To tap into the gold mine of data generated by Internet of Things (IoT) devices with unprecedented volume and value, there is an urgent need to efficiently and accurately label raw sensor data. To this end, we explore and leverage the hidden connections among the multimodal data collected by various sensing devices and propose to let different modal data complement and learn from each other. But it is challenging to align and fuse multimodal data without knowing their perception (and thus the correct labels). In this work, we propose MultiSense, a paradigm for automatically mining potential perception, cross-labelling each modal data, and then updating the learning models for recognizing human activity to achieve higher accuracy or even recognize new activities. We design innovative solutions for segmenting, aligning, and fusing multimodal data from different sensors, as well as model updating mechanism. We implement our framework and conduct comprehensive evaluations on a rich set of data. Our results demonstrate that MultiSense significantly improves the data usability and the power of the learning models. With nine diverse activities performed by users, our framework automatically labels multimodal sensing data generated by five different sensing mechanisms (video, smart watch, smartphone, audio, and wireless-channel) with an average accuracy 98.5%. Furthermore, it enables models of some modalities to learn unknown activities from other modalities and greatly improves the activity recognition ability.

With the popularity of deep learning applications, the privacy of training data has become a major concern as the data sources may be sensitive. Recent studies have found that deep learning models are vulnerable to privacy attacks, which are able to infer private training data from model parameters. To mitigate such attacks, differential privacy has been proposed to preserve data privacy by adding randomized noise to these models. However, since deep learning models usually consist of a large number of parameters and complicated layered structures, an overwhelming amount of noise is often inserted, which significantly degrades model accuracy. We seek a better tradeoff between model utility and data privacy, by choosing directions of noise w.r.t. the utility subspace. We propose an optimized mechanism for differentially-private stochastic gradient descent, and derive a closed-form solution. The form of the solution makes the mechanism ready to be deployed in real-world deep learning systems. Experimental results on a variety of models, datasets, and privacy settings show that our proposed mechanism achieves higher accuracies at the same privacy guarantee compared to the state-of-the-art methods. Further, we extend the privacy guarantee to a mutual information bound, and propose a general form to the utility-privacy problem.

Vibrations measurement is a crucial task in industrial systems, where vibration characteristics reflect health conditions and indicate anomalies of the devices. Previous approaches either work in an intrusive manner or fail to capture the micrometer-level vibrations. In this work, we propose mmVib, a practical approach to measure micrometer-level vibrations with mmWave radar. First, we derive a metric called Vibration Signal-to-Noise Ratio (VSNR) that highlights the directions of reducing measurement errors of tiny vibrations. Then, we introduce the design of mmVib based on the concept of Multi-Signal Consolidation (MSC) for the error reduction and multi-object measurement. We implement a prototype of mmVib, and the experiments show that it achieves 3.946% relative amplitude error and 0.02487% relative frequency error in median. Typically, the average amplitude error is only 3.174um when measuring the 100um-amplitude vibration at around 5 meters. Compared to two existing mmWave-based approaches, mmVib reduces the 80th-percentile amplitude error by 69.21% and 97.99% respectively.

Influence maximization through social networks has aroused tremendous interests nowadays. However, people's various expressions or feelings about a same idea often cause ambiguity via word of mouth. Consequently, the problem of how to maximize the spread of "effective information " still remains largely open. In this paper, we consider a practical setting where ideas can deviate from their original version to invalid forms during message passing, and make the first attempt to seek a union of users that maximizes the spread of effective influence, which is formulated as an Influence Maximization with Information Variation (IMIV) problem. To this end, we model the information as a vector, and quantify the difference of two arbitrary vectors as a distance by a matching function. We further establish a process where such distance increases with the propagation and ensure the recipient whose vector distance is less than a threshold can be effectively influenced. Due to the NP-hardness of IMIV, we greedily select users that can approximately maximize the estimation of effective propagation. Especially, for networks of small scales, we derive a condition under which all the users can be effectively influenced. Our models and theoretical findings are further consolidated through extensive experiments on real-world datasets.

近年来社交媒体在拓宽人们获取信息渠道的同时,也方便了虚假信息的传播,并造成了严重的负面影响.与传统互联网媒体相比,社交媒体包含的信息更加复杂多样,为内容可信性的判断带来了新的挑战.已有研究在分析社交媒体内容可信性时,对挖掘可信性影响因素进行了很多工作,但缺乏对噪音数据的处理,大量的无用推文会对推文可信性判断造成干扰,进而会影响事件层面的可信性判断,从大量噪音数据中筛选出真正有用的推文数据就显得尤为重要.在推文层面同时考虑用户的主题因素和从众行为,减少了从众转发等噪音数据在可信性判断过程中的作用,对社交媒体内容的可信性进行研究,采用贝叶斯网络建立了社交媒体内容可信性评价模型,并通过新浪微博公开数据集验证了模型的有效性.

