Machine Learning for 5G and Beyond:From ModelBased to Data-Driven Mobile Wireless Networks

During the past few decades,mobile wireless communications have experienced four generations of technological revolution,namely from 1 G to 4 G,and the deployment of the latest 5 G networks is expected to take place in 2019.One fundamental question is how we can push forward the development of mobile wireless communications while it has become an extremely complex and sophisticated system.We believe that the answer lies in the huge volumes of data produced by the network itself,and machine learning may become a key to exploit such information.In this paper,we elaborate why the conventional model-based paradigm,which has been widely proved useful in pre-5 G networks,can be less efficient or even less practical in the future 5 G and beyond mobile networks.Then,we explain how the data-driven paradigm,using state-of-the-art machine learning techniques,can become a promising solution.At last,we provide a typical use case of the data-driven paradigm,i.e.,proactive load balancing,in which online learning is utilized to adjust cell configurations in advance to avoid burst congestion caused by rapid traffic changes.

What is Discussed about COVID-19:A Multi-Modal Framework for Analyzing Microblogs from Sina Weibo without Human Labeling

Starting from late 2019,the new coronavirus disease(COVID-19)has become a global crisis.With the development of online social media,people prefer to express their opinions and discuss the latest news online.We have witnessed the positive influence of online social media,which helped citizens and governments track the development of this pandemic in time.It is necessary to apply artificial intelligence(AI)techniques to online social media and automatically discover and track public opinions posted online.In this paper,we take Sina Weibo,the most widely used online social media in China,for analysis and experiments.We collect multi-modal microblogs about COVID-19 from 2020/1/1 to 2020/3/31 with a web crawler,including texts and images posted by users.In order to effectively discover what is being discussed about COVID-19 without human labeling,we propose a unified multi-modal framework,including an unsupervised short-text topic model to discover and track bursty topics,and a self-supervised model to learn image features so that we can retrieve related images about COVID-19.Experimental results have shown the effectiveness and superiority of the proposed models,and also have shown the considerable application prospects for analyzing and tracking public opinions about COVID-19.

Pairwise tagging framework for end-to-end emotion-cause pair extraction

Emotion-cause pair extraction(ECPE)aims to extract all the pairs of emotions and corresponding causes in a document.It generally contains three subtasks,emotions extraction,causes extraction,and causal relations detection between emotions and causes.Existing works adopt pipelined approaches or multi-task learning to address the ECPE task.However,the pipelined approaches easily suffer from error propagation in real-world scenarios.Typical multi-task learning cannot optimize all tasks globally and may lead to suboptimal extraction results.To address these issues,we propose a novel framework,Pairwise Tagging Framework(PTF),tackling the complete emotion-cause pair extraction in one unified tagging task.Unlike prior works,PTF innovatively transforms all subtasks of ECPE,i.e.,emotions extraction,causes extraction,and causal relations detection between emotions and causes,into one unified clause-pair tagging task.Through this unified tagging task,we can optimize the ECPE task globally and extract more accurate emotion-cause pairs.To validate the feasibility and effectiveness of PTF,we design an end-to-end PTF-based neural network and conduct experiments on the ECPE benchmark dataset.The experimental results show that our method outperforms pipelined approaches significantly and typical multi-task learning approaches.

CA-DTS:A Distributed and Collaborative Task Scheduling Algorithm for Edge Computing Enabled Intelligent Road Network

Edge computing enabled Intelligent Road Network(EC-IRN)provides powerful and convenient computing services for vehicles and roadside sensing devices.The continuous emergence of transportation applications has caused a huge burden on roadside units(RSUs)equipped with edge servers in the Intelligent Road Network(IRN).Collaborative task scheduling among RSUs is an effective way to solve this problem.However,it is challenging to achieve collaborative scheduling among different RSUs in a completely decentralized environment.In this paper,we first model the interactions involved in task scheduling among distributed RSUs as a Markov game.Given that multi-agent deep reinforcement learning(MADRL)is a promising approach for the Markov game in decision optimization,we propose a collaborative task scheduling algorithm based on MADRL for EC-IRN,named CA-DTS,aiming to minimize the long-term average delay of tasks.To reduce the training costs caused by trial-and-error,CA-DTS specially designs a reward function and utilizes the distributed deployment and collective training architecture of counterfactual multi-agent policy gradient(COMA).To improve the stability of performance in large-scale environments,CA-DTS takes advantage of the action semantics network(ASN)to facilitate cooperation among multiple RSUs.The evaluation results of both the testbed and simulation demonstrate the effectiveness of our proposed algorithm.Compared with the baselines,CA-DTS can achieve convergence about 35%faster,and obtain average task delay that is lower by approximately 9.4%,9.8%,and 6.7%,in different scenarios with varying numbers of RSUs,service types,and task arrival rates,respectively.

