Mingliang Ma

Mingliang Ma, Abolfazl Safikhani

Deep neural networks are powerful tools to model observations over time with non-linear patterns. Despite the widespread use of neural networks in such settings, most theoretical developments of deep neural networks are under the assumption of independent observations, and theoretical results for temporally dependent observations are scarce. To bridge this gap, we study theoretical properties of deep neural networks on modeling non-linear time series data. Specifically, non-asymptotic bounds for prediction error of (sparse) feed-forward neural network with ReLU activation function is established under mixing-type assumptions. These assumptions are mild such that they include a wide range of time series models including auto-regressive models. Compared to independent observations, established convergence rates have additional logarithmic factors to compensate for additional complexity due to dependence among data points. The theoretical results are supported via various numerical simulation settings as well as an application to a macroeconomic data set.

Taoran Wang, Yanhui Li, Mingliang Ma et al.

Context: Along with developing Deep learning (DL) models, larger datasets and more complex model structures are applied, leading to rising computing resources and energy consumption, which is an alert that green DL models should receive more attention. Objective: This paper focuses on a novel view to analyze DL energy consumption: the effect of hyperparameters on the energy cost of DL models. Method: Our approach involves using mutation operators to simulate how practitioners adjust hyperparameters, such as epochs and learning rates. We train the original and mutated models separately and gather energy information and run-time performance metrics. Moreover, we focus on the parallel scenario where multiple DL models are trained in parallel. Results: To examine the effect of hyperparameters on energy consumption, we conducted extensive experiments on five real-world DL models. The results show that (1) many hyperparameters studied have a (positive or negative) correlation with energy consumption, (2) adjusting hyperparameters can make DL models greener, i.e., lead to less energy consumption without performance damage, and (3) in a parallel environment, energy consumption becomes more susceptible to change. Conclusions: We suggest that hyperparameters need more attention in developing DL models, as appropriately adjusting hyperparameters would cause green DL models.

Jiaming Ye, Mingliang Ma, Yun Lin et al.

Recent years have seen smart contracts are getting increasingly popular in building trustworthy decentralized applications. Previous research has proposed static and dynamic techniques to detect vulnerabilities in smart contracts. These tools check vulnerable contracts against several predefined rules. However, the emerging new vulnerable types and programming skills to prevent possible vulnerabilities emerging lead to a large number of false positive and false negative reports of tools. To address this, we propose Vulpedia, which mines expressive vulnerability signatures from contracts. Vulpedia is based on the relaxed assumption that the owner of contract is not malicious. Specifically, we extract structural program features from vulnerable and benign contracts as vulnerability signatures, and construct a systematic detection method based on detection rules composed of vulnerability signatures. Compared with the rules defined by state-of-the-arts, our approach can extract more expressive rules to achieve better completeness (i.e., detection recall) and soundness (i.e., precision). We further evaluate Vulpedia with four baselines (i.e., Slither, Securify, SmartCheck and Oyente) on the testing dataset consisting of 17,770 contracts. The experiment results show that Vulpedia achieves best performance of precision on 4 types of vulnerabilities and leading recall on 3 types of vulnerabilities meanwhile exhibiting the great efficiency performance.