Binary neural networks have become a promising research topic due to their advantages of fast inference speed and low energy consumption. However, most existing studies focus on binary convolutional neural networks, w...
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Binary neural networks have become a promising research topic due to their advantages of fast inference speed and low energy consumption. However, most existing studies focus on binary convolutional neural networks, while less attention has been paid to binary graph neural networks. A common drawback of existing studies on binary graph neural networks is that they still include lots of inefficient full-precision operations in multiplying three matrices and are therefore not efficient enough. In this paper, we propose a novel method, called re-quantization-based binary graph neural networks(RQBGN), for binarizing graph neural networks. Specifically, re-quantization, a necessary procedure contributing to the further reduction of superfluous inefficient full-precision operations, quantizes the results of multiplication between any two matrices during the process of multiplying three matrices. To address the challenges introduced by requantization, in RQBGN we first study the impact of different computation orders to find an effective one and then introduce a mixture of experts to increase the model capacity. Experiments on five benchmark datasets show that performing re-quantization in different computation orders significantly impacts the performance of binary graph neural network models, and RQBGN can outperform other baselines to achieve state-of-the-art performance.
Stochastic gradient descent(SGD) and its variants have been the dominating optimization methods in machine learning. Compared with SGD with small-batch training, SGD with large-batch training can better utilize the co...
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Stochastic gradient descent(SGD) and its variants have been the dominating optimization methods in machine learning. Compared with SGD with small-batch training, SGD with large-batch training can better utilize the computational power of current multi-core systems such as graphics processing units(GPUs)and can reduce the number of communication rounds in distributed training settings. Thus, SGD with large-batch training has attracted considerable attention. However, existing empirical results showed that large-batch training typically leads to a drop in generalization accuracy. Hence, how to guarantee the generalization ability in large-batch training becomes a challenging task. In this paper, we propose a simple yet effective method, called stochastic normalized gradient descent with momentum(SNGM), for large-batch training. We prove that with the same number of gradient computations, SNGM can adopt a larger batch size than momentum SGD(MSGD), which is one of the most widely used variants of SGD, to converge to an?-stationary point. Empirical results on deep learning verify that when adopting the same large batch size,SNGM can achieve better test accuracy than MSGD and other state-of-the-art large-batch training methods.
Many real-world datasets suffer from the unavoidable issue of missing values,and therefore classification with missing data has to be carefully handled since inadequate treatment of missing values will cause large ***...
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Many real-world datasets suffer from the unavoidable issue of missing values,and therefore classification with missing data has to be carefully handled since inadequate treatment of missing values will cause large *** this paper,we propose a random subspace sampling method,RSS,by sampling missing items from the corresponding feature histogram distributions in random subspaces,which is effective and efficient at different levels of missing *** most established approaches,RSS does not train on fixed imputed ***,we design a dynamic training strategy where the filled values change dynamically by resampling during ***,thanks to the sampling strategy,we design an ensemble testing strategy where we combine the results of multiple runs of a single model,which is more efficient and resource-saving than previous ensemble ***,we combine these two strategies with the random subspace method,which makes our estimations more robust and *** effectiveness of the proposed RSS method is well validated by experimental studies.
Mobile applications(apps for short)often need to display ***,inefficient image displaying(IID)issues are pervasive in mobile apps,and can severely impact app performance and user *** paper first establishes a descript...
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Mobile applications(apps for short)often need to display ***,inefficient image displaying(IID)issues are pervasive in mobile apps,and can severely impact app performance and user *** paper first establishes a descriptive framework for the image displaying procedures of IID *** on the descriptive framework,we conduct an empirical study of 216 real-world IID issues collected from 243 popular open-source Android apps to validate the presence and severity of IID issues,and then shed light on these issues’characteristics to support research on effective issue *** the findings of this study,we propose a static IID issue detection tool TAPIR and evaluate it with 243 real-world Android ***,49 and 64 previously-unknown IID issues in two different versions of 16 apps reported by TAPIR are manually confirmed as true positives,respectively,and 16 previously-unknown IID issues reported by TAPIR have been confirmed by developers and 13 have been ***,we further evaluate the performance impact of these detected IID issues and the performance improvement if they are *** results demonstrate that the IID issues detected by TAPIR indeed cause significant performance degradation,which further show the effectiveness and efficiency of TAPIR.
As a pivotal enabler of intelligent transportation system(ITS), Internet of vehicles(Io V) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive...
