Searcharxiv⌕ Search

arXiv subjects

Qing Wang

Publications and source records attributed to Qing Wang.

At least 307 records · Page 17Linked to original sources
arXiv2021

Multiple Intelligent Reflecting Surface aided Multi-user Weighted Sum-Rate Maximization using Manifold Optimization

Intelligent reflecting surface (IRS) are able to amend radio propagation condition tasks on account of its functional properties in phase shift optimizing. In fact, there exists geometry manifold in the base-station (BS) beamforming matrix and IRS reflection vector. Therefore, we propose a novel double manifold alternating optimization (DMAO) algorithm which makes use of differential geometry theory to improve optimization performance. First, we develop a multi-IRS multi-user system model to maximize the weighted sum-rate, which may lead to the non-convexity in our optimization procedure. Then in order to allocate an optimized coefficients to each BS antenna and IRS reflecting element, we present the beamforming matrix and reflection vector using complex sphere manifold and complex oblique manifold, respectively, which integrates the inner geometry structure and the constrains. By an innovative alternative iteration method, the system gradually converges an optimized stable state, which is associating with the maximized sum-rate. Furthermore, we quantize the IRS reflection coefficient considering the practical constrains. Experimental results demonstrated that our approach significantly outperforms the conventional methods in terms of weighted sum-rate.

eess.SP↗
arXiv2021

ISPY: Automatic Issue-Solution Pair Extraction from Community Live Chats

Collaborative live chats are gaining popularity as a development communication tool. In community live chatting, developers are likely to post issues they encountered (e.g., setup issues and compile issues), and other developers respond with possible solutions. Therefore, community live chats contain rich sets of information for reported issues and their corresponding solutions, which can be quite useful for knowledge sharing and future reuse if extracted and restored in time. However, it remains challenging to accurately mine such knowledge due to the noisy nature of interleaved dialogs in live chat data. In this paper, we first formulate the problem of issue-solution pair extraction from developer live chat data, and propose an automated approach, named ISPY, based on natural language processing and deep learning techniques with customized enhancements, to address the problem. Specifically, ISPY automates three tasks: 1) Disentangle live chat logs, employing a feedforward neural network to disentangle a conversation history into separate dialogs automatically; 2) Detect dialogs discussing issues, using a novel convolutional neural network (CNN), which consists of a BERT-based utterance embedding layer, a context-aware dialog embedding layer, and an output layer; 3) Extract appropriate utterances and combine them as corresponding solutions, based on the same CNN structure but with different feeding inputs. To evaluate ISPY, we compare it with six baselines, utilizing a dataset with 750 dialogs including 171 issue-solution pairs and evaluate ISPY from eight open source communities. The results show that, for issue-detection, our approach achieves the F1 of 76%, and outperforms all baselines by 30%. Our approach achieves the F1 of 63% for solution-extraction and outperforms the baselines by 20%.

cs.SE↗
arXiv2021

A duality between vertex superalgebras $L_{-3/2}(\mathfrak{osp}(1\vert 2))$ and $\mathcal V^{(2)}$ and generalizations to logarithmic vertex algebras

