Decoding brain signals can not only reveal Metaverse users' expectations but also early detect error-related behaviors such as stress, drowsiness, and motion sickness. For that, this article proposes a pioneering framework using wireless/over-the-air Brain-Computer Interface (BCI) to assist creation of virtual avatars as human representation in the Metaverse. Specifically, to eliminate the computational burden for Metaverse users' devices, we leverage Wireless Edge Servers (WES) that are popular in 5G architecture and therein URLLC, enhanced broadband features to obtain and process the brain activities, i.e., electroencephalography (EEG) signals (via uplink wireless channels). As a result, the WES can learn human behaviors, adapt system configurations, and allocate radio resources to create individualized settings and enhance user experiences. Despite the potential of BCI, the inherent noisy/fading wireless channels and the uncertainty in Metaverse users' demands and behaviors make the related resource allocation and learning/classification problems particularly challenging. We formulate the joint learning and resource allocation problem as a Quality-of-Experience (QoE) maximization problem that takes into the latency, brain classification accuracy, and resources of the system. To tackle this mixed integer programming problem, we then propose two novel algorithms that are (i) a hybrid learning algorithm to maximize the user QoE and (ii) a meta-learning algorithm to exploit the neurodiversity of the brain signals among multiple Metaverse users. The extensive experiment results with different BCI datasets show that our proposed algorithms can not only provide low delay for virtual reality (VR) applications but also can achieve high classification accuracy for the collected brain signals.
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Conversational search systems can improve user experience in digital libraries by facilitating a natural and intuitive way to interact with library content. However, most conversational search systems are limited to performing simple tasks and controlling smart devices. Therefore, there is a need for systems that can accurately understand the user's information requirements and perform the appropriate search activity. Prior research on intelligent systems suggested that it is possible to comprehend the functional aspect of discourse (search intent) by identifying the speech acts in user dialogues. In this work, we automatically identify the speech acts associated with spoken utterances and use them to predict the system-level search actions. First, we conducted a Wizard-of-Oz study to collect data from 75 search sessions. We performed thematic analysis to curate a gold standard dataset -- containing 1,834 utterances and 509 system actions -- of human-system interactions in three information-seeking scenarios. Next, we developed attention-based deep neural networks to understand natural language and predict speech acts. Then, the speech acts were fed to the model to predict the corresponding system-level search actions. We also annotated a second dataset to validate our results. For the two datasets, the best-performing classification model achieved maximum accuracy of 90.2% and 72.7% for speech act classification and 58.8% and 61.1%, respectively, for search act classification.
Peer-to-peer systems are the most resilient form of distributed computing, but the design of robust protocols for their coordination is difficult. This makes it hard to specify and reason about global behaviour of such systems. This paper presents swarm protocols to specify such systems from a global viewpoint. Swarm protocols are projected to machines, that is local specifications of peers. We take inspiration from behavioural types with a key difference: peers communicate through an event notification mechanism rather than through point-to-point message passing. Our goal is to adhere to the principles of local-first software where network devices collaborate on a common task while retaining full autonomy: every participating device can locally make progress at all times, not encumbered by unavailability of other devices or network connections. This coordination-free approach leads to inconsistencies that may emerge during computations. Our main result shows that under suitable well-formedness conditions for swarm protocols consistency is eventually recovered and the locally observable behaviour of conforming machines will eventually match the global specification. The model we propose elaborates on an existing industrial platform and provides the basis for tool support (sketched here and fully described in a companion artifact paper), wherefore we consider this work to be a viable step towards reasoning about local-first and peer-to-peer software systems.
Personalized Recommender System for Children's Book Recommendation with A Realtime Interactive Robot
In this paper we study the personalized book recommender system in a child-robot interactive environment. Firstly, we propose a novel text search algorithm using an inverse filtering mechanism that improves the efficiency. Secondly, we propose a user interest prediction method based on the Bayesian network and a novel feedback mechanism. According to children's fuzzy language input, the proposed method gives the predicted interests. Thirdly, the domain specific synonym association is proposed based on word vectorization, in order to improve the understanding of user intention. Experimental results show that the proposed recommender system has an improved performance and it can operate on embedded consumer devices with limited computational resources.
