arXiv subjects

Xiaowei Chen

Publications and source records attributed to Xiaowei Chen.

31 records · Page 2Linked to original sources

arXiv2023

Do Persuasive Designs Make Smartphones More Addictive? -- A Mixed-Methods Study on Chinese University Students

Persuasive designs become prevalent on smartphones, and an increasing number of users report having problematic smartphone use behaviours. Persuasive designs in smartphones might be accountable for the development and reinforcement of such problematic use. This paper uses a mixed-methods approach to study the relationship between persuasive designs and problematic smartphone use: (1) questionnaires (N=183) to investigate the proportion of participants having multiple problematic smartphone use behaviours and smartphone designs and applications (apps) that they perceived affecting their attitudes and behaviours, and (2) interviews (N=10) to deepen our understanding of users' observations and evaluations of persuasive designs. 25\% of the participants self-reported having multiple problematic smartphone use behaviours, with short video, social networking, game and learning apps perceived as most attitude and behaviour-affecting. Interviewees identified multiple persuasive designs in most of these apps and stated that persuasive designs prolonged their screen time, reinforced phone-checking habits, and caused distractions. Overall, this study provides evidence to argue that persuasive designs contribute to problematic smartphone use, potentially making smartphones more addictive. We end our study by discussing the ethical implications of persuasive designs that became salient in our study.

cs.HC↗

arXiv2023

DisPad: Flexible On-Body Displacement of Fabric Sensors for Robust Joint-Motion Tracking

The last few decades have witnessed an emerging trend of wearable soft sensors; however, there are important signal-processing challenges for soft sensors that still limit their practical deployment. They are error-prone when displaced, resulting in significant deviations from their ideal sensor output. In this work, we propose a novel prototype that integrates an elbow pad with a sparse network of soft sensors. Our prototype is fully bio-compatible, stretchable, and wearable. We develop a learning-based method to predict the elbow orientation angle and achieve an average tracking error of 9.82 degrees for single-user multi-motion experiments. With transfer learning, our method achieves the average tracking errors of 10.98 degrees and 11.81 degrees across different motion types and users, respectively. Our core contributions lie in a solution that realizes robust and stable human joint motion tracking across different device displacements.

cs.HC↗

arXiv2021

Distance and Hop-wise Structures Encoding Enhanced Graph Attention Networks

Numerous works have proven that existing neighbor-averaging Graph Neural Networks cannot efficiently catch structure features, and many works show that injecting structure, distance, position or spatial features can significantly improve performance of GNNs, however, injecting overall structure and distance into GNNs is an intuitive but remaining untouched idea. In this work, we shed light on the direction. We first extracting hop-wise structure information and compute distance distributional information, gathering with node's intrinsic features, embedding them into same vector space and then adding them up. The derived embedding vectors are then fed into GATs(like GAT, AGDN) and then Correct and Smooth, experiments show that the DHSEGATs achieve competitive result. The code is available at https://github.com/hzg0601/DHSEGATs.

cs.LG↗

arXiv2021

Semantic Borrowing for Generalized Zero-Shot Learning

Generalized zero-shot learning (GZSL) is one of the most realistic but challenging problems due to the partiality of the classifier to supervised classes, especially under the class-inductive instance-inductive (CIII) training setting, where testing data are not available. Instance-borrowing methods and synthesizing methods solve it to some extent with the help of testing semantics, but therefore neither can be used under CIII. Besides, the latter require the training process of a classifier after generating examples. In contrast, a novel non-transductive regularization under CIII called Semantic Borrowing (SB) for improving GZSL methods with compatibility metric learning is proposed in this paper, which not only can be used for training linear models, but also nonlinear ones such as artificial neural networks. This regularization item in the loss function borrows similar semantics in the training set, so that the classifier can model the relationship between the semantics of zero-shot and supervised classes more accurately during training. In practice, the information of semantics of unknown classes would not be available for training while this approach does NOT need it. Extensive experiments on GZSL benchmark datasets show that SB can reduce the partiality of the classifier to supervised classes and improve the performance of generalized zero-shot classification, surpassing inductive GZSL state of the arts.

