Recovering photorealistic and drivable full-body avatars is crucial for numerous applications, including virtual reality, 3D games, and tele-presence. Most methods, whether reconstruction or generation, require large numbers of human motion sequences and corresponding textured meshes. To easily learn a drivable avatar, a reasonable parametric body model with unified topology is paramount. However, existing human body datasets either have images or textured models and lack parametric models which fit clothes well. We propose a new parametric model SMPLX-Lite-D, which can fit detailed geometry of the scanned mesh while maintaining stable geometry in the face, hand and foot regions. We present SMPLX-Lite dataset, the most comprehensive clothing avatar dataset with multi-view RGB sequences, keypoints annotations, textured scanned meshes, and textured SMPLX-Lite-D models. With the SMPLX-Lite dataset, we train a conditional variational autoencoder model that takes human pose and facial keypoints as input, and generates a photorealistic drivable human avatar.

Social video platforms have emerged as significant channels for information dissemination, facilitating lively public discussions that often give rise to controversies. However, existing approaches to controversy detection primarily focus on textual features, which raises three key concerns: it underutilizes the potential of visual information available on social media platforms; it is ineffective when faced with incomplete or absent textual information; and the existing datasets fail to adequately address the need for comprehensive multimodal resources on social media platforms. To address these challenges, we construct a large-scale Multimodal Controversial Dataset (MMCD) in Chinese. Additionally, we propose a novel framework named Multi-view Controversy Detection (MVCD) to effectively model controversies from multiple perspectives. Through extensive experiments using state-of-the-art models on the MMCD, we demonstrate MVCD's effectiveness and potential impact.

To meet the imperative for sustainable low-carbon wireless communications, integrating distributed renewable energy sources with base stations is essential. However, there is often a mismatch between the energy generated by renewable sources and the energy required by base stations. To address this issue, optimization strategies such as traffic offloading and energy sharing are commonly employed. This paper introduces a spatial model based on stochastic geometry, mapping the spatial distribution of base stations and users, and quantifying the probability of coverage in cellular networks powered by renewable energy with energy-sharing and traffic offloading capabilities. Complementing this spatial analysis, we apply queuing theory to represent the base station energy status as a Markov chain, leading to a nuanced carbon emissions model post energy-sharing. Furthermore, a new metric called carbon efficiency is defined for accurately capturing the trade off between carbon emissions and performance of cellular networks. Simulations show that there is an optimal traffic offloading probability that can minimize carbon emissions in cellular networks while sacrificing the expected ergodic rate of users. These insights offer a foundation for developing optimization strategies that elevate the carbon efficiency of renewable-energy-driven cellular networks.

Federated learning (FL) has recently emerged as a promising paradigm that trains machine learning (ML) models on clients' devices in a distributed manner without the need of transmitting clients' data to the FL server. In many applications of ML (e.g., image classification), the labels of training data need to be generated manually by human agents (e.g., recognizing and annotating objects in an image), which are usually costly and error-prone. In this paper, we study FL with crowdsourced data labeling where the local data of each participating client of FL are labeled manually by the client. We consider the strategic behavior of clients who may not make desired effort in their local data labeling and local model computation (quantified by the mini-batch size used in the stochastic gradient computation), and may misreport their local models to the FL server. We first characterize the performance bounds on the training loss as a function of clients' data labeling effort, local computation effort, and reported local models, which reveal the impacts of these factors on the training loss. With these insights, we devise Labeling and Computation Effort and local Model Elicitation (LCEME) mechanisms which incentivize strategic clients to make truthful efforts as desired by the server in local data labeling and local model computation, and also report true local models to the server. The truthful design of the LCEME mechanism exploits the non-trivial dependence of the training loss on clients' hidden efforts and private local models, and overcomes the intricate coupling in the joint elicitation of clients' efforts and local models. Under the LCEME mechanism, we characterize the server’s optimal local computation effort assignments and analyze their performance. We evaluate the proposed FL algorithms with crowdsourced data labeling and the LCEME mechanism for the MNIST-based hand-written digit classification. The results corroborate the improved learning accuracy and cost-effectiveness of the proposed approaches.

Crowdsourcing has found a wide variety of applications, including spectrum sensing, traffic monitoring, as well as data annotation for machine learning based data analytics. To improve data accuracy and cost-effectiveness, workers’ data quality can be learned from their data in an online manner, which can be used for task assignment and data aggregation. However, crowdsourcing is vulnerable to data poisoning attacks, where the attacker reports malicious data to reduce aggregated data accuracy. In this paper, we study malicious data attacks on dynamic crowdsourcing where tasks are assigned and performed sequentially, and we explore online quality learning as a defense mechanism against the attack by finding malicious workers with low quality. We first focus on the asymptotic setting where workers’ quality is accurately learned by the requester, based on which we then turn to the general non-asymptotic setting where the quality is estimated online with errors. For each setting, we first characterize the conditions under which the attack strategy can effectively reduce the aggregated data accuracy. Our results show that the malicious noise variance needs to be within a certain range for the attack to be effective. Then we analyze the harm of effective attack strategies. It reveals that the regret of the online quality learning algorithm can be substantially increased from$\mathcal {O}(\log ^2T)$(upper bound) to$\Omega (T)$(lower bound) due to effective attacks. To further mitigate the attack, we also study median and maximum influence of estimation based data aggregation as defense mechanisms. Our results provide useful insights on the impacts of data poisoning attacks when online quality learning is used to defend against the attack. We evaluate the proposed attacks and defenses via extensive simulation results based on real-world data, which demonstrate the effectiveness of the attacks and defenses.

