arXiv subjects

Peng Cheng

Publications and source records attributed to Peng Cheng.

At least 253 records · Page 14Linked to original sources

arXiv2020

UNIFUZZ: A Holistic and Pragmatic Metrics-Driven Platform for Evaluating Fuzzers

A flurry of fuzzing tools (fuzzers) have been proposed in the literature, aiming at detecting software vulnerabilities effectively and efficiently. To date, it is however still challenging to compare fuzzers due to the inconsistency of the benchmarks, performance metrics, and/or environments for evaluation, which buries the useful insights and thus impedes the discovery of promising fuzzing primitives. In this paper, we design and develop UNIFUZZ, an open-source and metrics-driven platform for assessing fuzzers in a comprehensive and quantitative manner. Specifically, UNIFUZZ to date has incorporated 35 usable fuzzers, a benchmark of 20 real-world programs, and six categories of performance metrics. We first systematically study the usability of existing fuzzers, find and fix a number of flaws, and integrate them into UNIFUZZ. Based on the study, we propose a collection of pragmatic performance metrics to evaluate fuzzers from six complementary perspectives. Using UNIFUZZ, we conduct in-depth evaluations of several prominent fuzzers including AFL [1], AFLFast [2], Angora [3], Honggfuzz [4], MOPT [5], QSYM [6], T-Fuzz [7] and VUzzer64 [8]. We find that none of them outperforms the others across all the target programs, and that using a single metric to assess the performance of a fuzzer may lead to unilateral conclusions, which demonstrates the significance of comprehensive metrics. Moreover, we identify and investigate previously overlooked factors that may significantly affect a fuzzer's performance, including instrumentation methods and crash analysis tools. Our empirical results show that they are critical to the evaluation of a fuzzer. We hope that our findings can shed light on reliable fuzzing evaluation, so that we can discover promising fuzzing primitives to effectively facilitate fuzzer designs in the future.

cs.CR↗

arXiv2020

Deep Multi-Task Learning for Cooperative NOMA: System Design and Principles

Envisioned as a promising component of the future wireless Internet-of-Things (IoT) networks, the non-orthogonal multiple access (NOMA) technique can support massive connectivity with a significantly increased spectral efficiency. Cooperative NOMA is able to further improve the communication reliability of users under poor channel conditions. However, the conventional system design suffers from several inherent limitations and is not optimized from the bit error rate (BER) perspective. In this paper, we develop a novel deep cooperative NOMA scheme, drawing upon the recent advances in deep learning (DL). We develop a novel hybrid-cascaded deep neural network (DNN) architecture such that the entire system can be optimized in a holistic manner. On this basis, we construct multiple loss functions to quantify the BER performance and propose a novel multi-task oriented two-stage training method to solve the end-to-end training problem in a self-supervised manner. The learning mechanism of each DNN module is then analyzed based on information theory, offering insights into the proposed DNN architecture and its corresponding training method. We also adapt the proposed scheme to handle the power allocation (PA) mismatch between training and inference and incorporate it with channel coding to combat signal deterioration. Simulation results verify its advantages over orthogonal multiple access (OMA) and the conventional cooperative NOMA scheme in various scenarios.

cs.IT↗

arXiv2020

FastReID: A Pytorch Toolbox for General Instance Re-identification

General Instance Re-identification is a very important task in the computer vision, which can be widely used in many practical applications, such as person/vehicle re-identification, face recognition, wildlife protection, commodity tracing, and snapshop, etc.. To meet the increasing application demand for general instance re-identification, we present FastReID as a widely used software system in JD AI Research. In FastReID, highly modular and extensible design makes it easy for the researcher to achieve new research ideas. Friendly manageable system configuration and engineering deployment functions allow practitioners to quickly deploy models into productions. We have implemented some state-of-the-art projects, including person re-id, partial re-id, cross-domain re-id and vehicle re-id, and plan to release these pre-trained models on multiple benchmark datasets. FastReID is by far the most general and high-performance toolbox that supports single and multiple GPU servers, you can reproduce our project results very easily and are very welcome to use it, the code and models are available at https://github.com/JDAI-CV/fast-reid.

