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imBBO: An Improved Biogeography-Based Optimization Algorithm 13th

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imBBO: An Improved Biogeography-Based Optimization Algorithm

13th International Conference on Green, Pervasive, and Cloud Computing, GPC 2018

作者： Shi, Kai Yu, Huiqun Fan, Guisheng Yang, Xingguang Song, Zheng Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China The Third Research Institute of the Ministry of Public Security Shanghai China

ISBN: (纸本)9783030150921

Biogeography based Optimization (BBO) is a new evolutionary optimization algorithm based on the science of biogeography for global optimization. However, its direct-copying-based migration and random mutation operators make it easily possess local exploitation ability. To enhance the performance of BBO, we propose an improved BBO algorithm called imBBO. A hybrid migration operation is designed to further improve the population diversity and enhance the algorithm exploration ability. Empirical results demonstrate that our imBBO effectively gains the high optimization performance by comparing with the original BBO and three BBO variants for 23 out of 30 CEC’2017 benchmarks. Moreover, our imBBO presents a faster convergence speed. © 2019, Springer Nature Switzerland AG.

关键词： Global optimization

IRIS:A method for predicting in vivo RNA secondary structures using PARIS data

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Quantitative Biology 2020年第4期8卷 369-381页

作者： Jianyu Zhou Pan Li Wanwen Zeng Wenxiu Ma Zhipeng Lu Rui Jiang Qiangfeng Cliff Zhang Tao Jiang Bioinformatics Division BNRISTTsinghua UniversityBeijing 100084China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China MOE Key Laboratory of Bioinformatics Beijing Advanced Innovation Center for Structural BiologyCenter for Synthetic and Systems BiologyTsinghua-Peking Center for Life SciencesSchool of Life SciencesTsinghua UniversityBeijing 100084China College of Software Nankai UniversityTianjin 300071China Department of Statistics University of CaliforniaRiversideCA 92521USA Department of Pharmacology and Pharmaceutical Sciences University of Southern CaliforniaCA 90089USA Department of Automation Tsinghua UniversityBeijing 100084China Department of Computer Science and Engineering University of CaliforniaRiversideCA 92521USA

Background:RNA secondary structures play a pivotal role in posttranscriptional regulation and the functions of non-coding RNAs,yet in vivo RNA secondary structures remain ***(Psoralen Analysis of RNA Interactions and Structures)is a recently developed high-throughput sequencing-based approach that enables direct capture of RNA duplex structures in ***,the existence of incompatible,fuzzy pairing information obstructs the integration of PARIS data with the existing tools for reconstructing RNA secondary structure models at the single-base ***:We introduce IRIS,a method for predicting RNA secondary structure ensembles based on PARIS *** generates a large set of candidate RNA secondary structure models under the guidance of redistributed PARIS reads and then uses a Bayesian model to identify the optimal ensemble,according to both thermodynamic principles and PARIS ***:The predicted RNA structure ensembles by IRIS have been verified based on evolutionary conservation information and consistency with other experimental RNA structural *** is implemented in Python and freely available at http://***:IRIS capitalizes upon PARIS data to improve the prediction of in vivo RNA secondary structure *** expect that IRIS will enhance the application of the PARIS technology and shed more insight on in vivo RNA secondary structures.

关键词： RNA secondary structure PARIS data in vivo structure ensembles incompatible reads

An empirical study on optimal solutions selection strategies for effort-aware just-in-time software defect prediction 31

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An empirical study on optimal solutions selection strategies...

31st International Conference on software Engineering and Knowledge Engineering, SEKE 2019

作者： Yang, Xingguang Yu, Huiqun Fan, Guisheng Yang, Kang Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China Shanghai Engineering Research Center of Smart Energy Shanghai China

ISBN: (纸本)1891706489

Just-in-time software defect prediction (JIT-SDP) is an active topic in the filed of software engineering, and many methods have been proposed to solve this problem. state-of-the-art method MULTI applies multi-objective optimization algorithm to the effort-aware JIT-SDP problem, and obtains good average performance. Although the average performance of the MULTI method is high, there are many optimal solutions with poor performance. If an optimal solution is randomly selected, a poor prediction model may be obtained. In order to further improve the performance of the MULTI method, we propose three optimal solutions selection strategies: benefit priority (BP), cost priority (CP), and a compromise between cost and benefit (CCB). In order to compare and validate the effectiveness of the strategies, we conduct a large-scale empirical study on data sets of six open source projects. The experimental results show that, compared with the average performance of MULTI, the optimal solutions selection strategy based on BP has a significant improvement in ACC and Popt indicators. Therefore, we recommend using the BP-based optimal solutions selection strategy to improve the performance of MULTI when using the MULTI method to solve the effort-aware JIT-SDP problem. © 2019 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Multiobjective optimization

