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DF-Fit: A Robust Algorithm for Detection of Crystallographic Information in Atom Probe Tomography Data

MICROSCOPY AND MICROANALYSIS 2019年第2期25卷 331-337页

作者： Haley, Daniel Bagot, Paul A. J. Moody, Michael P. Univ Oxford Dept Mat 16 Parks Rd Oxford OX1 3PH England

We report on a new algorithm for the detection of crystallographic information in three-dimensional, as retained in atom probe tomography (APT), with improved robustness and signal detection performance. The algorithm is underpinned by one-dimensional distribution functions (DFs), as per existing algorithms, but eliminates an unnecessary parameter as compared to current methods. By examining traditional DFs in an automated fashion in real space, rather than using Fourier transform approaches, we utilize an error metric based upon the expected value for a spatially random distribution for detecting crystallography. We show cases where the metric is able to successfully obtain orientation information, and show that it can function with high levels of additive and displacive background noise. We additionally compare this metric to Fourier transform methods, showing fewer artifacts when examining simulated datasets. An extension of the approach is used to aid the automatic detection of high-quality data regions within an entire dataset, albeit with a large increase in computational cost. This extension is demonstrated on acquired aluminum and tungsten APT datasets, and shown to be able to discern regions of the data which have relatively improved spatial data quality. Finally, this program has been made available for use in other laboratories undertaking their own analyses.

关键词： atom probe computer program crystallography data analysis

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Data temperature-aware bloom filters for flash-based storage 21

Data temperature-aware bloom filters for flash-based storage

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21st IEEE International conference on high performance Computing and Communications, 17th IEEE International conference on Smart City and 5th IEEE International conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Li, Wei Zhang, Tianyun Zhang, Dafang Hu, Yupeng Xie, Kun Zhu, Ting College of Computer Science and Electronic Engineering Hunan University Changsha China Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore County United States

ISBN: (纸本)9781728120584

Hot data identification techniques for flash memory have attracted a plethora of research interests because of its crucial impact on performance and life span of SSD. Our proposed technique, Data Temperature-Aware Bloom Filters (DTABF), is suitable for the requirements of both the Buffer Management and the Flash Translation Layer in SSD. DTABF divides the access period of LPAs into n cycles. Thus, DTABF can record the access frequency of LPAs with a round-robin fashion in each cycle by combining one Counting Bloom filter and n Bloom filters. The access pattern of each LPA, indicated by the data access frequency, recency, and access frequency changes over time, is presented with n different data temperatures in n cycles. Specifically, we employ a bijective function to characterize the data temperature of each LPA. Based on the data temperature identified by DTABF, the data stored in LPAs, which presenting the similar access pattern, can be gathered in the same flash block in SSD. Meanwhile, those data occasionally becoming cold in the buffer can be effectively identified to avoid being meaninglessly replaced from the buffer. Analytical and experimental results show that DTABF can help to improve hit rate, write performance and alleviate write amplification, while achieving lower memory cost and computational complexity. © 2019 IEEE.

关键词： Data structures

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highly pipelined Accelerator for Convolutional Neural Network

Highly pipelined Accelerator for Convolutional Neural Networ...

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IEEE International conference on Consumer Electronics (ICCE)

作者： Kim, Junkyung Bae, HwangSik Min, Kyeong Yuk Chong, Jongwha Hanyang Univ Dept Elect Engn Seoul South Korea

ISBN: (纸本)9781538679104

We propose a highly pipelined accelerator (HIPA) for the convolutional neural network (CNN). The CNN achieves remarkable performance in computer vision application with high computational power and low energy efficiency than conventional algorithms. Recent CNN accelerators with energy efficiency perform pre-loading in the internal memory by reducing data transmission from off-chip. We focus on the input data loading time to the processing unit for higher throughput. HIPA reduces loading time with optimized internal memory structure. For throughput, we propose a continuous snake-scan order to maximize data reuse. HIPA shows the input data loading time reduction by over 37% from VGGNet-D, and speed up to 530GOp/s.

关键词： computer architecture Microsoft Windows Kernel Random access memory Convolution Loading Throughput

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A channel estimation algorithm based on the basis extension model for the high-speed railway environments 3

A channel estimation algorithm based on the basis extension ...

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3rd IEEE International conference on Electronic Information Technology and computer Engineering, EITCE 2019

作者： Xiao, Manlin Shi, Huajie Zheng, Qi Shanghai University of Engineering Science College of Urban Railway Transportation Shanghai China

ISBN: (纸本)9781728135847

The channel estimation is often a difficult task and has also attracted broad attention in wireless communications. This paper provides a fast time-varying channel model for the highspeed railway environments. Then, a channel estimation algorithm based on the basis extension model (BEM) is proposed for the Orthogonal Frequency Division Multiplexing (OFDM) system, which is often adopted in the high-speed moving communication systems. In simulations, the performance of the proposed channel estimation algorithm is compared under the different signal noise ratios (SNRs), Doppler frequency shifts and channel models which consist of different basis functions. © 2019 IEEE.

