arXiv subjects

Huan Chen

Publications and source records attributed to Huan Chen.

At least 55 records · Page 3Linked to original sources

arXiv2022

Toward An Optimal Selection of Dialogue Strategies: A Target-Driven Approach for Intelligent Outbound Robots

With the growth of the economy and society, enterprises, especially in the FinTech industry, have increasing demands of outbound calls for customers such as debt collection, marketing, anti-fraud calls, and so on. But a large amount of repetitive and mechanical work occupies most of the time of human agents, so the cost of equipment and labor for enterprises is increasing accordingly. At the same time, with the development of artificial intelligence technology in the past few decades, it has become quite common for companies to use new technologies such as Big Data and artificial intelligence to empower outbound call businesses. The intelligent outbound robot is a typical application of the artificial intelligence technology in the field of outbound call businesses. It is mainly used to communicate with customers in order to accomplish a certain target. It has the characteristics of low cost, high reuse, and easy compliance, which has attracted more attention from the industry. At present, there are two kinds of intelligent outbound robots in the industry but both of them still leave large room for improvement. One kind of them is based on a finite state machine relying on the configuration of jump conditions and corresponding nodes based on manual experience. This kind of intelligent outbound robot is also called a flow-based robot. For example, the schematic diagram of the working model of a flow-based robot for debt collection is shown in Fig.\ref{fig:label}. In each round, the robot will reply to the user with the words corresponding to each node.

cs.CL↗

arXiv2022

Auto-correlative weak-value amplification under strong noise background

By choosing more orthogonality between pre-selection and post-selection states, one can significantly improve the sensitivity in the general optical quantum metrology based on the weak-value amplification (WVA) approach. However, increasing the orthogonality decreases the probability of detecting photons and makes the weak measurement difficult, especially when the weak measurement is disturbed by strong noise and the pointer is drowned in noise with a negative-dB signal-to-noise ratio (SNR). In this article, we investigate a modified weak measurement protocol with a temporal pointer, namely, the auto-correlative weak-value amplification (AWVA) approach. Specifically, a small longitudinal time delay (tiny phase shift) $τ$ of a Gaussian pulse is measured by implementing two simultaneous auto-correlative weak measurements under Gaussian white noise with different SNR. The small quantities $τ$ are obtained by measuring the auto-correlation coefficient of the pulses instead of fitting the shift of the mean value of the probe in the standard WVA technique. Simulation results show that the AWVA approach outperforms the standard WVA technique in the time domain with smaller statistical errors, remarkably increasing the precision of weak measurement under strong noise background.

quant-ph↗

arXiv2022

Appending Information Reconciliation for Quantum Key Distribution

Information reconciliation (IR), which corrects the errors in the sifted keys, directly determines the secure key rate and the link distance of quantum key distribution (QKD) systems. In this article, we propose an appending information reconciliation (AIR) scheme based on polar codes, which achieves high efficiency and ultra-low failure probability simultaneously, by gradually disclosing the bit values of the polarized channels with high error probability. The experimental results show that the efficiency of the proposed AIR scheme is closer to the Shannon limit, compared with the state-of-the-art implemented polar codes-based IR schemes, with the overall failure probability around 1E-8, especially when performed with smaller block sizes. Moreover, the efficiency of the proposed AIR scheme is 1.046, when the block size is 1 Gb and the quantum bit error rate of 0.02. Therefore, the proposed AIR scheme can further eradicate the performance gap between theory and implementation for QKD systems.

quant-ph↗

arXiv2022

Influence of light quark loops on the Wigner phase with Dyson-Schwinger equations approach

We study the influence of light quark loops on the Wigner phase by solving coupled Dyson-Schwinger equations for quark propagator and gluon propagator. We take the gluon propagator in the Nambu phase from $N_f$ = 2 unquenched lattice QCD and choose various phenomenological models for the quark-gluon vertex. The gluon propagator in Winger phase is assumed to be different from that in the Nambu phase only due to the vacuum polarization of the quark loop. We obtain the Wigner solution of the coupled equations, compared with that from solving only the equation of the quark propagator. We discussed the corrections by the light quark loops and the dependence on various models of the quark-gluon vertex.

hep-ph↗

arXiv2021

2D wavelength-polarization dispersive microspectroscope based on a hybrid plasmonic helical nanostructure

