Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT), where a UAV can use its limited service coverage to harvest and disseminate data from IoT devices with low transmission abilities. The existing UAV-assisted data harvesting and dissemination schemes largely require UAVs to frequently fly between the IoTs and access points, resulting in extra energy and time costs. To reduce both energy and time costs, a key way is to enhance the transmission performance of IoT and UAVs. In this work, we introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination from multiple IoT clusters to remote base stations (BSs). Except for reducing these costs, another non-ignorable threat lies in the existence of the potential eavesdroppers, whereas the handling of eavesdroppers often increases the energy and time costs, resulting in a conflict with the minimization of the costs. Moreover, the importance of these goals may vary relatively in different applications. Thus, we formulate a multi-objective optimization problem (MOP) to simultaneously minimize the mission completion time, signal strength towards the eavesdropper, and total energy cost of the UAVs. We prove that the formulated MOP is an NP-hard, mixed-variable optimization, and large-scale optimization problem. Thus, we propose a swarm intelligence-based algorithm to find a set of candidate solutions with different trade-offs which can meet various requirements in a low computational complexity. We also show that swarm intelligence methods need to enhance solution initialization, solution update, and algorithm parameter update phases when dealing with mixed-variable optimization and large-scale problems. Simulation results demonstrate the proposed algorithm outperforms state-of-the-art swarm intelligence algorithms.
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Multi-view clustering (MVC) is a popular technique for improving clustering performance using various data sources. However, existing methods primarily focus on acquiring consistent information while often neglecting the issue of redundancy across multiple views. This study presents a new approach called Sufficient Multi-View Clustering (SUMVC) that examines the multi-view clustering framework from an information-theoretic standpoint. Our proposed method consists of two parts. Firstly, we develop a simple and reliable multi-view clustering method SCMVC (simple consistent multi-view clustering) that employs variational analysis to generate consistent information. Secondly, we propose a sufficient representation lower bound to enhance consistent information and minimise unnecessary information among views. The proposed SUMVC method offers a promising solution to the problem of multi-view clustering and provides a new perspective for analyzing multi-view data. To verify the effectiveness of our model, we conducted a theoretical analysis based on the Bayes Error Rate, and experiments on multiple multi-view datasets demonstrate the superior performance of SUMVC.
Unmanned aerial vehicle (UAV) is becoming increasingly important in modern civilian and military applications. However, its novel use cases is bottlenecked by conventional satellite and terrestrial localization technologies, and calling for complementary solutions. Multi-UAV mutual positioning can be a potential answer, but its accuracy and security are challenged by inaccurate and/or malicious measurements. This paper proposes a novel, robust, and secure approach to address these issues.
The development of deep neural networks (DNN) has significantly enhanced the performance of speaker verification (SV) systems in recent years. However, a critical issue that persists when applying DNN-based SV systems in practical applications is domain mismatch. To mitigate the performance degradation caused by the mismatch, domain adaptation becomes necessary. This paper introduces an approach to adapt DNN-based SV models by manipulating the learnable model inputs, inspired by the concept of adversarial reprogramming. The pre-trained SV model remains fixed and functions solely in the forward process, resembling a black-box model. A lightweight network is utilized to estimate the gradients for the learnable parameters at the input, which bypasses the gradient backpropagation through the black-box model. The reprogrammed output is processed by a two-layer backend learning module as the final adapted speaker embedding. The number of parameters involved in the gradient calculation is small in our design. With few additional parameters, the proposed method achieves both memory and parameter efficiency. The experiments are conducted in language mismatch scenarios. Using much less computation cost, the proposed method obtains close or superior performance to the fully finetuned models in our experiments, which demonstrates its effectiveness.
Large language models (LLMs) provide a new way to build chatbots by accepting natural language prompts. Yet, it is unclear how to design prompts to power chatbots to carry on naturalistic conversations while pursuing a given goal, such as collecting self-report data from users. We explore what design factors of prompts can help steer chatbots to talk naturally and collect data reliably. To this aim, we formulated four prompt designs with different structures and personas. Through an online study (N = 48) where participants conversed with chatbots driven by different designs of prompts, we assessed how prompt designs and conversation topics affected the conversation flows and users' perceptions of chatbots. Our chatbots covered 79% of the desired information slots during conversations, and the designs of prompts and topics significantly influenced the conversation flows and the data collection performance. We discuss the opportunities and challenges of building chatbots with LLMs.
NB-Fi is a new low-power wide-area network technology, which has become widely used for smart cities, smart grids, the Industrial Internet of Things, and telemetry applications. Although many countries use NB-Fi, almost no papers study NB-Fi, and its peak performance is unknown. This article aims to fill this gap by analyzing this technology and studying the problem of rate assignment in NB-Fi networks. For that, this article develops a mathematical model used to find the packet loss ratio, packet error rate, and the average delay for various rate assignment approaches. The performance evaluation results are used to develop the guidelines for NB-Fi configuration to optimize the network performance.
