OpenRaiser/Intern-Atlas huggingface dataset:
Contrastive Lexical Diffusion Coefficient: Quantifying the Stickiness of the Ordinary
Authors: Mohammadzaman Zamani, H. Andrew Schwartz
Venue: WWW
Year: 2021

Lexical phenomena, such as clusters of words, disseminate through social networks at different rates but most models of diffusion focus on the discrete adoption of new lexical phenomena (i.e. new topics or memes). It is possible much of lexical diffusion happens via the changing rates of existing word categories or concepts (those that are already being used, at least to some extent, regularly) rather than new ones. In this study we introduce a new metric, contrastive lexical diffusion (CLD) coefficient, which attempts to measure the degree to which ordinary language (here clusters of common words) catch on over friendship connections over time. For instance topics related to meeting and job are found to be sticky, while negative thinking and emotion, and global events, like ‘school orientation’ were found to be less sticky even though they change rates over time. We evaluate CLD coefficient over both quantitative and qualitative tests, studied over 6 years of language on Twitter. We find CLD predicts the spread of tweets and friendship connections, scores converge with human judgments of lexical diffusion (r=0.92), and CLD coefficients replicate across disjoint networks (r=0.85). Comparing CLD scores can help understand lexical diffusion: positive emotion words appear more diffusive than negative emotions, first-person plurals (we) score higher than other pronouns, and numbers and time appear non-contagious.
DYMOND: DYnamic MOtif-NoDes Network Generative Model
Authors: Giselle Zeno, Timothy La Fond, Jennifer Neville
Venue: WWW
Year: 2021

Motifs, which have been established as building blocks for network structure, move beyond pair-wise connections to capture longer-range correlations in connections and activity. In spite of this, there are few generative graph models that consider higher-order network structures and even fewer that focus on using motifs in models of dynamic graphs. Most existing generative models for temporal graphs strictly grow the networks via edge addition, and the models are evaluated using static graph structure metrics—which do not adequately capture the temporal behavior of the network. To address these issues, in this work we propose DYnamic MOtif-NoDes (DYMOND)—a generative model that considers (i) the dynamic changes in overall graph structure using temporal motif activity and (ii) the roles nodes play in motifs (e.g., one node plays the hub role in a wedge, while the remaining two act as spokes). We compare DYMOND to three dynamic graph generative model baselines on real-world networks and show that DYMOND performs better at generating graph structure and node behavior similar to the observed network. We also propose a new methodology to adapt graph structure metrics to better evaluate the temporal aspect of the network. These metrics take into account the changes in overall graph structure and the individual nodes’ behavior over time.
DeepVista: 16K Panoramic Cinema on Your Mobile Device
Authors: Wenxiao Zhang, Feng Qian, Bo Han, Pan Hui
Venue: WWW
Year: 2021

In this paper, we design, implement, and evaluate , which is to our knowledge the first consumer-class system that streams panoramic videos far beyond the ultra high-definition resolution (up to 16K) to mobile devices, offering truly immersive experiences. Such an immense resolution makes streaming video-on-demand (VoD) content extremely resource-demanding. To tackle this challenge, introduces a novel framework that leverages an edge server to perform efficient, intelligent, and quality-guaranteed content transcoding, by extracting from panoramic frames the viewport stream that will be delivered to the client. To support real-time transcoding of 16K content, employs several key mechanisms such as dual-GPU acceleration, lossless viewport extraction, deep viewport prediction, and a two-layer streaming design. Our extensive evaluations using real users’ viewport movement data indicate that outperforms existing solutions, and can smoothly stream 16K panoramic videos to mobile devices over diverse wireless networks including WiFi, LTE, and mmWave 5G.
DL Inference and Training Optimization Towards Speed and Scale
Authors: Minjia Zhang
Venue: WWW
Year: 2021

