Recently, cloud radio access network (C-RAN) with caching as a service (CaaS) was proposed to merge the functionalities of communication, computing, and caching (CCC) together. In this paper, we dissect the interactio...
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distributed software systems are becoming more and more complex *** is easy to find a huge amount of computing nodes in a nationwide or global information *** example,We Chat(Wei Xin),a well-known mobile application i...
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distributed software systems are becoming more and more complex *** is easy to find a huge amount of computing nodes in a nationwide or global information *** example,We Chat(Wei Xin),a well-known mobile application in China,has reached a record of 650 million monthly active users in the third quarter of *** the same time,researchers are starting to talk about software systems which have billions of lines of codes[1]or can last one hundred years.
The Internet based cyber-physical world has profoundly changed the information environment for the development of artificial intelligence(AI), bringing a new wave of AI research and promoting it into the new era of AI...
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The Internet based cyber-physical world has profoundly changed the information environment for the development of artificial intelligence(AI), bringing a new wave of AI research and promoting it into the new era of AI 2.0. As one of the most prominent characteristics of research in AI 2.0 era, crowd intelligence has attracted much attention from both industry and research communities. Specifically, crowd intelligence provides a novel problem-solving paradigm through gathering the intelligence of crowds to address challenges. In particular, due to the rapid development of the sharing economy, crowd intelligence not only becomes a new approach to solving scientific challenges, but has also been integrated into all kinds of application scenarios in daily life, e.g., online-tooffline(O2O) application, real-time traffic monitoring, and logistics management. In this paper, we survey existing studies of crowd intelligence. First, we describe the concept of crowd intelligence, and explain its relationship to the existing related concepts, e.g., crowdsourcing and human computation. Then, we introduce four categories of representative crowd intelligence platforms. We summarize three core research problems and the state-of-the-art techniques of crowd intelligence. Finally, we discuss promising future research directions of crowd intelligence.
As physical obstacles, vehicles have significant impacts on the efficient propagations of safety-related information in vehicular ad hoc networks (VANETs) by frequently obstructing the LOS links between transmitters a...
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New non-volatile memory （e.g., phase-change memory） provides fast access, large capacity, byteaddressability, and non-volatility features. These features, fast-byte-persistency, will bring new opportunities to fault...
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New non-volatile memory （e.g., phase-change memory） provides fast access, large capacity, byteaddressability, and non-volatility features. These features, fast-byte-persistency, will bring new opportunities to fault tolerance. We propose a fine-grained checkpoint based on non-volatile memory. We extend the current virtual memory manager to manage non-volatile memory, and design a persistent heap with support for fast allocation and checkpointing of persistent objects. To achieve a fine-grained checkpoint, we scatter objects across virtual pages and rely on hardware page-protection to monitor the modifications. In our system, two objects in different virtual pages may reside on the same physical page. Modifying one object would not interfere with the other object. This allows us to monitor and checkpoint objects smaller than 4096 bytes in a fine-grained way. Compared with previous page-grained based checkpoint mechanisms, our new checkpoint method can greatly reduce the data copied at checkpoint time and better leverage the limited bandwidth of non-volatile memory.
—Existing generalization theories analyze the generalization performance mainly based on the model complexity and training process. The ignorance of the task properties, which results from the widely used IID assumpt...
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Indoor-Outdoor scene classification problem have been proposed for almost 20 years and widely applied to general scene classification, image retrieval, image processing and robot application. But there is no consensus...
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ISBN:
(纸本)9781510845541
Indoor-Outdoor scene classification problem have been proposed for almost 20 years and widely applied to general scene classification, image retrieval, image processing and robot application. But there is no consensus on one particular scene classification technique that can solve the Indoor-Outdoor scene classification problem perfectly. As larger image dataset has been developed and machine learning technology especially deep learning based methods achieve remarkable performance in computer vision, we aim to provide guidance and direction for researchers to tackle the Indoor-Outdoor scene classification problem with more powerful and robust solution through concluding the Indoor-Outdoor scene classification approaches which have been proposed in last 20 years. In this paper, we review the Indoor-Outdoor scene classification including feature extraction, classifier and related dataset. Their advantages and disadvantages are discussed. At last we conclude some challenging problems remain unsolved and propose some potential solutions.
Image annotation generates a set of semantic labels that describe the contents of an input *** deep learning techniques have achieved significant success in many areas of image *** this paper,we present a multi-label ...
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Image annotation generates a set of semantic labels that describe the contents of an input *** deep learning techniques have achieved significant success in many areas of image *** this paper,we present a multi-label image annotation method that combines unsupervised object hypotheses generation and deep neural *** an image,object hypotheses are generated in an unsupervised *** we extract the image features for each hypothesis with a deep neural network *** combining the features of all hypotheses,we get the features of the entire ***,we calculate for each label the probability of that the label is correlated with the given *** can be trained in an end-to-end way using the standard backward propagation *** results on multiple benchmark datasets show that our method is better than the state-of-the-art ones.
Performance and energy consumption of high performance computing （HPC） interconnection networks have a great significance in the whole supercomputer, and building up HPC interconnection network simulation plat- form...
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Performance and energy consumption of high performance computing （HPC） interconnection networks have a great significance in the whole supercomputer, and building up HPC interconnection network simulation plat- form is very important for the research on HPC software and hardware technologies. To effectively evaluate the per- formance and energy consumption of HPC interconnection networks, this article designs and implements a detailed and clock-driven HPC interconnection network simulation plat- form, called HPC-NetSim. HPC-NetSim uses application- driven workloads and inherits the characteristics of the de- tailed and flexible cycle-accurate network simulator. Besides, it offers a large set of configurable network parameters in terms of topology and routing, and supports router＇s on/off states. We compare the simulated execution time with the real execution time of Tianhe-2 subsystem and the mean error is only 2.7%. In addition, we simulate the network behaviors with different network structures and low-power modes. The results are also consistent with the theoretical analyses.
GEMM is the main computational kernel in BLAS3. Its micro-kernel is either hand-crafted in assembly code or generated from C code by general-purpose compilers (guided by architecture-specific directives or auto-tuning...
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ISBN:
(纸本)9781509049318
GEMM is the main computational kernel in BLAS3. Its micro-kernel is either hand-crafted in assembly code or generated from C code by general-purpose compilers (guided by architecture-specific directives or auto-tuning). Therefore, either performance or portability suffers. We present a POrtable Compiler Approach, Poca, implemented in LLVM, to automatically generate and optimize this micro-kernel in an architecture-independent manner, without involving domain experts. The key insight is to leverage a wide range of architecture-specific abstractions already available in LLVM, by first generating a vectorized micro-kernel in the architecture-independent LLVM IR and then improving its performance by applying a series of domain-specific yet architecture-independent optimizations. The optimized micro-kernel drops easily in existing GEMM frameworks such as BLIS and OpenBLAS. Validation focuses on optimizing GEMM in double precision on two architectures. On Intel Sandybridge and AArch64 Cortex-A57, Poca's micro-kernels outperform expert-crafted assembly code by 2.35% and 7.54%, respectively, and both BLIS and OpenBLAS achieve competitive or better performance once their micro-kernels are replaced by Poca's.
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