检索结果-内蒙古大学图书馆

SSRN 2022年

作者： Hu, Shuwen Jing, Yi Li, Tao Wang, You-Gan Liu, Zhenyu Gao, Jing Tian, Yu-Chu School of Computer Science Queensland University of Technology Brisbane4001 Australia Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application for Agricultural and Animal Husbandry Hohhot010018 China School of Life Sciences Inner Mongolia Agricultural University Hohhot010018 China Institute for Learning Sciences & Teacher Education Brisbane Australia Australian Catholic University Brisbane4000 Australia School of Computer and Information Engineering Inner Mongolia Agriculture University Hohhot010018 China

The central biological clock controls countless aspects of mammalian physiology, such as sleep, metabolism, and immune system regulation. Gene regulatory relationships play an important role in understanding the mechanisms that underlie a variety of biological cellular processes. In principle, it is feasible to infer circadian gene regulatory relationships from time-series gene expression data. But the correlation-based inference of the relationships between genes does not give sufficient information about causation. Moreover, gene expression data usually have low-sampling observations and high dimensions, leading to difficulties in gene data analysis. In this paper, a new hybrid framework was proposed to infer circadian gene regulatory relationships from gene expression data of rats. For practical clustering computation of the high-dimensional problem, the framework hybridizes the fuzzy C-means clustering algorithm and dynamic time warping (DTW) distance. After a cluster of genes is identified for the target gene, Wilcoxon signed-rank test was conducted to obtain the number of significant genes in the specific cluster. Finally, a dynamic vector autoregressive method was applied to the selected significant gene expression profiles to identify a directed causal regulatory relationship from partial correlation. Circadian gene regulatory relationships are inferred from the gene expression data of rats based on the Aanat target gene. The results show that genes Pde10a, Atp7b, Prok2, Per1, Rhobtb3 and Dclk1 stand out, which have been known to be essential for the regulation of circadian behaviour. The potential relationships between genes Tspan15, Eprs, Eml5 and Fsbp with a circadian rhythm need further experimental research. © 2022, The Authors. All rights reserved.

关键词： Rats

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MSpace: Multi Security Namespace Management for Containers

Journal of Engineering Research

引用

Journal of engineering Research 2025年

作者： Hao Liang Gongshan He Jinhu Jiang National Digital Switching System Engineering & Technological Research Center Zhengzhou450002 China Key Laboratory of Cyberspace Security Ministry of Education Zhengzhou450002 China School of Computer Science Fudan University Shanghai200438 China Institute of Big Data Fudan University Shanghai200433 China

Existing system security frameworks like LSM are primarily designed for the operating system kernel and are not applicable to user-level container methods. This is primarily because the system security framework is designed and implemented within the operating system kernel, which is shared across the entire system and cannot fulfill the isolation requirements of containers. The security policies of multiple containers are intermixed in the underlying system kernel, which impacts each other and increases the overhead of monitoring, leading to inefficiencies in cloud servers running numerous containers. Improvements to the security of containers are ad hoc and partial, often lacking support for the universal SELinux mode. In this paper, we introduce MSpace, an enhanced kernel SELinux framework that supports containerized secure spaces with individual security policies and mechanisms. To enhance security, we first introduce an instantiation mechanism for virtual hook tables, which, in conjunction with the container’s namespace, ensures each has its own instantiated hook tables instead of a system-wide one, thereby isolating secure spaces. Subsequently, a security namespace method was devised to support container-specific private security policies. Lastly, to maintain compatibility, only the instantiation and deallocation interfaces were added while preserving the original interfaces unchanged. The results demonstrate that MSpace effectively alleviates the issue of using SELinux for internal containers, with an average reduction of 2.83% in system call latency compared to using host SELinux directly.

