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A Review of Artificial Fish Swarm Algorithms: Recent Advances and Applications

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arXiv 2020年

作者： Pourpanah, Farhad Wang, Ran Lim, Chee Peng Wang, Xi-Zhao Yazdani, Danial College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University China Department of Electrical and Computer Engineering University of Windsor Canada College of Mathematics and Statistics Shenzhen Key Lab. of Advanced Machine Learning and Applications Guangdong Key Lab. of Intelligent Information Processing Shenzhen University China Institute for Intelligent Systems Research and Innovation Deakin University Australia College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University China School of Computer Science and Engineering Southern University of Science and Technology China

The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming and following behaviors. Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an effective Swarm Intelligence (SI) methodology that has been widely applied to solve real-world optimization problems. Since its introduction in 2002, many improved and hybrid AFSA models have been developed to tackle continuous, binary, and combinatorial optimization problems. This paper aims to present a concise review of the continuous AFSA, encompassing the original ASFA, its improvements and hybrid models, as well as their associated applications. We focus on articles published in high-quality journals since 2013. Our review provides insights into AFSA parameters modifications, procedure and sub-functions. The main reasons for these enhancements and the comparison results with other hybrid methods are discussed. In addition, hybrid, multi-objective and dynamic AFSA models that have been proposed to solve continuous optimization problems are elucidated. We also analyse possible AFSA enhancements and highlight future research directions for advancing AFSA-based models. Copyright © 2020, The Authors. All rights reserved.

关键词： Multiobjective optimization

SenseMag: Enabling low-cost traffic monitoring using non-invasive magnetic sensing

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arXiv 2021年

作者： Wang, Kafeng Xiong, Haoyi Zhang, Jie Chen, Hongyang Dou, Dejing Xu, Cheng-Zhong Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Shenzhen China Big Data Lab Baidu Inc. Haidian Beijing China Key Laboratory of High Confidence Software Technologies Peking University Haidian Beijing China Research Center for Intelligent Network Zhejiang Lab Zhejiang Hangzhou China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau Tapia Macau China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

The operation and management of intelligent transportation systems (ITS), such as traffic monitoring, relies on real-time data aggregation of vehicular traffic information, including vehicular types (e.g., cars, trucks, and buses), in the critical roads and highways. While traditional approaches based on vehicular-embedded GPS sensors or camera networks would either invade drivers’ privacy or require high deployment cost, this paper introduces a low-cost method, namely SenseMag, to recognize the vehicular type using a pair of non-invasive magnetic sensors deployed on the straight road section. SenseMag filters out noises and segments received magnetic signals by the exact time points that the vehicle arrives or departs from every sensor node. Further, SenseMag adopts a hierarchical recognition model to first estimate the speed/velocity, then identify the length of vehicle using the predicted speed, sampling cycles, and the distance between the sensor nodes. With the vehicle length identified and the temporal/spectral features extracted from the magnetic signals, SenseMag classify the types of vehicles accordingly. Some semi-automated learning techniques have been adopted for the design of filters, features, and the choice of hyper-parameters. Extensive experiment based on real-word field deployment (on the highways in Shenzhen, China) shows that SenseMag significantly outperforms the existing methods in both classification accuracy and the granularity of vehicle types (i.e., 7 types by SenseMag versus 4 types by the existing work in comparisons). To be specific, our field experiment results validate that SenseMag is with at least 90% vehicle type classification accuracy and less than 5% vehicle length classification error. Copyright © 2021, The Authors. All rights reserved.

关键词： Sensor nodes

R-LDA: Profiling RDF Datasets Using Knowledge-Based Topic Modeling

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R-LDA: Profiling RDF Datasets Using Knowledge-Based Topic Mo...

