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Peng Liu

Publications and source records attributed to Peng Liu.

At least 379 records · Page 21Linked to original sources
arXiv2020

Regularised Text Logistic Regression: Key Word Detection and Sentiment Classification for Online Reviews

Online customer reviews have become important for managers and executives in the hospitality and catering industry who wish to obtain a comprehensive understanding of their customers' demands and expectations. We propose a Regularized Text Logistic (RTL) regression model to perform text analytics and sentiment classification on unstructured text data, which automatically identifies a set of statistically significant and operationally insightful word features, and achieves satisfactory predictive classification accuracy. We apply the RTL model to two online review datasets, Restaurant and Hotel, from TripAdvisor. Our results demonstrate satisfactory classification performance compared with alternative classifiers with a highest true positive rate of 94.9%. Moreover, RTL identifies a small set of word features, corresponding to 3% for Restaurant and 20% for Hotel, which boosts working efficiency by allowing managers to drill down into a much smaller set of important customer reviews. We also develop the consistency, sparsity and oracle property of the estimator.

stat.ML↗
arXiv2020

ServeNet: A Deep Neural Network for Web Services Classification

Automated service classification plays a crucial role in service discovery, selection, and composition. Machine learning has been widely used for service classification in recent years. However, the performance of conventional machine learning methods highly depends on the quality of manual feature engineering. In this paper, we present a novel deep neural network to automatically abstract low-level representation of both service name and service description to high-level merged features without feature engineering and the length limitation, and then predict service classification on 50 service categories. To demonstrate the effectiveness of our approach, we conduct a comprehensive experimental study by comparing 10 machine learning methods on 10,000 real-world web services. The result shows that the proposed deep neural network can achieve higher accuracy in classification and more robust than other machine learning methods.

cs.LG↗
arXiv2020

Randomized Last-Level Caches Are Still Vulnerable to Cache Side-Channel Attacks! But We Can Fix It

Cache randomization has recently been revived as a promising defense against conflict-based cache side-channel attacks. As two of the latest implementations, CEASER-S and ScatterCache both claim to thwart conflict-based cache side-channel attacks using randomized skewed caches. Unfortunately, our experiments show that an attacker can easily find a usable eviction set within the chosen remap period of CEASER-S and increasing the number of partitions without dynamic remapping, such as ScatterCache, cannot eliminate the threat. By quantitatively analyzing the access patterns left by various attacks in the LLC, we have newly discovered several problems with the hypotheses and implementations of randomized caches, which are also overlooked by the research on conflict-based cache side-channel attack. However, cache randomization is not a false hope and it is an effective defense that should be widely adopted in future processors. The newly discovered problems are corresponding to flaws associated with the existing implementation of cache randomization and are fixable. Several new defense techniques are proposed in this paper. our experiments show that all the newly discovered vulnerabilities of existing randomized caches are fixed within the current performance budget. We also argue that randomized set-associative caches can be sufficiently strengthened and possess a better chance to be actually adopted in commercial processors than their skewed counterparts as they introduce less overhaul to the existing cache structure.

cs.CR↗
arXiv2020

Informational properties for Einstein-Maxwell-Dilaton Gravity

We study the information quantities, including the holographic entanglement entropy (HEE), mutual information (MI) and entanglement of purification (EoP), over Gubser-Rocha model. The remarkable property of this model is the zero entropy density at ground state, in term of which we expect to extract novel, even singular informational properties in zero temperature limit. Surprisedly, we do not observe any singular behavior of entanglement-related physical quantities under the zero temperature limit. Nevertheless, we find a peculiar property from Gubser-Rocha model that in low temperature region, the HEE decreases with the increase of temperature, which is contrary to that in most holographic models. We argue that this novel phenomenon is brought by the singular property of the zero temperature limit, of which the analytical verification is present. In addition, we also compare the features of the information quantities in Gubser-Rocha model with those in Reissner-Nordstrom Anti-de Sitter (RN-AdS) black hole model. It is shown that the HEE and MI of Gubser-Rocha model are always larger than those of RN-AdS model, while the EoP behaves in an opposite way. Our results indicate that MI and EoP could have different abilities in describing mixed state entanglement.