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CASIA-Face-Africa: A large-scale African face image database

arXiv

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arXiv 2021年

作者： Muhammad, Jawad Wang, Yunlong Wang, Caiyong Zhang, Kunbo Sun, Zhenan The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044 China

Face recognition is a popular and well-studied area with wide applications in our society. However, racial bias had been proven to be inherent in most State Of The Art (SOTA) face recognition systems. Many investigative studies on face recognition algorithms have reported higher false positive rates of African subjects cohorts than the other cohorts. Lack of large-scale African face image databases in public domain is one of the main restrictions in studying the racial bias problem of face recognition. To this end, we collect a face image database namely CASIA-Face-Africa which contains 38,546 images of 1,183 African subjects. Multi-spectral cameras are utilized to capture the face images under various illumination settings. Demographic attributes and facial expressions of the subjects are also carefully recorded. For landmark detection, each face image in the database is manually labeled with 68 facial keypoints. A group of evaluation protocols are constructed according to different applications, tasks, partitions and scenarios. The performances of SOTA face recognition algorithms without retraining are reported as baselines. The proposed database along with its face landmark annotations, evaluation protocols and preliminary results form a good benchmark to study the essential aspects of face biometrics for African subjects, especially face image preprocessing, face feature analysis and matching, facial expression recognition, sex/age estimation, ethnic classification, face image generation, etc. The database can be downloaded from our website* Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

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Nirvp as a Proxy for Gross Primary Productivity Across Different Biomes and Climate Zones: Performance and Limitations

SSRN

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SSRN 2022年

作者： Chen, Siyuan Liu, Liangyun Sui, Lichun Liu, Xinjie Li, Xing Qian, Xiaojin International Research Center of Big Data for Sustainable Development Goals Beijing100094 China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China College of Geological Engineering and Geomatics Chang’ an University Xi’an710054 China Research Institute of Agriculture and Life Sciences Seoul National University Seoul08826 Korea Republic of School of Geographic and Biologic Information Nanjing University of Posts and Telecommunications Nanjing210023 China

Near-infrared (NIR) reflectance (ρNIR) and radiance-based proxies are promising new methods for remotely estimating vegetation photosynthesis. Yet how well such NIR-based proxies estimate gross primary productivity (GPP), and their limitations, are still unclear across biomes and climate zones. Here, we aimed to evaluate the performance and uncover the limitations of near-infrared radiation reflected by vegetation (NIRvP) for GPP estimation, in comparison with absorbed photosynthetically active radiation (APAR), solar-induced fluorescence (SIF) and a satellite GPP product, across different biomes and climate zones. Overall, NIRvP showed stronger relationship with eddy covariance (EC) GPP when compared with APAR, SIF, and MOD17A2H GPP across most biomes except evergreen broadleaf forests (EBF), open shrublands (OSH), and savannas (SAV). This demonstrates the superiority of NIRvP for estimating GPP across most biomes. However, weaker NIRvP-GPP relationships were also found when focusing on specific climate zones. Specifically, NIRvP was better at predicting GPP in most climate zones due to positive correlations between ρNIR and light use efficiency (LUE), usually with similar seasonal patterns of radiation, air temperature (TA), and precipitation, suggesting a positive contribution of ρNIR to the NIRvP–GPP relationship. But a poorer performance of NIRvP with GPP was found in few zones with uncorrelated or even negative correlations between ρNIR and LUE, where seasonal patterns of radiation, TA and precipitation differed, including EBF over equatorial monsoon, dry summer and fully humid warm temperate climates, SAV over dry winter equatorial climates and arid steppe climates, as well as OSH over arid desert climates. Our results indicated a synergistic driving effect of APAR and ρNIR for a robust NIRvP–GPP relationship across most biomes and climate zones. We also revealed limitations of using NIRvP in the GPP estimation of certain climate zones, which is important for

关键词： Vegetation

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C olumnSGD: A Column-oriented Framework for Distributed Stochastic Gradient Descent

C olumnSGD: A Column-oriented Framework for Distributed Stoc...

