374 research outputs found
Adversarial Variational Embedding for Robust Semi-supervised Learning
Semi-supervised learning is sought for leveraging the unlabelled data when
labelled data is difficult or expensive to acquire. Deep generative models
(e.g., Variational Autoencoder (VAE)) and semisupervised Generative Adversarial
Networks (GANs) have recently shown promising performance in semi-supervised
classification for the excellent discriminative representing ability. However,
the latent code learned by the traditional VAE is not exclusive (repeatable)
for a specific input sample, which prevents it from excellent classification
performance. In particular, the learned latent representation depends on a
non-exclusive component which is stochastically sampled from the prior
distribution. Moreover, the semi-supervised GAN models generate data from
pre-defined distribution (e.g., Gaussian noises) which is independent of the
input data distribution and may obstruct the convergence and is difficult to
control the distribution of the generated data. To address the aforementioned
issues, we propose a novel Adversarial Variational Embedding (AVAE) framework
for robust and effective semi-supervised learning to leverage both the
advantage of GAN as a high quality generative model and VAE as a posterior
distribution learner. The proposed approach first produces an exclusive latent
code by the model which we call VAE++, and meanwhile, provides a meaningful
prior distribution for the generator of GAN. The proposed approach is evaluated
over four different real-world applications and we show that our method
outperforms the state-of-the-art models, which confirms that the combination of
VAE++ and GAN can provide significant improvements in semisupervised
classification.Comment: 9 pages, Accepted by Research Track in KDD 201
Machine Learning for Cyberattack Detection
Machine learning has become rapidly utilized in cybersecurity, rising from almost non-existent to currently over half of cybersecurity techniques utilized commercially. Machine learning is advancing at a rapid rate, and the application of new learning techniques to cybersecurity have not been investigate yet. Current technology trends have led to an abundance of household items containing microprocessors all connected within a private network. Thus, network intrusion detection is essential for keeping these networks secure. However, network intrusion detection can be extremely taxing on battery operated devices. The presented work presents a cyberattack detection system based on a multilayer perceptron neural network algorithm. To show that this system can operate at low power, the algorithm was executed on two commercially available minicomputer systems including the Raspberry PI 3 and the Asus Tinkerboard. An analysis of accuracy, power, energy, and timing was performed to study the tradeoffs necessary when executing these algorithms at low power. Our results show that these low power implementations are feasible, and a scan rate of more than 226,000 packets per second can be achieved from a system that requires approximately 5W to operate with greater than 99% accuracy
Academic competitions
Academic challenges comprise effective means for (i) advancing the state of
the art, (ii) putting in the spotlight of a scientific community specific
topics and problems, as well as (iii) closing the gap for under represented
communities in terms of accessing and participating in the shaping of research
fields. Competitions can be traced back for centuries and their achievements
have had great influence in our modern world. Recently, they (re)gained
popularity, with the overwhelming amounts of data that is being generated in
different domains, as well as the need of pushing the barriers of existing
methods, and available tools to handle such data. This chapter provides a
survey of academic challenges in the context of machine learning and related
fields. We review the most influential competitions in the last few years and
analyze challenges per area of knowledge. The aims of scientific challenges,
their goals, major achievements and expectations for the next few years are
reviewed
Student Activity Recognition in Classroom Environments using Transfer Learning
The recent advances in artificial intelligence and deep learning facilitate
automation in various applications including home automation, smart
surveillance systems, and healthcare among others. Human Activity Recognition
is one of its emerging applications, which can be implemented in a classroom
environment to enhance safety, efficiency, and overall educational quality.