Mobile edge computing enables resource-limited edge clouds (ECs) in federation to help each other with resource-hungry yet delay-sensitive service requests. Contrary to common practice, we acknowledge that mobile services are heterogeneous and the limited storage resources of ECs allow only a subset of services to be placed at the same time. This paper presents a jointly optimized design of cooperative placement and scheduling framework, named JCPS, that pursues social cost minimization over time while ensuring diverse user demands. Our main contribution is a novel perspective on cost reduction by exploiting the spatial-temporal diversities in workload and resource cost among federated ECs. To build a practical edge cloud federation system, we have to consider two major challenges: user deadline preference and ECs' strategic behaviors. We first formulate and solve the problem of spatially strategic optimization without deadline awareness, which is proved ArP-hard. By leveraging user deadline tolerance, we develop a Lyapunov-based deadline-driven joint cooperative mechanism under the scenario where the workload and resource information of ECs are known for one-shot global cost minimization. The service priority imposed by deadline urgency drives time-critical placement and scheduling, which, combined with cooperative control, enables workloads migrated across different times and ECs. Given selfishness of individual ECs, we further design an auction-based cooperative mechanism to elicit truthful bids on workload and resource cost. Rigorous theoretical analysis and extensive simulations are performed, validating the efficiency of JCPS in realizing cost reduction and user satisfaction.

Mobile video streaming occupies three-quarters of today's cellular network traffic. The quality of mobile videos becomes increasingly important for video providers to attract more users. For example, they invest in network bandwidth resources and conduct adaptive bitrate techniques to improve video quality. Prior adaptive bitrate (ABR) algorithms perform well under given throughput traces on broadband and WiFi networks. They may perform poorly for mobile video streaming due to the high network dynamics of cellular networks. To study the properties of throughput traces under cellular networks, we collect 4G network throughput traces for over four months in two large cities, Beijing and Suzhou in China. We derive the environment-specific Markov property of throughputs in the dataset. Accordingly, we propose NEIVA, an environment identification based technique to adaptively predict future throughput for different types of environments. We also implement NEIVA and integrate it with the state-of-the-art ABR algorithm, model predictive control (MPC) approach in our testbed for experiments. By emulating mobile video streaming under throughput traces in our dataset, NEIVA achieves 20 - 25 percent improvement on throughput prediction accuracy comparing to baseline predictors. Meanwhile, NEIVA achieves 11 - 20 percent user QoE improvement over MPC with baseline predictors.

The von Neumann graph entropy is a measure of graph complexity based on the Laplacian spectrum. It has recently found applications in various learning tasks driven by the networked data. However, it is computationally demanding and hard to interpret using simple structural patterns. Due to the close relation between the Laplacian spectrum and the degree sequence, we conjecture that the structural information, defined as the Shannon entropy of the normalized degree sequence, might be a good approximation of the von Neumann graph entropy that is both scalable and interpretable. In this work, we thereby study the difference between the structural information and the von Neumann graph entropy named as entropy gap. Based on the knowledge that the degree sequence is majorized by the Laplacian spectrum, we for the first time prove that the entropy gap is between 0 and log(2) e in any undirected unweighted graphs. Consequently we certify that the structural information is a good approximation of the von Neumann graph entropy that achieves provable accuracy, scalability, and interpretability simultaneously. This approximation is further applied to two entropyrelated tasks: network design and graph similarity measure, where a novel graph similarity measure and the corresponding fast algorithms are proposed. Meanwhile, we show empirically and theoretically that maximizing the von Neumann graph entropy can effectively hide the community structure, and then propose an alternative metric called spectral polarization to guide the community obfuscation. Our experimental results on graphs of various scales and types show that the very small entropy gap readily applies to a wide range of simple/weighted graphs. As an approximation of the von Neumann graph entropy, the structural information is the only one that achieves both high efficiency and high accuracy among the prominent methods. It is at least two orders of magnitude faster than SLaQ (Tsitsulin et al., 2020) with comparable accuracy. Our structural information based methods also exhibit superior performance in downstream tasks such as entropy-driven network design, graph comparison, and community obfuscation.

Recent years have witnessed the proliferation of electric vehicles (EVs) that enable environment-friendly commuting and traveling. However, the increasing number of EVs inevitably create massive charging demands that are challenging to satisfy. Oftentimes in practice, EVs have to wait in queues for a long time outside charging stations before chargers become available. To address this challenge, we fully capture the elasticity of EVs' charging demands in response to the charging prices, and propose a dynamic charging pricing mechanism that jointly controls the lengths of the demand queues at multiple charging stations and maximizes the charging platform's long-term profit for offering charging services. Clearly, such an approach is more feasible than the financially and temporally expensive way of constructing extra charging facilities. Technically, we augment the Lyapunov stochastic optimization technique to decompose the challenging long-term decision-making problem into a series of single-time-slot optimization programs which require zero knowledge of future system parameters. However, due to the correlation of charging demands among different stations, the aforementioned optimization program in each time slot is non-convex. We handle the non-convexity by jointly constructing independent sets of charging stations and adapting the block coordinate descent method to iteratively obtain approximately optimal charging prices. Through rigorous theoretical analysis and extensive simulations based on the real-world dataset in the Chinese city Shenzhen which consists of 4000 taxis and 171 charging stations, we demonstrate that our control policy ensures an arbitrarily close-to-optimal profit with a flexible trade-off between the profit and queue lengths, has a low computational complexity, and requires zero knowledge of future system dynamics.