cs.CV↗

arXiv2020

Evidence the ferromagnetic order on CoSb layer of LaCoSb$_2$

The emergence of unconventional superconductivity is generally considered to be related to spin fluctuations. Unveiling the intriguing behaviors of spin fluctuations in parent compounds with layered transition-metal ions may shed light on the search for exotic unconventional superconductors. Here, based on the framework of the first-principles calculations, we theoretically propose that LaCoSb$_2$ is a weak antiferromagnetic layered metal with an in-plane ferromagnetic moment of 0.88 $μ_B$ at the Co sites, as a candidate parent compound of the cobalt-based superconductors. Importantly, this theoretical finding is experimentally supported by our magnetization measurements on polycrystalline samples of LaCo$_{0.78}$Sb$_2$. Following the symmetry analysis, we suggest a possible $p$-wave superconductivity hosted in doped LaCoSb$_2$ emerging at the verge of ferromagnetic spin fluctuations, which implies potential applications in topological quantum computing in future.

cond-mat.supr-con↗

arXiv2020

Experimental evidence of monolayer AlB$_2$ with symmetry-protected Dirac cones

Monolayer AlB$_2$ is composed of two atomic layers: honeycomb borophene and triangular aluminum. In contrast with the bulk phase, monolayer AlB$_2$ is predicted to be a superconductor with a high critical temperature. Here, we demonstrate that monolayer AlB$_2$ can be synthesized on Al(111) via molecular beam epitaxy. Our theoretical calculations revealed that the monolayer AlB$_2$ hosts several Dirac cones along the $Γ$--M and $Γ$--K directions; these Dirac cones are protected by crystal symmetries and are thus resistant to external perturbations. The extraordinary electronic structure of the monolayer AlB$_2$ was confirmed via angle-resolved photoemission spectroscopy measurements. These results are likely to stimulate further research interest to explore the exotic properties arising from the interplay of Dirac fermions and superconductivity in two-dimensional materials.

cond-mat.mtrl-sci↗

arXiv2020

Quantum oscillations and electronic structure in the large-Chern number topological chiral semimetal PtGa

We report the magnetoresistance(MR), de Haas-van Alphen (dHvA) oscillations and the electronic structures of single crystal PtGa. The large unsaturated MR is observed with the magnetic field B//[111]. Evident dHvA oscillations with B//[001] configuration have been observed, from which twelve fundamental frequencies are extracted and the spin-orbit coupling effect (SOC) induced band splitting is revealed. The light cyclotron effective masses are extracted from the fitting by the thermal damping term of the Lifshitz-Kosevich (LK) formula. Combining with the calculated frequencies from the first-principles calculations, the dHvA frequencies F$_1$/F$_3$ and F$_{11}$/F$_{12}$ are confirmed to originate from the electron pockets at $Γ$ and R, respectively. The first-principles calculations also reveal the existence of spin-3/2 RSW fermion and time-reversal (TR) doubling of the spin-1 excitation at $Γ$ and R with large Chern number $\pm4$ when SOC is included.

cond-mat.mtrl-sci↗

arXiv2020

Lightweight and Unobtrusive Data Obfuscation at IoT Edge for Remote Inference

Executing deep neural networks for inference on the server-class or cloud backend based on data generated at the edge of Internet of Things is desirable due primarily to the limited compute power of edge devices and the need to protect the confidentiality of the inference neural networks. However, such a remote inference scheme incurs concerns regarding the privacy of the inference data transmitted by the edge devices to the curious backend. This paper presents a lightweight and unobtrusive approach to obfuscate the inference data at the edge devices. It is lightweight in that the edge device only needs to execute a small-scale neural network; it is unobtrusive in that the edge device does not need to indicate whether obfuscation is applied. Extensive evaluation by three case studies of free spoken digit recognition, handwritten digit recognition, and American sign language recognition shows that our approach effectively protects the confidentiality of the raw forms of the inference data while effectively preserving the backend's inference accuracy.