Minimal Gated Unit for Recurrent Neural Networks

Recurrent neural networks(RNN) have been very successful in handling sequence data.However,understanding RNN and finding the best practices for RNN learning is a difficult task,partly because there are many competing and complex hidden units,such as the long short-term memory(LSTM) and the gated recurrent unit(GRU).We propose a gated unit for RNN,named as minimal gated unit(MGU),since it only contains one gate,which is a minimal design among all gated hidden units.The design of MGU benefits from evaluation results on LSTM and GRU in the literature.Experiments on various sequence data show that MGU has comparable accuracy with GRU,but has a simpler structure,fewer parameters,and faster training.Hence,MGU is suitable in RNN s applications.Its simple architecture also means that it is easier to evaluate and tune,and in principle it is easier to study MGU s properties theoretically and empirically.

Software Defect Detection with ROCUS

Software defect detection aims to automatically identify defective software modules for efficient software test in order to improve the quality of a software system.Although many machine learning methods have been successfully applied to the task,most of them fail to consider two practical yet important issues in software defect detection.First,it is rather difficult to collect a large amount of labeled training data for learning a well-performing model;second,in a software system there are usually much fewer defective modules than defect-free modules,so learning would have to be conducted over an imbalanced data set.In this paper,we address these two practical issues simultaneously by proposing a novel semi-supervised learning approach named Rocus.This method exploits the abundant unlabeled examples to improve the detection accuracy,as well as employs under-sampling to tackle the class-imbalance problem in the learning process.Experimental results of real-world software defect detection tasks show that Rocus is effective for software defect detection.Its performance is better than a semi-supervised learning method that ignores the class-imbalance nature of the task and a class-imbalance learning method that does not make effective use of unlabeled data.

Intelligent Development Environment and Software Knowledge Graph

软件聪明的开发在软件设计成为了最重要的研究趋势之一。在这篇论文，我们提出了二个关键概念聪明的开发环境(IntelliDE ) 和软件知识图第一次。IntelliDE 是在软件，大数据被聚集的一个生态系统， mined 并且分析了在软件开发的生命周期提供聪明的帮助。我们在场它的建筑学并且讨论它的关键研究问题和挑战。软件知识图是一个软件知识代表和管理框架，它在 IntelliDE 起一个重要作用。我们学习它的概念并且介绍一些水泥详细说明并且显示出它怎么能被构造并且利用的例子。

Derivative-free reinforcement learning:a review

Reinforcement learning is about learning agent models that make the best sequential decisions in unknown environments.In an unknown environment,the agent needs to explore the environment while exploiting the collected information,which usually forms a sophisticated problem to solve.Derivative-free optimization,meanwhile,is capable of solving sophisticated problems.It commonly uses a sampling-andupdating framework to iteratively improve the solution,where exploration and exploitation are also needed to be well balanced.Therefore,derivative-free optimization deals with a similar core issue as reinforcement learning,and has been introduced in reinforcement learning approaches,under the names of learning classifier systems and neuroevolution/evolutionary reinforcement learning.Although such methods have been developed for decades,recently,derivative-free reinforcement learning exhibits attracting increasing attention.However,recent survey on this topic is still lacking.In this article,we summarize methods of derivative-free reinforcement learning to date,and organize the methods in aspects including parameter updating,model selection,exploration,and parallel/distributed methods.Moreover,we discuss some current limitations and possible future directions,hoping that this article could bring more attentions to this topic and serve as a catalyst for developing novel and efficient approaches.

The complexity of variable minimal formulas

Based on the common properties of logic formulas:equivalence and satisfiability,the concept of variable minimal formulas with property preservation is introduced.A formula is variable minimal if the resulting sub-formulas with any variable omission will change the given property.Some theoretical results of two classes:variable minimal equivalence(VME) and variable minimal satisfiability(VMS) are studied.We prove that VME is NP-complete,and VMS is in DP and coNP-hard.