We introduce a subalgebra $\overline F$ of the Clifford vertex superalgebra ($bc$ system) which is completely reducible as a $L^{Vir} (-2,0)$-module, $C_2$-cofinite, but it is not conformal and it is not isomorphic to the symplectic fermion algebra $\mathcal{SF}(1)$. We show that $\mathcal{SF}(1)$ and $\overline{F}$ are in an interesting duality, since $\overline{F}$ can be equipped with the structure of a $\mathcal{SF}(1)$-module and vice versa. Using the decomposition of $\overline F$ and a free-field realization from arXiv:1711.11342, we decompose $L_k(\mathfrak{osp}(1\vert 2))$ at the critical level $k=-3/2$ as a module for $L_k(\mathfrak{sl}(2))$. The decomposition of $L_k(\mathfrak{osp}(1\vert 2))$ is exactly the same as of the $N=4$ superconformal vertex algebra with central charge $c=-9$, denoted by $\mathcal V^{(2)}$. Using the duality between $\overline{F}$ and $\mathcal{SF}(1)$, we prove that $L_k(\mathfrak{osp}(1\vert 2))$ and $\mathcal V^{(2)}$ are in the duality of the same type. As an application, we construct and classify all irreducible $L_k(\mathfrak{osp}(1\vert 2))$-modules in the category $\mathcal O$ and the category $\mathcal R$ which includes relaxed highest weight modules. We also describe the structure of the parafermion algebra $N_{-3/2}(\mathfrak{osp}(1\vert 2))$ as a $N_{-3/2}(\mathfrak{sl}(2))$-module. We extend this example, and for each $p \ge 2$, we introduce a non-conformal vertex algebra $\mathcal A^{(p)}_{new}$ and show that $\mathcal A^{(p)}_{new} $ is isomorphic to the doublet vertex algebra as a module for the Virasoro algebra. We also construct the vertex algebra $ \mathcal V^{(p)} _{new}$ which is isomorphic to the logarithmic vertex algebra $\mathcal V^{(p)}$ as a module for $\widehat{\mathfrak{sl}}(2)$.

math.QA↗
arXiv2021

Deep Graph Memory Networks for Forgetting-Robust Knowledge Tracing

Tracing a student's knowledge is vital for tailoring the learning experience. Recent knowledge tracing methods tend to respond to these challenges by modelling knowledge state dynamics across learning concepts. However, they still suffer from several inherent challenges including: modelling forgetting behaviours and identifying relationships among latent concepts. To address these challenges, in this paper, we propose a novel knowledge tracing model, namely \emph{Deep Graph Memory Network} (DGMN). In this model, we incorporate a forget gating mechanism into an attention memory structure in order to capture forgetting behaviours dynamically during the knowledge tracing process. Particularly, this forget gating mechanism is built upon attention forgetting features over latent concepts considering their mutual dependencies. Further, this model has the capability of learning relationships between latent concepts from a dynamic latent concept graph in light of a student's evolving knowledge states. A comprehensive experimental evaluation has been conducted using four well-established benchmark datasets. The results show that DGMN consistently outperforms the state-of-the-art KT models over all the datasets. The effectiveness of modelling forgetting behaviours and learning latent concept graphs has also been analyzed in our experiments.

cs.LG↗
arXiv2021

VTLayout: Fusion of Visual and Text Features for Document Layout Analysis

Documents often contain complex physical structures, which make the Document Layout Analysis (DLA) task challenging. As a pre-processing step for content extraction, DLA has the potential to capture rich information in historical or scientific documents on a large scale. Although many deep-learning-based methods from computer vision have already achieved excellent performance in detecting \emph{Figure} from documents, they are still unsatisfactory in recognizing the \emph{List}, \emph{Table}, \emph{Text} and \emph{Title} category blocks in DLA. This paper proposes a VTLayout model fusing the documents' deep visual, shallow visual, and text features to localize and identify different category blocks. The model mainly includes two stages, and the three feature extractors are built in the second stage. In the first stage, the Cascade Mask R-CNN model is applied directly to localize all category blocks of the documents. In the second stage, the deep visual, shallow visual, and text features are extracted for fusion to identify the category blocks of documents. As a result, we strengthen the classification power of different category blocks based on the existing localization technique. The experimental results show that the identification capability of the VTLayout is superior to the most advanced method of DLA based on the PubLayNet dataset, and the F1 score is as high as 0.9599.

cs.IR↗
arXiv2021

A First Look at Developers' Live Chat on Gitter

Modern communication platforms such as Gitter and Slack play an increasingly critical role in supporting software teamwork, especially in open source development.Conversations on such platforms often contain intensive, valuable information that may be used for better understanding OSS developer communication and collaboration. However, little work has been done in this regard. To bridge the gap, this paper reports a first comprehensive empirical study on developers' live chat, investigating when they interact, what community structures look like, which topics are discussed, and how they interact. We manually analyze 749 dialogs in the first phase, followed by an automated analysis of over 173K dialogs in the second phase. We find that developers tend to converse more often on weekdays, especially on Wednesdays and Thursdays (UTC), that there are three common community structures observed, that developers tend to discuss topics such as API usages and errors, and that six dialog interaction patterns are identified in the live chat communities. Based on the findings, we provide recommendations for individual developers and OSS communities, highlight desired features for platform vendors, and shed light on future research directions. We believe that the findings and insights will enable a better understanding of developers' live chat, pave the way for other researchers, as well as a better utilization and mining of knowledge embedded in the massive chat history.