In Time-Triggered (TT) or time-sensitive networks, the transmission of a TT frame is required to be scheduled at a precise time instant for industrial distributed real-time control systems. Other (or {\em best-effort} (BE)) frames are forwarded in a BE manner. Under this scheduling strategy, the transmission of a TT frame must wait until its scheduled instant even if it could have been transmitted sooner. On the other hand, BE frames are transmitted whenever possible but may miss deadlines or may even be dropped due to congestion. As a result, TT transmission and BE delivery are incompatible with each other. To remedy this incompatibility, we propose a synergistic switch architecture (SWA) for TT transmission with BE delivery to dynamically improve the end-to-end (e2e) latency of TT frames by opportunistically exploiting BE delivery. Given a TT frame, the SWA generates and transmits a cloned copy with BE delivery. The first frame arriving at the receiver device is delivered with a configured jitter and the other copy ignored. So, the SWA achieves shorter latency and controllable jitter, the best of both worlds. We have implemented SWA using FPGAs in an industry-strength TT switches and used four test scenarios to demonstrate SWA's improvements of e2e latency and controllable jitter over the state-of-the-art TT transmission scheme.
Accurate deformable object manipulation (DOM) is essential for achieving autonomy in robotic surgery, where soft tissues are being displaced, stretched, and dissected. Many DOM methods can be powered by simulation, which ensures realistic deformation by adhering to the governing physical constraints and allowing for model prediction and control. However, real soft objects in robotic surgery, such as membranes and soft tissues, have complex, anisotropic physical parameters that a simulation with simple initialization from cameras may not fully capture. To use the simulation techniques in real surgical tasks, the "real-to-sim" gap needs to be properly compensated. In this work, we propose an online, adaptive parameter tuning approach for simulation optimization that (1) bridges the real-to-sim gap between a physics simulation and observations obtained 3D perceptions through estimating a residual mapping and (2) optimizes its stiffness parameters online. Our method ensures a small residual gap between the simulation and observation and improves the simulation's predictive capabilities. The effectiveness of the proposed mechanism is evaluated in the manipulation of both a thin-shell and volumetric tissue, representative of most tissue scenarios. This work contributes to the advancement of simulation-based deformable tissue manipulation and holds potential for improving surgical autonomy.
We consider unmanned aerial vehicle (UAV)-enabled wireless systems where downlink communications between a multi-antenna UAV and multiple users are assisted by a hybrid active-passive reconfigurable intelligent surface (RIS). We aim at a fairness design of two typical UAV-enabled networks, namely the static-UAV network where the UAV is deployed at a fixed location to serve all users at the same time, and the mobile-UAV network which employs the time division multiple access protocol. In both networks, our goal is to maximize the minimum rate among users through jointly optimizing the UAV's location/trajectory, transmit beamformer, and RIS coefficients. The resulting problems are highly nonconvex due to a strong coupling between the involved variables. We develop efficient algorithms based on block coordinate ascend and successive convex approximation to effectively solve these problems in an iterative manner. In particular, in the optimization of the mobile-UAV network, closed-form solutions to the transmit beamformer and RIS passive coefficients are derived. Numerical results show that a hybrid RIS equipped with only 4 active elements and a power budget of 0 dBm offers an improvement of 38%-63% in minimum rate, while that achieved by a passive RIS is only about 15%, with the same total number of elements.
Spectral clustering (SC) is a popular clustering technique to find strongly connected communities on a graph. SC can be used in Graph Neural Networks (GNNs) to implement pooling operations that aggregate nodes belonging to the same cluster. However, the eigendecomposition of the Laplacian is expensive and, since clustering results are graph-specific, pooling methods based on SC must perform a new optimization for each new sample. In this paper, we propose a graph clustering approach that addresses these limitations of SC. We formulate a continuous relaxation of the normalized minCUT problem and train a GNN to compute cluster assignments that minimize this objective. Our GNN-based implementation is differentiable, does not require to compute the spectral decomposition, and learns a clustering function that can be quickly evaluated on out-of-sample graphs. From the proposed clustering method, we design a graph pooling operator that overcomes some important limitations of state-of-the-art graph pooling techniques and achieves the best performance in several supervised and unsupervised tasks.
Graph neural networks (GNNs) have emerged as a powerful paradigm for embedding-based entity alignment due to their capability of identifying isomorphic subgraphs. However, in real knowledge graphs (KGs), the counterpart entities usually have non-isomorphic neighborhood structures, which easily causes GNNs to yield different representations for them. To tackle this problem, we propose a new KG alignment network, namely AliNet, aiming at mitigating the non-isomorphism of neighborhood structures in an end-to-end manner. As the direct neighbors of counterpart entities are usually dissimilar due to the schema heterogeneity, AliNet introduces distant neighbors to expand the overlap between their neighborhood structures. It employs an attention mechanism to highlight helpful distant neighbors and reduce noises. Then, it controls the aggregation of both direct and distant neighborhood information using a gating mechanism. We further propose a relation loss to refine entity representations. We perform thorough experiments with detailed ablation studies and analyses on five entity alignment datasets, demonstrating the effectiveness of AliNet.
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