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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Liu, Zejie Hu, Xiaoyu Zhou, Deyu Li, Lin Zhang, Xu Xiang, Yanzheng Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Nanjing Origin Information Technology Company China

Currently, researchers focus on generating codes from the requirement documents. However, current approaches still perform poorly on some requirements needing complex problem-solving skills. In reality, to tackle such complex requirements, instead of directly translating requirement documents into codes, software engineers write codes via unified modeling language diagrams, such as flowcharts, an intermediate tool to analyze and visualize the system. Therefore, we propose a new source code generation task, that is, to generate source code from flowcharts with texts. We manually construct a benchmark dataset containing 320 flowcharts with their corresponding source codes. Obviously, it is not straightforward to employ the current approaches for the new source code generation task since (1) the flowchart is a graph that contains various structures, including loop, selection, and others which is different from texts;(2) the connections between nodes in the flowchart are abundant and diverse which need to be carefully handled. To solve the above problems, we propose a two-stage code generation model. In the first stage, a structure recognition model is employed to transform the flowchart into pseudo-code containing the structural conventions of a typical programming language such as while, if. In the second stage, a code generation model is employed to convert the pseudo-code into code. Experimental results show that the proposed approach can achieve some improvement over the baselines. © 2022 Association for Computational Linguistics.

关键词： Flowcharting

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BCTCP:A Feedback-Based Congestion Control Method

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China Communications 2020年第6期17卷 13-25页

作者： Yuyu Zhao Guang Cheng Weici Zhang Xin Chen Jin Li School of Cyber Science&Engineering Southeast UniversityNanjing 211189China Key Laboratory of Computer Network and Information Integration of Ministry of Education Southeast UniversityNanjing 211189China Key Laboratory of Computer Network Technology of Jiangsu Province China Huawei Technologies Co. Ltd.No 101Software AvenueNanjing 211189China

Delay and throughput are the two network indicators that users most care *** congestion control methods try to occupy buffer aggressively until packet loss being detected,causing high delay and *** AQM and ECN can highly reduce packet drop rate and delay,however they may also lead to low *** queue size of routers properly means a lot to congestion control *** traffic size varying around bottleneck bandwidth creates some degree of persistent queue in the router,which brings in additional delay into network unwillingly,but a corporation between sender and router can keep it under *** persistent queue not only keeps routers being fully utilized all the time,but also lower the variation of throughput and delay,achieving the balance between delay and *** this paper,we present BCTCP(Buffer Controllable TCP),a congestion control protocol based on explicit feedback from *** requires sender,receiver and routers cooperating with each other,in which senders adjust their sending rate according to the multiple bit load factor information from *** keeps queue length of bottleneck under control,leading to very good delay and utilization result,making it more applicable to complex network environments.

关键词： explicit congestion control active queue management delay variation low latency high throughput

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RBAS: A real-Time user behavior analysis system for Internet TV in cloud computing 16

RBAS: A real-Time user behavior analysis system for Internet...

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11th International Conference on Future Internet Technologies, CFI 2016

作者： Zhu, Chengang Cheng, Guang Guo, Xiaojun Wang, Yuxiang School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing211189 China

ISBN: (纸本)9781450341813

The characteristic of Internet TV user behavior is quite essential for designers to optimize resource schedule and improve user experience. With the rapid development of Internet, both Internet TV users and STB (set top boxes) models are booming. This brings a large amount of behavior data which requires matching computing and storage resource to process. Therefore, scalable Internet TV user behavior analysis becomes more difficult. As a solution, cloud computing framework such as Hive is emerged. But limited by performance, it's not an appropriate choice for interactive analysis or real-Time data exploration. In this paper, we present a real-Time Internet TV user behavior analysis system with advantages of high concurrency, low latency and good transportability. Firstly, we design an event capture scheme, consisted of agents embedded in STBs and capture server clusters, to capture every manipulation performed by users. Secondly, we develop a SQL-on-Hadoop engine with distributed transactional management to decrease the response time. The engine has excellent query performance and ability to interactively query various data sources in different Hadoop formats. Lastly, we evaluate RBAS in a commercial Internet TV platform of 16 million registered users. The results show that, with a 32-node cluster, the system can effectively process 10.2 TB of behavior data every day, which is about 40x faster than original Hive-based system. © Copyright 2016 ACM.

