The resource allocation problem that is posed by cloud services has long been a popular research topic. The existing auction mechanisms focus primarily on maximizing social welfare, but they often result in lower revenue for cloud service providers. The virtual valuation-based combinatorial auction (VVCA) mechanism can increase the revenue that is obtained by service providers while satisfying dominant strategy incentive compatibility (DSIC). In this study, we innovatively apply the VVCA mechanism to address a time-varying resource allocation problem that involves heterogeneous servers (HTs) in cloud services and effectively increase the revenue that is received by cloud service providers. We begin by transforming the HT problem into an integer programming model with time-varying and resource constraint features. Afterward, we provide the theoretical basis for using the VVCA mechanism to solve the aforementioned problem and provide the DSIC proof. On this basis, we design three progressively more effective mechanisms using the VVCA mechanism. (1) We develop a random mechanism$\rm {HT\_{V}VC{A^{m}}}$and prove that it has a logarithmic approximation ratio, thus offering a better lower bound guarantee than the existing approach does. (2) We propose a gradient-based optimization mechanism$\rm {HT\_{V}VC{A^ * }}$to approximate the optimal revenue. (3) We design an optimal revenue algorithm called HT_VVCANET on the basis of the transformer architecture that is used in deep learning; this algorithm achieves a good balance between execution efficiency and effectiveness. In the experiments, we implement these mechanisms, which significantly increase the revenue that is received by cloud service providers over that yielded by other benchmark mechanisms.

Radio Frequency Fingerprint Identification (RFFI), which leverages hardware-specific impairments in Internet of Things (IoT) devices, is widely used for device authentication and spoofing attack detection to enhance communication security. However, the existing RFFI methods heavily depend on large-scale training datasets in deep learning (DL), with severe overfitting issues if the training samples are scarce. This paper proposes a few-shot learning framework that combines feature augmentation and meta-learning to overcome these challenges. A novel data augmentation technique based on grad class activation maps (Grad-CAM) is introduced to address the scarcity of training samples, which generates augmented samples by adjusting the weights of receptive fields in feature maps, forming an auxiliary dataset for training. In meta-training, the auxiliary dataset is used to construct tasks comprising support and query sets. By extracting common features from the limited sample size, the framework trains a meta-model with robust generalization capabilities. In the deployment phase, a fine-tuning strategy further optimizes the classifier using a small labeled dataset from new IoT devices, allowing rapid adaptation with high accuracy. The proposed framework is evaluated on the large-scale open-source dataset, achieving an accuracy of 94.1% under 8-way 5-shot, with only 25 samples per device in meta-training. Meta-learning boosts performance by 15%-20%, with meta-training feature augmentation further increasing accuracy by 5%-6.6%. Compared to baseline methods, the proposed framework improves the accuracy by 30%, which outperforms the state-of-the-art algorithms by 4%-25%.

Mobile crowdsensing service (MCS) providers recruit users to complete data collection tasks with an incentive mechanism. How to maximize the utility of service providers has long been a popular topic in MCS research. Applying the existing reverse auction mechanism to an MCS may result in excessively high payments, thereby reducing the utility of the MCS provider. The affine maximizer auction (AMA) mechanism increases the revenue of service providers and meets dominant-strategy incentive-compatible (DSIC) characteristics. However, the AMA mechanism is a forward auction mechanism and cannot be applied to MCSs. Inspired by the AMA mechanism, this paper innovatively proposes a reverse affine maximizer auction (RAMA) mechanism to solve the task allocation problem of MCSs, effectively improving the MCS provider utility. Specifically, we construct a RAMA theoretical model and prove that the mechanism satisfies DSIC characteristics. For the discrete MCS task allocation problem, we use the reverse virtual valuation combinatorial auction (RVVCA) mechanism, a subclass of RAMA, to design a random mechanism RVVCA$^{t}$and prove that the RVVCA$^{t}$has a logarithmic approximate ratio. For the differentiable MCS task allocation problem, we use the deep learning transformer framework to design RAMANet, which can fit an exponential number of allocation solutions and output the optimal allocation and payment. We experimentally compare the algorithms of the RAMA family we propose, which use affine maximization, with existing state-of-the-art algorithms, demonstrating that the proposed algorithms significantly improve MCS provider utility.