引用

International Conference on data engineering

作者： Zhipeng Zhang Wentao Wu Jiawei Jiang Lele Yu Bin Cui Ce Zhang School of EECS & Key Laboratory of High Confidence Software Technologies (MOE) Peking University Microsoft Research Redmond USA ETH Zurich Switzerland Tencent Inc Center for Data Science Peking University & National Engineering Laboratory for Big Data Analysis and Applications

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Distributed machine learning (ML) has triggered tremendous research interest in recent years. Stochastic gradient descent (SGD) is one of the most popular algorithms for training ML models, and has been implemented in almost all distributed ML systems, such as Spark MLlib, Petuum, MXNet, and TensorFlow. However, current implementations often incur huge communication and memory overheads when it comes to large models. One important reason for this inefficiency is the row-oriented scheme (RowSGD) that existing systems use to partition the training data, which forces them to adopt a centralized model management strategy that leads to vast amount of data exchange over the network. We propose a novel, column-oriented scheme (ColumnSGD) that partitions training data by columns rather than by rows. As a result, ML model can be partitioned by columns as well, leading to a distributed configuration where individual data and model partitions can be collocated on the same machine. Following this locality property, we develop a simple yet powerful computation framework that significantly reduces communication overheads and memory footprints compared to RowSGD, for large-scale ML models such as generalized linear models (GLMs) and factorization machines (FMs). We implement ColumnSGD on top of Apache Spark, and study its performance both analytically and experimentally. Experimental results on both public and real-world datasets show that ColumnSGD is up to 930× faster than MLlib, 63× faster than Petuum, and 14× faster than MXNet.

关键词： Conferences data engineering Radio frequency

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Overview of the Tenth Dialog System Technology Challenge: DSTC10

引用

IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 32卷 765-778页

作者： Yoshino, Koichiro Chen, Yun-Nung Crook, Paul Kottur, Satwik Li, Jinchao Hedayatnia, Behnam Moon, Seungwhan Fei, Zhengcong Li, Zekang Zhang, Jinchao Feng, Yang Zhou, Jie Kim, Seokhwan Liu, Yang Jin, Di Papangelis, Alexandros Gopalakrishnan, Karthik Hakkani-Tur, Dilek Damavandi, Babak Geramifard, Alborz Hori, Chiori Shah, Ankit Zhang, Chen Li, Haizhou Sedoc, Joao D'haro, Luis F. Banchs, Rafael Rudnicky, Alexander Guardian Robot Project R-IH RIKEN 2-2-2 Hikaridai Seika Shoraku619-0288 Japan Information Science Nara Institute of Science and Technology Ikoma630-0101 Japan Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan Inc. Palo AltoCA95054 United States Alexa AI *** Inc. SunnyvaleCA94089 United States Meta Seattle RedmondWA98052 United States Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Tencent AI Lab Beijing Beijing China Kexueyuan South Road Zhongguancun Beijing100190 China Beijing 100190 China Alexa AI *** Inc. SunnyvaleCA United States 1120 Enterprise way Sunnyvale94089 United States *** Inc. SeattleWA United States Menlo Park CA United States Audio and Speech Group Mitsubishi Electric Research Laboratories CambridgeMA02139-1955 United States Carnegie Mellon University Department of Language and Information Technologies or just Carnegie Mellon University Pittsburgh United States National University of Singapore Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Shenzhen Research Institute of Big Data School of Data Science Chinese University of Hong Kong Shenzhen518172 China New York University New YorkNY United States ETSI de Telecomunicacion - Speech Technology and Machine Learning Group Universidad Politecnica de Madrid Ciudad Universitaria Madrid28040 Spain Nanyang Technological University Singapore Singapore Carnegie Mellon University PittsburghPA United States

This article introduces the Tenth Dialog System Technology Challenge (DSTC-10). This edition of the DSTC focuses on applying end-to-end dialog technologies for five distinct tasks in dialog systems, namely 1. Incorporation of Meme images into open domain dialogs, 2. knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations, 3. Situated Interactive Multimodal dialogs, 4. Reasoning for Audio Visual Scene-Aware Dialog, and 5. Automatic Evaluation and Moderation of Open-domainDialogue Systems. This article describes the task definition, provided datasets, baselines, and evaluation setup for each track. We also summarize the results of the submitted systems to highlight the general trends of the state-of-the-art technologies for the tasks. © 2023 The Authors.