Self-Supervised Task Augmentation for Few-Shot Intent Detection

Few-shot intent detection is a practical challenge task,because new intents are frequently emerging and collecting large-scale data for them could be costly.Meta-learning,a promising technique for leveraging data from previous tasks to enable efficient learning of new tasks,has been a popular way to tackle this problem.However,the existing meta-learning models have been evidenced to be overfitting when the meta-training tasks are insufficient.To overcome this challenge,we present a novel self-supervised task augmentation with meta-learning framework,namely STAM.Firstly,we introduce the task augmentation,which explores two different strategies and combines them to extend meta-training tasks.Secondly,we devise two auxiliary losses for integrating self-supervised learning into meta-learning to learn more generalizable and transferable features.Experimental results show that STAM can achieve consistent and considerable performance improvement to existing state-of-the-art methods on four datasets.

ZenLDA: Large-Scale Topic Model Training on Distributed Data-Parallel Platform

Recently, topic models such as Latent Dirichlet Allocation(LDA) have been widely used in large-scale web mining. Many large-scale LDA training systems have been developed, which usually prefer a customized design from top to bottom with sophisticated synchronization support. We propose an LDA training system named ZenLDA, which follows a generalized design for the distributed data-parallel platform. The novelty of ZenLDA consists of three main aspects:(1) it converts the commonly used serial Collapsed Gibbs Sampling(CGS) inference algorithm to a Monte-Carlo Collapsed Bayesian(MCCB) estimation method, which is embarrassingly parallel;(2)it decomposes the LDA inference formula into parts that can be sampled more efficiently to reduce computation complexity;(3) it proposes a distributed LDA training framework, which represents the corpus as a directed graph with the parameters annotated as corresponding vertices and implements ZenLDA and other well-known inference methods based on Spark. Experimental results indicate that MCCB converges with accuracy similar to that of CGS, while running much faster. On top of MCCB, the ZenLDA formula decomposition achieved the fastest speed among other well-known inference methods. ZenLDA also showed good scalability when dealing with large-scale topic models on the data-parallel platform. Overall, ZenLDA could achieve comparable and even better computing performance with state-of-the-art dedicated systems.

A Semi-Supervised Attention Model for Identifying Authentic Sneakers

To protect consumers and those who manufacture and sell the products they enjoy,it is important to develop convenient tools to help consumers distinguish an authentic product from a counterfeit one.The advancement of deep learning techniques for fine-grained object recognition creates new possibilities for genuine product identification.In this paper,we develop a Semi-Supervised Attention(SSA)model to work in conjunction with a large-scale multiple-source dataset named YSneaker,which consists of sneakers from various brands and their authentication results,to identify authentic sneakers.Specifically,the SSA model has a self-attention structure for different images of a labeled sneaker and a novel prototypical loss is designed to exploit unlabeled data within the data structure.The model draws on the weighted average of the output feature representations,where the weights are determined by an additional shallow neural network.This allows the SSA model to focus on the most important images of a sneaker for use in identification.A unique feature of the SSA model is its ability to take advantage of unlabeled data,which can help to further minimize the intra-class variation for more discriminative feature embedding.To validate the model,we collect a large number of labeled and unlabeled sneaker images and perform extensive experimental studies.The results show that YSneaker together with the proposed SSA architecture can identify authentic sneakers with a high accuracy rate.

Launch of the Excellent Young Computer Scientists Forum

Since Volume 10, 2016, Frontiers of Computer Science established an "NSFC Excellent Young Scholars Forum", which aims to publishing articles from recipients of the NSFC (National Science Foundation of China) Excellent Young Scholars Program. During the past three years, 37 articles have been published and this forum has received very positive feedbacks from readers.

Integrating heterogeneous thesauruses for Chinese synonyms

1 Introduction L exical semantic resource plays an important role in natural language processing.So far,many lexical semantic resources have been developed by the world-wide linguists,such as WordNet[1],ConceptNet[2]in English,HowNet[3],Chinese Concept Dictionary(CCD)[4],and Tongyici-Cilin(Cilin)[5]in Chinese,Diferent recourses usually have different focuses and structures,while some of them are also closely rclated and could be complementary to each other.As a result,the integration of several resources may be more useful than only using one of them for a certain purpose.