cs.SE↗
arXiv2021

OwlEyes-Online: A Fully Automated Platform for Detecting and Localizing UI Display Issues

Graphical User Interface (GUI) provides visual bridges between software apps and end users. However, due to the compatibility of software or hardware, UI display issues such as text overlap, blurred screen, image missing always occur during GUI rendering on different devices. Because these UI display issues can be found directly by human eyes, in this paper, we implement an online UI display issue detection tool OwlEyes-Online, which provides a simple and easy-to-use platform for users to realize the automatic detection and localization of UI display issues. The OwlEyes-Online can automatically run the app and get its screenshots and XML files, and then detect the existence of issues by analyzing the screenshots. In addition, OwlEyes-Online can also find the detailed area of the issue in the given screenshots to further remind developers. Finally, OwlEyes-Online will automatically generate test reports with UI display issues detected in app screenshots and send them to users. The OwlEyes-Online was evaluated and proved to be able to accurately detect UI display issues. Tool Link: http://www.owleyes.online:7476 Github Link: https://github.com/franklinbill/owleyes Demo Video Link: https://youtu.be/002nHZBxtCY

cs.SE↗
arXiv2021

The Use of Bandit Algorithms in Intelligent Interactive Recommender Systems

In today's business marketplace, many high-tech Internet enterprises constantly explore innovative ways to provide optimal online user experiences for gaining competitive advantages. The great needs of developing intelligent interactive recommendation systems are indicated, which could sequentially suggest users the most proper items by accurately predicting their preferences, while receiving the up-to-date feedback to refine the recommendation results, continuously. Multi-armed bandit algorithms, which have been widely applied into various online systems, are quite capable of delivering such efficient recommendation services. However, few existing bandit models are able to adapt to new changes introduced by the modern recommender systems.

cs.IR↗
arXiv2021

Multi-Level Transfer Learning from Near-Field to Far-Field Speaker Verification

In far-field speaker verification, the performance of speaker embeddings is susceptible to degradation when there is a mismatch between the conditions of enrollment and test speech. To solve this problem, we propose the feature-level and instance-level transfer learning in the teacher-student framework to learn a domain-invariant embedding space. For the feature-level knowledge transfer, we develop the contrastive loss to transfer knowledge from teacher model to student model, which can not only decrease the intra-class distance, but also enlarge the inter-class distance. Moreover, we propose the instance-level pairwise distance transfer method to force the student model to preserve pairwise instances distance from the well optimized embedding space of the teacher model. On FFSVC 2020 evaluation set, our EER on Full-eval trials is relatively reduced by 13.9% compared with the fusion system result on Partial-eval trials of Task2. On Task1, compared with the winner's DenseNet result on Partial-eval trials, our minDCF on Full-eval trials is relatively reduced by 6.3%. On Task3, the EER and minDCF of our proposed method on Full-eval trials are very close to the result of the fusion system on Partial-eval trials. Our results also outperform other competitive domain adaptation methods.

cs.SD↗
arXiv2021

Episode Adaptive Embedding Networks for Few-shot Learning

Few-shot learning aims to learn a classifier using a few labelled instances for each class. Metric-learning approaches for few-shot learning embed instances into a high-dimensional space and conduct classification based on distances among instance embeddings. However, such instance embeddings are usually shared across all episodes and thus lack the discriminative power to generalize classifiers according to episode-specific features. In this paper, we propose a novel approach, namely \emph{Episode Adaptive Embedding Network} (EAEN), to learn episode-specific embeddings of instances. By leveraging the probability distributions of all instances in an episode at each channel-pixel embedding dimension, EAEN can not only alleviate the overfitting issue encountered in few-shot learning tasks, but also capture discriminative features specific to an episode. To empirically verify the effectiveness and robustness of EAEN, we have conducted extensive experiments on three widely used benchmark datasets, under various combinations of different generic embedding backbones and different classifiers. The results show that EAEN significantly improves classification accuracy about $10\%$ to $20\%$ in different settings over the state-of-the-art methods.