Microspectrometer features remarkable portability and integration, and is prospected to provide quick detection tools to daily life and revolutionary techniques to researchers. For the extremely finite footprint, microspectrometer can hardly work to analyze the polarization feature by placing polarizer in the optical path like conventional spectrometers. Here, we demonstrate a novel 2D wavelength-polarization dispersive microspectroscope based on carbon nanocoil with plasmonic Au nanopariticles (Au/CNC) as a dispersive component. Explored by the microspectrum and Fourier-space microscopy, a unique 2D dispersive characteristic of the Au/CNC is revealed. Along the axis of the coil, Au/CNC disperses light as wavelength to bands of different diffraction orders like a grating. Wavelength of the incident light can be obtained from the position of the signal lines in a quite large visible-near-infrared wavelength range with an acceptable resolution. In the direction perpendicular to the axis of the coil, incident light is dispersed as polarization with bright and dark areas. We can distinguish left- and right-circularly-polarized light, and also obtain the polarization orientation of linearly-polarized light. Based on this effect, a wonderful 2D wavelength-polarization microspectrometer can be built. It not only fulfills the wavelength analysis with a tiny dispersive component, but also simultaneously knows the polarization feature of the incident light in one shot. Moreover, this powerful tool can further evolve new revolutionary techniques via integrated with other systems.

physics.optics↗

arXiv2021

Automated Climate Analyses Using Knowledge Graph

The FAIR (Findable, Accessible, Interoperable, Reusable) data principles are fundamental for climate researchers and all stakeholders in the current digital ecosystem. In this paper, we demonstrate how relational climate data can be "FAIR" and modeled using RDF, in line with Semantic Web technologies and our Climate Analysis ontology. Thus, heterogeneous climate data can be stored in graph databases and offered as Linked Data on the Web. As a result, climate researchers will be able to use the standard SPARQL query language to query these sources directly on the Web. In this paper, we demonstrate the usefulness of our SPARQL endpoint for automated climate analytics. We illustrate two sample use cases that establish the advantage of representing climate data as knowledge graphs.

cs.DB↗

arXiv2021

An Interoperable Open Data Portal for Climate Analysis

This work proposes an open interoperable data portal that offers access to a Web-wide climate domain knowledge graph created for Ireland and England's NOAA climate daily data. There are three main components contributing to this data portal: the first is the upper layer schema of the knowledge graph -- the climate analysis (CA) ontology -- the second is an ad hoc SPARQL server by which to store the graph data and provide public Web access, the last is a dereferencing engine deployed to resolve URIs for entity information. Our knowledge graph form of NOAA climate data facilitates the supply of semantic climate information to researchers and offers a variety of semantic applications that can be built on top of it.

cs.DB↗

arXiv2021

Network Security Modeling using NetFlow Data: Detecting Botnet attacks in IP Traffic

Cybersecurity, security monitoring of malicious events in IP traffic, is an important field largely unexplored by statisticians. Computer scientists have made significant contributions in this area using statistical anomaly detection and other supervised learning methods to detect specific malicious events. In this research, we investigate the detection of botnet command and control (C&C) hosts in massive IP traffic. We use the NetFlow data, the industry standard for monitoring of IP traffic for exploratory analysis and extracting new features. Using statistical as well as deep learning models, we develop a statistical intrusion detection system (SIDS) to predict traffic traces identified with malicious attacks. Employing interpretative machine learning techniques, botnet traffic signatures are derived. These models successfully detected botnet C&C hosts and compromised devices. The results were validated by matching predictions to existing blacklists of published malicious IP addresses.

cs.CR↗

arXiv2021

Airborne Quantum Key Distribution with Boundary Layer Effects

Airborne quantum key distribution (QKD) is now becoming a flexible bond between terrestrial fiber and satellite, which is an efficient solution to establish a mobile, on-demand, and real-time coverage quantum network. Furthermore, When the aircraft is flying at a high speed, usually larger than 0.3 Ma, the produced boundary layer will impair the performance of aircraft-based QKD. The boundary layer would introduce random wavefront aberration, jitter and extra intensity attenuation to the transmitted photons. However, previous airborne QKD implementations only considered the influences from atmospheric turbulence and molecular scattering, but ignored the boundary layer effects. In this article, we propose a detailed performance evaluation scheme of airborne QKD with boundary layer effects and estimate the overall photon transmission efficiency, quantum bit error rate and final secure key rate. Through simulations and modeling, in our proposed airborne QKD scenario, the boundary layer would introduce 3.5dB loss to the transmitted photons and decrease 70.7% of the secure key rate, which shows that the aero-optical effects caused by the boundary layer can not be ignored. With tolerated quantum bit error rate set to 10%, the suggested quantum communication azimuth angle between the aircraft and the ground station is within 60 degrees. Moreover, the optimal beacon laser module and adaptive optics module are suggested to be employed to improve the performance of airborne QKD system. Our detailed airborne QKD evaluation study can be performed to the future airborne quantum communication designs.

quant-ph↗

arXiv2021

Improving Conversational Recommendation System by Pretraining on Billions Scale of Knowledge Graph

