This paper investigates the tracking problem of a smooth coordinate-invariant trajectory using dual quaternion algebra. The proposed architecture consists of a cascade structure in which the outer-loop MPC performs real-time smoothing of the manipulator's end-effector twist while an inner-loop kinematic controller ensures tracking of the instantaneous desired end-effector pose. Experiments on a $7$-DoF Franka Emika Panda robotic manipulator validate the proposed method demonstrating its application to constraint the robot twists, accelerations and jerks within prescribed bounds.

Smart cities stand as pivotal components in the ongoing pursuit of elevating urban living standards, facilitating the rapid expansion of urban areas while efficiently managing resources through sustainable and scalable innovations. In this regard, as emerging technologies like Artificial Intelligence (AI), the Internet of Things (IoT), big data analytics, and fog and edge computing have become increasingly prevalent, smart city applications grapple with various challenges, including the potential for unauthorized disclosure of confidential and sensitive data. The seamless integration of emerging technologies has played a vital role in sustaining the dynamic pace of their development. This paper explores the substantial potential and applications of Deep Learning (DL), Federated Learning (FL), IoT, Blockchain, Natural Language Processing (NLP), and large language models (LLMs) in optimizing ICT processes within smart cities. We aim to spotlight the vast potential of these technologies as foundational elements that technically strengthen the realization and advancement of smart cities, underscoring their significance in driving innovation within this transformative urban milieu. Our discourse culminates with an exploration of the formidable challenges that DL, FL, IoT, Blockchain, NLP, and LLMs face within these contexts, and we offer insights into potential future directions.

The popularity of federated learning (FL) is on the rise, along with growing concerns about data privacy in artificial intelligence applications. FL facilitates collaborative multi-party model learning while simultaneously ensuring the preservation of data confidentiality. Nevertheless, the problem of statistical heterogeneity caused by the presence of diverse client data distributions gives rise to certain challenges, such as inadequate personalization and slow convergence. In order to address the above issues, this paper offers a brief summary of the current research progress in the field of personalized federated learning (PFL). It outlines the PFL concept, examines related techniques, and highlights current endeavors. Furthermore, this paper also discusses potential further research and obstacles associated with PFL.

Multimodal data pervades various domains, including healthcare, social media, and transportation, where multimodal graphs play a pivotal role. Machine learning on multimodal graphs, referred to as multimodal graph learning (MGL), is essential for successful artificial intelligence (AI) applications. The burgeoning research in this field encompasses diverse graph data types and modalities, learning techniques, and application scenarios. This survey paper conducts a comparative analysis of existing works in multimodal graph learning, elucidating how multimodal learning is achieved across different graph types and exploring the characteristics of prevalent learning techniques. Additionally, we delineate significant applications of multimodal graph learning and offer insights into future directions in this domain. Consequently, this paper serves as a foundational resource for researchers seeking to comprehend existing MGL techniques and their applicability across diverse scenarios.

Information retrieval is a rapidly evolving field of information retrieval, which is characterized by a continuous refinement of techniques and technologies, from basic hyperlink-based navigation to sophisticated algorithm-driven search engines. This paper aims to provide a comprehensive overview of the evolution of Information Retrieval Technology, with a particular focus on the role of Large Language Models (LLMs) in bridging the gap between traditional search methods and the emerging paradigm of answer retrieval. The integration of LLMs in the realms of response retrieval and indexing signifies a paradigm shift in how users interact with information systems. This paradigm shift is driven by the integration of large language models (LLMs) like GPT-4, which are capable of understanding and generating human-like text, thus enabling them to provide more direct and contextually relevant answers to user queries. Through this exploration, we seek to illuminate the technological milestones that have shaped this journey and the potential future directions in this rapidly changing field.

Visually Rich Form Understanding (VRFU) poses a complex research problem due to the documents' highly structured nature and yet highly variable style and content. Current annotation schemes decompose form understanding and omit key hierarchical structure, making development and evaluation of end-to-end models difficult. In this paper, we propose a novel F1 metric to evaluate form parsers and describe a new content-agnostic, tree-based annotation scheme for VRFU: TreeForm. We provide methods to convert previous annotation schemes into TreeForm structures and evaluate TreeForm predictions using a modified version of the normalized tree-edit distance. We present initial baselines for our end-to-end performance metric and the TreeForm edit distance, averaged over the FUNSD and XFUND datasets, of 61.5 and 26.4 respectively. We hope that TreeForm encourages deeper research in annotating, modeling, and evaluating the complexities of form-like documents.

