arXiv subjects

Ye Yang

Publications and source records attributed to Ye Yang.

At least 55 records · Page 3Linked to original sources

arXiv2022

A proof-of-principle demonstration of quantum microwave photonics

With the rapid development of microwave photonics, which has expanded to numerous applications of commercial importance, eliminating the emerging bottlenecks becomes of vital importance. For example, as the main branch of microwave photonics, radio-over-fiber technology provides high bandwidth, low-loss, and long-distance propagation capability, facilitating wide applications ranging from telecommunication to wireless networks. With ultrashort pulses as the optical carrier, huge capacity is further endowed. However, the wide bandwidth of ultrashort pulses results in the severe vulnerability of high-frequency RF signals to fiber dispersion. With a time-energy entangled biphoton source as the optical carrier and combined with the single-photon detection technique, a quantum microwave photonics method is proposed and demonstrated experimentally. The results show that it not only realizes unprecedented nonlocal RF signal modulation with strong resistance to the dispersion associated with ultrashort pulse carriers but provides an alternative mechanism to effectively distill the RF signal out from the dispersion. Furthermore, the spurious-free dynamic range of both the nonlocally modulated and distilled RF signals has been significantly improved. With the ultra-weak detection and high-speed processing advantages endowed by the low-timing-jitter single-photon detection, the quantum microwave photonics method opens up new possibilities in modern communication and networks.

quant-ph↗

arXiv2022

An Empirical Investigation of Worker Communities in TopCoder

Software crowdsourcing platforms employ extrinsic rewards such as rating or ranking systems to motivate workers. Such rating systems are noisy and provide limited knowledge about workers' preferences and performance. To develop better understanding of worker reliability and trustworthiness in software crowdsourcing, this paper reports an empirical study conducted on more than one year's real-world data from TopCoder, one of the leading software crowdsourcing platforms. To do so, first, we create a bipartite network of active workers based on common task registrations. Then, we use the Clauset-Newman-Moore graph clustering algorithm to identify worker clusters in the network. Finally, we conduct an empirical evaluation to measure and analyze workers' behavior per identified community in the platform by workers' rating. More specifically, workers' behavior is analyzed based on their performances in terms of reliability, trustworthiness, and success; their preferences in terms of efficiency and elasticity; and strategies in terms of comfort, confidence, and deceitfulness. The main result of this study identified four communities of active workers: mixed-ranked, high-ranked, mid-ranked, and low-ranked. This study shows that the low-ranked community associates with the highest reliable workers with an average reliability of 25%, while the mixed-ranked community contains the most trustworthy workers with average trustworthiness of 16%. Such empirical evidence is beneficial to help exploring resourcing options while understanding the relations among unknown resources to improve task success.

cs.SE↗

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 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

An Evolutionary Algorithm for Task Scheduling in Crowdsourced Software Development

The complexity of software tasks and the uncertainty of crowd developer behaviors make it challenging to plan crowdsourced software development (CSD) projects. In a competitive crowdsourcing marketplace, competition for shared worker resources from multiple simultaneously open tasks adds another layer of uncertainty to the potential outcomes of software crowdsourcing. These factors lead to the need for supporting CSD managers with automated scheduling to improve the visibility and predictability of crowdsourcing processes and outcomes. To that end, this paper proposes an evolutionary algorithm-based task scheduling method for crowdsourced software development. The proposed evolutionary scheduling method uses a multiobjective genetic algorithm to recommend an optimal task start date. The method uses three fitness functions, based on project duration, task similarity, and task failure prediction, respectively. The task failure fitness function uses a neural network to predict the probability of task failure with respect to a specific task start date. The proposed method then recommends the best tasks start dates for the project as a whole and each individual task so as to achieve the lowest project failure ratio. Experimental results on 4 projects demonstrate that the proposed method has the potential to reduce project duration by a factor of 33-78%.

cs.SE↗

arXiv2021

CrowdSim: A Hybrid Simulation Model for Failure Prediction in Crowdsourced Software Development

A typical crowdsourcing software development(CSD) marketplace consists of a list of software tasks as service demands and a pool of freelancer developers as service suppliers. Highly dynamic and competitive CSD market places may result in task failure due to unforeseen risks, such as increased competition over shared worker supply, or uncertainty associated with workers' experience and skills, and so on. To improve CSD effectiveness, it is essential to better understand and plan with respect to dynamic worker characteristics and risks associated with CSD processes. In this paper, we present a hybrid simulation model, CrowdSim, to forecast crowdsourcing task failure risk in competitive CSD platforms. CrowdSim is composed of three layered components: the macro-level reflects the overall crowdsourcing platform based on system dynamics,the meso-level represents the task life cycle based on discrete event simulation, and the micro-level models the crowd workers' decision-making processes based on agent-based simulation. CrowdSim is evaluated through three CSD decision scenarios to demonstrate its effectiveness, using a real-world historical dataset and the results demonstrate CrowdSim's potential in empowering crowdsourcing managers to explore crowdsourcing outcomes with respect to different task scheduling options.