关键词： Job analysis

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Accelerated evolution of an Lhx2 enhancer shapes mammalian social hierarchies

引用

细胞研究（英文版） 2020年第5期30卷 408-420页

作者： Yuting Wang Guangyi Dai Zhili Gu Guopeng Liu Ke Tang Yi-Hsuan Pan Yujie Chen Xin Lin Nan Wu Haoshan Chen Su Feng Shou Qiu Hongduo Sun Qian Li Chuan Xu Yanan Mao Yong Edward Zhang Philipp Khaitovich Yan-Ling Wang Qunxiu Liu Jing-Dong Jackie Han Zhen Shao Gang Wei Chun Xu Naihe Jing Haipeng Li CAS Key Laboratory of Computational Biology CAS-MPG Partner Institute for Computational Biology Shanghai Institute of Nutrition and Health Chinese Academy of SciencesYueyang Road 320 200031 Shanghai China University of Chinese Academy of Sciences Beijing China Precise Genome Engineering Center School of Life SciencesGuangzhou University Guangzhou 510405 Guangdong China Key Laboratory of Brain Functional Genomics of Ministry of Education School of Life Science East China Normal University 200062 Shanghai China Bio-Med Big Data Center CAS Key Laboratory of Computational Biology CAS-MPG Partner Institute for Computational Biology Shanghai Institute of Nutrition and Health Chinese Academy of Sciences Yueyang Road 320 200031 Shanghai China Institute of Neuroscience CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Yueyang Road 320 200031 Shanghai China State Key Laboratory of Cell Biology Institute of Biochemistry and Cell Biology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Yueyang Road 320 200031 Shanghai China Key Laboratory of Zoological Systematics and Evolution & State Key Laboratory of Integrated Management of Pest Insects and Rodents Institute of Zoology Chinese Academy of Sciences 100101 Beijing China Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences 650223 Kunming China State Key Laboratory of Stem cell and Reproductive Biology Institute of Zoology Chinese Academy of Sciences 100101 Beijing China Shanghai Zoological Park 200335 Shanghai China

Social hierarchies emerged during evolution,and social rank influences behavior and health of ***,the evolutionary mechanisms of social hierarchy are still unknown in *** we developed a new method and performed a genome-wide screening for identifying regions with accelerated evolution in the ancestral lineage of placental mammals,where mammalian social hierarchies might have initially *** functional analyses were conducted for the most accelerated region designated as placental-accelerated sequence 1 (PAS1,P=3.15 × 10-18).Multiple pieces of evidence show that PAS1 is an enhancer of the transcription factor gene Lhx2 involved in brain ***1s isolated from various amniotes showed different cis-regulatory activity in vitro,and affected the expression of Lhx2 differently in the nervous system of mouse ***1 knock-out mice lack social ***1 knock-in mouse models demonstrate that PAS1s determine the social dominance and subordinate of adult mice,and that social ranks could even be turned over by mutated *** homozygous mutant mice had normal huddled sleeping behavior,motor coordination and ***,PAS1-Lhx2 modulates social hierarchies and is essential for establishing social stratification in amniotes,and positive Darwinian selection on PAS1 plays pivotal roles in the occurrence of mammalian social hierarchies.

关键词： Bioinformatics Comparative genomics

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Incorporating Multiple Features to Predict Bug Fixing Time with Neural Networks

Incorporating Multiple Features to Predict Bug Fixing Time w...