cs.LG↗

arXiv2020

CoinMagic: A Differential Privacy Framework for Ring Signature Schemes

By allowing users to obscure their transactions via including "mixins" (chaff coins), ring signature schemes have been widely used to protect a sender's identity of a transaction in privacy-preserving blockchain systems, like Monero and Bytecoin. However, recent works point out that the existing ring signature scheme is vulnerable to the "chain-reaction" analysis (i.e., the spent coin in a given ring signature can be deduced through elimination). Especially, when the diversity of mixins is low, the spent coin will have a high risk to be detected. To overcome the weakness, the ring signature should be consisted of a set of mixins with high diversity and produce observations having "similar" distributions for any two coins. In this paper, we propose a notion, namely $ε$-coin-indistinguishability ($ε$-CI), to formally define the "similar" distribution guaranteed through a differential privacy scheme. Then, we formally define the CI-aware mixins selection problem with disjoint-superset constraint (CIA-MS-DS), which aims to find a mixin set that has maximal diversity and satisfies the constraints of $ε$-CI and the budget. In CIA-MS-DS, each ring signature is either disjoint with or the superset of its preceding ring signatures. We prove that CIA-MS-DS is NP-hard and thus intractable. To solve the CIA-MS-DS problem, we propose two approximation algorithms, namely the Progressive Algorithm and the Game Theoretic Algorithm, with theoretic guarantees. Through extensive experiments on both real data sets and synthetic data sets, we demonstrate the efficiency and the effectiveness of our approaches.

cs.CR↗

arXiv2020

Tunable magnetic properties in van der Waals crystals (Fe$_{1-x}$Co$_x$)$_5$GeTe$_2$

We report the doping effects of cobalt on van der Waals (vdW) magnet Fe$_5$GeTe$_2$. A series of (Fe$_{1-x}$Co$_x$)$_5$GeTe$_2$ (0$\leq$x$\leq$0.44) single crystals have been successfully grown, their structural, magnetic and transport properties are investigated. For x=0.20, The Curie temperature $T_C$ increases from 276~K to 337~K. Moreover, the magnetic easy-axis is reoriented to the $ab$-plane from the $c$-axis in undoped Fe$_5$GeTe$_2$ with largely enhanced magnetic anisotropy. These magnetic properties would make (Fe$_{0.8}$Co$_{0.2}$)$_5$GeTe$_2$ more effective in stabilizing magnetic order in the two-dimensional limit. A complex magnetic phase diagram is identified on the higher doping side. The x=0.44 crystal first orders ferromagnetically at $T_C$=363~K then undergoes an antiferromagnetic transition at $T_N$=335~K. Furthermore magnetic-field-induced spin-flop transitions are observed for the AFM ground state. Our work reveals (Fe$_{1-x}$Co$_x$)$_5$GeTe$_2$ as promising candidates for developing new spin-related applications and proposes a method to engineer the magnetic properties of vdW magnet.

cond-mat.mes-hall↗

arXiv2020

Simulating Performance of ML Systems with Offline Profiling

We advocate that simulation based on offline profiling is a promising approach to better understand and improve the complex ML systems. Our approach uses operation-level profiling and dataflow based simulation to ensure it offers a unified and automated solution for all frameworks and ML models, and is also accurate by considering the various parallelization strategies in a real system.

