319 research outputs found
Mean-Variance-Skewness Portfolio Selection Model Based on RBF-GA
The classical Markowitz’s mean-variance model in modern investment science uses variance as risk measure while it ignores the asymmetry of the return distribution. This article introduces skewness, V-type transaction costs, cardinality constraint and initial investment proportion, and builds a new class of nonlinear multi-objective portfolio model (mean-variance-skewness portfolio selection model). To solve the model, we develop a genetic algorithm(GA) which contains radial basis function(RBF) neural network, called RBF-GA. The experimental results show that the proposed model is more effective and more realistic than others
Exploring enterprises competition: From a perspective of massive recruitment texts mining
Extant research has made limited efforts to conduct competitive intelligence analysis based on recruitment texts. To fill the gap, this study proposes a method for deriving and analyzing competitive relationships, identifying competition paths, and calculating asymmetric competitiveness degrees, from the recruitment texts on e-recruiting websites. Specifically, this study developed a competitive evaluation index system for companies’ skill needs and resource base based on 53,171 job descriptions and 42,641 company profiles published by companies across 8 industries (including 35 industry segments) using automated text processing methods. Furthermore, in order to identify competitive paths and calculate the degree of asymmetric competitiveness, this study proposes a modified bipartite graph approach (i.e., MBGA) for competitive intelligence analysis of recruitment texts based on the competition evaluation index system. Experiments on a real-world dataset of the representative companies clearly validated the effectiveness of the method. Compared to the five state-of-the-art methods, MBGA performs better in disclosing the overall competition and is more accurate in terms of the error rating ratio (i.e., ERR) of the competition
Little Exploration is All You Need
The prevailing principle of "Optimism in the Face of Uncertainty" advocates
for the incorporation of an exploration bonus, generally assumed to be
proportional to the inverse square root of the visit count (),
where is the number of visits to a particular state-action pair. This
approach, however, exclusively focuses on "uncertainty," neglecting the
inherent "difficulty" of different options. To address this gap, we introduce a
novel modification of standard UCB algorithm in the multi-armed bandit problem,
proposing an adjusted bonus term of , where , that
accounts for task difficulty. Our proposed algorithm, denoted as UCB, is
substantiated through comprehensive regret and risk analyses, confirming its
theoretical robustness. Comparative evaluations with standard UCB and Thompson
Sampling algorithms on synthetic datasets demonstrate that UCB not only
outperforms in efficacy but also exhibits lower risk across various
environmental conditions and hyperparameter settings
Low-Mid Adversarial Perturbation against Unauthorized Face Recognition System
In light of the growing concerns regarding the unauthorized use of facial
recognition systems and its implications on individual privacy, the exploration
of adversarial perturbations as a potential countermeasure has gained traction.
However, challenges arise in effectively deploying this approach against
unauthorized facial recognition systems due to the effects of JPEG compression
on image distribution across the internet, which ultimately diminishes the
efficacy of adversarial perturbations. Existing JPEG compression-resistant
techniques struggle to strike a balance between resistance, transferability,
and attack potency. To address these limitations, we propose a novel solution
referred to as \emph{low frequency adversarial perturbation} (LFAP). This
method conditions the source model to leverage low-frequency characteristics
through adversarial training. To further enhance the performance, we introduce
an improved \emph{low-mid frequency adversarial perturbation} (LMFAP) that
incorporates mid-frequency components for an additive benefit. Our study
encompasses a range of settings to replicate genuine application scenarios,
including cross backbones, supervisory heads, training datasets, and testing
datasets. Moreover, we evaluated our approaches on a commercial black-box API,
\texttt{Face++}. The empirical results validate the cutting-edge performance
achieved by our proposed solutions.Comment: published in Information Science
An optimization approach for localization refinement of candidate traffic signs
We propose a localisation refinement approach for
candidate traffic signs. Previous traffic sign localisation approaches
which place a bounding rectangle around the sign do
not always give a compact bounding box, making the subsequent
classification task more difficult. We formulate localisation
as a segmentation problem, and incorporate prior knowledge
concerning color and shape of traffic signs. To evaluate the
effectiveness of our approach, we use it as an intermediate step
between a standard traffic sign localizer and a classifier. Our
experiments use the well-known GTSDB benchmark as well as
our new CTSDB (Chinese Traffic Sign Detection Benchmark).
This newly created benchmark is publicly available, and goes
beyond previous benchmark datasets: it has over 5,000 highresolution
images containing more than 14,000 traffic signs
taken in realistic driving conditions. Experimental results show
that our localization approach significantly improves bounding
boxes when compared to a standard localizer, thereby allowing
a standard traffic sign classifier to generate more accurate
classification results
Numerical modelling for reinforced concrete response to blast load: understanding the demands on material models
A comparative study of modelling RC slab response to blast loading with two typical concrete material models
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