In contrast to traditional mechanism design, the posted pricing mechanism can quickly determine the winning user and ensure the revenue of the seller through a predetermined price. Additionally, the posted pricing mechanism inherently possesses economic properties such as truthfulness and individual rationality. These properties make it an ideal method for solving online task allocation problems for mobile crowdsensing services (MCSs). The challenge in posted pricing mechanism design is being able to find reasonable posted prices under complex MCS task constraints. This paper presents an innovative posted pricing mechanism to solve a general point of interest (POI)-based online MCS task allocation problem. We transform the problem into an integer programming model with the goal of maximizing the total utility of the system while satisfying various constraints. We prove that under any user arrival order, there must exist a posted price structure that can ensure that the total utility of the system is approximately optimal, with an approximation ratio of$1/(d+1)$in the worst case. With the support of theoretical analysis, the posted price calculation can be completed using only a simple gradient descent algorithm. Compared with existing methods, our solution achieves very good results in terms of total utility and the task completion ratio, indicating that it can effectively improve the efficiency and service quality of MCSs.

To address issues of group-level fairness in machine learning, it is natural to adjust model parameters based on specific fairness objectives over a sensitive-attributed validation set. Such an adjustment procedure can be cast within a meta-learning framework. However, naive integration of fairness goals via meta-learning can cause hypergradient conflicts for subgroups, resulting in unstable convergence and compromising model performance and fairness. To navigate this issue, we frame the resolution of hypergradient conflicts as a multi-player cooperative bargaining game. We introduce a two-stage meta-learning framework in which the first stage involves the use of a Nash Bargaining Solution (NBS) to resolve hypergradient conflicts and steer the model toward the Pareto front, and the second stage optimizes with respect to specific fairness goals. Our method is supported by theoretical results, notably a proof of the NBS for gradient aggregation free from linear independence assumptions, a proof of Pareto improvement, and a proof of monotonic improvement in validation loss. We also show empirical effects across various fairness objectives in six key fairness datasets and two image classification tasks.

We present differentially private algorithms for high-dimensional mean estimation. Previous private estimators on distributions over $\mathbb{R}^d$ suffer from a curse of dimensionality, as they require $\Omega(d^{1/2})$ samples to achieve non-trivial error, even in cases where $O(1)$ samples suffice without privacy. This rate is unavoidable when the distribution is isotropic, namely, when the covariance is a multiple of the identity matrix. Yet, real-world data is often highly anisotropic, with signals concentrated on a small number of principal components. We develop estimators that are appropriate for such signals---our estimators are $(\varepsilon,\delta)$-differentially private and have sample complexity that is dimension-independent for anisotropic subgaussian distributions. Given $n$ samples from a distribution with known covariance-proxy $\Sigma$ and unknown mean $\mu$, we present an estimator $\hat{\mu}$ that achieves error, $\|\hat{\mu}-\mu\|_2\leq \alpha$, as long as $n\gtrsim \text{tr}(\Sigma)/\alpha^2+ \text{tr}(\Sigma^{1/2})/(\alpha\varepsilon)$. We show that this is the optimal sample complexity for this task up to logarithmic factors. Moreover, for the case of unknown covariance, we present an algorithm whose sample complexity has improved dependence on the dimension, from $d^{1/2}$ to $d^{1/4}$.

Robust radio frequency fingerprint identification (RFFI) is crucial for physical layer authentication, while it suffers from channel effects and requires extra overhead to increase recognition accuracy (RA). To address this, an efficient channel-robust RFFI scheme is proposed, employing a specialized multi-task learning (MTL) framework to direct the neural network (NN) toward extracting channel-robust features. In addition, receiver collaboration (RC) is utilized for data augmentation and output calibration. Experimental results demonstrate that the RA is significantly increased from 51.72% to 99.97% when using the open-resource Wi-Fi signal datasets collected from different time periods. Meanwhile, the requirements for extra data transmission, NN structure, and feature crafting in the inferring stage are dramatically simplified.