Data crowdsourcing is a promising paradigm that leverages the “wisdom” of a potentially large crowd of “workers” in many application domains. Quality-aware crowdsourcing is beneficial as it makes use of workers’ data quality to perform task allocation and data aggregation. However, a worker’s quality and data can be her private information that she may have incentive to misreport to the crowdsourcing requester. Moreover, a worker’s quality and data can depend on her sensitive information (e.g., location), which can be inferred from the outcomes of task allocation and data aggregation by an adversary. In this paper, we devise Privacy-preserving crowdsourcing mechanisms for truthful Data Quality Elicitation (PDQE). In these mechanisms, we design differentially private task allocation and data aggregation algorithms to prevent the inference of a worker’s quality and data from the outcomes of these algorithms. In the meantime, the mechanisms also incentivize workers to truthfully report their quality and data and make desired efforts. We first focus on the mechanisms for a single task (S-PDQE) and then extend it to the case of multiple tasks (M-PDQE). We further show that both the mechanisms achieve a bounded performance gap compared to the optimal strategy. We evaluate the proposed mechanisms using simulations based on real-world data, which corroborate their highly-desired properties on truthful data quality elicitation, data accuracy and privacy protection.

In wireless federated learning (WFL), machine learning (ML) models are trained distributively on wireless edge devices without the need of collecting data from the devices. In such a setting, the quality of a local model update heavily depends on the variance of the local stochastic gradient, determined by the mini-batch data size used to compute the update. In this paper, we explore quality-aware distributed computation for WFL where user devices share limited communication resources, using mini-batch size as a "knob" to control the quality of users’ local updates. In particular, we study joint mini-batch size design and communication scheduling, with the goal of minimizing the training loss as well as the training time of the FL algorithm. For the case of IID data, we first characterize the optimal communication scheduling and the optimal minibatch sizes. Then we develop a greedy algorithm that finds the optimal set of participating users with an approximation ratio. For the case of non-IID data, we first characterize the optimal communication structure and the optimal mini-batch sizes. Then we develop algorithms that find the optimal communication order for some special cases. Our findings provide useful insights for the computation-communication co-design for WFL. We evaluate the proposed mini-batch size design and communication scheduling using simulations, which corroborate improved learning accuracy and learning time.

Wireless federated learning (WFL) trains machine learning (ML) models on wireless edge devices in a distributed manner without the need of collecting data from users. In WFL, the quality of a local model update depends on the variance of the local stochastic gradient, determined by the mini-batch data size used to compute the update. In this paper, we study quality-aware distributed computation for WFL with non-convex problems and asynchronous algorithms, using mini-batch size as a "knob" to control the quality of users' local updates. We first characterize performance bounds on the training loss as a function of local updates' quality over the training process, for both non-convex and asynchronous settings. Our findings reveal that the impact of a local update's quality on the training loss 1) increases with the stepsize used for that local update for non- convex learning, and 2) increases when there are more other users' local updates which are coupled with that local update (depending on the update delays) for asynchronous learning. Based on these useful insights, we design channel-aware adaptive algorithms that determine users' mini-batch sizes over the training process, based on the impacts of local updates' quality on the training loss as well as users' wireless channel conditions (which determine the update delays) and computation costs. We evaluate the proposed quality- aware adaptive algorithms using simulations, which demonstrate improved learning accuracy and learning cost.

Crowdsensing has found a broad range of applications (e.g., spectrum sensing, environmental monitoring) by leveraging the "wisdom" of a potentially large crowd of "workers" (i.e., mobile users). One important class of applications use the data collected from crowdsensing for data analytics via machine learning (e.g., for wireless indoor localization). To exploit the potential of crowdsensing for machine learning, it is beneficial for the crowdsensing requester to know and make use of the quality of worker's data. In this paper, based on a general linear regression model of machine learning, we devise truthful quality-aware crowdsensing mechanisms for quality and effort elicitation, which incentivize workers to truthfully report their private worker quality to the requester, and make effort as desired by the requester. The truthful design of the mechanisms overcomes the differences of ground truths of workers' tasks, and the coupling in the joint elicitation of workers' quality, effort, and data. Under the mechanisms, we investigated the socially optimal and the requester's optimal effort assignments, and analyze their performance. We show that the requester's optimal assignment is determined by the "virtual quality" rather than the highest quality among workers, which depends on the worker's quality and the quality's distribution. Simulation results are provided which demonstrate the truthfulness of the mechanisms and the performance of the optimal effort assignments.