cs.LG↗

arXiv2021

Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning

Multi-layered network exploration (MuLaNE) problem is an important problem abstracted from many applications. In MuLaNE, there are multiple network layers where each node has an importance weight and each layer is explored by a random walk. The MuLaNE task is to allocate total random walk budget $B$ into each network layer so that the total weights of the unique nodes visited by random walks are maximized. We systematically study this problem from offline optimization to online learning. For the offline optimization setting where the network structure and node weights are known, we provide greedy based constant-ratio approximation algorithms for overlapping networks, and greedy or dynamic-programming based optimal solutions for non-overlapping networks. For the online learning setting, neither the network structure nor the node weights are known initially. We adapt the combinatorial multi-armed bandit framework and design algorithms to learn random walk related parameters and node weights while optimizing the budget allocation in multiple rounds, and prove that they achieve logarithmic regret bounds. Finally, we conduct experiments on a real-world social network dataset to validate our theoretical results.

cs.LG↗

arXiv2020

Pandemic risk management: resources contingency planning and allocation

Repeated history of pandemics, such as SARS, H1N1, Ebola, Zika, and COVID-19, has shown that pandemic risk is inevitable. Extraordinary shortages of medical resources have been observed in many parts of the world. Some attributing factors include the lack of sufficient stockpiles and the lack of coordinated efforts to deploy existing resources to the location of greatest needs. The paper investigates contingency planning and resources allocation from a risk management perspective, as opposed to the prevailing supply chain perspective. The key idea is that the competition of limited critical resources is not only present in different geographical locations but also at different stages of a pandemic. This paper draws on an analogy between risk aggregation and capital allocation in finance and pandemic resources planning and allocation for healthcare systems. The main contribution is to introduce new strategies for optimal stockpiling and allocation balancing spatio-temporal competitions of medical supply and demand.

q-fin.RM↗

arXiv2020

Sampling Graphlets of Multi-layer Networks: A Restricted Random Walk Approach

Graphlets are induced subgraph patterns that are crucial to the understanding of the structure and function of a large network. A lot of efforts have been devoted to calculating graphlet statistics where random walk based approaches are commonly used to access restricted graphs through the available application programming interfaces (APIs). However, most of them merely consider individual networks while overlooking the strong coupling between different networks. In this paper, we estimate the graphlet concentration in multi-layer networks with real-world applications. An inter-layer edge connects two nodes in different layers if they belong to the same person. The access to a multi-layer network is restrictive in the sense that the upper layer allows random walk sampling, whereas the nodes of lower layers can be accessed only though the inter-layer edges and only support random node or edge sampling. To cope with this new challenge, we define a suit of two-layer graphlets and propose a novel random walk sampling algorithm to estimate the proportion of all the 3-node graphlets. An analytical bound on the sampling steps is proved to guarantee the convergence of our unbiased estimator. We further generalize our algorithm to explore the tradeoff between the estimated accuracies of different graphlets when the sample size is split on different layers. Experimental evaluation on real-world and synthetic multi-layer networks demonstrate the accuracy and high efficiency of our unbiased estimators.

cs.SI↗

arXiv2020

Evolution of Ethereum: A Temporal Graph Perspective

Ethereum is one of the most popular blockchain systems that supports more than half a million transactions every day and fosters miscellaneous decentralized applications with its Turing-complete smart contract machine. Whereas it remains mysterious what the transaction pattern of Ethereum is and how it evolves over time. In this paper, we study the evolutionary behavior of Ethereum transactions from a temporal graph point of view. We first develop a data analytics platform to collect external transactions associated with users as well as internal transactions initiated by smart contracts. Three types of temporal graphs, user-to-user, contract-to-contract and user-contract graphs, are constructed according to trading relationship and are segmented with an appropriate time window. We observe a strong correlation between the size of user-to-user transaction graph and the average Ether price in a time window, while no evidence of such linkage is shown at the average degree, average edge weights and average triplet closure duration. The macroscopic and microscopic burstiness of Ethereum transactions is validated. We analyze the Gini indexes of the transaction graphs and the user wealth in which Ethereum is found to be very unfair since the very beginning, in a sense, "the rich is already very rich".

cs.SI↗

arXiv2019

Super-resolution Imaging of the Fluorescent Dipole Assembly with Polarized Structured Illumination Microscopy