hep-th↗
arXiv2020

Outcome-Guided Disease Subtyping for High-Dimensional Omics Data

High-throughput microarray and sequencing technology have been used to identify disease subtypes that could not be observed otherwise by using clinical variables alone. The classical unsupervised clustering strategy concerns primarily the identification of subpopulations that have similar patterns in gene features. However, as the features corresponding to irrelevant confounders (e.g. gender or age) may dominate the clustering process, the resulting clusters may or may not capture clinically meaningful disease subtypes. This gives rise to a fundamental problem: can we find a subtyping procedure guided by a pre-specified disease outcome? Existing methods, such as supervised clustering, apply a two-stage approach and depend on an arbitrary number of selected features associated with outcome. In this paper, we propose a unified latent generative model to perform outcome-guided disease subtyping constructed from omics data, which improves the resulting subtypes concerning the disease of interest. Feature selection is embedded in a regularization regression. A modified EM algorithm is applied for numerical computation and parameter estimation. The proposed method performs feature selection, latent subtype characterization and outcome prediction simultaneously. To account for possible outliers or violation of mixture Gaussian assumption, we incorporate robust estimation using adaptive Huber or median-truncated loss function. Extensive simulations and an application to complex lung diseases with transcriptomic and clinical data demonstrate the ability of the proposed method to identify clinically relevant disease subtypes and signature genes suitable to explore toward precision medicine.

stat.ME↗
arXiv2020

Speaker Independent and Multilingual/Mixlingual Speech-Driven Talking Head Generation Using Phonetic Posteriorgrams

Generating 3D speech-driven talking head has received more and more attention in recent years. Recent approaches mainly have following limitations: 1) most speaker-independent methods need handcrafted features that are time-consuming to design or unreliable; 2) there is no convincing method to support multilingual or mixlingual speech as input. In this work, we propose a novel approach using phonetic posteriorgrams (PPG). In this way, our method doesn't need hand-crafted features and is more robust to noise compared to recent approaches. Furthermore, our method can support multilingual speech as input by building a universal phoneme space. As far as we know, our model is the first to support multilingual/mixlingual speech as input with convincing results. Objective and subjective experiments have shown that our model can generate high quality animations given speech from unseen languages or speakers and be robust to noise.

eess.AS↗
arXiv2020

Towards classification parity across cohorts

Recently, there has been a lot of interest in ensuring algorithmic fairness in machine learning where the central question is how to prevent sensitive information (e.g. knowledge about the ethnic group of an individual) from adding "unfair" bias to a learning algorithm (Feldman et al. (2015), Zemel et al. (2013)). This has led to several debiasing algorithms on word embeddings (Qian et al. (2019) , Bolukbasi et al. (2016)), coreference resolution (Zhao et al. (2018a)), semantic role labeling (Zhao et al. (2017)), etc. Most of these existing work deals with explicit sensitive features such as gender, occupations or race which doesn't work with data where such features are not captured due to privacy concerns. In this research work, we aim to achieve classification parity across explicit as well as implicit sensitive features. We define explicit cohorts as groups of people based on explicit sensitive attributes provided in the data (age, gender, race) whereas implicit cohorts are defined as groups of people with similar language usage. We obtain implicit cohorts by clustering embeddings of each individual trained on the language generated by them using a language model. We achieve two primary objectives in this work : [1.] We experimented and discovered classification performance differences across cohorts based on implicit and explicit features , [2] We improved classification parity by introducing modification to the loss function aimed to minimize the range of model performances across cohorts.