The application of deep learning models presents significant improvement to many services and products in Microsoft. However, it is challenging to provide efficient computation and memory capabilities for both DNN workload inference and training given that the model size and complexities keep increasing. From the serving aspect, many DL models suffer from long inference latency and high cost, preventing their deployment in production. On the training side, large-scale model training often requires complex refactoring of models and access to prohibitively expensive GPU clusters, which are not always accessible to many practitioners. We want to deliver solid solutions and systems while exploring the cutting-edge techniques to address these issues. In this talk, I will introduce our experience and lessons from designing and implementing optimizations for both DNN serving and training at large scale with remarkable compute and memory efficiency improvement and infrastructure cost reduction.
A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation
Authors: Yin Zhang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Lichan Hong, Ed H. Chi
Venue: WWW
Year: 2021

Highly skewed long-tail item distribution is very common in recommendation systems. It significantly hurts model performance on tail items. To improve tail-item recommendation, we conduct research to transfer knowledge from head items to tail items, leveraging the rich user feedback in head items and the semantic connections between head and tail items. Specifically, we propose a novel dual transfer learning framework that jointly learns the knowledge transfer from both model-level and item-level: 1. The model-level knowledge transfer builds a generic meta-mapping of model parameters from few-shot to many-shot model. It captures the implicit data augmentation on the model-level to improve the representation learning of tail items. 2. The item-level transfer connects head and tail items through item-level features, to ensure a smooth transfer of meta-mapping from head items to tail items. The two types of transfers are incorporated to ensure the learned knowledge from head items can be well applied for tail item representation learning in the long-tail distribution settings. Through extensive experiments on two benchmark datasets, results show that our proposed dual transfer learning framework significantly outperforms other state-of-the-art methods for tail item recommendation in hit ratio and NDCG. It is also very encouraging that our framework further improves head items and overall performance on top of the gains on tail items.
WiseTrans: Adaptive Transport Protocol Selection for Mobile Web Service
Authors: Jia Zhang, Enhuan Dong, Zili Meng, Yuan Yang, Mingwei Xu, Sijie Yang, Miao Zhang, Yang Yue
Venue: WWW
Year: 2021

To improve the performance of mobile web service, a new transport protocol, QUIC, has been recently proposed. However, for large-scale real-world deployments, deciding whether and when to use QUIC in mobile web service is challenging. Complex temporal correlation of network conditions, high spatial heterogeneity of users in a nationwide deployment, and limited resources on mobile devices all affect the selection of transport protocols. In this paper, we present WiseTrans to adaptively switch transport protocols for mobile web service online and improve the completion time of web requests. WiseTrans introduces machine learning techniques to deal with temporal heterogeneity, makes decisions with historical information to handle spatial heterogeneity, and switches transport protocols at the request level to reach both high performance and acceptable overhead. We implement WiseTrans on two platforms (Android and iOS) in a popular mobile web service application of Baidu. Comprehensive experiments demonstrate that WiseTrans can reduce request completion time by up to 26.5% on average compared to the usage of a single protocol.
Community Value Prediction in Social E-commerce
Authors: Guozhen Zhang, Yong Li, Yuan Yuan, Fengli Xu, Hancheng Cao, Yujian Xu, Depeng Jin
Venue: WWW
Year: 2021

The phenomenal success of the newly-emerging social e-commerce has demonstrated that utilizing social relations is becoming a promising approach to promote e-commerce platforms. In this new scenario, one of the most important problems is to predict the value of a community formed by closely connected users in social networks due to its tremendous business value. However, few works have addressed this problem because of 1) its novel setting and 2) its challenging nature that the structure of a community has complex effects on its value. To bridge this gap, we develop a Multi-scale Structure-aware Community value prediction network (MSC) that jointly models the structural information of different scales, including peer relations, community structure, and inter-community connections, to predict the value of given communities. Specifically, we first proposed a Masked Edge Learning Graph Convolutional Network (MEL-GCN) based on a novel masked propagation mechanism to model peer influence. Then, we design a Pair-wise Community Pooling (PCPool) module to capture critical community structures. Finally, we model the inter-community connections by distinguishing intra-community edges from inter-community edges and employing a Multi-aggregator Framework (MAF). Extensive experiments on a large-scale real-world social e-commerce dataset demonstrate our method’s superior performance over state-of-the-art baselines, with a relative performance gain of 11.40%, 10.01%, and 10.97% in MAE, RMSE, and NRMSE, respectively. Further ablation study shows the effectiveness of our designed components. Our code and dataset are available1.
Distributed Fog Computing Based on Improved LT codes for Deep Learning in Web of Things
Authors: Lei Zhang, Jie Liu, Fuquan Zhang, Yu Mao
Venue: WWW
Year: 2021