This paper proposes a system for detecting and recognizing the activities of
students in a classroom environment. The dataset has been structured and
recorded by the authors since a standard dataset for this task was not
available at the time of this study. Transfer learning, a widely adopted method
within the field of deep learning, has proven to be helpful in complex tasks
like image and video processing. Pretrained models including VGG-16, ResNet-50,
InceptionV3, and Xception are used for feature extraction and classification
tasks. Xception achieved an accuracy of 93%, on the novel classroom dataset,
outperforming the other three models in consideration. The system proposed in
this study aims to introduce a safer and more productive learning environment
for students and educators.Comment: 6 pages, 12 figures, accepted at the IEEE International Conference on
Computational Intelligence, Networks and Security (ICCINS) 202
MDFRCNN: Malware Detection using Faster Region Proposals Convolution Neural Network
Technological advancement of smart devices has opened up a new trend: Internet of Everything (IoE), where all devices are connected to the web. Large scale networking benefits the community by increasing connectivity and giving control of physical devices. On the other hand, there exists an increased โThreatโ of an โAttackโ. Attackers are targeting these devices, as it may provide an easier โbackdoor entry to the usersโ networkโ.MALicious softWARE (MalWare) is a major threat to user security. Fast and accurate detection of malware attacks are the sine qua non of IoE, where large scale networking is involved. The paper proposes use of a visualization technique where the disassembled malware code is converted into gray images, as well as use of Image Similarity based Statistical Parameters (ISSP) such as Normalized Cross correlation (NCC), Average difference (AD), Maximum difference (MaxD), Singular Structural Similarity Index Module (SSIM), Laplacian Mean Square Error (LMSE), MSE and PSNR. A vector consisting of gray image with statistical parameters is trained using a Faster Region proposals Convolution Neural Network (F-RCNN) classifier. The experiment results are promising as the proposed method includes ISSP with F-RCNN training. Overall training time of learning the semantics of higher-level malicious behaviors is less. Identification of malware (testing phase) is also performed in less time. The fusion of image and statistical parameter enhances system performance with greater accuracy. The benchmark database from Microsoft Malware Classification challenge has been used to analyze system performance, which is available on the Kaggle website. An overall average classification accuracy of 98.12% is achieved by the proposed method
๊ณผ๊ฑฐ ๊ฐ๊ฒฉ ๋ฐ ํฌ์ํ ํธ์์ ์ด์ฉํ ์ฃผ๊ฐ ๋ณ๋ ์์ธก
ํ์๋