cs.LG↗
arXiv2020

Logic Bugs in IoT Platforms and Systems: A Review

In recent years, IoT platforms and systems have been rapidly emerging. Although IoT is a new technology, new does not mean simpler (than existing networked systems). Contrarily, the complexity (of IoT platforms and systems) is actually being increased in terms of the interactions between the physical world and cyberspace. The increased complexity indeed results in new vulnerabilities. This paper seeks to provide a review of the recently discovered logic bugs that are specific to IoT platforms and systems. In particular, 17 logic bugs and one weakness falling into seven categories of vulnerabilities are reviewed in this survey.

cs.CR↗
arXiv2020

Experimental composable security decoy-state quantum key distribution using time-phase encoding

Quantum key distribution (QKD) promises provably secure communications. In order to improve the secret key rate, combining a biased basis choice with the decoy-state method is proposed. Concomitantly, there is a basis-independent detection efficiency condition, which usually cannot be satisfied in a practical system, such as the time-phase encoding. Fortunately, this flaw has been recently removed theoretically and experimentally using the fact that the expected yields of single-photon states prepared in two bases stay the same for a given measurement basis. However, the security proofs do not fully consider the finite-key effects for general attacks. In this work, we provide the rigorous finite-key security bounds for four-intensity decoy-state BB84 QKD against coherent attacks in the universally composable framework. Furthermore, we build a time-phase encoding system with 200 MHz clocked to implement this protocol, in which the real-time secret key rate is more than 60 kbps over 50 km single-mode fiber.

quant-ph↗
arXiv2020

Secure and efficient synchronization scheme for quantum key distribution

To establish a time reference frame between two users in quantum key distribution, a synchronization calibration process is usually applied for the case of using gated mode single-photon detectors (SPDs). Traditionally, the synchronization calibration is independently implemented by the line length measurement for each SPD. However, this will leave a loophole which has been experimentally demonstrated by a special attack. Here, we propose an alternative synchronization scheme by fixing the relative delay of the signal time window among all SPDs and jointly performing the line length measurement with multiple SPDs under combining low-precision with high-precision synchronization. The new scheme is not only immune to the vulnerability but also improves the synchronization time from usually a few seconds to tens of milliseconds.

quant-ph↗
arXiv2020

Practical Verification of MapReduce Computation Integrity via Partial Re-execution

Big data processing is often outsourced to powerful, but untrusted cloud service providers that provide agile and scalable computing resources to weaker clients. However, untrusted cloud services do not ensure the integrity of data and computations while clients have no control over the outsourced computation or no means to check the correctness of the execution. Despite a growing interest and recent progress in verifiable computation, the existing techniques are still not practical enough for big data processing due to high verification overhead. In this paper, we present a solution called V-MR (Verifiable MapReduce), which is a framework that verifies the integrity of MapReduce computation outsourced in the untrusted cloud via partial re-execution. V-MR is practically effective and efficient in that (1) it can detect the violation of MapReduce computation integrity and identify the malicious workers involved in the that produced the incorrect computation. (2) it can reduce the overhead of verification via partial re-execution with carefully selected input data and program code using program analysis. The experiment results of a prototype of V-MR show that V-MR can verify the integrity of MapReduce computation effectively with small overhead for partial re-execution.

cs.CR↗
arXiv2020

Change of measure under the hard-to-borrow model

As the Securities and Exchange Commission(SEC) has implemented a new regulation on short-sellings, short-sellers are required to repurchase stocks once the clearing risk rises to a certain level. Avellaneda and Lipkin proposed a fully coupled SDE system to describe the mechanism which is referred as Hard-To-Borrow(HTB) models. Guiyuan Ma obtained the PDE system for both American and European options. There is a technical error in Guiyuan Ma where two correlated Brownian motion should be converted before change of measure. In this paper, I will provide supplement conditions.

q-fin.MF↗
arXiv2020

Regression and Learning with Pixel-wise Attention for Retinal Fundus Glaucoma Segmentation and Detection