International Conference on Semantic Computing

作者： Seyedamin Pouriyeh Mehdi Allahyaril Gong Cheng Hamid Reza Arabnia Krys Kochut Maurizio Atzori Information Technology Department Kennesaw State University Marietta GA USA Computer Science Department Georgia Southern University Statesboro USA National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Computer Science Department University of Georgia Athens GA USA Math/CS Department University of Cagliari Italy

Recently, Linked Open Data (LOD) has experienced an exponential growth via publishing huge volume of datasets on the Web. This vast amount of information needs to be searched, queried, and interlinked easier than before. It is recommended that potential data publishers provide recapitulative information about their datasets published on the Web. This information, which functions as metadata, will facilitate those datasets to be discovered easily. As it is not always the case, we are faced with a large number of datasets without a proper profile, leading to a high demand for different data profiling techniques. In this paper, we focus on RDF dataset profiling utilizing unsupervised machine learning techniques, namely knowledge based topic modeling. We also investigate the use of Wikipedia categories to represent the topics identified in an RDF dataset. In the proposed model, we extract a number of representative topics for an RDF dataset and annotate them with Wikipedia categories. The union of the assigned categories serves as a profile of the dataset, in a sense that it provides an overall characterization of the content of the dataset.

关键词： Resource description framework Encyclopedias Electronic publishing Coherence Computational modeling

Defensive Few-shot Learning

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arXiv 2019年

作者： Li, Wenbin Wang, Lei Zhang, Xingxing Qi, Lei Huo, Jing Gao, Yang Luo, Jiebo The State Key Laboratory for Novel Software Technology Nanjing University 210023 China The School of Computing and Information Technology University of Wollongong Australia The Department of Computer Science Tsinghua University China The School of Computer Science and Engineering Southeast University China The Department of Computer Science University of Rochester United States

This paper investigates a new challenging problem called defensive few-shot learning in order to learn a robust few-shot model against adversarial attacks. Simply applying the existing adversarial defense methods to few-shot learning cannot effectively solve this problem. This is because the commonly assumed sample-level distribution consistency between the training and test sets can no longer be met in the few-shot setting. To address this situation, we develop a general defensive few-shot learning (DFSL) framework to answer the following two key questions: (1) how to transfer adversarial defense knowledge from one sample distribution to another? (2) how to narrow the distribution gap between clean and adversarial examples under the few-shot setting? To answer the first question, we propose an episode-based adversarial training mechanism by assuming a task-level distribution consistency to better transfer the adversarial defense knowledge. As for the second question, within each few-shot task, we design two kinds of distribution consistency criteria to narrow the distribution gap between clean and adversarial examples from the feature-wise and prediction-wise perspectives, respectively. Extensive experiments demonstrate that the proposed framework can effectively make the existing few-shot models robust against adversarial attacks. Code is available at https://***/WenbinLee/***. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

Towards a programming framework for activity-oriented context-aware applications

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Frontiers of computer science 2017年第6期11卷 987-1006页

作者： Xuansong LI Xianping TAO Jian LU State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing 210000 China

Context-aware system is an emerging research area in recent years. Context plays an important role in these systems. In most existing work, context is treated as all rel- ative elements in the environment of an application, and the scope of context is predefined by the developers during the development. However, it is difficult to analyze, specify, and organize everything in the environment accurately and com- pletely; and even when it is possible, the developed applica- tions are difficult to extend or modify as the requests for en- vironment may change over time. In this paper, we focus on activity-oriented context-aware （AOCA） applications where the requests for environment are highly dependent on user activities, and propose a programming framework for devel- oping AOCA applications. In particular, we first present a concept model for describing the notions of activity-oriented context. Next, based on the concept model, we describe the details of the programming framework as well as a develop- ment tool. Moreover, we provide a platform to support the runtime of AOCA applications, and demonstrate the advan- tages of our programming framework through experimental evaluations.

