Searcharxiv⌕ Search

arXiv subjects

Mingwei Zhang

Publications and source records attributed to Mingwei Zhang.

29 records · Page 2Linked to original sources
arXiv2023

Extra electron reflections in concentrated alloys do not necessitate short-range order

In many concentrated alloys of current interest, the observation of diffuse superlattice intensities by transmission electron microscopy has been attributed to chemical short-range order. We briefly review these findings and comment on the plausibility of widespread interpretations, noting the absence of expected peaks, conflicts with theoretical predictions, and the possibility of alternative explanations.

cond-mat.mtrl-sci↗
arXiv2023

Decentralized Threshold Signatures with Dynamically Private Accountability

Threshold signatures are a fundamental cryptographic primitive used in many practical applications. As proposed by Boneh and Komlo (CRYPTO'22), TAPS is a threshold signature that is a hybrid of privacy and accountability. It enables a combiner to combine t signature shares while revealing nothing about the threshold t or signing quorum to the public and asks a tracer to track a signature to the quorum that generates it. However, TAPS has three disadvantages: it 1) structures upon a centralized model, 2) assumes that both combiner and tracer are honest, and 3) leaves the tracing unnotarized and static. In this work, we introduce Decentralized, Threshold, dynamically Accountable and Private Signature (DeTAPS) that provides decentralized combining and tracing, enhanced privacy against untrusted combiners (tracers), and notarized and dynamic tracing. Specifically, we adopt Dynamic Threshold Public-Key Encryption (DTPKE) to dynamically notarize the tracing process, design non-interactive zero knowledge proofs to achieve public verifiability of notaries, and utilize the Key-Aggregate Searchable Encryption to bridge TAPS and DTPKE so as to awaken the notaries securely and efficiently. In addition, we formalize the definitions and security requirements for DeTAPS. Then we present a generic construction and formally prove its security and privacy. To evaluate the performance, we build a prototype based on SGX2 and Ethereum.

cs.CR↗
arXiv2023

Adaptive Distributed Filtering of DDoS Traffic on the Internet

Despite the proliferation of traffic filtering capabilities throughout the Internet, attackers continue to launch distributed denial-of-service (DDoS) attacks to successfully overwhelm the victims with DDoS traffic. In this paper, we introduce a distributed filtering system that leverages nodes distributed along the paths of DDoS traffic to filter the DDoS traffic. In particular, we focus on adaptive distributed filtering, a new direction in filtering DDoS traffic. In our design, a subscriber to the distributed filtering service can act on behalf of a DDoS victim and generate filtering rules that not only adapt to the most suitable and effective filtering granularity (e.g., IP source address and a port number vs. an individual IP address vs. IP prefixes at different lengths), but also adapt to the preferences of the subscriber (e.g., maximum coverage of DDoS traffic vs. minimum collateral damage from dropping legitimate traffic vs. minimum number of rules). We design an efficient algorithm that can generate rules adaptive toward filtering granularities and objectives, which can further help determine where to deploy generated rules for the best efficacy. We evaluated our system through both large-scale simulations based on real-world DDoS attack traces and pilot studies. Our evaluations confirm that our algorithm can generate rules that adapt to every distinct filtering objective and achieve optimal results. We studied the success rate and distribution of rule deployment under different Internet-scale rule deployment profiles, and found a small number of autonomous systems can contribute disproportionately to the defense. Our pilot studies also show our adaptive distributed filtering system can effectively defend against real-world DDoS attack traces in real time.

cs.NI↗
arXiv2021

CSSR: A Context-Aware Sequential Software Service Recommendation Model

We propose a novel software service recommendation model to help users find their suitable repositories in GitHub. Our model first designs a novel context-induced repository graph embedding method to leverage rich contextual information of repositories to alleviate the difficulties caused by the data sparsity issue. It then leverages sequence information of user-repository interactions for the first time in the software service recommendation field. Specifically, a deep-learning based sequential recommendation technique is adopted to capture the dynamics of user preferences. Comprehensive experiments have been conducted on a large dataset collected from GitHub against a list of existing methods. The results illustrate the superiority of our method in various aspects.

cs.IR↗
arXiv2021

Fast Online Planning for Bipedal Locomotion via Centroidal Model Predictive Gait Synthesis

The planning of whole-body motion and step time for bipedal locomotion is constructed as a model predictive control (MPC) problem, in which a sequence of optimization problems needs to be solved online. While directly solving these problems is extremely time-consuming, we propose a predictive gait synthesizer to offer immediate solutions. Based on the full-dimensional model, a library of gaits with different speeds and periods is first constructed offline. Then the proposed gait synthesizer generates real-time gaits at 1kHz by synthesizing the gait library based on the online prediction of centroidal dynamics. We prove that the constructed MPC problem can ensure the uniform ultimate boundedness (UUB) of the CoM states and show that our proposed gait synthesizer can provide feasible solutions to the MPC optimization problems. Simulation and experimental results on a bipedal robot with 8 degrees of freedom (DoF) are provided to show the performance and robustness of this approach.

cs.RO↗
arXiv2020

Generalized Operating Procedure for Deep Learning: an Unconstrained Optimal Design Perspective

