from home for part of the week. The Ada Lovelace Institute is recruiting to the newly created position of Associate Director, Data & AI Law and Policy to join our senior leadership team and develop a comprehensive strategy for informing and influencing public policy, regulatory initiatives and legislative debates on data and AI policy and regulation, in the UK and beyond. In the past five years, AI and other tech regulation has become politically palatable, practically achievable and even commercially desirable in jurisdictions around the world. The year 2022 alone has seen a significant global uptick in proposals for the regulation of AI technologies, online markets, social media platforms and other digital technologies, such as the European Union Directive on AI liability, a forthcoming AI regulation whitepaper in the UK, and similar initiatives in jurisdictions such as Canada and Brazil. At the same time, data regulation is being reformed and iterated in the UK, EU and beyond.

LONDON, Sept. 27, 2022 (GLOBE NEWSWIRE) -- According to The Business Research Company's research report on the high energy lasers market, the emergence of artificial intelligence is gaining popularity among the high energy lasers industry trends. Many companies operating in the market are focused on developing AI-based products to get a competitive advantage. For instance, in April 2022, the US Navy successfully tested the Layered Laser Defense (LLD), a laser weapon designed and developed by Lockheed Martin, a US-based aerospace, arms, defense, information security, and technology company. This is the Layered Laser Defense (LLD). It can use a high-power laser to counter unmanned aerial systems and fast-attack boats, as well as track inbound air threats, support combat identification, and conduct battle damage assessments of engaged targets.

Knowledge graphs represent factual knowledge about the world as relationships between concepts and are critical for intelligent decision making in enterprise applications. New knowledge is inferred from the existing facts in the knowledge graphs by encoding the concepts and relations into low-dimensional feature vector representations. The most effective representations for this task, called Knowledge Graph Embeddings (KGE), are learned through neural network architectures. Due to their impressive predictive performance, they are increasingly used in high-impact domains like healthcare, finance and education. However, are the black-box KGE models adversarially robust for use in domains with high stakes? This thesis argues that state-of-the-art KGE models are vulnerable to data poisoning attacks, that is, their predictive performance can be degraded by systematically crafted perturbations to the training knowledge graph. To support this argument, two novel data poisoning attacks are proposed that craft input deletions or additions at training time to subvert the learned model's performance at inference time. These adversarial attacks target the task of predicting the missing facts in knowledge graphs using KGE models, and the evaluation shows that the simpler attacks are competitive with or outperform the computationally expensive ones. The thesis contributions not only highlight and provide an opportunity to fix the security vulnerabilities of KGE models, but also help to understand the black-box predictive behaviour of KGE models.

Smart energy performance monitoring and optimisation at the supplier and consumer levels is essential to realising smart cities. In order to implement a more sustainable energy management plan, it is crucial to conduct a better energy forecast. The next-generation smart meters can also be used to measure, record, and report energy consumption data, which can be used to train machine learning (ML) models for predicting energy needs. However, sharing fine-grained energy data and performing centralised learning may compromise users' privacy and leave them vulnerable to several attacks. This study addresses this issue by utilising federated learning (FL), an emerging technique that performs ML model training at the user level, where data resides. We introduce FedTrees, a new, lightweight FL framework that benefits from the outstanding features of ensemble learning. Furthermore, we developed a delta-based early stopping algorithm to monitor FL training and stop it when it does not need to continue. The simulation results demonstrate that FedTrees outperforms the most popular federated averaging (FedAvg) framework and the baseline Persistence model for providing accurate energy forecasting patterns while taking only 2% of the computation time and 13% of the communication rounds compared to FedAvg, saving considerable amounts of computation and communication resources.

Demands for minimum parameter setup in machine learning models are desirable to avoid time-consuming optimization processes. The $k$-Nearest Neighbors is one of the most effective and straightforward models employed in numerous problems. Despite its well-known performance, it requires the value of $k$ for specific data distribution, thus demanding expensive computational efforts. This paper proposes a $k$-Nearest Neighbors classifier that bypasses the need to define the value of $k$. The model computes the $k$ value adaptively considering the data distribution of the training set. We compared the proposed model against the standard $k$-Nearest Neighbors classifier and two parameterless versions from the literature. Experiments over 11 public datasets confirm the robustness of the proposed approach, for the obtained results were similar or even better than its counterpart versions.

Continual learning aims to train a model incre-mentally on a sequence of tasks without forgetting previous knowledge. Although continual learning has been widely studied in computer vision, its application to Vision+Language tasks is not that straightforward, as settings can be parameterized in multiple ways according to their input modalities. In this paper, we present a detailed study of how different settings affect performance for Visual Question Answering. We first propose three plausible task formulations and demonstrate their impact on the performance of continual learning algorithms. We break down several factors of task similarity, showing that performance and sensitivity to task order highly depend on the shift of the output distribution. We also investigate the potential of pretrained models and compare the robustness of transformer models with different visual embeddings. Finally, we provide an analysis interpreting model representations and their impact on forgetting. Our results highlight the importance of stabilizing visual representations in deeper layers.

Conditional language models are predominantly trained with maximum likelihood estimation (MLE), giving probability mass to sparsely observed target sequences. While MLE trained models assign high probability to plausible sequences given the context, the model probabilities often do not accurately rank-order generated sequences by quality. This has been empirically observed in beam search decoding as output quality degrading with large beam sizes, and decoding strategies benefiting from heuristics such as length normalization and repetition-blocking. In this work, we introduce sequence likelihood calibration (SLiC) where the likelihood of model generated sequences are calibrated to better align with reference sequences in the model's latent space. With SLiC, decoding heuristics become unnecessary and decoding candidates' quality significantly improves regardless of the decoding method. Furthermore, SLiC shows no sign of diminishing returns with model scale, and presents alternative ways to improve quality with limited training and inference budgets. With SLiC, we exceed or match SOTA results on a wide range of generation tasks spanning abstractive summarization, question generation, abstractive question answering and data-to-text generation, even with modest-sized models.