Fluorescence polarization microscopy images both the intensity and orientation of fluorescent dipoles, which plays a vital role in studying the molecular structure and dynamics of bio-complex. However, it is difficult to resolve the dipole assemblies on the subcellular structure and their dynamics in living cells with super-resolution. Here we report polarized structured illumination microscopy (pSIM), which decouples the entangled spatial and angular structured illumination through interpreting the dipoles in spatio-angular hyperspace. We demonstrate its application on a series of biological filamentous systems such as cytoskeleton networks and lambda-DNA, and report the dynamics of short actin sliding through myosin-coated surface. Further, pSIM reveals "side-by-side" organization of the actin ring structure in the membrane-associated periodic skeleton in hippocampal neurons. It also images the dipole dynamics of green fluorescent proteins labeled to the microtubules in live U2OS cells. pSIM can be applied directly to a large variety of commercial or home-built SIM systems.

physics.optics↗

arXiv2019

CEP-stable high-energy ytterbium doped fiber amplifier

We report on the CEP stabilization of an Yb-doped fiber amplifier system delivering 30 microjoules, 100 fs pulses at 100 kHz repetition rate. A single shot, every shot, measurement of the CEP stability based on a simple f-2f interferometer is performed, yielding a CEP standard deviation of 320 mrad rms over 1 s. Long-term stability is also assessed, with 380 mrad measured over one hour. This level of performance is allowed by a hybrid architecture including a passively CEP-stabilized front-end based on difference frequency generation, and an active CEP stabilization loop for the fiber amplifier system, acting on a telecom-grade integrated LiNbO3 phase modulator. These results demonstrate the full compatibility of Yb-doped high repetition rate laser for attoscience.

physics.app-ph↗

arXiv2018

Community Exploration: From Offline Optimization to Online Learning

We introduce the community exploration problem that has many real-world applications such as online advertising. In the problem, an explorer allocates limited budget to explore communities so as to maximize the number of members he could meet. We provide a systematic study of the community exploration problem, from offline optimization to online learning. For the offline setting where the sizes of communities are known, we prove that the greedy methods for both of non-adaptive exploration and adaptive exploration are optimal. For the online setting where the sizes of communities are not known and need to be learned from the multi-round explorations, we propose an `upper confidence' like algorithm that achieves the logarithmic regret bounds. By combining the feedback from different rounds, we can achieve a constant regret bound.

cs.LG↗

arXiv2018

Citation Analysis of Innovative ICT and Advances of Governance (2008-2017)

This paper opens by introducing the Internet Plus Government (IPG), a new government initiative emerging in the last decade. To understand benefits and challenges associated with this initiative worldwide, we conducted analyses on research articles published in the e-governance area between 2008 and 2017. Content analysis and citation analysis were performed on 2105 articles to address three questions: (1) What types of new ICT have been adopted in the IPG initiative in the past decade? (2) How did scholars investigate interactions between the new ICTs and governance core to IPG? (3) How did the new ICTs interact and shape while also being shaped by the evolution of governance in the past decade? Our analysis suggests that IPG initiative has enriched the government information infrastructure. It presented opportunities to accumulate and use huge volume of data for better decision making and proactive government-citizen interaction. At the same time, the advance of open data, the widespread use of social media and the potential of data analytics also generated great pressure to address challenging questions and issues in the domain of e-democracy.

cs.SI↗

arXiv2016

A General Framework for Estimating Graphlet Statistics via Random Walk

Graphlets are induced subgraph patterns and have been frequently applied to characterize the local topology structures of graphs across various domains, e.g., online social networks (OSNs) and biological networks. Discovering and computing graphlet statistics are highly challenging. First, the massive size of real-world graphs makes the exact computation of graphlets extremely expensive. Secondly, the graph topology may not be readily available so one has to resort to web crawling using the available application programming interfaces (APIs). In this work, we propose a general and novel framework to estimate graphlet statistics of "any size". Our framework is based on collecting samples through consecutive steps of random walks. We derive an analytical bound on the sample size (via the Chernoff-Hoeffding technique) to guarantee the convergence of our unbiased estimator. To further improve the accuracy, we introduce two novel optimization techniques to reduce the lower bound on the sample size. Experimental evaluations demonstrate that our methods outperform the state-of-the-art method up to an order of magnitude both in terms of accuracy and time cost.

cs.SI↗