With the rapid development of the Web of Things, there have been a lot of sensors deployed. Advanced knowledge can be achieved by deep learning method and easier integration with open Web standards. A large number of the data generated by sensors required extra processing resources due to the limited resources of the sensors. Due to the limitation of bandwidth or requirement of low latency, it is impossible to transfer such large amounts of data to cloud servers for processing. Thus, the concept of distributed fog computing has been proposed to process such big data into knowledge in real-time. Large scale fog computing system is built using cheap devices, denotes as fog nodes. Therefore, the resiliency to fog node failures should be considered in design of distributed fog computing. LT codes (LTC) have important applications in the design of modern distributed computing, which can reduce the latency of the computing tasks, such as matrix multiplication in deep learning methods. In this paper, we consider that fog nodes may be failure, and an improved LT codes are applied to matrix multiplication of distributed fog computing process to reduce latency. Numerical results show that the improved LTC based scheme can reduce average overhead and degree simultaneously, which reduce the latency and computation complexity of distributed fog computing.
Adapting to Context-Aware Knowledge in Natural Conversation for Multi-Turn Response Selection
Authors: Chen Zhang, Hao Wang, Feijun Jiang, Hongzhi Yin
Venue: WWW
Year: 2021

Virtual assistants aim to build a human-like conversational agent. However, current human-machine conversations still cannot make users feel intelligent enough to build a continued dialog over time. Some responses from agents are usually inconsistent, uninformative, less-engaging and even memoryless. In recent years, most researchers have tried to employ conversation context and external knowledge, e.g. wiki pages and knowledge graphs, into the model which only focuses on solving some special conversation problems in local perspectives. Few researchers are dedicated to the whole capability of the conversational agent which is endowed with abilities of not only passively reacting the conversation but also proactively leading the conversation. In this paper, we first explore the essence of conversations among humans by analyzing real dialog records. We find that some conversations revolve around the same context and topic, and some require additional information or even move on to a new topic. Base on that, we conclude three conversation modes shown in Figure 1 and try to solve how to adapt to them for a continuous conversation. To this end, we define “Adaptive Knowledge-Grounded Conversations” (AKGCs) where the knowledge is to ground the conversation within a multi-turn context by adapting to three modes. To achieve AKGC, a model called MNDB is proposed to model natural dialog behaviors for multi-turn response selection. To ensure a consistent response, MNDB constructs a multi-turn context flow. Then, to mimic user behaviors of incorporating knowledge in natural conversations, we design a ternary-grounding network along with the context flow. In this network, to gain the ability to adapt to diversified conversation modes, we exploit multi-view semantical relations among response candidates, context and knowledge. Thus, three adaptive matching signals are extracted for final response selection. Evaluation results on two benchmarks indicate that MNDB can significantly outperform state-of-the-art models.
ReACt: A Resource-centric Access Control System for Web-app Interactions on Android
Authors: Xin Zhang, Yifan Zhang
Venue: WWW
Year: 2021