关键词： pervasive computing context-awareness,context-aware programming framework activity-orientedcontext

Mining API usage change rules for software framework evolution

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science China(Information sciences) 2018年第5期61卷 104-106页

作者： Ping YU Fei YANG Chun CAO Hao HU Xiaoxing MA State Key Laboratory for Novel Software Technology Nanjing University Department of Computer Science and Technology Nanjing University

Dear editor,software frameworks or libraries are frequently evolving APIs(application programming interfaces)to include new features,improve performance or fix *** relying on these APIs should be updated according to these *** modern IDEs(integrated development environments)such as Eclipse and Intelli J IDEA are able to provide compile error reports or warnings to nonexistent or deprecated ***,developers still have to search for precise replacement APIs and check their usage to fix bugs or

关键词： API AUC

Masked non-autoregressive image captioning

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arXiv 2019年

作者： Gao, Junlong Meng, Xi Wang, Shiqi Li, Xia Wang, Shanshe Ma, Siwei Gao, Wen Peking University Shenzhen Graduate School National Key Lab for Novel Software Technology Nanjing University Institute of Digital Media Peking University Peng Cheng Laboratory Department of Computer Science City University of Hong Kong Hong Kong

Existing captioning models often adopt the encoder-decoder architecture, where the decoder uses autoregressive decoding to generate captions, such that each token is generated sequentially given the preceding generated tokens. However, autoregressive decoding results in issues such as sequential error accumulation, slow generation, improper semantics and lack of diversity. Non-autoregressive decoding has been proposed to tackle slow generation for neural machine translation but suffers from multimodality problem due to the indirect modeling of the target distribution. In this paper, we propose masked non-autoregressive decoding to tackle the issues of both autoregressive decoding and non-autoregressive decoding. In masked non-autoregressive decoding, we mask several kinds of ratios of the input sequences during training, and generate captions parallelly in several stages from a totally masked sequence to a totally non-masked sequence in a compositional manner during inference. Experimentally our proposed model can preserve semantic content more effectively and can generate more diverse captions. Copyright © 2019, The Authors. All rights reserved.

关键词： Decoding

Chasing errors using biasing automata 8th

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Chasing errors using biasing automata

8th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2018

作者： Bu, Lei Peled, Doron Shen, Dashuan Tzirulnikov, Yael State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science Bar Ilan University Ramat Gan Israel

ISBN: (纸本)9783030034207

Randomized testing is a lightweight approach for searching for bugs. It presents a tradeoff between the number of testing experiments performed and the probability to find errors. An important challenge in random testing is when the errors that we try to detect are scattered with very low probability among the different executions, forming a "rare event". We suggest here the use of a "biasing automaton", which observes the tested sequence and controls the distribution of the different choices of extending it. By the careful selection of a biasing automaton, we can increase the chance of errors to be found and consequently reduce the number of tests we need to perform. The biasing automaton is constructed through repeated testing of variants of the system under test. We show how to construct biasing automata based on genetic programming. © Springer Nature Switzerland AG 2018.

关键词： Genetic programming

Cyan Droid: stable and effective energy inefficiency diagnosis for Android apps

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science China(Information sciences) 2017年第1期60卷 51-68页

作者： Qiwei LI Chang XU Yepang LIU Chun CAO Xiaoxing MA Jian LU¨ State Key Laboratory for Novel Software Technology Nanjing University Department of Computer Science and Technology Nanjing University Department of Computer Science and Engineering The Hong Kong University of Science and Technology

Smartphones are an indispensable part of people’s daily lives. Smartphone apps often use phone sensors to probe their users’ physical environmental conditions to provide services. However, sensing operations can be energy-consumptive, and thus the obtained sensory data should be effectively utilized by apps for their users’ benefits. Existing studies disclosed that many real-world smartphone apps have poor utilization of sensory data, and this causes serious energy waste. To diagnose such energy bugs, a recent technique GreenD roid automatically generates sensory data, tracks their propagation and analyzes their utilization in an app. However,we observe that GreenD roid’s sensory data generation is random and this can negatively affect its stability and effectiveness. Our study reported that GreenD roid might miss energy bugs that require specific sensory data to manifest. To address this problem, we propose a novel approach to systematically generating multi-dimensional sensory data. For effective diagnosis, we also propose to consider app state changes at a finer granularity. We implemented our approach as a prototype tool CyanD roid, and evaluated it using four real-world Android apps and hundreds of their mutants. Our results confirmed that CyanD roid is more stable and effective in energy inefficiency diagnosis for sensory data underutilization issues.

关键词： smartphone sensory data utilization energy inefficiency white-box sampling dynamic tainting