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As a pivotal enabler of intelligent transportation system(ITS), Internet of vehicles(Io V) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive,and privacy-aware vehicular applications in Io V result in the transformation from cloud computing to edge computing,which enables tasks to be offloaded to edge nodes(ENs) closer to vehicles for efficient execution. In ITS environment,however, due to dynamic and stochastic computation offloading requests, it is challenging to efficiently orchestrate offloading decisions for application requirements. How to accomplish complex computation offloading of vehicles while ensuring data privacy remains challenging. In this paper, we propose an intelligent computation offloading with privacy protection scheme, named COPP. In particular, an Advanced Encryption Standard-based encryption method is utilized to implement privacy protection. Furthermore, an online offloading scheme is proposed to find optimal offloading policies. Finally, experimental results demonstrate that COPP significantly outperforms benchmark schemes in the performance of both delay and energy consumption.
In this paper,we consider the exact quantum query complexity of two fundamental symmetric functions.1)MOD_(m)^(n),which calculates the Hamming weight of an-bit string modulo;2)EXACT_(k,l)^(n),which determines if the H...
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In this paper,we consider the exact quantum query complexity of two fundamental symmetric functions.1)MOD_(m)^(n),which calculates the Hamming weight of an-bit string modulo;2)EXACT_(k,l)^(n),which determines if the Hamming weight of an-bit string is exactly k or *** these two symmetric functions have received considerable attention,their exact quantum query complexities have not been fully ***,our results are as follows:1)We design an optimal quantum query algorithm to compute MOD_(m)^(n)exactly and thus provide a tight characterization of its exact quantum query complexity,which settles a previous *** on this algorithm,we demonstrate that a broad class of symmetric functions is not evasive in the quantum model,i.e.,there exist quantum algorithms to compute these functions exactly when the number of queries is less than their input size.2)By proposing a quantum algorithm that utilizes the minimum number of queries to compute EXACT_(k,l)^(n)exactly for some specific values of k and l,we give a tight characterization of its exact quantum query complexity in these scenarios.
The transformer architecture [1] has been widely used for natural language processing(NLP) tasks. Under the inspiration of its excellent performance in NLP, transformer-based models [2, 3] have established many new re...
The transformer architecture [1] has been widely used for natural language processing(NLP) tasks. Under the inspiration of its excellent performance in NLP, transformer-based models [2, 3] have established many new records in various computer vision tasks. However, most vision transformers(Vi Ts) suffer from large model sizes, large run-time memory consumption, and high computational costs. Therefore, impending needs exist to develop and deploy lightweight and efficient vision transformers.
With the rapid development of deep learning, current deep models can learn a fixed number of classes with high performance. However, in our ever-changing world, data often come from the open environment, which is with...
With the rapid development of deep learning, current deep models can learn a fixed number of classes with high performance. However, in our ever-changing world, data often come from the open environment, which is with stream format or available temporarily due to privacy issues. As a result, the classification model should learn new classes incrementally instead of restarting the training process.
Automated test generation tools enable test automation and further alleviate the low efficiency caused by writing hand-crafted test ***,existing automated tools are not mature enough to be widely used by software test...
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Automated test generation tools enable test automation and further alleviate the low efficiency caused by writing hand-crafted test ***,existing automated tools are not mature enough to be widely used by software testing *** paper conducts an empirical study on the state-of-the-art automated tools for Java,i.e.,EvoSuite,Randoop,JDoop,JTeXpert,T3,and *** design a test workflow to facilitate the process,which can automatically run tools for test generation,collect data,and evaluate various ***,we conduct empirical analysis on these six tools and their related techniques from different aspects,i.e.,code coverage,mutation score,test suite size,readability,and real fault detection *** discuss about the benefits and drawbacks of hybrid techniques based on experimental ***,we introduce our experience in setting up and executing these tools,and summarize their usability and ***,we give some insights into automated tools in terms of test suite readability improvement,meaningful assertion generation,test suite reduction for random testing tools,and symbolic execution integration.
Though obstruction-free progress property is weaker than other non-blocking properties including lock-freedom and wait-freedom,it has advantages that have led to the use of obstruction-free implementations for softwar...
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Though obstruction-free progress property is weaker than other non-blocking properties including lock-freedom and wait-freedom,it has advantages that have led to the use of obstruction-free implementations for software transactional memory(STM)and in anonymous and fault-tolerant distributed ***,existing work can only verify obstruction-freedom of specific data structures(e.g.,STM and list-based algorithms).In this paper,to fill this gap,we propose a program logic that can formally verify obstruction-freedom of practical implementations,as well as verify linearizability,a safety property,at the same *** also propose informal principles to extend a logic for verifying linearizability to verifying *** this approach,the existing proof for linearizability can be reused directly to construct the proof for both linearizability and ***,we have successfully applied our logic to verifying a practical obstruction-free double-ended queue implementation in the first classic paper that has proposed the definition of obstruction-freedom.
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