ผ๋ฌธ(์์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ์ปดํจํฐ๊ณตํ๋ถ, 2021.8. ๊ฐ์ .Given historical stock prices and sparse tweets mentioning the stocks to predict, how can we precisely predict stock price movement? Many market analysts strive to use a large amount of information for prediction. However, they confront more noise when utilizing larger data for prediction. Thus, existing methods use only historical prices, or those along with a small amount of refined data such as news articles or tweets mentioning target stocks. However, they have the following limitations: 1) using only historical prices gives low performance since they have insufficient information, 2) news articles lack timeliness compared to social medias for predicting stock price movement, and 3) the previous methods using tweets do not handle stocks without tweets mentioning them.
In this paper, we propose GLT (Stock Price Movement Prediction using Global and Local Trends of Tweets), an accurate stock price movement prediction method that works without tweets mentioning target stocks. GLT pre-trains both of stock and tweet representations in a self-supervised way. Then, GLT generates global and local tweet trends which represent global public opinion and the local trends related to target stocks, respectively. The trend vectors are combined to accurately predict stock price movement. Experimental results show that GLT provides the state-ofthe-art accuracy for stock price movement prediction.๊ณผ๊ฑฐ ์ฃผ๊ฐ์ ์์ธกํ ์ฃผ์์ ์ธ๊ธํ๋ ํฌ์ํ ํธ์์ด ์ฃผ์ด์ก์ ๋ ์ฃผ๊ฐ ๋ณ๋์ ์ด๋ป๊ฒ ์ ํํ๊ฒ ์์ธกํ ์ ์์๊น? ๋ง์ ์์ฅ ๋ถ์๊ฐ๋ค์ ์์ธก์ ์ํด ๋ง์ ์์ ์ ๋ณด๋ฅผ ์ฌ์ฉํ๋ ค๊ณ ๋
ธ๋ ฅํฉ๋๋ค. ํ์ง๋ง ์์ธก์ ์ํด ๋ ๋ง์ ์์ ๋ฐ์ดํฐ๋ฅผ ์ฌ์ฉํ ์ ๋ก ๋ ๋ง์ ๋
ธ์ด์ฆ์ ์ง๋ฉดํฉ๋๋ค. ๋ฐ๋ผ์ ๊ธฐ์กด ๋ฐฉ๋ฒ์ ๊ณผ๊ฑฐ ์ฃผ์ ๊ฐ๊ฒฉ๋ง ์ฌ์ฉํ๊ฑฐ๋ ๋ด์ค ๊ธฐ์ฌ ํน์ ๋์ ์ฃผ์์ ์ธ๊ธํ๋ ํธ์๊ณผ ๊ฐ์ ์๋์ ์ ์ ๋ ๋ฐ์ดํฐ๋ฅผ ์ฌ์ฉ ํฉ๋๋ค. ๊ทธ๋ฌ๋ ๊ธฐ์กด ๋ฐฉ๋ฒ๋ค์ ๋ค์๊ณผ ๊ฐ์ ํ๊ณ๊ฐ ์์ต๋๋ค: 1) ๊ณผ๊ฑฐ ์ฃผ์ ๊ฐ๊ฒฉ๋ง ์ฌ์ฉํ๋ฉด ์ ๋ณด๊ฐ ๋ถ์กฑํ์ฌ ์ฑ๋ฅ์ด ์ ํ๋๊ณ , 2) ๋ด์ค ๊ธฐ์ฌ๋ ์ฃผ๊ฐ ๋ณ๋์ ์์ธกํ๋ ๋ฐ ์์
๋ฏธ๋์ด์ ๋นํด ์ ์์ฑ์ด ๋ถ์กฑํ๋ฉฐ, 3) ํธ์์ ์ฌ์ฉํ๋ ์ด์ ๋ฐฉ๋ฒ๋ค์ ํธ์์ด ์ธ๊ธํ์ง ์์ ์ฃผ์๋ค์ ์ฒ๋ฆฌํ์ง ๋ชปํฉ๋๋ค.
๋ณธ ๋
ผ๋ฌธ์์๋ ๋ชฉํ ์ฃผ์์ ์ธ๊ธํ๋ ํธ์ ์์ด๋ ์๋ํ๋ ์ ํํ ์ฃผ๊ฐ ๋ณ๋ ์์ธก ๋ฐฉ๋ฒ์ธ GLT (Stock Price Movement Prediction using Global and Local Trends of Tweets)๋ฅผ ์ ์ํฉ๋๋ค. GLT๋ ์๊ฐ ๊ฐ๋
๋ฐฉ์์ ํ์ฉํ์ฌ ์ฃผ์ ๋ฐ ํธ์ ์๋ฒ ๋ฉ์ ์ฌ์ ํ์ตํฉ๋๋ค. ๊ทธ๋ฐ ๋ค์ GLT๋ ๊ฐ๊ฐ ๊ธ๋ก๋ฒ ์ฌ๋ก ๊ณผ ๋ชฉํ ์ฃผ์๊ณผ ๊ด๋ จ๋ ํธ๋ ๋ ๋ฅผ ๋ํ๋ด๋ ๊ธ๋ก๋ฒ ๋ฐ ๋ก์ปฌ ํธ์ ํธ๋ ๋๋ฅผ ์์ฑํฉ๋๋ค. ์ด๋ฌํ ์ถ์ธ ๋ฒกํฐ๋ค์ ์ฃผ๊ฐ ๋ณ๋์ ์ ํํ๊ฒ ์์ธกํ๋๋ฐ ๊ธฐ์ฌํฉ๋๋ค. ์คํ ๊ฒฐ๊ณผ์ ๋ฐ๋ฅด๋ฉด GLT๋ ์ฃผ๊ฐ ๋ณ๋ ์ ์ธก์์ ์ต๊ณ ์์ค์ ์ ํ๋๋ฅผ ์ ๊ณตํฉ๋๋ค.I. Introduction 1
II. Related Work 4
2.1 Stock Price Movement Prediction 4
2.2 Attentive LSTM 4
III. Proposed Method 6
3.1 Overview 6
3.2 Self-supervised Pre-training for Representing Tweets and Stocks 7
3.3 Global Tweet Trend 10
3.4 Local Tweet Trend 11
3.5 Stock Movement Prediction 11
IV. Experiment 13
4.1 Experiment Setting 13
4.2 Classification Performance 15
4.3 Ablation Study 16
4.4 Hyperparameter Robustness 16
V. Conclusion 18
References 19
Abstract in Korean 23์
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