cs.SE↗

arXiv2021

Quantum microwave photonics

By harnessing quantum superposition and entanglement, remarkable progress has sprouted over the past three decades from different areas of research in communication computation and simulation. To further improve the processing ability of microwave pho-tonics, here, we have demonstrated a quantum microwave photonic processing system using a low jitter superconducting nanowire single photon detector (SNSPD) and a time-correlated single-photon counting (TCSPC) module. This method uniquely combines extreme optical sensitivity, down to a single-photon level (below -100 dBm), and wide processing bandwidth, twice higher than the transmission bandwidth of the cable. Moreover, benefitted from the trigger, the system can selectively process the desired RF signal and attenuates the other in-tense noise and undesired RF components even the power is 15dB greater than the desired signal power. Using this method we show microwave phase shifting and frequency filtering for the desired RF signal on the single-photon level. Besides its applications in space and under-water communications and testing and qualification of pre-packaged photonic modulators and detectors. This RF signal processing capability at the single-photon level can lead to significant development in the high-speed quantum processing method.

physics.app-ph↗

arXiv2020

Isotropic or anisotropic screening in black phosphorous: can doping tip the balance?

Black phosphorus (BP), a layered van der Waals (vdW) crystal, has unique in-plane band anisotropy and many resulting anisotropy properties such as the effective mass, electron mobility, optical absorption, thermal conductivity and plasmonic dispersion. However, whether anisotropic or isotropic charge screening exist in BP remains a controversial issue. Based on first-principles calculations, we study the screening properties in both of single-layer and bulk BP, especially concerning the role of doping. Without charge doping, the single-layer and bulk-phase BP show slight anisotropic screening. Electron and hole doping can increase the charge screening of BP and significantly change the relative static dielectric tensor elements along two different in-plane directions. We further study the charge density change induced by potassium (K) adatom near the BP surface, under different levels of charge doping. The calculated two-dimensional (2D) charge redistribution patterns also confirm that doping can greatly affect the screening feature and tip the balance between isotropic and anisotropic screening. We corroborate that screening in BP exhibit slight intrinsic anisotropy and doping has significant influence on its screening property.

cond-mat.mtrl-sci↗

arXiv2020

Study on Patterns and Effect of Task Diversity in Software Crowdsourcing

Context: The success of software crowdsourcing depends on steady tasks supply and active worker pool. Existing analysis reveals an average task failure ratio of 15.7% in software crowdsourcing market. Goal: The objective of this study is to empirically investigate patterns and effect of task diversity in software crowdsourcing platform in order to improve the success and efficiency of software crowdsourcing. Method: We propose a conceptual task diversity model, and develop an approach to measuring and analyzing task diversity.More specifically, this includes grouping similar tasks, ranking them based on their competition level and identifying the dominant attributes that distinguish among these levels, and then studying the impact of task diversity on task success and worker performance in crowdsourcing platform. The empirical study is conducted on more than one year's real-world data from TopCoder, the leading software crowdsourcing platform. Results: We identified that monetary prize and task complexity are the dominant attributes that differentiate among different competition levels. Based on these dominant attributes, we found three task diversity patterns (configurations) from workers behavior perspective: responsive to prize, responsive to prize and complexity and over responsive to prize. This study supports that1) responsive to prize configuration provides highest level of task density and workers' reliability in a platform; 2) responsive to prize and complexity configuration leads to attracting high level of trustworthy workers; 3) over responsive to prize configuration results in highest task stability and the lowest failure ratio in the platform for not high similar tasks.