We identify and survey five mechanisms through which web content interacts with mobile apps. While useful, these web-app interaction mechanisms cause various notable security vulnerabilities on mobile apps or web content. The root cause is lack of proper access control mechanisms for web-app interactions on mobile OSes. Existing solutions usually adopt either an origin-centric design or a code-centric design, and suffer from one or several of the following limitations: coarse protection granularity, poor flexibility in terms of access control policy establishment, and incompatibility with existing apps/OSes due to the need of modifying the apps and/or the underlying OS. More importantly, none of the existing works can organically deal with all the five web-app interaction mechanisms. In this paper, we propose ReACt, a novel Resource-centric Access Control design that can coherently work with all the web-app interaction mechanisms while addressing the above-mentioned limitations. We have implemented a prototype system on Android, and performed extensive evaluation on it. The evaluation results show that our system works well with existing commercial off-the-shelf Android apps and different versions of Android OS, and it can achieve the design goals with small overhead.
Unsupervised Semantic Association Learning with Latent Label Inference
Authors: Yanzhao Zhang, Richong Zhang, Jaein Kim, Xudong Liu, Yongyi Mao
Venue: WWW
Year: 2021

In this paper, we unify a diverse set of learning tasks in NLP, semantic retrieval and related areas, under a common umbrella, which we call unsupervised semantic association learning (USAL). Examples of this generic task include word sense disambiguation, answer selection and question retrieval. We then present a novel modeling framework to tackle such tasks. The framework introduces, under the deep learning paradigm, a latent label indexing the true target in the candidate target set. An EM algorithm is then developed for learning the deep model and inferring the latent variables, principled under variational techniques and noise contrastive estimation. We apply the model and algorithm to several semantic retrieval benchmark tasks and the superior performance of the proposed approach is demonstrated via empirical studies.
Temporal Analysis of the Entire Ethereum Blockchain Network
Authors: Lin Zhao, Sourav Sen Gupta, Arijit Khan, Robby Luo
Venue: WWW
Year: 2021

With over 42 billion USD market capitalization (October 2020), Ethereum is the largest public blockchain that supports smart contracts. Recent works have modeled transactions, tokens, and other interactions in the Ethereum blockchain as static graphs to provide new observations and insights by conducting relevant graph analysis. Surprisingly, there is much less study on the evolution and temporal properties of these networks. In this paper, we investigate the evolutionary nature of Ethereum interaction networks from a temporal graphs perspective. We study the growth rate and model of four Ethereum blockchain networks, active lifespan and update rate of high-degree vertices. We detect anomalies based on temporal changes in global network properties, and forecast the survival of network communities in succeeding months leveraging on the relevant graph features and machine learning models.
AutoDim: Field-aware Embedding Dimension Searchin Recommender Systems
Authors: Xiangyu Zhao, Haochen Liu, Hui Liu, Jiliang Tang, Weiwei Guo, Jun Shi, Sida Wang, Huiji Gao, Bo Long
Venue: WWW
Year: 2021

Practical large-scale recommender systems usually contain thousands of feature fields from users, items, contextual information, and their interactions. Most of them empirically allocate a unified dimension to all feature fields, which is memory inefficient. Thus it is highly desired to assign various embedding dimensions to different feature fields according to their importance and predictability. Due to the large amounts of feature fields and the nuanced relationship between embedding dimensions with feature distributions and neural network architectures, manually allocating embedding dimensions in practical recommender systems can be challenging. To this end, we propose an AutoML-based framework (AutoDim) in this paper, which can automatically select dimensions for different feature fields in a data-driven fashion. Specifically, we first proposed an end-to-end differentiable framework that can calculate the weights over various dimensions in a soft and continuous manner for feature fields, and an AutoML-based optimization algorithm; then, we derive a hard and discrete embedding component architecture according to the maximal weights and retrain the whole recommender framework. We conduct extensive experiments on benchmark datasets to validate the effectiveness of AutoDim.
Curriculum CycleGAN for Textual Sentiment Domain Adaptation with Multiple Sources
Authors: Sicheng Zhao, Yang Xiao, Jiang Guo, Xiangyu Yue, Jufeng Yang, Ravi Krishna, Pengfei Xu, Kurt Keutzer
Venue: WWW
Year: 2021