Observing retinal fundus images by an ophthalmologist is a major diagnosis approach for glaucoma. However, it is still difficult to distinguish the features of the lesion solely through manual observations, especially, in glaucoma early phase. In this paper, we present two deep learning-based automated algorithms for glaucoma detection and optic disc and cup segmentation. We utilize the attention mechanism to learn pixel-wise features for accurate prediction. In particular, we present two convolutional neural networks that can focus on learning various pixel-wise level features. In addition, we develop several attention strategies to guide the networks to learn the important features that have a major impact on prediction accuracy. We evaluate our methods on the validation dataset and The proposed both tasks' solutions can achieve impressive results and outperform current state-of-the-art methods. \textit{The code is available at \url{https://github.com/cswin/RLPA}}.

eess.IV↗
arXiv2019

Holographic superconductor induced by charge density waves

Understanding the role of charge density wave (CDW) in high-temperature superconductivity is a longstanding challenge in condensed matter physics. We construct a holographic superconductor model in which the $U(1)$ symmetry is spontaneously broken only due to the presence of CDWs, rather than previously known free charges with constant density. Below the critical temperature of superconductivity, CDW phase and superconducting phase coexist, which is also justified by the numerical results of optical conductivity. The competitive and cooperative relations between CDW phase and superconducting phase are observed. This work supports the opinion that the appearance of pseudo-gap in CDW phase promotes the pre-pairing of electrons as well as holes such that the formation of superconductivity benefits from the presence of CDW.

hep-th↗
arXiv2019

Mixed State Entanglement for Holographic Axion Model

We study the mixed state entanglement in a holographic axion model. We find that the holographic entanglement entropy (HEE), mutual information (MI) and entanglement of purification (EoP) exhibit very distinct behaviors with system parameters. The HEE exhibits universal monotonic behavior with system parameters, while the behaviors of MI and EoP relate to the specific system parameters and configurations. We find that MI and EoP can characterize mixed state entanglement better than HEE since they are less affected by thermal effects. Moreover, we argue that EoP is more suitable for describing mixed state entanglement than MI. Because the MI of large configurations are still dictated by the thermal entropy, while the EoP will never be controlled only by the thermal effects.

hep-th↗
arXiv2019

Maximizing Mutual Information for Tacotron

End-to-end speech synthesis methods already achieve close-to-human quality performance. However compared to HMM-based and NN-based frame-to-frame regression methods, they are prone to some synthesis errors, such as missing or repeating words and incomplete synthesis. We attribute the comparatively high utterance error rate to the local information preference of conditional autoregressive models, and the ill-posed training objective of the model, which describes mostly the training status of the autoregressive module, but rarely that of the condition module. Inspired by InfoGAN, we propose to maximize the mutual information between the text condition and the predicted acoustic features to strengthen the dependency between them for CAR speech synthesis model, which would alleviate the local information preference issue and reduce the utterance error rate. The training objective of maximizing mutual information can be considered as a metric of the dependency between the autoregressive module and the condition module. Experiment results show that our method can reduce the utterance error rate.

eess.AS↗
arXiv2019

The properties of neutron star from realistic nucleon-nucleon interaction within relativistic Hartree-Fock model

The properties of neutron star are studied in the framework of relativistic Hartree-Fock (RHF) model with realistic nucleon-nucleon ($NN$) interactions, i.e. Bonn potentials. The strong repulsion of $NN$ interaction at short range is properly removed by the unitary correlation operator method (UCOM). Meanwhile, the tensor correlation is neglected due to the very rich neutron environment in neutron star, where the total isospin of two nucleons can be approximately regarded as $T=1$. The equations of state of neutron star matter are calculated in $β$ equilibrium and charge neutrality conditions. The properties of neutron star, such as mass, radius, and tidal deformability are obtained by solving the Tolman-Oppenheimer-Volkoff equation and tidal equation. The maximum masses of neutron from Bonn A, B, C potentials are around $2.2M_\odot$. The radius are $12.40-12.91$ km at $1.4M_\odot$, respectively. The corresponding tidal deformabilities are $Λ_{1.4}=293-355$. All of these properties are satisfied the recent observables from the astronomical and gravitational wave devices and are consistent with the results from the relativistic Brueckner-Hartree-Fock model.

nucl-th↗