Deep learning (DL) has brought about remarkable breakthrough in processing images, video and speech due to its efficacy in extracting highly abstract representation and learning very complex functions. However, there is seldom operating procedure reported on how to make it for real use cases. In this paper, we intend to address this problem by presenting a generalized operating procedure for DL from the perspective of unconstrained optimal design, which is motivated by a simple intension to remove the barrier of using DL, especially for those scientists or engineers who are new but eager to use it. Our proposed procedure contains seven steps, which are project/problem statement, data collection, architecture design, initialization of parameters, defining loss function, computing optimal parameters, and inference, respectively. Following this procedure, we build a multi-stream end-to-end speaker verification system, in which the input speech utterance is processed by multiple parallel streams within different frequency range, so that the acoustic modeling can be more robust resulting from the diversity of features. Trained with VoxCeleb dataset, our experimental results verify the effectiveness of our proposed operating procedure, and also show that our multi-stream framework outperforms single-stream baseline with 20 % relative reduction in minimum decision cost function (minDCF).

cs.LG↗
arXiv2020

A Knowledge Graph based Approach for Mobile Application Recommendation

With the rapid prevalence of mobile devices and the dramatic proliferation of mobile applications (apps), app recommendation becomes an emergent task that would benefit both app users and stockholders. How to effectively organize and make full use of rich side information of users and apps is a key challenge to address the sparsity issue for traditional approaches. To meet this challenge, we proposed a novel end-to-end Knowledge Graph Convolutional Embedding Propagation Model (KGEP) for app recommendation. Specifically, we first designed a knowledge graph construction method to model the user and app side information, then adopted KG embedding techniques to capture the factual triplet-focused semantics of the side information related to the first-order structure of the KG, and finally proposed a relation-weighted convolutional embedding propagation model to capture the recommendation-focused semantics related to high-order structure of the KG. Extensive experiments conducted on a real-world dataset validate the effectiveness of the proposed approach compared to the state-of-the-art recommendation approaches.

cs.IR↗
arXiv2018

Multiplicity Dependence of Charged Particle, $ϕ$ Meson and Multi-strange Particle Productions in p+p Collisions at $\sqrt{\rm s}$ = 200 GeV with PYTHIA Simulation

We report the multiplicity dependence of charged particle productions for $π^{\pm}$, $K^{\pm}$, $p$, $\overline{p}$ and $ϕ$ meson at $|y| < 1.0$ in p+p collisions at $\sqrt{\rm s}$ = 200 GeV with $\rm PYTHIA$ simulation. The impact of parton multiple interactions and gluon contributions is studied and found to be possible sources of the particle yields splitting as a function of $p_T$ with respect to multiplicity. No obvious particle species dependence for the splitting is observed. The multiplicity dependence on ratios of $K^-/π^-$, $K^+/π^+$, $\overline{p}/π^-$, $p/π^+$ and $Λ/K^{0}_{s}$ in mid-rapidity in p+p collisions is found following the similar tendency as that in Au+Au collisions at $\sqrt{s_{NN}}$ = 200 GeV from RHIC, which heralds the similar underlying initial production mechanisms despite the differences in the initial colliding systems.

hep-ph↗
arXiv2017

Semi-supervised classification for dynamic Android malware detection

A growing number of threats to Android phones creates challenges for malware detection. Manually labeling the samples into benign or different malicious families requires tremendous human efforts, while it is comparably easy and cheap to obtain a large amount of unlabeled APKs from various sources. Moreover, the fast-paced evolution of Android malware continuously generates derivative malware families. These families often contain new signatures, which can escape detection when using static analysis. These practical challenges can also cause traditional supervised machine learning algorithms to degrade in performance. In this paper, we propose a framework that uses model-based semi-supervised (MBSS) classification scheme on the dynamic Android API call logs. The semi-supervised approach efficiently uses the labeled and unlabeled APKs to estimate a finite mixture model of Gaussian distributions via conditional expectation-maximization and efficiently detects malwares during out-of-sample testing. We compare MBSS with the popular malware detection classifiers such as support vector machine (SVM), $k$-nearest neighbor (kNN) and linear discriminant analysis (LDA). Under the ideal classification setting, MBSS has competitive performance with 98\% accuracy and very low false positive rate for in-sample classification. For out-of-sample testing, the out-of-sample test data exhibit similar behavior of retrieving phone information and sending to the network, compared with in-sample training set. When this similarity is strong, MBSS and SVM with linear kernel maintain 90\% detection rate while $k$NN and LDA suffer great performance degradation. When this similarity is slightly weaker, all classifiers degrade in performance, but MBSS still performs significantly better than other classifiers.

cs.CR↗
arXiv2016

Some extremal results on complete degenerate hypergraphs

Let $K^{(r)}_{s_1,s_2,\cdots,s_r}$ be the complete $r$-partite $r$-uniform hypergraph and $ex(n,K^{(r)}_{s_1,s_2,\cdots,s_r})$ be the maximum number of edges in any $n$-vertex $K^{(r)}_{s_1,s_2,\cdots,s_r}$-free $r$-uniform hypergraph. It is well-known in the graph case that $ex(n,K_{s,t})=Θ(n^{2-1/s})$ when $t$ is sufficiently larger than $s$. In this note, we generalize the above to hypergraphs by showing that if $s_r$ is sufficiently larger than $s_1,s_2,\cdots,s_{r-1}$ then $$ex(n, K^{(r)}_{s_1,s_2,\cdots,s_r})=Θ\left(n^{r-\frac{1}{s_1s_2\cdots s_{r-1}}}\right).$$ This follows from a more general Turán type result we establish in hypergraphs, which also improves and generalizes some recent results of Alon and Shikhelman. The lower bounds of our results are obtained by the powerful random algebraic method of Bukh. Another new, perhaps unsurprising insight which we provide here is that one can also use the random algebraic method to construct non-degenerate (hyper-)graphs for various Turán type problems. The asymptotics for $ex(n, K^{(r)}_{s_1,s_2,\cdots,s_r})$ is also proved by Verstraëte independently with a different approach.

math.CO↗