cs.DC↗

arXiv2020

Universal mechanical exfoliation of large-area 2D crystals

Two-dimensional (2D) materials provide extraordinary opportunities for exploring phenomena arising in atomically thin crystals. Beginning with the first isolation of graphene, mechanical exfoliation has been a key to provide high-quality 2D materials but despite improvements it is still limited in yield, lateral size and contamination. Here we introduce a contamination-free, one-step and universal Au-assisted mechanical exfoliation method and demonstrate its effectiveness by isolating 40 types of single-crystalline monolayers, including elemental 2D crystals, metal-dichalcogenides, magnets and superconductors. Most of them are of millimeter-size and high-quality, as shown by transfer-free measurements of electron microscopy, photo spectroscopies and electrical transport. Large suspended 2D crystals and heterojunctions were also prepared with high-yield. Enhanced adhesion between the crystals and the substrates enables such efficient exfoliation, for which we identify a common rule that underpins a universal route for producing large-area monolayers and thus supports studies of fundamental properties and potential application of 2D materials.

cond-mat.mes-hall↗

arXiv2020

Evolution of superconductivity and antiferromagnetic order in Ba(Fe$_{0.92-x}$Co$_{0.08}$V$_x$)$_2$As$_2$

The vanadium doping effects on superconductivity and magnetism of iron pnictides are investigated in Ba(Fe$_{0.92-x}$Co$_{0.08}$V$_x$)$_2$As$_2$ by transport, susceptibility and neutron scattering measurements. The doping of magnetic impurity V causes a fast suppression of superconductivity with T$_c$ reduced at a rate of 7.4~K/1\%V. On the other hand, the long-range commensurate $C$-type antiferromagnetic order is recovered upon the V doping. The value of ordered magnetic moments of Ba(Fe$_{0.92-x}$Co$_{0.08}$V$_x$)$_2$As$_2$ follows a dome-like evolution versus doping concentration x. A possible Griffiths-type antiferromagnetic region of multiple coexisting phases in the phase diagram of Ba(Fe$_{0.92-x}$Co$_{0.08}$V$_x$)$_2$As$_2$ is identified, in accordance with previous theoretical predictions based on a cooperative behavior of the magnetic impurities and the conduction electrons mediating the Ruderman-Kittel-Kasuya-Yosida interactions between them.

cond-mat.supr-con↗

arXiv2020

Optimal Scheduling of Multiple Sensors over Lossy and Bandwidth Limited Channels

This work considers the sensor scheduling for multiple dynamic processes. We consider $n$ linear dynamic processes, the state of each process is measured by a sensor, which transmits their local state estimates over wireless channels to a remote estimator with certain communication costs. In each time step, only a portion of the sensors is allowed to transmit data to the remote estimator and the packet might be lost due to unreliability of the wireless channels. Our goal is to find a scheduling policy which coordinates the sensors in a centralized manner to minimize the total expected estimation error of the remote estimator and the communication costs. We formulate the problem as a Markov decision process. We develop an algorithm to check whether there exists a deterministic stationary optimal policy. We show the optimality of monotone policies, which saves computational effort of finding an optimal policy and facilitates practical implementation. Nevertheless, obtaining an exact optimal policy still suffers from curse of dimensionality when the number of processes are large. We further provide an index-based heuristics to avoid brute force computation. Numerical examples are presented to illustrate our theoretical results.

eess.SY↗

arXiv2020

Spectrum Intelligent Radio: Technology, Development, and Future Trends

The advent of Industry 4.0 with massive connectivity places significant strains on the current spectrum resources, and challenges the industry and regulators to respond promptly with new disruptive spectrum management strategies. The current radio development, with certain elements of intelligence, is nowhere near showing an agile response to the complex radio environments. Following the line of intelligence, we propose to classify spectrum intelligent radio into three streams: classical signal processing, machine learning (ML), and contextual adaptation. We focus on the ML approach, and propose a new intelligent radio architecture with three hierarchical forms: perception, understanding, and reasoning. The proposed perception method achieves fully blind multi-level spectrum sensing. The understanding method accurately predicts the primary users' coverage across a large area, and the reasoning method performs a near-optimal idle channel selection. Opportunities, challenges, and future visions are also discussed for the realization of a fully intelligent radio.