Since the inception of human research studies, researchers often need to interact with participants on a set schedule to collect data. While some human research is automated, most is not; which costs researchers both time and money. Usually, user-provided data collection consists of surveys administered via telephone or email. While these methods are simplest, they are tedious for the survey administrators, which could incur fatigue and potentially lead to collection mistakes. A solution to this was the creation of "chatbots". Early developments relied on mostly rule-based tactics (e.g. ELIZA), which were suitable for uniform input. However, as the complexity of interactions increases, rule-based systems begin breaking down since there exist a variety of ways for a user to express the same intention. This is especially true when tracking states within a research study (or protocol). Recently, natural language processing (NLP) models and, subsequently, virtual assistants have become increasingly more sophisticated when communicating with users. Examples of these efforts range from research studies to commercial health products. This project leverages recent advancements in conversational artificial intelligence (AI), speech-to-text, natural language understanding (NLU), and finite-state machines to automate protocols, specifically in research settings. This application must be generalized, fully customizable, and irrespective of any research study. These parameters allow new research protocols to be created quickly once envisioned. With this in mind, I present SmartState, a fully-customizable, state-driven protocol manager combined with supporting AI components to autonomously manage user data and intelligently determine the intention of users through chat and end device interactions to drive protocols.
In this paper, we investigate the employment of reconfigurable intelligent surfaces (RISs) into vehicle platoons, functioning in tandem with a base station (BS) in support of the high-precision location tracking. In particular, the use of a RIS imposes additional structured sparsity that, when paired with the initial sparse line-of-sight (LoS) channels of the BS, facilitates beneficial group sparsity. The resultant group sparsity significantly enriches the energies of the original direct-only channel, enabling a greater concentration of the LoS channel energies emanated from the same vehicle location index. Furthermore, the burst sparsity is exposed by representing the non-line-of-sight (NLoS) channels as their sparse copies. This thus constitutes the philosophy of the diverse sparsities of interest. Then, a diverse dynamic layered structured sparsity (DiLuS) framework is customized for capturing different priors for this pair of sparsities, based upon which the location tracking problem is formulated as a maximum a posterior (MAP) estimate of the location. Nevertheless, the tracking issue is highly intractable due to the ill-conditioned sensing matrix, intricately coupled latent variables associated with the BS and RIS, and the spatialtemporal correlations among the vehicle platoon. To circumvent these hurdles, we propose an efficient algorithm, namely DiLuS enabled spatial-temporal platoon localization (DiLuS-STPL), which incorporates both variational Bayesian inference (VBI) and message passing techniques for recursively achieving parameter updates in a turbo-like way. Finally, we demonstrate through extensive simulation results that the localization relying exclusively upon a BS and a RIS may achieve the comparable precision performance obtained by the two individual BSs, along with the robustness and superiority of our proposed algorithm as compared to various benchmark schemes.
In recent years, there has been a significant increase in attention towards designing incentive mechanisms for federated learning (FL). Tremendous existing studies attempt to design the solutions using various approaches (e.g., game theory, reinforcement learning) under different settings. Yet the design of incentive mechanism could be significantly biased in that clients' performance in many applications is stochastic and hard to estimate. Properly handling this stochasticity motivates this research, as it is not well addressed in pioneering literature. In this paper, we focus on cross-device FL and propose a multi-level FL architecture under the real scenarios. Considering the two properties of clients' situations: uncertainty, correlation, we propose FL Incentive Mechanism based on Portfolio theory (FL-IMP). As far as we are aware, this is the pioneering application of portfolio theory to incentive mechanism design aimed at resolving FL resource allocation problem. In order to more accurately reflect practical FL scenarios, we introduce the Federated Learning Agent-Based Model (FL-ABM) as a means of simulating autonomous clients. FL-ABM enables us to gain a deeper understanding of the factors that influence the system's outcomes. Experimental evaluations of our approach have extensively validated its effectiveness and superior performance in comparison to the benchmark methods.