Sentiment analysis of user-generated reviews or comments on products and services in social networks can help enterprises to analyze the feedback from customers and take corresponding actions for improvement. To mitigate large-scale annotations on the target domain, domain adaptation (DA) provides an alternate solution by learning a transferable model from other labeled source domains. Existing multi-source domain adaptation (MDA) methods either fail to extract some discriminative features in the target domain that are related to sentiment, neglect the correlations of different sources and the distribution difference among different sub-domains even in the same source, or cannot reflect the varying optimal weighting during different training stages. In this paper, we propose a novel instance-level MDA framework, named curriculum cycle-consistent generative adversarial network (C-CycleGAN), to address the above issues. Specifically, C-CycleGAN consists of three components: (1) pre-trained text encoder which encodes textual input from different domains into a continuous representation space, (2) intermediate domain generator with curriculum instance-level adaptation which bridges the gap across source and target domains, and (3) task classifier trained on the intermediate domain for final sentiment classification. C-CycleGAN transfers source samples at instance-level to an intermediate domain that is closer to the target domain with sentiment semantics preserved and without losing discriminative features. Further, our dynamic instance-level weighting mechanisms can assign the optimal weights to different source samples in each training stage. We conduct extensive experiments on three benchmark datasets and achieve substantial gains over state-of-the-art DA approaches. Our source code is released at: https://github.com/WArushrush/Curriculum-CycleGAN.
UserSim: User Simulation via Supervised GenerativeAdversarial Network
Authors: Xiangyu Zhao, Long Xia, Lixin Zou, Hui Liu, Dawei Yin, Jiliang Tang
Venue: WWW
Year: 2021

With the recent advances in Reinforcement Learning (RL), there have been tremendous interests in employing RL for recommender systems. However, directly training and evaluating a new RL-based recommendation algorithm needs to collect users’ real-time feedback in the real system, which is time/effort consuming and could negatively impact users’ experiences. Thus, it calls for a user simulator that can mimic real users’ behaviors to pre-train and evaluate new recommendation algorithms. Simulating users’ behaviors in a dynamic system faces immense challenges – (i) the underlying item distribution is complex, and (ii) historical logs for each user are limited. In this paper, we develop a user simulator based on a Generative Adversarial Network (GAN). To be specific, the generator captures the underlying distribution of users’ historical logs and generates realistic logs that can be considered as augmentations of real logs; while the discriminator not only distinguishes real and fake logs but also predicts users’ behaviors. The experimental results based on benchmark datasets demonstrate the effectiveness of the proposed simulator.
Taxonomy-aware Learning for Few-Shot Event Detection
Authors: Jianming Zheng, Fei Cai, Wanyu Chen, Wengqiang Lei, Honghui Chen
Venue: WWW
Year: 2021

Event detection classifies unlabeled sentences into event labels, which can benefit numerous applications, including information retrieval, question answering and script learning. One of the major obstacles to event detection in reality is insufficient training data. To deal with the low-resources problem, we investigate few-shot event detection in this paper and propose TaLeM, a novel taxonomy-aware learning model, consisting of two components, i.e., the taxonomy-aware self-supervised learning framework (TaSeLF) and the taxonomy-aware prototypical networks (TaPN). Specifically, TaSeLF mines the taxonomy-aware distance relations to increases the training examples, which alleviates the generalization bottleneck brought by the insufficient data. TaPN introduces the Poincaré embeddings to represent the label taxonomy, and integrates them into a task-adaptive projection networks, which tackles problems of the class centroids distribution and the taxonomy-aware embedding distribution in the vanilla prototypical networks. Extensive experiments in the four types of meta tasks demonstrate the superiority of our proposal over the strong baselines, and further verify the effectiveness and importance of modeling the label taxonomy. Besides, TaSeLF can be a flexible plug-in for the other taxonomy-based few-shot classification tasks.