【Paddle打比赛】讯飞赛题—学术论文分类挑战赛0.8+Baseline

/home/aistudio/data/data100192
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['cs.CL', 'cs.NE', 'cs.DL', 'cs.CV', 'cs.LG', 'cs.DS', 'cs.IR', 'cs.RO', 'cs.DM', 'cs.CR', 'cs.AR', 'cs.NI', 'cs.AI', 'cs.SE', 'cs.CG', 'cs.LO', 'cs.SY', 'cs.GR', 'cs.PL', 'cs.SI', 'cs.OH', 'cs.HC', 'cs.MA', 'cs.GT', 'cs.ET', 'cs.FL', 'cs.CC', 'cs.DB', 'cs.DC', 'cs.CY', 'cs.CE', 'cs.MM', 'cs.NA', 'cs.PF', 'cs.OS', 'cs.SD', 'cs.SC', 'cs.MS', 'cs.GL']
{0: 'cs.CL', 1: 'cs.NE', 2: 'cs.DL', 3: 'cs.CV', 4: 'cs.LG', 5: 'cs.DS', 6: 'cs.IR', 7: 'cs.RO', 8: 'cs.DM', 9: 'cs.CR', 10: 'cs.AR', 11: 'cs.NI', 12: 'cs.AI', 13: 'cs.SE', 14: 'cs.CG', 15: 'cs.LO', 16: 'cs.SY', 17: 'cs.GR', 18: 'cs.PL', 19: 'cs.SI', 20: 'cs.OH', 21: 'cs.HC', 22: 'cs.MA', 23: 'cs.GT', 24: 'cs.ET', 25: 'cs.FL', 26: 'cs.CC', 27: 'cs.DB', 28: 'cs.DC', 29: 'cs.CY', 30: 'cs.CE', 31: 'cs.MM', 32: 'cs.NA', 33: 'cs.PF', 34: 'cs.OS', 35: 'cs.SD', 36: 'cs.SC', 37: 'cs.MS', 38: 'cs.GL'}

Download https://bj.bcebos.com/paddlehub/paddlehub_dev/ernie_v2_eng_large_2.0.2.tar.gz
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[2021-08-01 17:43:16,200] [    INFO] - Successfully installed ernie_v2_eng_large-2.0.2
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[2021-08-01 17:43:16,205] [    INFO] - Downloading ernie_v2_eng_large.pdparams from https://paddlenlp.bj.bcebos.com/models/transformers/ernie_v2_large/ernie_v2_eng_large.pdparams
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训练数据集拼接后的标题和摘要的最大长度为3713
训练数据集拼接后的标题和摘要的最小长度为69
训练数据集拼接后的标题和摘要的平均长度为1131.28478
测试数据集拼接后的标题和摘要的最大长度为3501
测试数据集拼接后的标题和摘要的最小长度为74
测试数据集拼接后的标题和摘要的平均长度为1127.0977

[2021-08-01 17:44:12,576] [    INFO] - Downloading vocab.txt from https://paddlenlp.bj.bcebos.com/models/transformers/ernie_v2_large/vocab.txt
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[2021-08-01 17:46:28,717] [    INFO] - Found /home/aistudio/.paddlenlp/models/ernie-2.0-large-en/vocab.txt

text=Enforcing Label and Intensity Consistency for IR Target Detection   This study formulates the IR target detection as a binary classification
problem of each pixel. Each pixel is associated with a label which indicates
whether it is a target or background pixel. The optimal label set for all the
pixels of an image maximizes aposteriori distribution of label configuration
given the pixel intensities. The posterior probability is factored into (or
proportional to) a conditional likelihood of the intensity values and a prior
probability of label configuration. Each of these two probabilities are
computed assuming a Markov Random Field (MRF) on both pixel intensities and
their labels. In particular, this study enforces neighborhood dependency on
both intensity values, by a Simultaneous Auto Regressive (SAR) model, and on
labels, by an Auto-Logistic model. The parameters of these MRF models are
learned from labeled examples. During testing, an MRF inference technique,
namely Iterated Conditional Mode (ICM), produces the optimal label for each
pixel. The detection performance is further improved by incorporating temporal
information through background subtraction. High performances on benchmark
datasets demonstrate effectiveness of this method for IR target detection.
	label=cs.CV
text=Saliency Preservation in Low-Resolution Grayscale Images   Visual salience detection originated over 500 million years ago and is one of
nature's most efficient mechanisms. In contrast, many state-of-the-art
computational saliency models are complex and inefficient. Most saliency models
process high-resolution color (HC) images; however, insights into the
evolutionary origins of visual salience detection suggest that achromatic
low-resolution vision is essential to its speed and efficiency. Previous
studies showed that low-resolution color and high-resolution grayscale images
preserve saliency information. However, to our knowledge, no one has
investigated whether saliency is preserved in low-resolution grayscale (LG)
images. In this study, we explain the biological and computational motivation
for LG, and show, through a range of human eye-tracking and computational
modeling experiments, that saliency information is preserved in LG images.
Moreover, we show that using LG images leads to significant speedups in model
training and detection times and conclude by proposing LG images for fast and
efficient salience detection.
	label=cs.CV
text=RT-Gang: Real-Time Gang Scheduling Framework for Safety-Critical Systems   In this paper, we present RT-Gang: a novel real-time gang scheduling
framework that enforces a one-gang-at-a-time policy. We find that, in a
multicore platform, co-scheduling multiple parallel real-time tasks would
require highly pessimistic worst-case execution time (WCET) and schedulability
analysis - even when there are enough cores - due to contention in shared
hardware resources such as cache and DRAM controller. In RT-Gang, all threads
of a parallel real-time task form a real-time gang and the scheduler globally
enforces the one-gang-at-a-time scheduling policy to guarantee tight and
accurate task WCET. To minimize under-utilization, we integrate a
state-of-the-art memory bandwidth throttling framework to allow safe execution
of best-effort tasks. Specifically, any idle cores, if exist, are used to
schedule best-effort tasks but their maximum memory bandwidth usages are
strictly throttled to tightly bound interference to real-time gang tasks. We
implement RT-Gang in the Linux kernel and evaluate it on two representative
embedded multicore platforms using both synthetic and real-world DNN workloads.
The results show that RT-Gang dramatically improves system predictability and
the overhead is negligible.
	label=cs.DC
text=AI Enabling Technologies: A Survey   Artificial Intelligence (AI) has the opportunity to revolutionize the way the
United States Department of Defense (DoD) and Intelligence Community (IC)
address the challenges of evolving threats, data deluge, and rapid courses of
action. Developing an end-to-end artificial intelligence system involves
parallel development of different pieces that must work together in order to
provide capabilities that can be used by decision makers, warfighters and
analysts. These pieces include data collection, data conditioning, algorithms,
computing, robust artificial intelligence, and human-machine teaming. While
much of the popular press today surrounds advances in algorithms and computing,
most modern AI systems leverage advances across numerous different fields.
Further, while certain components may not be as visible to end-users as others,
our experience has shown that each of these interrelated components play a
major role in the success or failure of an AI system. This article is meant to
highlight many of these technologies that are involved in an end-to-end AI
system. The goal of this article is to provide readers with an overview of
terminology, technical details and recent highlights from academia, industry
and government. Where possible, we indicate relevant resources that can be used
for further reading and understanding.
	label=cs.AI