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

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 ‘Threat’ of an ‘Attack’. Attackers are targeting these devices, as it may provide an easier ‘backdoor entry to the users’ network’.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

과거 가격 및 희소한 트윗을 이용한 주가 변동 예측

학위논문(석사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2021.8. 강유.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.과거 주가와 예측할 주식을 언급하는 희소한 트윗이 주어졌을 때 주가 변동을 어떻게 정확하게 예측할 수 있을까? 많은 시장 분석가들은 예측을 위해 많은 양의 정 보를 사용하려고 노력합니다. 하지만 예측을 위해 더 많은 양의 데이터를 사용할수 록 더 많은 노이즈에 직면합니다. 따라서 기존 방법은 과거 주식 가격만 사용하거나 뉴스 기사 혹은 대상 주식을 언급하는 트윗과 같은 소량의 정제된 데이터를 사용 합니다. 그러나 기존 방법들은 다음과 같은 한계가 있습니다: 1) 과거 주식 가격만 사용하면 정보가 부족하여 성능이 저하되고, 2) 뉴스 기사는 주가 변동을 예측하는 데 소셜 미디어에 비해 적시성이 부족하며, 3) 트윗을 사용하는 이전 방법들은 트윗이 언급하지 않은 주식들을 처리하지 못합니다. 본 논문에서는 목표 주식을 언급하는 트윗 없이도 작동하는 정확한 주가 변동 예측 방법인 GLT (Stock Price Movement Prediction using Global and Local Trends of Tweets)를 제안합니다. GLT는 자가 감독 방식을 활용하여 주식 및 트윗 임베딩을 사전 학습합니다. 그런 다음 GLT는 각각 글로벌 여론과 목표 주식과 관련된 트렌드 를 나타내는 글로벌 및 로컬 트윗 트렌드를 생성합니다. 이러한 추세 벡터들은 주가 변동을 정확하게 예측하는데 기여합니다. 실험 결과에 따르면 GLT는 주가 변동 예 측에서 최고 수준의 정확도를 제공합니다.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 23석

Photonic integrated reconfigurable linear processors as neural network accelerators

Reconfigurable linear optical processors can be used to perform linear transformations and are instrumental in effectively computing matrix–vector multiplications required in each neural network layer. In this paper, we characterize and compare two thermally tuned photonic integrated processors realized in silicon-on-insulator and silicon nitride platforms suited for extracting feature maps in convolutional neural networks. The reduction in bit resolution when crossing the processor is mainly due to optical losses, in the range 2.3–3.3 for the silicon-on-insulator chip and in the range 1.3–2.4 for the silicon nitride chip. However, the lower extinction ratio of Mach–Zehnder elements in the latter platform limits their expressivity (i.e., the capacity to implement any transformation) to 75%, compared to 97% of the former. Finally, the silicon-on-insulator processor outperforms the silicon nitride one in terms of footprint and energy efficiency

Spiking Neural Networks for Computational Intelligence:An Overview

Deep neural networks with rate-based neurons have exhibited tremendous progress in the last decade. However, the same level of progress has not been observed in research on spiking neural networks (SNN), despite their capability to handle temporal data, energy-efficiency and low latency. This could be because the benchmarking techniques for SNNs are based on the methods used for evaluating deep neural networks, which do not provide a clear evaluation of the capabilities of SNNs. Particularly, the benchmarking of SNN approaches with regards to energy efficiency and latency requires realization in suitable hardware, which imposes additional temporal and resource constraints upon ongoing projects. This review aims to provide an overview of the current real-world applications of SNNs and identifies steps to accelerate research involving SNNs in the future

Towards Evaluating User Profiling Methods Based on Explicit Ratings on Item Features

In order to improve the accuracy of recommendations, many recommender systems nowadays use side information beyond the user rating matrix, such as item content. These systems build user profiles as estimates of users' interest on content (e.g., movie genre, director or cast) and then evaluate the performance of the recommender system as a whole e.g., by their ability to recommend relevant and novel items to the target user. The user profile modelling stage, which is a key stage in content-driven RS is barely properly evaluated due to the lack of publicly available datasets that contain user preferences on content features of items. To raise awareness of this fact, we investigate differences between explicit user preferences and implicit user profiles. We create a dataset of explicit preferences towards content features of movies, which we release publicly. We then compare the collected explicit user feature preferences and implicit user profiles built via state-of-the-art user profiling models. Our results show a maximum average pairwise cosine similarity of 58.07\% between the explicit feature preferences and the implicit user profiles modelled by the best investigated profiling method and considering movies' genres only. For actors and directors, this maximum similarity is only 9.13\% and 17.24\%, respectively. This low similarity between explicit and implicit preference models encourages a more in-depth study to investigate and improve this important user profile modelling step, which will eventually translate into better recommendations