arXiv subjects

Weiwei Yang

Publications and source records attributed to Weiwei Yang.

47 records · Page 3Linked to original sources

arXiv2021

Inducing a hierarchy for multi-class classification problems

In applications where categorical labels follow a natural hierarchy, classification methods that exploit the label structure often outperform those that do not. Un-fortunately, the majority of classification datasets do not come pre-equipped with a hierarchical structure and classical flat classifiers must be employed. In this paper, we investigate a class of methods that induce a hierarchy that can similarly improve classification performance over flat classifiers. The class of methods follows the structure of first clustering the conditional distributions and subsequently using a hierarchical classifier with the induced hierarchy. We demonstrate the effectiveness of the class of methods both for discovering a latent hierarchy and for improving accuracy in principled simulation settings and three real data applications.

stat.ML↗

arXiv2020

A partition-based similarity for classification distributions

Herein we define a measure of similarity between classification distributions that is both principled from the perspective of statistical pattern recognition and useful from the perspective of machine learning practitioners. In particular, we propose a novel similarity on classification distributions, dubbed task similarity, that quantifies how an optimally-transformed optimal representation for a source distribution performs when applied to inference related to a target distribution. The definition of task similarity allows for natural definitions of adversarial and orthogonal distributions. We highlight limiting properties of representations induced by (universally) consistent decision rules and demonstrate in simulation that an empirical estimate of task similarity is a function of the decision rule deployed for inference. We demonstrate that for a given target distribution, both transfer efficiency and semantic similarity of candidate source distributions correlate with empirical task similarity.

stat.ML↗

arXiv2020

Design of a Privacy-Preserving Data Platform for Collaboration Against Human Trafficking

Case records on victims of human trafficking are highly sensitive, yet the ability to share such data is critical to evidence-based practice and policy development across government, business, and civil society. We present new methods to anonymize, publish, and explore such data, implemented as a pipeline generating three artifacts: (1) synthetic data mitigating the privacy risk that published attribute combinations might be linked to known individuals or groups; (2) aggregate data mitigating the utility risk that synthetic data might misrepresent statistics needed for official reporting; and (3) visual analytics interfaces to both datasets mitigating the accessibility risk that privacy mechanisms or analysis tools might not be understandable and usable by all stakeholders. We present our work as a design study motivated by the goal of transforming how the world's largest database of identified victims is made available for global collaboration against human trafficking.

cs.HC↗

arXiv2020

Energy-Efficient Trajectory Design for UAV-Enabled Communication Under Malicious Jamming

In this letter, we investigate a UAV-enabled communication system, where a UAV is deployed to communicate with the ground node (GN) in the presence of multiple jammers. We aim to maximize the energy efficiency (EE) of the UAV by optimizing its trajectory, subject to the UAV's mobility constraints. However, the formulated problem is difficult to solve due to the non-convex and fractional form of the objective function. Thus, we propose an iterative algorithm based on successive convex approximation (SCA) technique and Dinkelbach's algorithm to solve it. Numerical results show that the proposed algorithm can strike a better balance between the throughput and energy consumption by the optimized trajectory and thus improve the EE significantly as compared to the benchmark algorithms.

cs.IT↗

arXiv2020

Covert Communications with Constrained Age of Information

In this letter, we consider the requirement of information freshness in covert communications for the first time. With artificial noise (AN) generated from a full-duplex (FD) receiver, we formulate a covertness maximization problem under the average age of information (AoI) constraint to optimize the transmit probability of information signal. In particular, the transmit probability not only represents the generation rate of information signal but also represents the prior probability of the alternative hypothesis in covert communications, which builds up a bridge between information freshness and communication covertness. Our analysis shows that the best transmit probability is not always 0.5, which differs from the equal prior probabilities assumption in most related works on covert communications. Furthermore, the limitation of average AoI enlarges the transmit probability at the cost of the covertness reduction and leads to a positive lower bound on the information transmit power for non-zero covertness.

cs.IT↗

arXiv2020

Using Mobility for Electrical Load Forecasting During the COVID-19 Pandemic

The novel coronavirus (COVID-19) pandemic has posed unprecedented challenges for the utilities and grid operators around the world. In this work, we focus on the problem of load forecasting. With strict social distancing restrictions, power consumption profiles around the world have shifted both in magnitude and daily patterns. These changes have caused significant difficulties in short-term load forecasting. Typically algorithms use weather, timing information and previous consumption levels as input variables, yet they cannot capture large and sudden changes in socioeconomic behavior during the pandemic. In this paper, we introduce mobility as a measure of economic activities to complement existing building blocks of forecasting algorithms. Mobility data acts as good proxies for the population-level behaviors during the implementation and subsequent easing of social distancing measures. The major challenge with such dataset is that only limited mobility records are associated with the recent pandemic. To overcome this small data problem, we design a transfer learning scheme that enables knowledge transfer between several different geographical regions. This architecture leverages the diversity across these regions and the resulting aggregated model can boost the algorithm performance in each region's day-ahead forecast. Through simulations for regions in the US and Europe, we show our proposed algorithm can outperform conventional forecasting methods by more than three-folds. In addition, we demonstrate how the proposed model can be used to project how electricity consumption would recover based on different mobility scenarios.

eess.SP↗

arXiv2020

Robust and Secure Beamforming for Intelligent Reflecting Surface Aided mmWave MISO Systems