关键词： Cloud computing

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A Prediction-Based Fuzzy Method for Multi-objective Microservice Workflows Scheduling 18th

A Prediction-Based Fuzzy Method for Multi-objective Microse...

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18th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Xu, Jingwen Chen, Long Li, Xiaoping Wang, Shuang School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing211189 China

ISBN: (纸本)9789819996391

Microservice workflows are widely used in real-time mobile computing scenarios such as face recognition and speech recognition. The key challenge is to develop efficient, stable, and robust algorithms capable of handling uncertain and fuzzy workflow tasks. In this paper, we consider the real-time microservice workflow fuzzy scheduling problem under VM-Container two-tier resources. A novel model based on triangle fuzzy numbers is formulated. The model encompasses two metrics: cost and degree of satisfaction. In response, this paper proposes an ARIMA prediction-based workflow fuzzy scheduling method (PFSM), comprising five key components: prediction of workflow arrival number, predistribution of task sub-deadlines, ordering of the task pool, task scheduling strategy, and resource management. To assess the performance of the proposed algorithms, several comparison algorithms are selected for analysis, and their performance differences are evaluated using ANOVA. The experimental results demonstrate the significant superiority of the proposed algorithms over the other compared algorithms in terms of overall performance. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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A Traffic Flow Prediction Based Task Offloading Method in Vehicular Edge Computing 18th

A Traffic Flow Prediction Based Task Offloading Method in V...

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18th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Xie, Liqiong Chen, Long Li, Xiaoping Wang, Shuang School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing211189 China

ISBN: (纸本)9789819996391

Vehicular Edge Computing (VEC) emerges as a promising paradigm by deploying computation and storage resources on edge servers located in close proximity to vehicles, such as roadside units or base stations. This proximity enables VEC to provide abundant resources and low-latency services, catering to the computational needs of vehicles. However, the dynamic nature of traffic flow presents new challenges in terms of task offloading decisions and resource allocation within VEC environments. This paper addresses the task offloading problem in VEC systems, considering the impact of dynamic traffic flow. To address this problem, we formulate an integer programming model that captures the essence of the studied scenario. To devise an efficient solution, we propose a novel traffic flow prediction-based heuristic algorithm (TFPVTO). TFPVTO incorporates different rules and strategies to generate an optimal offloading task sequence, make informed offloading decisions, and allocate resources effectively. To assess the performance of the proposed algorithm, we utilize a real-world traffic flow dataset. In order to fine-tune and optimize the algorithm’s components, we employ a multi-factor analysis of variance (ANOVA) technique. The proposed TFPVTO algorithm is then rigorously compared against other state-of-the-art algorithms, namely TAVF, MONSA, and RANDOM. Through extensive experiments and statistical analysis, we demonstrate the effectiveness and efficiency of the proposed algorithm in terms of task offloading performance. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Resource allocation

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Optimization of charging and data collection in wireless rechargeable sensor networks 1

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2nd International Conference on Human Centered Computing, HCC 2016

作者： Hu, Cheng Wang, Yun Key Lab of Computer Network and Information Integration MOE Southeast University Nanjing211189 China

ISBN: (数字)9783319318547

ISBN: (纸本)9783319318530

Wireless Rechargeable Sensor networks (WRSNs) can significantly prolong the lifetime by employing a mobile charger to replenish the depleted energy of the sensor nodes. This paper considers the scenario that the mobile charger cannot maintain full operation of the WRSN. In this case, the first and foremost problem is how to allocate the insufficient energy to the sensor nodes and decide their corresponding data sensing rates. The objective is to maximize the utility of data collection, which concerns data fairness as well as data amount. To address this problem, we first formulate and simplify its optimization form. Then based on two observations, we propose the BalanceFlow scheme, which coordinates data sensing of the sensor nodes with charging of the mobile charger. Numerical results show that, the BalanceFlow scheme achieves near optimal utility compared with the optimization results. © Springer International Publishing Switzerland 2016.