cs.HC↗

arXiv2020

Graph Neural Networks-based Clustering for Social Internet of Things

In this paper, we propose a machine learning process for clustering large-scale social Internet-of-things (SIoT) devices into several groups of related devices sharing strong relations. To this end, we generate undirected weighted graphs based on the historical dataset of IoT devices and their social relations. Using the adjacency matrices of these graphs and the IoT devices' features, we embed the graphs' nodes using a Graph Neural Network (GNN) to obtain numerical vector representations of the IoT devices. The vector representation does not only reflect the characteristics of the device but also its relations with its peers. The obtained node embeddings are then fed to a conventional unsupervised learning algorithm to determine the clusters accordingly. We showcase the obtained IoT groups using two well-known clustering algorithms, specifically the K-means and the density-based algorithm for discovering clusters (DBSCAN). Finally, we compare the performances of the proposed GNN-based clustering approach in terms of coverage and modularity to those of the deterministic Louvain community detection algorithm applied solely on the graphs created from the different relations. It is shown that the framework achieves promising preliminary results in clustering large-scale IoT systems.

cs.SI↗

arXiv2020

Computational Resource Allocation for Edge Computing in Social Internet-of-Things

The heterogeneity of the Internet-of-things (IoT) network can be exploited as a dynamic computational resource environment for many devices lacking computational capabilities. A smart mechanism for allocating edge and mobile computers to match the need of devices requesting external computational resources is developed. In this paper, we employ the concept of Social IoT and machine learning to downgrade the complexity of allocating appropriate edge computers. We propose a framework that detects different communities of devices in SIoT enclosing trustworthy peers having strong social relations. Afterwards, we train a machine learning algorithm, considering multiple computational and non-computational features of the requester as well as the edge computers, to predict the total time needed to process the required task by the potential candidates belonging to the same community of the requester. By applying it to a real-world data set, we observe that the proposed framework provides encouraging results for mobile computer allocation.

cs.SI↗

arXiv2020

A Trustworthy Recruitment Process for Spatial Mobile Crowdsourcing in Large-scale Social IoT

Spatial Mobile Crowdsourcing (SMCS) can be leveraged by exploiting the capabilities of the Social Internet-of-Things (SIoT) to execute spatial tasks. Typically, in SMCS, a task requester aims to recruit a subset of IoT devices and commission them to travel to the task location. However, because of the exponential increase of IoT networks and their diversified devices (e.g., multiple brands, different communication channels, etc.), recruiting the appropriate devices/workers is becoming a challenging task. To this end, in this paper, we develop a recruitment process for SMCS platforms using automated SIoT service discovery to select trustworthy workers satisfying the requester requirements. The method we purpose includes mainly two stages: 1) a worker filtering stage, aiming at reducing the workers' search space to a subset of potential trustworthy candidates using the Louvain community detection algorithm (CD) applied to SIoT relation graphs. Next, 2) a selection process stage that uses an Integer Linear Program (ILP) to determine the final set of selected devices/workers. The ILP maximizes a worker efficiency metric incorporating the skills/specs level, recruitment cost, and trustworthiness level of the recruited IoT devices. Selected experiments analyze the performance of the proposed CD-ILP algorithm using a real-world dataset and show its superiority in providing an effective recruitment strategy compared to an existing stochastic algorithm.

cs.SI↗

arXiv2020

Automated Service Discovery for Social Internet-of-Things Systems

In this paper, we propose to design an automated service discovery process to allow mobile crowdsourcing task requesters select a small set of devices out of a large-scale Internet-of-things (IoT) network to execute their tasks. To this end, we proceed by dividing the large-scale IoT network into several virtual communities whose members share strong social IoT relations. Two community detection algorithms, namely Louvain and order statistics local method (OSLOM) algorithms, are investigated and applied to a real-world IoT dataset to form non-overlapping and overlapping IoT devices groups. Afterwards, a natural language process (NLP)-based approach is executed to handle crowdsourcing textual requests and accordingly find the list of IoT devices capable of effectively accomplishing the tasks. This is performed by matching the NLP outputs, e.g., type of application, location, required trustworthiness level, with the different detected communities. The proposed approach effectively helps in automating and reducing the service discovery procedure and recruitment process for mobile crowdsourcing applications.

cs.SI↗

arXiv2020

Learned Enrichment of Top-View Grid Maps Improves Object Detection

We propose an object detector for top-view grid maps which is additionally trained to generate an enriched version of its input. Our goal in the joint model is to improve generalization by regularizing towards structural knowledge in form of a map fused from multiple adjacent range sensor measurements. This training data can be generated in an automatic fashion, thus does not require manual annotations. We present an evidential framework to generate training data, investigate different model architectures and show that predicting enriched inputs as an additional task can improve object detection performance.

cs.CV↗

arXiv2020

Quantized Auger Recombination of Polaronic Self-trapped Excitons in Bulk Iron Oxide