关键词： Orthogonal frequency division multiplexing

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Towards efficient content discovery in NDN with enhanced component-based bloom filters 21

Towards efficient content discovery in NDN with enhanced com...

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作者： Zhang, Cuiping Wang, Yunmin School of Electronic and Computer Engineering Peking University China Shenzhen Key Lab of Information Theory and Future Network Architecture Future Network PKU Lab of National Major Research Infrastructure PKU Institute of Big Data Technology Shenzhen Engineering Lab of Converged Networking Technology Huawei and PKU Jointly Engineering Lab of Future Network Based on SDN Shenzhen Graduate School Peking University China

ISBN: (纸本)9781728120584

Variable-length hierarchical names of Named data networking (NDN) content bring about intuitive issues for both content routing and large-scale forwarding. Bloom filters can accelerate hash-based NDN content discovery with a false positive probability. In this paper, we propose ECBF, a component-based bloom filter scheme to enhance the reliability of bloom filters. We exploit the hierarchical characteristics of NDN content names and store them into different bloom filters in a component-based way, and then we present a correlation validation policy to check whether the names exist. Finally, we introduce an ECBF-based content discovery scheme BCD. ECBF and the correlation validation value are stored in a fast on-chip memory, and cryptographic check values of content names and related information are respectively stored in an off-chip memory of intermediate routers. This design not only improves the effectiveness of content discovery, but also reduces the additional memory throughput. We experimentally demonstrate that BCD can provide a more than twice reduction of false probability compared with the primary bloom filter methods. Besides, BCD can economize about 40% memory consumption. © 2019 IEEE.

关键词： Probability

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Smart Ponzi Scheme Detection using Federated Learning

Smart Ponzi Scheme Detection using Federated Learning

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IEEE International conference on high performance Computing and Communications (HPCC)

作者： Shuhui Fan Haoran Xu Shaojing Fu Ming Xu College of Computer National University of Defense Technology Changsha China Sate Key Laboratory of Cryptology Beijing China

ISBN: (纸本)9781728176499;9781728176505

Decentralized applications (DApp) have opened up new heights for the use of blockchain and cryptocurrencies, but users with poor intentions utilize DApp websites to promote smart Ponzi schemes, causing huge losses to inexperienced investors. To alert potential investors, DApp websites tend to mark high-risk DApps uploaded by users. So far, previous works have only trained the classifiers of smart Ponzi schemes in a unilateral way. However, smart contract dataset is very skewed, there are much fewer smart Ponzi schemes than non-Ponzi schemes. Besides, due to data security and privacy, different DApp websites are generally not allowed to share their users' data and information. As a result, it is difficult for a certain DApp website to learn the patterns of smart Ponzi schemes and detect them *** paper proposes a novel smart Ponzi scheme detection framework named SPSD-FL (Smart Ponzi Scheme Detection in Federated Learning) for the first time to address these problems. Instead of unilateral training, our method fulfills a secure aggregation of local gradient histograms by a horizontal federated learning based on XGBoost algorithm, in which we leverage a data augmentation method and labeled smart Ponzi schemes' code features, dapp submitters' information, and investors' information to implement the collaborative training of the detection model between different DApp websites without sharing the original training data and protecting the user's sensitive information. Experiments conducted on the real dataset show that our method can be trained substantially more data-efficiently and the SPSD-FL model achieves an average F-score of 96.55%, which is better than the unilateral training model and no less than that of the centralized training model.

关键词： Training Histograms Computational modeling high performance computing Smart contracts Training data Collaboration

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Decision Support System for Teacher performance Assessment of SMK Nusantara 1 Ciputat Based on AHP and TOPSIS 1

Decision Support System for Teacher Performance Assessment o...

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1st International conference on computer Science and Applied Mathematic, ICCSAM 2018

作者： Hafizah, Husnul Napitupulu, Darmawan Adiyarta, Krisna Windarto, Agus Perdana Program Studi Magister Ilmu Komputer Universitas Budi Luhur Jakarta Indonesia Research Center for Quality System and Testing Technology Indonesian Institute of Sciences Banten Indonesia STIKOM Tunas Bangsa Sudirman Street Pematangsiantar Medan Indonesia

SMK Nusantara 1 Ciputat is a vocational high school that has seven vocational schools including: Multimedia, Software Engineering, computer and Network Engineering, Marketing, Accounting, Catering and Hospitality Services. To improve the quality of education in schools, principals are required to know information about the performance of educators in their schools. At present, the Ciputat 1 Vocational School principal has difficulty in making decisions on teacher performance appraisal because there is no weighting on teacher performance appraisal competencies that refer to Minister of National Education Regulation 16/2007 concerning Academic Qualification Standards and Teacher Competencies. Therefore, the AHP method is used to determine the weight of teacher competency and TOPSIS is used to produce a ranking on teacher alternatives according to the weight of the competencies determined by the AHP method. The results of weighting teacher competency with the AHP method have tested the consistency of the ratio of 0.088, this value is said Consistent because it is still below 0.1. Based on the weighting of the teacher's competency, an alternative ranking is obtained in the assessment of teacher performance using the TOPSIS method with the highest teacher's alternative value of 0.8052. © Published under licence by IOP Publishing Ltd.