Conversational Recommender Systems (CRSs) in E-commerce platforms aim to recommend items to users via multiple conversational interactions. Click-through rate (CTR) prediction models are commonly used for ranking candidate items. However, most CRSs are suffer from the problem of data scarcity and sparseness. To address this issue, we propose a novel knowledge-enhanced deep cross network (K-DCN), a two-step (pretrain and fine-tune) CTR prediction model to recommend items. We first construct a billion-scale conversation knowledge graph (CKG) from information about users, items and conversations, and then pretrain CKG by introducing knowledge graph embedding method and graph convolution network to encode semantic and structural information respectively.To make the CTR prediction model sensible of current state of users and the relationship between dialogues and items, we introduce user-state and dialogue-interaction representations based on pre-trained CKG and propose K-DCN.In K-DCN, we fuse the user-state representation, dialogue-interaction representation and other normal feature representations via deep cross network, which will give the rank of candidate items to be recommended.We experimentally prove that our proposal significantly outperforms baselines and show it's real application in Alime.

cs.IR↗

arXiv2021

Free-Running Long-Distance Reference-Frame-Independent Quantum Key Distribution

Rapidly and randomly drifted reference frames will shorten the link distance and decrease the secure key rate of realistic quantum key distribution (QKD) systems. However, an actively or inappropriately implemented calibration scheme will increase complexity of the systems and may open security loopholes. In this article, we present a free-running reference-frame-independent (RFI) QKD scheme, where measurement events are classified into multiple slices with the same misalignment variation of reference frames and each slice performs the post-processing procedure individually. We perform the free-running RFI QKD experiment with a fiber link of 100km and the misalignment of the reference frame between Alice and Bob is varied more than 29 periods in a 50.7-hour experiment test. The average secure key rate is about 734 bps with a total loss of 31.5 dB, which achieves the state-of-art performance of the long-distance RFI QKD implementations. Our free-running RFI scheme can be efficiently adapted into the satellite-to-ground and drone based mobile communication scenarios, as it can be performed with rapidly varying reference frame and a loss more than 40 dB, where no secure key can be obtained by the original RFI scheme.

quant-ph↗

arXiv2021

An Emotion-controlled Dialog Response Generation Model with Dynamic Vocabulary

In response generation task, proper sentimental expressions can obviously improve the human-like level of the responses. However, for real application in online systems, high QPS (queries per second, an indicator of the flow capacity of on-line systems) is required, and a dynamic vocabulary mechanism has been proved available in improving speed of generative models. In this paper, we proposed an emotion-controlled dialog response generation model based on the dynamic vocabulary mechanism, and the experimental results show the benefit of this model.

cs.CL↗

arXiv2021

A Layered Grouping Random Access Scheme Based on Dynamic Preamble Selection for Massive Machine Type Communications

Massive machine type communication (mMTC) has been identified as an important use case in Beyond 5G networks and future massive Internet of Things (IoT). However, for the massive multiple access in mMTC, there is a serious access preamble collision problem if the conventional 4-step random access (RA) scheme is employed. Consequently, a range of grantfree (GF) RA schemes were proposed. Nevertheless, if the number of cellular users (devices) significantly increases, both the energy and spectrum efficiency of the existing GF schemes still rapidly degrade owing to the much longer preambles required. In order to overcome this dilemma, a layered grouping strategy is proposed, where the cellular users are firstly divided into clusters based on their geographical locations, and then the users of the same cluster autonomously join in different groups by using optimum energy consumption (Opt-EC) based K-means algorithm. With this new layered cellular architecture, the RA process is divided into cluster load estimation phase and active group detection phase. Based on the state evolution theory of approximated message passing algorithm, a tight lower bound on the minimum preamble length for achieving a certain detection accuracy is derived. Benefiting from the cluster load estimation, a dynamic preamble selection (DPS) strategy is invoked in the second phase, resulting the required preambles with minimum length. As evidenced in our simulation results, this two-phase DPS aided RA strategy results in a significant performance improvement

cs.IT↗

arXiv2021

The equation of state and radial oscillations of neutron stars

We investigate radial oscillations of pure neutron stars and hybrid stars, employing equations of state of nuclear matter from Brueckner-Hartree-Fock theory, and of quark matter from the Dyson-Schwinger quark model, performing a Gibbs construction for the mixed phase in hybrid stars. We calculate the eigenfrequencies and corresponding oscillation functions. Our results for the zero points of the first-order radial oscillation frequencies give the maximum mass of stable neutron stars, consistent with the common criterion $dM/dρ_c=0$. Possible observations of the radial oscillation frequencies could help to learn more about the equation of state, predict the maximum mass of neutron stars more precisely, and indicate the presence of quark matter.