MIS: A Multi-Identifier Management and Resolution System Based on Consortium Blockchain in Metaverse
The metaverse gradually evolves into a virtual world containing a series of interconnected sub-metaverses. Diverse digital resources, including identities, contents, services, and supporting data, are key components of the sub-metaverse. Therefore, a Domain Name System (DNS)-like system is necessary for efficient management and resolution. However, the legacy DNS was designed with security vulnerabilities and trust risks due to centralized issues. Blockchain is used to mitigate these concerns due to its decentralized features. Additionally, it supports identity management as a default feature, making it a natural fit for the metaverse. While there are several DNS alternatives based on the blockchain, they either manage only a single type of identifiers or isolate identities from other sorts of identifiers, making it difficult for sub-metaverses to coexist and connect with each other. This paper proposes a Multi-Identifier management and resolution System (MIS) in the metaverse, supporting the registration, resolution, and inter-translation functions. The basic MIS is portrayed as a four-tier architecture on a consortium blockchain due to its manageability, enhanced security, and efficiency properties. On-chain data is lightweight and compressed to save on storage while accelerating reading and writing operations. The resource data is encrypted based on the attributes of the sub-metaverse in the storage tier for privacy protection and access control. For users with decentralization priorities, a modification named EMIS is built on top of Ethereum. Finally, MIS is implemented on two testbeds and is available online as the open-source system. The first testbed consists of 4 physical servers located in the UK and Malaysia while the second is made up of 200 virtual machines (VMs) spread over 26 countries across all 5 continents on Google Cloud.
We consider the problem of allocating orders to multiple stations and sequencing the interlinked order and rack processing flows in each station in the robot-assisted KIVA warehouse. The various decisions involved in the problem, which are closely associated and must be solved in real time, are often tackled separately for ease of treatment. However, exploiting the synergy between order assignment and picking station scheduling benefits picking efficiency. We develop a comprehensive mathematical model that takes the synergy into consideration to minimize the total number of rack visits. To solve this intractable problem, we develop an efficient algorithm based on simulated annealing and beam search. Computational studies show that our proposed approach outperforms the rule-based greedy policy and the independent picking station scheduling method in terms of solution quality, saving over one-third and one-fifth of rack visits compared with the former and latter, respectively.
Along with the massive growth of the Internet from the 1990s until now, various innovative technologies have been created to bring users breathtaking experiences with more virtual interactions in cyberspace. Many virtual environments with thousands of services and applications, from social networks to virtual gaming worlds, have been developed with immersive experience and digital transformation, but most are incoherent instead of being integrated into a platform. In this context, metaverse, a term formed by combining meta and universe, has been introduced as a shared virtual world that is fueled by many emerging technologies, such as fifth-generation networks and beyond, virtual reality, and artificial intelligence (AI). Among such technologies, AI has shown the great importance of processing big data to enhance immersive experience and enable human-like intelligence of virtual agents. In this survey, we make a beneficial effort to explore the role of AI in the foundation and development of the metaverse. We first deliver a preliminary of AI, including machine learning algorithms and deep learning architectures, and its role in the metaverse. We then convey a comprehensive investigation of AI-based methods concerning six technical aspects that have potentials for the metaverse: natural language processing, machine vision, blockchain, networking, digital twin, and neural interface, and being potential for the metaverse. Subsequently, several AI-aided applications, such as healthcare, manufacturing, smart cities, and gaming, are studied to be deployed in the virtual worlds. Finally, we conclude the key contribution of this survey and open some future research directions in AI for the metaverse.
Deployment of Internet of Things (IoT) devices and Data Fusion techniques have gained popularity in public and government domains. This usually requires capturing and consolidating data from multiple sources. As datasets do not necessarily originate from identical sensors, fused data typically results in a complex data problem. Because military is investigating how heterogeneous IoT devices can aid processes and tasks, we investigate a multi-sensor approach. Moreover, we propose a signal to image encoding approach to transform information (signal) to integrate (fuse) data from IoT wearable devices to an image which is invertible and easier to visualize supporting decision making. Furthermore, we investigate the challenge of enabling an intelligent identification and detection operation and demonstrate the feasibility of the proposed Deep Learning and Anomaly Detection models that can support future application that utilizes hand gesture data from wearable devices.
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