In this letter, we investigate the robust and secure beamforming (RSBF) in an intelligent reflecting surface (IRS) aided millimeter wave (mmWave) multiple input single output (MISO) system, where multiple single antenna eavesdroppers (Eves) are arbitrarily distributed nearby the legitimate receiver. Considering the channel state information (CSI) of Eves' channels is imperfectly known at the legitimate transmitter, the RSBF design problems to maximize the worst case of achievable secrecy rate (ASR) are formulated under the total transmission power and unit-modulus constraints. Since the problems are difficult to solve optimally due to their nonconvexity and coupled variables, we substitute the wiretap channels by a weighted combination of discrete samples and propose a RSBF scheme based on alternating optimization and semidefinite relaxation (SDR) techniques, for both colluding and noncolluding eavesdropping scenarios. Simulation results show that the proposed RSBF scheme can effectively improve the ASR and also outperforms other benchmark schemes.

cs.IT↗

arXiv2019

Wireless Key Generation from Imperfect Channel State Information: Performance Analysis and Improvements

The basis of generating secret key from the common wireless channel at two communication parties is reciprocity. However, due to non-simultaneous measurements and unavoidable estimation error, bias always exists and key disagreement occurs. In this paper, performance analysis and improvements for key generation exploiting imperfect channel statement information (CSI) is studied. Firstly, the closed-form expression for key disagreement rate (KDR) is derived to evaluate the key mismatch caused by time delay and estimation error. Secondly, the energy efficiency involving power allocation between key generation and private data transmission is presented. After that, a neural network based prediction (NNBP) algorithm is proposed to improve the key consistency. Simulation results verify the theoretical analysis and show that NNBP achieves significant improvements. And it's interesting to find that NNBP algorithm achieves a higher performance gain in low signal-to-noise ration (SNR) and high delay condition, which is different from other methods, like guard band based quantizer (GBBQ).

cs.IT↗

arXiv2018

Massive MIMO relaying with linear precoding in correlated channels under limited feedback

In this paper we study on a massive MIMO relay system with linear precoding under the conditions of imperfect channel state information at the transmitter (CSIT) and per-user channel transmit correlation. In our system the source-relay channels are massive multiple-input multiple-output (MIMO) ones and the relay-destination channels are massive multiple-input single-output (MISO) ones. Large random matrix theory (RMT) is used to derive a deterministic equivalent of the signal-to-interference-plus-noise ratio (SINR) at each user in massive MIMO amplify-forward and decode-forward (M-MIMO-ADF) relaying with regularized zero-forcing (RZF) precoding, as the number of transmit antennas and users M,K approaches to infinity and M>>K. In this paper we obtain a closed-form expression for the deterministic equivalent of h^H_kW(hat)_lh(hat)_k, and we give two theorems and a corollary to derive the deterministic equivalent of the SINR at each user. Simulation results show that the deterministic equivalent of the SINR at each user in M-MIMO-ADF relaying and the results of Theorem 1, Theorem 2, Proposition 1 and Corollary 1 are accurate.

eess.SP↗

arXiv2015

Joint Relay and Jammer Selection Improves the Physical Layer Security in the Face of CSI Feedback Delays

We enhance the physical-layer security (PLS) of amplify-and-forward relaying networks with the aid of joint relay and jammer selection (JRJS), despite the deliterious effect of channel state information (CSI) feedback delays. Furthermore, we conceive a new outage-based characterization approach for the JRJS scheme. The traditional best relay selection (TBRS) is also considered as a benchmark. We first derive closed-form expressions of both the connection outage probability (COP) and of the secrecy outage probability (SOP) for both the TBRS and JRJS schemes. Then, a reliable-and-secure connection probability (RSCP) is defined and analyzed for characterizing the effect of the correlation between the COP and SOP introduced by the corporate source-relay link. The reliability-security ratio (RSR) is introduced for characterizing the relationship between the reliability and security through the asymptotic analysis. Moreover, the concept of effective secrecy throughput is defined as the product of the secrecy rate and of the RSCP for the sake of characterizing the overall efficiency of the system, as determined by the transmit SNR, secrecy codeword rate and the power sharing ratio between the relay and jammer. The impact of the direct source-eavesdropper link and additional performance comparisons with respect to other related selection schemes are further included. Our numerical results show that the JRJS scheme outperforms the TBRS method both in terms of the RSCP as well as in terms of its effective secrecy throughput, but it is more sensitive to the feedback delays. Increasing the transmit SNR will not always improve the overall throughput. Moreover, the RSR results demonstrate that upon reducing the CSI feedback delays, the reliability improves more substantially than the security degrades, implying an overall improvement in terms of the security-reliability tradeoff.

cs.IT↗

arXiv2014

When Does Relay Transmission Give a More Secure Connection in Wireless Ad Hoc Networks?

Relay transmission can enhance coverage and throughput, while it can be vulnerable to eavesdropping attacks due to the additional transmission of the source message at the relay. Thus, whether or not one should use relay transmission for secure communication is an interesting and important problem. In this paper, we consider the transmission of a confidential message from a source to a destination in a decentralized wireless network in the presence of randomly distributed eavesdroppers. The source-destination pair can be potentially assisted by randomly distributed relays. For an arbitrary relay, we derive exact expressions of secure connection probability for both colluding and non-colluding eavesdroppers. We further obtain lower bound expressions on the secure connection probability, which are accurate when the eavesdropper density is small. By utilizing these lower bound expressions, we propose a relay selection strategy to improve the secure connection probability. By analytically comparing the secure connection probability for direct transmission and relay transmission, we address the important problem of whether or not to relay and discuss the condition for relay transmission in terms of the relay density and source-destination distance. These analytical results are accurate in the small eavesdropper density regime.

cs.IT↗