nucl-th↗

arXiv2020

A Fast Numerical solution of the quark's Dyson-Schwinger equation with Ball-Chiu vertex

In this paper, we present two feasible and efficient methods to numerically solve the quark's Dyson-Schwinger (qDSE), the qDSE is mathematical systems of nonlinear integral equations of the second kind with high degrees of freedom. It is difficult to analytically solve the qDSE due to its non-linearity and the singularity. Normally we discrete the singular integral equation by Gauss Legendre integral integration formula, then the approximate solutions of integral equation are obtained by iterative method. The main difficulty in the progress is the unknown function, which is the quark's propagator at vacuum and at finite chemical potential, occurs inside and outside the integral sign. Because of the singularity, the unknown function inside the integral sign need to be interpolate with high precision. Normally traditional numerical examples show the interpolation will cost a lot of CPU time. In this case, we provide two effective and efficient methods to optimize the numerical calculation, one is we put forward a modified interpolation method to replace the traditional method. Besides, the technique of OpenMP and automatic parallelization in GCC is another method which has widely used in modern scientific computation. Finally, we compare CPU time with different algorithm and our numerical results show the efficiency of the proposed methods.

math.NA↗

arXiv2020

Powerful t-SNE technique leading to clear separation of type-2 AGN and HII galaxies in BPT diagrams

Narrow emission-line galaxies can be distinguished in the well-known BPT diagrams through narrow emission line properties. However, there are no boundaries visible to the naked eye between type-2 AGN and HII galaxies in BPT diagrams, besides the extreme dividing lines expected by theoretical photoionization models. Here, based on powerful t-SNE technique applied to the local narrow emission-line galaxies in SDSS DR15, type-2 AGN and HII galaxies can be clearly separated in the t-SNE determined two-dimensional projected map, and then the dividing lines can be mathematically determined in BPT diagrams, leading to charming harmonization of the theoretical expectations and the actual results from real observed properties. The results not only provide an interesting and robust method to determine the dividing lines in BPT diagrams through the powerful t-SNE technique, but also lead to further confirmation on previously defined composite galaxies more efficiently classified in the BPT diagram of [OIII]/H$β$ versus [NII]/H$α$.

astro-ph.GA↗

arXiv2020

Hacking single-photon avalanche detector in quantum key distribution via pulse illumination

Quantum key distribution (QKD) has been proved to be information-theoretically secure in theory. Unfortunately, the imperfect devices in practice compromise its security. Thus, to improve the security property of practical QKD systems, a commonly used method is to patch the loopholes in the existing QKD systems. However, in this work, we show an adversary's capability of exploiting the imperfection of the patch itself to bypass the patch. Specifically, we experimentally demonstrate that, in the detector under test, the patch of photocurrent monitor against the detector blinding attack can be defeated by the pulse illumination attack proposed in this paper. We also analyze the secret key rate under the pulse illumination attack, which theoretically confirmed that Eve can conduct the attack to learn the secret key. This work indicates the importance of inspecting the security loopholes in a detection unit to further understand their impacts on a QKD system. The method of pulse illumination attack can be a general testing item in the security evaluation standard of QKD.

quant-ph↗

arXiv2020

PulseGAN: Learning to generate realistic pulse waveforms in remote photoplethysmography

Remote photoplethysmography (rPPG) is a non-contact technique for measuring cardiac signals from facial videos. High-quality rPPG pulse signals are urgently demanded in many fields, such as health monitoring and emotion recognition. However, most of the existing rPPG methods can only be used to get average heart rate (HR) values due to the limitation of inaccurate pulse signals. In this paper, a new framework based on generative adversarial network, called PulseGAN, is introduced to generate realistic rPPG pulse signals through denoising the chrominance signals. Considering that the cardiac signal is quasi-periodic and has apparent time-frequency characteristics, the error losses defined in time and spectrum domains are both employed with the adversarial loss to enforce the model generating accurate pulse waveforms as its reference. The proposed framework is tested on the public UBFC-RPPG database in both within-database and cross-database configurations. The results show that the PulseGAN framework can effectively improve the waveform quality, thereby enhancing the accuracy of HR, the heart rate variability (HRV) and the interbeat interval (IBI). The proposed method achieves the best performance compared to the denoising autoencoder (DAE) and CHROM, with the mean absolute error of AVNN (the average of all normal-to-normal intervals) improving 20.85% and 41.19%, and the mean absolute error of SDNN (the standard deviation of all NN intervals) improving 20.28% and 37.53%, respectively, in the cross-database test. This framework can be easily extended to other existing deep learning based rPPG methods, which is expected to expand the application scope of rPPG techniques.

eess.IV↗