cs.IT↗

arXiv2019

Analysis of Moving Target Defense Against False Data Injection Attacks on Power Grid

Recent studies have considered thwarting false data injection (FDI) attacks against state estimation in power grids by proactively perturbing branch susceptances. This approach is known as moving target defense (MTD). However, despite of the deployment of MTD, it is still possible for the attacker to launch stealthy FDI attacks generated with former branch susceptances. In this paper, we prove that, an MTD has the capability to thwart all FDI attacks constructed with former branch susceptances only if (i) the number of branches $l$ in the power system is not less than twice that of the system states $n$ (i.e., $l \geq 2n$, where $n + 1$ is the number of buses); (ii) the susceptances of more than $n$ branches, which cover all buses, are perturbed. Moreover, we prove that the state variable of a bus that is only connected by a single branch (no matter it is perturbed or not) can always be modified by the attacker. Nevertheless, in order to reduce the attack opportunities of potential attackers, we first exploit the impact of the susceptance perturbation magnitude on the dimension of the \emph{stealthy attack space}, in which the attack vector is constructed with former branch susceptances. Then, we propose that, by perturbing an appropriate set of branches, we can minimize the dimension of the \emph{stealthy attack space} and maximize the number of covered buses. Besides, we consider the increasing operation cost caused by the activation of MTD. Finally, we conduct extensive simulations to illustrate our findings with IEEE standard test power systems.

eess.SY↗

arXiv2019

Quantum oscillations and electronic structures in large Chern number semimetal RhSn

We report the magnetoresistance, Hall effect, de Haas-van Alphen (dHvA) oscillations and the electronic structures of single crystal RhSn, which is a typical material of CoSi family holding a large Chern number. The large unsaturated magnetoresistance is observed with B//[001]. The Hall resistivity curve indicates that RhSn is a multi-band system with high mobility. Evident quantum oscillations have been observed, from which the light effective masses are extracted. Ten fundamental frequencies are extracted after the fast Fourier transform analysis of the dHvA oscillations with B//[001] configuration. The two low frequencies F$_1$ and F$_2$ do not change obviously and the two high frequencies F$_9$ and F$_{10}$ evolve into four when B rotates from B//[001] to B//[110], which is consistent with the band structure in the first-principles calculations with spin-orbit coupling (SOC). The extracted Berry phases of the relative pockets show a good agreement with the Chern number $\pm4$ (with SOC) in the first-principles calculations. Above all, our studies indicate that RhSn is an ideal platform to study the unconventional chiral fermions and the surface states.

cond-mat.mes-hall↗

arXiv2019

Reinforcement learning for bandwidth estimation and congestion control in real-time communications

Bandwidth estimation and congestion control for real-time communications (i.e., audio and video conferencing) remains a difficult problem, despite many years of research. Achieving high quality of experience (QoE) for end users requires continual updates due to changing network architectures and technologies. In this paper, we apply reinforcement learning for the first time to the problem of real-time communications (RTC), where we seek to optimize user-perceived quality. We present initial proof-of-concept results, where we learn an agent to control sending rate in an RTC system, evaluating using both network simulation and real Internet video calls. We discuss the challenges we observed, particularly in designing realistic reward functions that reflect QoE, and in bridging the gap between the training environment and real-world networks.

cs.NI↗

arXiv2019

Challenges of Privacy-Preserving Machine Learning in IoT

The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. However, the extensive data collection and processing in IoT also engender various privacy concerns. This paper provides a taxonomy of the existing privacy-preserving machine learning approaches developed in the context of cloud computing and discusses the challenges of applying them in the context of IoT. Moreover, we present a privacy-preserving inference approach that runs a lightweight neural network at IoT objects to obfuscate the data before transmission and a deep neural network in the cloud to classify the obfuscated data. Evaluation based on the MNIST dataset shows satisfactory performance.

cs.CR↗