关键词： Sensor nodes

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Label distribution for learning with noisy labels 29

Label distribution for learning with noisy labels

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Liu, Yun-Peng Xu, Ning Zhang, Yu Geng, Xin MOE Key Laboratory of Computer Network and Information Integration China School of Computer Science and Engineering Southeast University Nanjing210096 China

ISBN: (纸本)9780999241165

The performances of deep neural networks (DNNs) crucially rely on the quality of labeling. In some situations, labels are easily corrupted, and therefore become noisy labels. Thus, designing algorithms that deal with noisy labels is of great importance for learning robust DNNs. However, it is difficult to distinguish between noisy labels and clean labels, which becomes the bottleneck of many methods. To address the problem, this paper proposes a novel method named Label Distribution based Confidence Estimation (LDCE). LDCE estimates the confidence of the observed labels based on label distribution. Then, the boundary between clean labels and noisy labels becomes clear according to confidence scores. To verify the effectiveness of the method, LDCE is combined with the existing learning algorithm to train robust DNNs. Experiments on both synthetic and real-world datasets substantiate the superiority of the proposed algorithm against state-of-the-art methods. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Deep neural networks

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Execution recovery in transactional composite service

Execution recovery in transactional composite service

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2013 IEEE 20th International Conference on Web Services, ICWS 2013

作者： Cao, Jiuxin Zhou, Tao Zhu, Gongrui Liu, Bo Luo, Junzhou School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration of MoE of China Southeast University Nanjing 211189 China

ISBN: (纸本)9780768550251

In the composite service which runs for a long time under the heterogeneous and loose-coupled circumstance, the failure of service tends to occur. The transaction and recovery mechanism is urgently needed in order to guarantee the end-to-end QoS of the workflow and satisfy the user requirement. In this paper we address the composite service recovery issue in the way of substitution with the consideration of QoS constraint, based on our previous research work of the transactional construction and processing rules and the global optimization service selection algorithm, TSSA. Firstly, the service execution graph (SEG) is introduced and a service execution solution selection algorithm is proposed to choose one from the solution set of TSSA which has the highest success rate of recovery. Then, the concepts of execution backup path and switch cost are introduced and a search algorithm is presented to search for the optimal backup path when current service failure occurs. Meanwhile, a local induction algorithm based on positive feedback is described which could rapidly construct an transactional execution path when no backup execution path can be found in SEG and also guarantees the transactional constraint of composite service. Finally, experimental results show the recovery algorithm proposed in this paper is efficient and has high reliability. © 2013 IEEE.

关键词： Recovery

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Position Prediction Social-Relationship-Based on Multi-order Markov Model 3

Position Prediction Social-Relationship-Based on Multi-order...

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3rd International Conference on Advanced Cloud and Big Data, CBD 2015

作者： Zou, Yue Zhang, Sanfeng College of Software Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration in Southeast University Ministry of Education Southeast University Nanjing China

ISBN: (纸本)9781467385374

Opportunistic network mobile user-nodes location prediction can help to collect the data, improve the dynamic network and help to decrease the problems which include the delay, low success rate, high storage space energy consume and so on, which are the results of the blindness in the process of delivery. In this paper, a mobile node location prediction algorithm base on the Multi-Order Markov prediction model as the initial forecast, and then use social-relationship which is closely linked to the node to optimize the algorithm. Experimental results show that the proposed algorithm is better than the single Multi-Order Markov model. © 2015 IEEE.

关键词： Location

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Adaptive sampling for openflow network measurement methods 17

Adaptive sampling for openflow network measurement methods

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12th International Conference on Future Internet Technologies, CFI 2017

作者： Cheng, Guang Yu, Jun School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing211198 China

ISBN: (纸本)9781450353328

OpenFlow provides a statistics collection scheme for per-flow and aggregate metrics at a user-specified frequency. However, periodic polling for flow statistics cannot well balance the tradeofi between measurement accuracy and limited control channel bandwidth. To further discuss the resource/accuracy tradeofi, this paper extends OpenFlow protocol to add sampling action for each monitoring flow entry, and systematically sample the matching packets to infer the flow-level statistics. Although traffic sampling can somehow be error-prone, it will provide near-real-Time measurements of flow dynamics and determine its accurate polling frequency, which the polling-based approach cannot achieve. In this paper, we propose a per-flow sampling solution to instruct the controller to adaptively adjust polling frequency, then evaluate it in the context of link utilization monitoring. © 2017 Association for Computing Machinery.

关键词： Software defined networking

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