The Auger recombination in bulk semiconductors can depopulate the charge carriers in a non-radiative way, which, fortunately, only has detrimental impact on optoelectronic device performance under the condition of high carrier density because the restriction arising from concurrent momentum and energy conservation limits the Auger rate. Here, we surprisingly found that the Auger recombination in bulk Fe2O3 films was more efficient than narrow-bandgap high-mobility semiconductors that were supposed to have much higher Auger rate constants than metal oxides. The Auger process in Fe2O3 was ascribed to the Coulombically coupled self-trapped excitons (STEs), which was enhanced by the relaxation of momentum conservation because of the strong spatial localization of these STEs. Furthermore, due to this localization effect the kinetic traces of the STE annihilation for different STE densities exhibited characteristics of quantized Auger recombination, and we demonstrated that these traces could be simultaneously modeled by taking into account the quantized Auger rates.

physics.chem-ph↗

arXiv2019

Homogeneous hierarchical NiMoO4@NiMoO4 nanostructure as a high-performance anode material for electrochemical energy storage

Here we report the extraordinary electrochemical energy storage capability of NiMoO4@NiMoO4 homogeneous hierarchical nanosheet-on-nanowire-arrays (SOWAs) synthesized on nickel substrate by a two-stage hydrothermal process. Comparatively speaking, the SOWAs electrode displays improved electrochemical performances than the bare NiMoO4 nanowire arrays. Such improvements can be ascribed to the characteristic homogeneous hierarchical structure which not only effectively increases the active surface areas for fast charge transfer, but also reduces the electrode resistance significantly by eliminating the potential barrier at the nanowire/nanosheet junction, which is usually an issue in other reported heterogeneous architectures. We further evaluate the performances of the SOWAs by constructing an asymmetric hybrid supercapacitor (ASC) with the SOWAs and activated carbon (AC). The optimized ASC shows excellent electrochemical performances with 47.2 Wh/kg in energy density at 1.38 kW/kg at 0-1.2 V. Moreover, the specific capacity retention can be as high as 91.4% after 4000 cycles, illustrating the remarkable cycling stability of the NiMoO4@NiMoO4//AC ASC device. Our results show that this unique NiMoO4@NiMoO4 SOWAs display great prospect for future energy storage applications

physics.app-ph↗

arXiv2018

Tensor-Based Channel Estimation for Dual-Polarized Massive MIMO Systems

The 3GPP suggests to combine dual polarized (DP) antenna arrays with the double directional (DD) channel model for downlink channel estimation. This combination strikes a good balance between high-capacity communications and parsimonious channel modeling, and also brings limited feedback schemes for downlink channel state information within reach---since such channel can be fully characterized by several key parameters. However, most existing channel estimation work under the DD model has not yet considered DP arrays, perhaps because of the complex array manifold and the resulting difficulty in algorithm design. In this paper, we first reveal that the DD channel with DP arrays at the transmitter and receiver can be naturally modeled as a low-rank tensor, and thus the key parameters of the channel can be effectively estimated via tensor decomposition algorithms. On the theory side, we show that the DD-DP parameters are identifiable under very mild conditions, by leveraging identifiability of low-rank tensors. Furthermore, a compressed tensor decomposition algorithm is developed for alleviating the downlink training overhead. We show that, by using judiciously designed pilot structure, the channel parameters are still guaranteed to be identified via the compressed tensor decomposition formulation even when the size of the pilot sequence is much smaller than what is needed for conventional channel identification methods, such as linear least squares and matched filtering. Numerical simulations are presented to showcase the effectiveness of the proposed methods.

eess.SP↗

arXiv2018

Crowdtesting : When is The Party Over?

Trade-offs such as "how much testing is enough" are critical yet challenging project decisions in software engineering. Most existing approaches adopt risk-driven or value-based analysis to prioritize test cases and minimize test runs. However, none of these is applicable to the emerging crowd testing paradigm where task requesters typically have no control over online crowdworkers's dynamic behavior and uncertain performance. In current practice, deciding when to close a crowdtesting task is largely done by guesswork due to lack of decision support. This paper intends to fill this gap by introducing automated decision support for monitoring and determining appropriate time to close the crowdtesting tasks. First, this paper investigates the necessity and feasibility of close prediction of crowdtesting tasks based on industrial dataset. Then,it designs 8 methods for close prediction, based on various models including the bug trend, bug arrival model, capture-recapture model.Finally, the evaluation is conducted on 218 crowdtesting tasks from one of the largest crowdtesting platforms in China, and the results show that a median of 91% bugs can be detected with 49% saved cost.

cs.SE↗