An integrated physical-layer security scheme is proposed, which combines dynamic physical-layer secret key generation (SKG) and static device fingerprint authentication (DFA) for secure transmission in frequency division duplex (FDD) systems. The proposed approach employs random forest (RF) algorithms to achieve high-speed SKG and utilizes autoencoders (AE) to realize high-accuracy DFA. Besides, channel interference to DFA is minimized during the integration. Experimental results demonstrated that the SKG achieved a key generation rate (KGR) of 57.71 Kbps, while the open-set DFA recognition accuracy reached 99.60%, with a miss alarm rate (MAR) of 0.93%. The proposed scheme successfully integrates physical-layer identity recognition and a fast key generation with excellent performance.

Building machines that can reason about physical events and their causal relationships is crucial for flexible interaction with the physical world. However, most existing physical and causal reasoning benchmarks are exclusively based on synthetically generated events and synthetic natural language descriptions of the causal relationships. This design brings up two issues. First, there is a lack of diversity in both event types and natural language descriptions; second, causal relationships based on manually-defined heuristics are different from human judgments. To address both shortcomings, we present the CLEVRER-Humans benchmark, a video reasoning dataset for causal judgment of physical events with human labels. We employ two techniques to improve data collection efficiency: first, a novel iterative event cloze task to elicit a new representation of events in videos, which we term Causal Event Graphs (CEGs); second, a data augmentation technique based on neural language generative models. We convert the collected CEGs into questions and answers to be consistent with prior work. Finally, we study a collection of baseline approaches for CLEVRER-Humans question-answering, highlighting great challenges set forth by our benchmark.

Humans have the remarkable ability to recognize and acquire novel visual concepts in a zero-shot manner. Given a high-level, symbolic description of a novel concept in terms of previously learned visual concepts and their relations, humans can recognize novel concepts without seeing any examples. Moreover, they can acquire new concepts by parsing and communicating symbolic structures using learned visual concepts and relations. Endowing these capabilities in machines is pivotal in improving their generalization capability at inference time. In this work, we introduce Zero-shot Concept Recognition and Acquisition (ZeroC), a neuro-symbolic architecture that can recognize and acquire novel concepts in a zero-shot way. ZeroC represents concepts as graphs of constituent concept models (as nodes) and their relations (as edges). To allow inference time composition, we employ energy-based models (EBMs) to model concepts and relations. We design ZeroC architecture so that it allows a one-to-one mapping between a symbolic graph structure of a concept and its corresponding EBM, which for the first time, allows acquiring new concepts, communicating its graph structure, and applying it to classification and detection tasks (even across domains) at inference time. We introduce algorithms for learning and inference with ZeroC. We evaluate ZeroC on a challenging grid-world dataset which is designed to probe zero-shot concept recognition and acquisition, and demonstrate its capability.

A digital image encryption algorithm based on dynamic deoxyribonucleic acid coding and chaotic operations using hyper digital chaos in frequency‐domain is proposed and demonstrated, where both the amplitude and phase components in frequency‐domain are diffused and scrambled. The proposed encryption algorithm is evaluated through various evaluations of key parameters such as histogram uniformity, entropy, and correlation. Excellent performance of the encrypted image is achieved to resist the statistical attacks, which implies that the statistical properties of the original image are completely destroyed. In the encryption procedure, each cipher pixel is affected by all of the plain‐pixels as well as cipher‐pixels, due to the implementation of chaotic diffusion and scrambling operations, which increases the sensitivity of the encrypted image to the plain‐text, and improves the security against any differential attacks. Moreover, due to the high sensitivity introduced by the hyper digital chaos, a huge key space is provided for the encrypted image to ensure the high security level, thus the encryption algorithm has a strong secure capability against the brute‐force attacks.