关键词： Decision support systems

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Decoding both intention and learning strategies from EEG signals 7

Decoding both intention and learning strategies from EEG sig...

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7th International Winter conference on Brain-computer Interface (BCI)

作者： Kim, Dongjae Lee, Sang Wan Korea Adv Inst Sci & Technol Program Brain & Cognit Engn Dept Bio & Brain Engn Daejeon South Korea Korea Adv Inst Sci & Technol K1 Inst Hlth Sci & Technol K1 Inst Al Daejeon South Korea

ISBN: (纸本)9781538681169

Despite the fact that a majority of Brain-computer Interface (BCI) studies have focused on decoding signals related to movement, decoding intention underlying movement might be equally important. Fundamental properties of electroencephalography (EEG) constrain temporal resolutions for directly decoding movement kinematics, so estimating intention pertaining to movement, which would help us predict both long and short term human behaviors, may be an alternative solution for overcoming this issue. To estimate movement intention, recent studies found it helpful to consider hierarchical control between two distinctive learning strategies in reinforcement learning: a goal-directed and a habitual strategy. In the previous study, we suggested a proof of concept, called a model-based BCI framework, that can distinguish different learning strategies from electroencephalography (EEG) data. To advance this concept, we proposed intention decoders based on simple long short-term memory models (LSTM). To test the effect of intention estimation on prediction performance, we trained two versions of intention decoders: one with and the other without making use of underlying learning strategies decoded by the model-based BCI. The simulation results demonstrated that estimation performance of intention decoders with model-based BCI is significantly better (84%) than the one without model-based BCI (77%). We argued that by using model-based BCI, we can not only estimate learning strategy but also improve decoding performance of movement intention with high accuracy.

关键词： EEG model-based BCI LSTM movement intention

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PLINER: Isolating Lines of Floating-Point Code for Compiler-Induced Variability 20

PLINER: Isolating Lines of Floating-Point Code for Compiler-...

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Supercomputing conference

作者： Hui Guo Ignacio Laguna Cindy Rubio-González University of California Davis Davis CA USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore CA USA

ISBN: (数字)9781728199986

ISBN: (纸本)9781728199993

Scientific applications are often impacted by numerical inconsistencies when using different compilers or when a compiler is used with different optimization levels; such inconsistencies hinder reproducibility and can be hard to diagnose. We present PLINER, a tool to automatically pinpoint code lines that trigger compiler-induced variability. PLINER uses a novel approach to enhance floating-point precision at different levels of code granularity, and performs a guided search to identify locations affected by numerical inconsistencies. We demonstrate PLINER on a real-world numerical inconsistency that required weeks to diagnose, which PLINER isolates in minutes. We also evaluate PLiNER on 100 synthetic programs, and the NAS Parallel Benchmarks (NPB). On the synthetic programs, PLiNER detects the affected lines of code 87% of the time while the stateof-the-art approach only detects the affected lines 6% of the time. Furthermore, PLINER successfully isolates all numerical inconsistencies found in the NPB.

关键词： Productivity program processors high performance computing computer architecture Tools Reliability Optimization

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Symbolic Verification of Message Passing Interface programs

Symbolic Verification of Message Passing Interface Programs

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International conference on Software Engineering (ICSE)

作者： Hengbiao Yu Zhenbang Chen Xianjin Fu Ji Wang Zhendong Su Jun Sun Chun Huang Wei Dong National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China ETH Zurich Switzerland School of Information Systems Singapore Management University Singapore

ISBN: (数字)9781450371216

ISBN: (纸本)9781728165196

Message passing is the standard paradigm of programming in high-performance computing. However, verifying Message Passing Interface (MPI) programs is challenging, due to the complex program features (such as non-determinism and non-blocking operations). In this work, we present MPI symbolic verifier (MPI-SV), the first symbolic execution based tool for automatically verifying MPI programs with non-blocking operations. MPI-SV combines symbolic execution and model checking in a synergistic way to tackle the challenges in MPI program verification. The synergy improves the scalability and enlarges the scope of verifiable properties. We have implemented MPI-SV and evaluated it with 111 real-world MPI verification tasks. The pure symbolic execution-based technique successfully verifies 61 out of the 111 tasks (55%) within one hour, while in comparison, MPI-SV verifies 100 tasks (90%). On average, compared with pure symbolic execution, MPI-SV achieves 19x speedups on verifying the satisfaction of the critical property and 5x speedups on finding violations.

关键词： Message passing Model checking Tools Task analysis Tuning Standards Software engineering

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