Mu-En Wu

Bio: Mu-En Wu is an academic researcher from National Taipei University of Technology. The author has contributed to research in topics: Trading strategy & Kelly criterion. The author has an hindex of 13, co-authored 87 publications receiving 647 citations. Previous affiliations of Mu-En Wu include Academia Sinica & Nanyang Technological University.

A secure authenticated and key exchange scheme for fog computing

Abstract: Fog computing architecture is used in various environments such as smart manufacturing, vehicular ad hoc networks. However, as an extension of cloud computing, inheriting security challenges of clo...

Dual RSA and Its Security Analysis

Abstract: We present new variants of an RSA whose key generation algorithms output two distinct RSA key pairs having the same public and private exponents. This family of variants, called dual RSA, can be used in scenarios that require two instances of RSA with the advantage of reducing the storage requirements for the keys. Two applications for dual RSA, blind signatures and authentication/secrecy, are proposed. In addition, we also provide the security analysis of dual RSA. Compared to normal RSA, the security boundary should be raised when applying dual RSA to the types of small-d, small-e, and rebalanced-RSA.

Applied attention-based LSTM neural networks in stock prediction

Abstract: Prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Many studies predict stock price movements using deep learning models. Although the attention mechanism has gained popularity recently in neural machine translation, little focus has been devoted to attention-based deep learning models for stock prediction. This paper proposes an attention-based long short-term memory model to predict stock price movement and make trading strategies

Decentralized E-Voting Systems Based on the Blockchain Technology

Abstract: This research is aimed to design a decentralized e-voting system. The core idea is to combine the blockchain technology with secret sharing scheme and homomorphic encryption in order to realize the decentralized e-voting application without a trusted third party. It provides a public and transparent voting process while protecting the anonymity of voter’s identity, the privacy of data transmission and verifiability of ballots during the billing phase.

Portfolio management system in equity market neutral using reinforcement learning

Abstract: Portfolio management involves position sizing and resource allocation. Traditional and generic portfolio strategies require forecasting of future stock prices as model inputs, which is not a trivial task since those values are difficult to obtain in the real-world applications. To overcome the above limitations and provide a better solution for portfolio management, we developed a Portfolio Management System (PMS) using reinforcement learning with two neural networks (CNN and RNN). A novel reward function involving Sharpe ratios is also proposed to evaluate the performance of the developed systems. Experimental results indicate that the PMS with the Sharpe ratio reward function exhibits outstanding performance, increasing return by 39.0% and decreasing drawdown by 13.7% on average compared to the reward function of trading return. In addition, the proposed PMS_CNN model is more suitable for the construction of a reinforcement learning portfolio, but has 1.98 times more drawdown risk than the PMS_RNN. Among the conducted datasets, the PMS outperforms the benchmark strategies in TW50 and traditional stocks, but is inferior to a benchmark strategy in the financial dataset. The PMS is profitable, effective, and offers lower investment risk among almost all datasets. The novel reward function involving the Sharpe ratio enhances performance, and well supports resource-allocation for empirical stock trading.

PORs: Proofs of Retrievability for Large Files

Abstract: In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

The Economics Of Money Banking And Financial Markets

Abstract: Thank you very much for reading the economics of money banking and financial markets. Maybe you have knowledge that, people have search numerous times for their favorite readings like this the economics of money banking and financial markets, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they cope with some harmful virus inside their laptop.

Benefits of depth in neural networks

Abstract: For any positive integer $k$, there exist neural networks with $\Theta(k^3)$ layers, $\Theta(1)$ nodes per layer, and $\Theta(1)$ distinct parameters which can not be approximated by networks with $\mathcal{O}(k)$ layers unless they are exponentially large --- they must possess $\Omega(2^k)$ nodes. This result is proved here for a class of nodes termed "semi-algebraic gates" which includes the common choices of ReLU, maximum, indicator, and piecewise polynomial functions, therefore establishing benefits of depth against not just standard networks with ReLU gates, but also convolutional networks with ReLU and maximization gates, sum-product networks, and boosted decision trees (in this last case with a stronger separation: $\Omega(2^{k^3})$ total tree nodes are required).