cs.CV↗
arXiv2021

Improved Gate Reliability of p-GaN Gate HEMTs by Gate Doping Engineering

We present a novel p-GaN gate HEMT structure with reduced hole concentration near the Schottky interface by doping engineering in MOCVD, which aims at lowering the electric field across the gate. By employing an additional unintentionally doped GaN layer, the gate leakage current is suppressed and the gate breakdown voltage is boosted from 10.6 to 14.6 V with negligible influence on the threshold voltage and on-resistance. Time-dependent gate breakdown measurements reveal that the maximum gate drive voltage increases from 6.2 to 10.6 V for a 10-year lifetime with a 1% gate failure rate. This method effectively expands the operating voltage margin of the p-GaN gate HEMTs without any other additional process steps.

physics.app-ph↗
arXiv2021

Pose2Drone: A Skeleton-Pose-based Framework for Human-Drone Interaction

Drones have become a common tool, which is utilized in many tasks such as aerial photography, surveillance, and delivery. However, operating a drone requires more and more interaction with the user. A natural and safe method for Human-Drone Interaction (HDI) is using gestures. In this paper, we introduce an HDI framework building upon skeleton-based pose estimation. Our framework provides the functionality to control the movement of the drone with simple arm gestures and to follow the user while keeping a safe distance. We also propose a monocular distance estimation method, which is entirely based on image features and does not require any additional depth sensors. To perform comprehensive experiments and quantitative analysis, we create a customized testing dataset. The experiments indicate that our HDI framework can achieve an average of 93.5\% accuracy in the recognition of 11 common gestures. The code is available at: https://github.com/Zrrr1997/Pose2Drone

cs.CV↗
arXiv2021

Dialogue Disentanglement in Software Engineering: How Far are We?

Despite the valuable information contained in software chat messages, disentangling them into distinct conversations is an essential prerequisite for any in-depth analyses that utilize this information. To provide a better understanding of the current state-of-the-art, we evaluate five popular dialog disentanglement approaches on software-related chat. We find that existing approaches do not perform well on disentangling software-related dialogs that discuss technical and complex topics. Further investigation on how well the existing disentanglement measures reflect human satisfaction shows that existing measures cannot correctly indicate human satisfaction on disentanglement results. Therefore, in this paper, we introduce and evaluate a novel measure, named DLD. Using results of human satisfaction, we further summarize four most frequently appeared bad disentanglement cases on software-related chat to insight future improvements. These cases include (i) ignoring interaction patterns; (ii) ignoring contextual information; (iii) mixing up topics; and (iv) ignoring user relationships. We believe that our findings provide valuable insights on the effectiveness of existing dialog disentanglement approaches and these findings would promote a better application of dialog disentanglement in software engineering.

cs.SE↗
arXiv2021

Query-by-Sketch: Scaling Shortest Path Graph Queries on Very Large Networks

Computing shortest paths is a fundamental operation in processing graph data. In many real-world applications, discovering shortest paths between two vertices empowers us to make full use of the underlying structure to understand how vertices are related in a graph, e.g. the strength of social ties between individuals in a social network. In this paper, we study the shortest-path-graph problem that aims to efficiently compute a shortest path graph containing exactly all shortest paths between any arbitrary pair of vertices on complex networks. Our goal is to design an exact solution that can scale to graphs with millions or billions of vertices and edges. To achieve high scalability, we propose a novel method, Query-by-Sketch (QbS), which efficiently leverages offline labelling (i.e., precomputed labels) to guide online searching through a fast sketching process that summarizes the important structural aspects of shortest paths in answering shortest-path-graph queries. We theoretically prove the correctness of this method and analyze its computational complexity. To empirically verify the efficiency of QbS, we conduct experiments on 12 real-world datasets, among which the largest dataset has 1.7 billion vertices and 7.8 billion edges. The experimental results show that QbS can answer shortest-path graph queries in microseconds for million-scale graphs and less than half a second for billion-scale graphs.