Trust plays an essential role in an individual's decision-making. Traditional trust prediction models rely on pairwise correlations to infer potential relationships between users. However, in the real world, interactions between users are usually complicated rather than pairwise only. Hypergraphs offer a flexible approach to modeling these complex high-order correlations (not just pairwise connections), since hypergraphs can leverage hyperedeges to link more than two nodes. However, most hypergraph-based methods are generic and cannot be well applied to the trust prediction task. In this paper, we propose an Adaptive Hypergraph Network for Trust Prediction (AHNTP), a novel approach that improves trust prediction accuracy by using higher-order correlations. AHNTP utilizes Motif-based PageRank to capture high-order social influence information. In addition, it constructs hypergroups from both node-level and structure-level attributes to incorporate complex correlation information. Furthermore, AHNTP leverages adaptive hypergraph Graph Convolutional Network (GCN) layers and multilayer perceptrons (MLPs) to generate comprehensive user embeddings, facilitating trust relationship prediction. To enhance model generalization and robustness, we introduce a novel supervised contrastive learning loss for optimization. Extensive experiments demonstrate the superiority of our model over the state-of-the-art approaches in terms of trust prediction accuracy. The source code of this work can be accessed via https://github.com/Sherry-XU1995/AHNTP.

Albrecht and Stone (2018) state that modeling of changing behaviors remains an open problem "due to the essentially unconstrained nature of what other agents may do". In this work we evaluate the adaptability of neural artificial agents towards assumed partner behaviors in a collaborative reference game. In this game success is achieved when a knowledgeable Guide can verbally lead a Follower to the selection of a specific puzzle piece among several distractors. We frame this language grounding and coordination task as a reinforcement learning problem and measure to which extent a common reinforcement training algorithm (PPO) is able to produce neural agents (the Guides) that perform well with various heuristic Follower behaviors that vary along the dimensions of confidence and autonomy. We experiment with a learning signal that in addition to the goal condition also respects an assumed communicative effort. Our results indicate that this novel ingredient leads to communicative strategies that are less verbose (staying silent in some of the steps) and that with respect to that the Guide's strategies indeed adapt to the partner's level of confidence and autonomy. Figure 1: An exemplary interaction between a Guide and a Follower that controls the gripper (the black dot).

The quest to improve scalar performance numbers on predetermined benchmarks seems to be deeply engraved in deep learning. However, the real world is seldom carefully curated and applications are seldom limited to excelling on test sets. A practical system is generally required to recognize novel concepts, refrain from actively including uninformative data, and retain previously acquired knowledge throughout its lifetime. Despite these key elements being rigorously researched individually, the study of their conjunction, open world lifelong learning, is only a recent trend. To accelerate this multifaceted field's exploration, we introduce its first monolithic and much-needed baseline. Leveraging the ubiquitous use of batch normalization across deep neural networks, we propose a deceptively simple yet highly effective way to repurpose standard models for open world lifelong learning. Through extensive empirical evaluation, we highlight why our approach should serve as a future standard for models that are able to effectively maintain their knowledge, selectively focus on informative data, and accelerate future learning.

Persuasion, as one of the crucial abilities in human communication, has garnered extensive attention from researchers within the field of intelligent dialogue systems. We humans tend to persuade others to change their viewpoints, attitudes or behaviors through conversations in various scenarios (e.g., persuasion for social good, arguing in online platforms). Developing dialogue agents that can persuade others to accept certain standpoints is essential to achieving truly intelligent and anthropomorphic dialogue system. Benefiting from the substantial progress of Large Language Models (LLMs), dialogue agents have acquired an exceptional capability in context understanding and response generation. However, as a typical and complicated cognitive psychological system, persuasive dialogue agents also require knowledge from the domain of cognitive psychology to attain a level of human-like persuasion. Consequently, the cognitive strategy-enhanced persuasive dialogue agent (defined as CogAgent), which incorporates cognitive strategies to achieve persuasive targets through conversation, has become a predominant research paradigm. To depict the research trends of CogAgent, in this paper, we first present several fundamental cognitive psychology theories and give the formalized definition of three typical cognitive strategies, including the persuasion strategy, the topic path planning strategy, and the argument structure prediction strategy. Then we propose a new system architecture by incorporating the formalized definition to lay the foundation of CogAgent. Representative works are detailed and investigated according to the combined cognitive strategy, followed by the summary of authoritative benchmarks and evaluation metrics. Finally, we summarize our insights on open issues and future directions of CogAgent for upcoming researchers.