Crosslink-net: Double-branch encoder segmentation network via fusing vertical and horizontal convolutions

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arXiv 2021年

作者： Yu, Qian Qi, Lei Zhou, Luping Wang, Lei Yin, Yilong Shi, Yinghuan Wang, Wuzhang Gao, Yang School of Data and Computer Science Shandong Women's University Jinan250300 China The State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China School of Computer Science and Engineering Southeast University Nanjing211189 China The School of Electrical and Information Engineering University of Sydney NSW2006 Australia The School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia The School of Soft Shandong University Jinan250100 China Department of Respiratory and Critical Care Medicine Shandong Chest Hospital Jinan250013 China

Accurate image segmentation plays a crucial role in medical image analysis, yet it faces great challenges of various shapes, diverse sizes, and blurry boundaries. To address these difficulties, square kernel-based encoder-decoder architecture has been proposed and widely used, but its performance remains still unsatisfactory. To further cope with these challenges, we present a novel double-branch encoder architecture. Our architecture is inspired by two observations: 1) Since the discrimination of features learned via square convolutional kernels needs to be further improved, we propose to utilize non-square vertical and horizontal convolutional kernels in the double-branch encoder, so features learned by the two branches can be expected to complement each other. 2) Considering that spatial attention can help models to better focus on the target region in a large-sized image, we develop an attention loss to further emphasize the segmentation on small-sized targets. Together, the above two schemes give rise to a novel double-branch encoder segmentation framework for medical image segmentation, namely Crosslink-Net. The experiments validate the effectiveness of our model on four datasets. The code is released at https://***/Qianyu1226/*** Codes 68T07 Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

Operation-aware neural networks for user response prediction

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arXiv 2019年

作者： Yang, Yi Xu, Baile Shen, Furao Zhao, Jian State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing University China State Key Laboratory for Novel Software Technology School of Electronic Science and Engineering Nanjing University China

User response prediction makes a crucial contribution to the rapid development of online advertising system and recommendation system. The importance of learning feature interactions has been emphasized by many works. Many deep models are proposed to automatically learn high-order feature interactions. Since most features in advertising system and recommendation system are high-dimensional sparse features, deep models usually learn a low-dimensional distributed representation for each feature in the bottom layer. Besides traditional fully-connected architectures, some new operations, such as convolutional operations and product operations, are proposed to learn feature interactions better. In these models, the representation is shared among different operations. However, the best representation for different operations may be different. In this paper, we propose a new neural model named Operation-aware Neural Networks (ONN) which learns different representations for different operations. Our experimental results on two large-scale real-world ad click/conversion datasets demonstrate that ONN consistently outperforms the state-of-the-art models in both offline-training environment and online-training environment. Copyright © 2019, The Authors. All rights reserved.

关键词： Recommender systems

Secondary power diagram, dual of secondary polytope 9th

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Secondary power diagram, dual of secondary polytope

9th International Conference on Numerical Geometry, Grid Generation, and Scientific Computing, celebrating the 150th anniversary of Georgy F. Voronoi, NUMGRID 2018

作者： Lei, Na Chen, Wei Luo, Zhongxuan Si, Hang Gu, Xianfeng DUT-RU ISE Dalian University of Technology Dalian China School of Software Technology Dalian University of Technology Dalian China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian China Berlin Germany Department of Computer Science Stony Brook University Stony Brook NY United States

ISBN: (纸本)9783030234355

An ingenious construction of Gel’fand et al. (Discriminants, Resultants, and Multidimensional Determinants. Birkhäuser, Basel, 1994) geometrizes the triangulations of a point configuration, such that all coherent triangulations form a convex polytope, the so-called secondary polytope. The secondary polytope can be treated as a weighted Delaunay triangulation in the space of all possible coherent triangulations. Naturally, it should have a dual diagram. In this work, we explicitly construct the secondary power diagram, which is the power diagram of the space of all possible power diagrams with non-empty boundary cells. Secondary power diagram gives an alternative proof for the classical secondary polytope theorem based on Alexandrov theorem. Furthermore, secondary power diagram theory shows one can transform a non-degenerated coherent triangulation to another non-degenerated coherent triangulation by a sequence of bistellar modifications, such that all the intermediate triangulations are non-degenerated and coherent. © Springer Nature Switzerland AG 2019.