cs.DB↗
arXiv2021

Potential model prediction of fully-heavy tetraquarks $QQ\bar{Q}\bar{Q}$ ($Q=c, b$)

The fully-heavy tetraquark states $QQ\bar{Q}\bar{Q}$ ($Q=c, b$) are systematically investigated by means of a nonrelativistic potential model. The model is based on the lattice QCD study of the two-body $Q\bar{Q}$ interaction, which exhibits a spin-independent Cornell potential along with a spin-spin interaction. The four-body problem is implemented with a highly accurate computational approach, the Gaussian expansion method. The complex scaling method is also employed so that the bound, resonance, and scattering states can be treated on the same footing. Complete set of the four-body configurations, including meson-meson, diquark-antidiquark, and K-type configurations, as well as their couplings, are considered for spin-parity quantum numbers $J^{PC}=0^{++}$, $1^{+-}$, and $2^{++}$ in the $S$-wave channel. No $S$-wave bound state is found either in the fully-charm tetraquark system or in the fully-bottom one. However, several narrow resonances, with widths less than 10 MeV in two-meson strong decay, are found in both of these two systems. For the fully-charm case, the recently reported resonance structures between 6.2 and 7.2 GeV in the di-$J/ψ$ invariant spectrum by the LHCb collaboration, can be well identified in our calculation. For the fully-bottom case, resonances with masses between 18.9 and 19.6 GeV are found. The predicted resonances can be further examined in future experiments.

hep-ph↗
arXiv2021

$(G,χ_ϕ)$-equivariant $ϕ$-coordinated modules for vertex algebras

To give a unified treatment on the association of Lie algebras and vertex algebras, we study $(G,χ_ϕ)$-equivariant $ϕ$-coordinated quasi modules for vertex algebras, where $G$ is a group with $χ_ϕ$ a linear character of $G$ and $ϕ$ is an associate of the one-dimensional additive formal group. The theory of $(G,χ_ϕ)$-equivariant $ϕ$-coordinated quasi modules for nonlocal vertex algebra is established in \cite{JKLT}. In this paper, we concentrate on the context of vertex algebras. We establish several conceptual results, including a generalized commutator formula and a general construction of vertex algebras and their $(G,χ_ϕ)$-equivariant $ϕ$-coordinated quasi modules. Furthermore, for any conformal algebra $\mathcal{C}$, we construct a class of Lie algebras $\widehat{\mathcal{C}}_ϕ[G]$ and prove that restricted $\widehat{\mathcal{C}}_ϕ[G]$-modules are exactly $(G,χ_ϕ)$-equivariant $ϕ$-coordinated quasi modules for the universal enveloping vertex algebra of $\mathcal{C}$. As an application, we determine the $(G,χ_ϕ)$-equivariant $ϕ$-coordinated quasi modules for affine and Virasoro vertex algebras.

math.QA↗
arXiv2021

The NPU System for the 2020 Personalized Voice Trigger Challenge

This paper describes the system developed by the NPU team for the 2020 personalized voice trigger challenge. Our submitted system consists of two independently trained subsystems: a small footprint keyword spotting (KWS) system and a speaker verification (SV) system. For the KWS system, a multi-scale dilated temporal convolutional (MDTC) network is proposed to detect wake-up word (WuW). For SV system, Write something here. The KWS predicts posterior probabilities of whether an audio utterance contains WuW and estimates the location of WuW at the same time. When the posterior probability ofWuW reaches a predefined threshold, the identity information of triggered segment is determined by the SV system. On evaluation dataset, our submitted system obtains detection costs of 0.081and 0.091 in close talking and far-field tasks, respectively.

cs.SD↗