引用

International Conference on Software Maintenance (ICSM)

作者： Wei Yuan Yuan Xiong Hailong Sun Xudong Liu SKLSDE Lab School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China SKLSDE Lab School of Software Beihang University Beijing China

Debugging is a well-known time-consuming task, and knowing how long it would take to resolve bugs is of great importance for allocating the limited resources in a software development team. However, it is challenging to predict bug fixing time since fixing bugs is subject to a plethora of uncertain factors such as types of bugs, program complexity and developers' abilities. Existing work mainly focuses on developers' activities in a bug lifecycle and ignores other important factors. In light of the limitations of existing work, we propose a novel approach to predicting the bug fixing time by incorporating a comprehensive set of relevant features. Specifically, we consider four types of features including developers' activities, developers' sentiments, semantics of bugs, and efforts caused by understanding and analyzing source code, and design particular neural networks to take advantage of these features and make them work efficiently. Experimental results on four real application datasets demonstrate that on the one hand, our approach outperforms the state-of-the-art by over 5% in accuracy and 7.3% in F1-score on average; on the other hand, each type of the features considered in our approach plays an important part in the prediction.

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An Image Enhancement Method for Few-shot Classification

Research Square

引用

Research Square 2021年

作者： Wu, Yirui Wu, Benze Zhang, Yunfei Wan, Shaohua Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Jiangning Jiangsu Nanjing211100 China College of Computer and Information Hohai University Jiangning Jiangsu Nanjing211100 China School of Information and Safety Engineering Zhongnan University of Economics and Law Hongshan Hubei Wuhan430073 China

With the development of 5G/6G, IoT, and cloud systems, the amount of data generated, transmitted, and calculated is increasing, and fast and effective close-range image classification becomes more and more important. But many methods require a large number of samples to support in order to achieve sufficient functions. This allows the entire network to zoom in to meet a large number of effective feature extractions, which reduces the efficiency of small sample classification to a certain extent. In order to solve these problems, we propose an image enhancement method for the problems of few-shot classification. This method is an expanded convolutional network with data enhancement function. This network can not only meet the features required for image classification without increasing the number of samples, but also has the advantage of using a large number of effective features without sacrificing efficiency. structure. The cutout structure can enhance the matrix in the data image input process by adding a fixed area 0 mask. The structure of FAU uses dilated convolution and uses the characteristics of the sequence to improve the efficiency of the network. We conduct a comparative experiment on the miniImageNet and CUB datasets, and the proposed method is superior to the comparative method in terms of effectiveness and efficiency measurement in the 1-shot and 5-shot cases. © 2021, CC BY.

关键词： Image enhancement

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Privacy-preserving cloud-aided broad learning system

arXiv

引用

arXiv 2021年

作者： Liu, Haiyang Zhang, Hanlin Guo, Li Yu, Jia Lin, Jie College of Computer Science and Technology Qingdao University Qingdao266071 China Institute of Big Data Technology and Smart City Qingdao University Qingdao266071 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Electronic and Information Engineering Xian Jiaotong University Xian710049 China

With the rapid development of artificial intelligence and the advent of the 5G era, deep learning has received extensive attention from researchers. Broad Learning System (BLS) is a new deep learning model proposed recently, which shows its effectiveness in many fields, such as image recognition and fault detection. However, the training process still requires vast computations, and therefore cannot be accomplished by some resource-constrained devices. To solve this problem, the resource-constrained device can outsource the BLS algorithm to cloud servers. Nevertheless, some security challenges also follow with the use of cloud computing, including the privacy of the data and the correctness of returned results. In this paper, we propose a secure, efficient, and verifiable outsourcing algorithm for BLS. This algorithm not only improves the efficiency of the algorithm on the client but also ensures that the clients sensitive information is not leaked to the cloud server. In addition, in our algorithm, the client can verify the correctness of returned results with a probability of almost 1. Finally, we analyze the security and efficiency of our algorithm in theory and prove our algorithms feasibility through experiments. Copyright © 2021, The Authors. All rights reserved.

关键词： Outsourcing

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