关键词： Triangulation

sCompile: Critical Path Identification and Analysis for Smart Contracts 21st

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sCompile: Critical Path Identification and Analysis for Smar...

21st International Conference on Formal Engineering Methods, ICFEM 2019

作者： Chang, Jialiang Gao, Bo Xiao, Hao Sun, Jun Cai, Yan Yang, Zijiang Department of Computer Science Western Michigan University KalamazooMI49009 United States Pillar of Information System Technology and Design Singapore University of Technology and Design Singapore Singapore School of Information Systems Singapore Management University Singapore Singapore State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030324087

Ethereum smart contracts are an innovation built on top of the blockchain technology, which provides a platform for automatically executing contracts in an anonymous, distributed, and trusted way. The problem is magnified by the fact that smart contracts, unlike ordinary programs, cannot be patched easily once deployed. It is important for smart contracts to be checked against potential vulnerabilities. In this work, we propose an alternative approach to automatically identify critical program paths (with multiple function calls including inter-contract function calls) in a smart contract, rank the paths according to their criticalness, discard them if they are infeasible or otherwise present them with user friendly warnings for user inspection. We identify paths which involve monetary transaction as critical paths, and prioritize those which potentially violate important properties. For scalability, symbolic execution techniques are only applied to top ranked critical paths. Our approach has been implemented in a tool called sCompile, which has been applied to 36,099 smart contracts. The experiment results show that sCompile is efficient, i.e., 5 s on average for one smart contract. Furthermore, we show that many known vulnerabilities can be captured if user inspects as few as 10 program paths generated by sCompile. Lastly, sCompile discovered 224 unknown vulnerabilities with a false positive rate of 15.4% before user inspection. © 2019, Springer Nature Switzerland AG.

关键词： Smart contract

Quantum Experiments and Hypergraphs: Multi-Photon Sources for Quantum Interference, Quantum Computation and Quantum Entanglement

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arXiv 2020年

作者： Gu, Xuemei Chen, Lijun Krenn, Mario State Key Laboratory for Novel Software Technology Nanjing University 163 Xianlin Avenue Qixia District Nanjing City210023 China Department of Chemistry & Computer Science University of Toronto Canada & Vector Institute for Artificial Intelligence Toronto Canada

We introduce the concept of hypergraphs to describe quantum optical experiments with probabilistic multi-photon sources. Every hyperedge represents a correlated photon source, and every vertex stands for an optical output path. Such general graph description provides new insights for producing complex high-dimensional multi-photon quantum entangled states, which go beyond limitations imposed by pair creation via spontaneous parametric down-conversion. Furthermore, properties of hypergraphs can be investigated experimentally. For example, the NP-Complete problem of deciding whether a hypergraph has a perfect matchin can be answered by experimentally detecting multi-photon events in quantum experiments. By introducing complex weights in hypergraphs, we show a general many-particle quantum interference and manipulating entanglement in a pictorial way. Our work paves the path for the development of multi-photon high-dimensional state generation and might inspire new applications of quantum computations using hypergraph mappings. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph theory

Rubik: A hierarchical architecture for efficient graph learning

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arXiv 2020年

作者： Chen, Xiaobing Wang, Yuke Xie, Xinfeng Hu, Xing Basak, Abanti Liang, Ling Yan, Mingyu Deng, Lei Ding, Yufei Du, Zidong Chen, Yunji Xie, Yuan State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Computer Science University of California Santa Barbara United States Department of Electrical and Computer Engineering University of California Santa Barbara United States

Graph convolutional network (GCN) emerges as a promising direction to learn the inductive representation in graph data commonly used in widespread applications, such as E-commerce, social networks, and knowledge graphs. However, learning from graphs is non-trivial because of its mixed computation model involving both graph analytics and neural network computing. To this end, we decompose the GCN learning into two hierarchical paradigms: graph-level and node-level computing. Such a hierarchical paradigm facilitates the software and hardware accelerations for GCN learning. We propose a lightweight graph reordering methodology, incorporated with a GCN accelerator architecture that equips a customized cache design to fully utilize the graph-level data reuse. We also propose a mapping methodology aware of data reuse and task-level parallelism to handle various graphs inputs effectively. Results show that Rubik accelerator design improves energy efficiency by 26.3x to 1375.2x than GPU platforms across different datasets and GCN models. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks