中国科学院数学与系统科学研究院期刊网

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    Yao YUE, Yuying SUN, Kuo YANG, Shouyang WANG
    Journal of Systems Science and Information. 2023, 11(2): 139-159. https://doi.org/10.21078/JSSI-2023-139-21

    Since Bitcoin came into the world, modelling and analyzing the underlying characteristics of Bitcoin has attracted increasing attention. This paper uses a framework including decomposition, reconstruction and extraction method (DRE) to analyze price fluctuations based on ultra-high-frequency data from Dec.1, 2019, to Nov.30, 2021. First, the ensemble mode decomposition (EMD) is employed to decompose the Bitcoin hourly spot price into 13 intrinsic mode functions (IMF) plus a residual. Second, the IMFs are reconstructed into high-frequency components, low-frequency components and a trend based on fine-to-coarse reconstruction. Furthermore, the intraday volatility analysis based on LM test is applied on 15-minutes frequency data to detect discontinuous jump arrivals and extract jump from realized quadratic variation. Empirical results show that three components of reconstruction can be identified as short term fluctuations process caused by microstructure noise, the shocks affected by major events, and a long-term trend based on inelastic supply and rigid demand. We find that approximately 40% of jumps can be matched with the news from the public news database (Factiva), and the jump sizes are larger than that of stock markets. This finding indicates that the Bitcoin market has more irregularly noise and unforeseen shocks from unscheduled events.

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    Qiao HU, Jiayin QI
    Journal of Systems Science and Information. 2023, 11(2): 160-178. https://doi.org/10.21078/JSSI-2023-160-19

    The resumption of production after the "suspension" caused by the COVID-19 has emerged as an urgent problem for many enterprises and the government. The resumption of production is actually a dynamic evolution problem from 0 to 1 (100%). This paper constructs a general game model and a dynamic replication system for the resumption of production and government support, and gives theorems for the construction of the model. It analyzes the evolution mechanism and scenario conditions for the convergence of enterprise strategies to the "resumption of production" strategy, takes the resumption of production of hog farmers as an example to carry out a study on the regulation of countermeasures to resume hog production, and explores systemic countermeasures and suggestions for the rapid convergence of farmers' strategies to the "resumption of work and production" strategy. The study found that the production resuming behavior system dynamics evolution game regulation model provides a systematic model and method for the study of resumption countermeasures, a general regulation model for the resumption ratio from 0 to 1 (100%), and a systematic idea, method and model for exploring the "precise strategy" system to promote the rapid resumption of production.

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    Yang CHEN, Jian XU
    Journal of Systems Science and Information. 2023, 11(2): 179-203. https://doi.org/10.21078/JSSI-2023-179-25

    Based on heterogeneity extraction, this paper analyzes four potential characteristics of the supervisory board, they are Individual Heterogeneity of the Supervisory Member (Internal Heterogeneity), Organization Size of the Supervisory Board (Organization Size), Structural Characteristics of the Supervisory Board (Structural Characteristics) and Identity Background of the Supervisory Board (Identity Background); and verifies the impact and action path of the potential characteristics on irregularities. Then, systematically evaluates the micro enterprise organization construction and corporate governance behavior by using the methods of factor analysis and Heckman two-stage model. Empirical research shows that the scale of corporate assets does have an important impact on corporate irregularities and the governance of the board of supervisors. Under the regulation of the company scale, the three potential characteristics: Organization Size, Identity Background and Structural Characteristics have played a significant inhibitory role on irregularities, and the Internal Heterogeneity has no significant effect. When using violation behavior as an alternative variable of supervision performance, the sample selection deviation will be caused by the lack of information disclosure. This paper suggests that we should pay attention to the team of the board of supervisors scientifically and reasonably, weaken the appropriate personalized differences within the board of supervisors, and comprehensively consider the interaction between the company scale, asset quality and the performance of the board of supervisors when formulating the corporate internal management system.

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    Ping ZHAO, Shouyang WANG
    Journal of Systems Science and Information. 2023, 11(6): 655-670. https://doi.org/10.21078/JSSI-2023-0068

    We conduct an empirical analysis of Shanghai-Hong Kong Stock Connect to reveal the dynamic impacts of stock connect trading activity on the stock pool's Amihud illiquidity proxy, index return, and CNY-HKD exchange rate. From pairwise conditional g causality analysis, we note a mutual significant causal connection between northbound net buying volume and Shanghai stock exchange return on all frequency levels. Meanwhile, we find a significant causal impact on the Shanghai portfolio's liquidity from northbound net buying volume. And there is a significant causal impact from the southbound net buying volume on Hang Seng Index return. Both are significant at the low-frequency level. In particular, northbound trading activity stimulates the Shanghai portfolio's liquidity in the low trading activity regime from the threshold VAR analysis. In robust analysis, we find similar significant dynamic causal connection and stimulation effects for the northbound trades when replacing Amihud illiquidity with the turnover rate. The result might relate to the investment behaviors looking for opportunity in the low trading activity regime. In contrast, the investors' beliefs may vary in the high trading activity regime, which weakens the connection between trading activities and other factors like liquidity.

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    Huapeng WANG, Fangzhou HE, Lianquan WU
    Journal of Systems Science and Information. 2023, 11(2): 264-276. https://doi.org/10.21078/JSSI-2023-264-13

    In recent years, various speech embedding methods based on deep learning have been proposed and have shown better performance in speaker verification. Those new technologies will inevitably promote the development of forensic speaker verification. We propose a new forensic speaker verification method based on embeddings trained with loss function called generalized end-to-end (GE2E) loss. First, a long short-term memory (LSTM) based deep neural network (DNN) is trained as the embedding extractor, then the cosine similarity scores between embeddings from same speaker comparison pairs and different speaker comparison pairs are trained to represent within-speaker model and between-speaker model respectively, and finally, the cosine similarity scores between the questioned embeddings and enrolled embeddings are evaluated in the above two models to get the likelihood ratio (LR) value. On the subset of LibriSpeech, test-other-500, we achieve a new state of the art. Both all the same speaker comparison pairs and different speaker comparison pairs get correct results and can provide considerable strong evidence strength for courts.

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    Han YANG, Jun LI
    Journal of Systems Science and Information. 2023, 11(2): 204-218. https://doi.org/10.21078/JSSI-2023-204-15

    Contrastive learning, a self-supervised learning method, is widely used in image representation learning. The core idea is to close the distance between positive sample pairs and increase the distance between negative sample pairs in the representation space. Siamese networks are the most common structure among various current contrastive learning models. However, contrastive learning using positive and negative sample pairs on large datasets is computationally expensive. In addition, there are cases where positive samples are mislabeled as negative samples. Contrastive learning without negative sample pairs can still learn good representations. In this paper, we propose a simple framework for contrastive learning of image classification (SimCLIC). SimCLIC simplifies the Siamese network and is able to learn the representation of an image without negative sample pairs and momentum encoders. It is mainly by perturbing the image representation generated by the encoder to generate different contrastive views. We apply three representation perturbation methods, namely, history representation, representation dropoput, and representation noise. We conducted experiments on several benchmark datasets to compare with current popular models, using image classification accuracy as a measure, and the results show that our SimCLIC is competitive. Finally, we did ablation experiments to verify the effect of different hyperparameters and structures on the model effectiveness.

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    Zhaohao WEI, Jichang DONG, Zhi DONG
    Journal of Systems Science and Information. 2023, 11(6): 671-690. https://doi.org/10.21078/JSSI-2023-0039

    Based on the different premium volatility characteristics of various systematic factors in the A-share market, this paper constructs six representative high-frequency volatility prediction models that consider multiple complex risk structures. On this basis, a detailed comparative analysis of the differences in volatility characteristics among various factors is conducted, and the optimal prediction and early warning framework for the A-share market is proposed. Research shows that: 1) The volatility research results only for individual market indexes are not universally representative. 2) The fluctuation characteristics among different systematic factors and their respective optimal prediction model frameworks generally have significant differences, that is, there is no single fixed combination of model parameters. 3) Complex risk characteristics such as long memory, measurement errors, and high-frequency jump fluctuations obviously exist in the A-share market. The optimal forecast and early warning framework for the A-share market can be constructed by a combination of models that consider one or more of the above risk characteristics. The above conclusions have important practical reference value for the risk warning and prevention of the A-share market and the formulation of related policies.

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    Shiyong LI, Yanan ZHANG, Wei SUN
    Journal of Systems Science and Information. 2023, 11(2): 219-244. https://doi.org/10.21078/JSSI-2023-219-26

    It is a hot issue to allocate resources using auction mechanisms in vehicular fog computing (VFC) with cloud and edge collaboration. However, most current research faces the limitation of only considering single type resource allocation, which cannot satisfy the resource requirements of users. In addition, the resource requirements of users are satisfied with a fixed amount of resources during the usage time, which may result in high cost of users and even cause a waste of resources. In fact, the actual resource requirements of users may change with time. Besides, existing allocation algorithms in the VFC of cloud and edge collaboration cannot be directly applied to time-varying multidimensional resource allocation. Therefore, in order to minimize the cost of users, we propose a reverse auction mechanism for the time-varying multidimensional resource allocation problem (TMRAP) in VFC with cloud and edge collaboration based on VFC parking assistance and transform the resource allocation problem into an integer programming (IP) model. And we also design a heuristic resource allocation algorithm to approximate the solution of the model. We apply a dominant-resource-based strategy for resource allocation to improve resource utilization and obtain the lowest cost of users for resource pricing. Furthermore, we prove that the algorithm satisfies individual rationality and truthfulness, and can minimize the cost of users and improve resource utilization through comparison with other similar methods. Above all, we combine VFC smart parking assistance with reverse auction mechanisms to encourage resource providers to offer resources, so that more vehicle users can obtain services at lower prices and relieve traffic pressure.

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    Maolin CHENG, Bin LIU
    Journal of Systems Science and Information. 2023, 11(2): 245-263. https://doi.org/10.21078/JSSI-2023-245-19

    The conventional grey GM(2, 1) model built for the fast growing time sequence generally has big errors. To improve the modeling precision, the paper improves from the following two aspects: First, the paper transforms the accumulated generating sequence of original time sequence quantitatively to make the transformed time sequence have the better adaptability to the model; second, the paper extends the conventional grey GM(2, 1) model's structure to make the extended model meet the variation law of fast growing sequence better. The extended grey model is called the GM(2, 1, Σexp(ct)) model. The paper offers the parameter optimization method and the solving method of time response sequence of GM(2, 1, Σexp(ct)) model. Using the model and methods proposed, the paper builds the GM(2, 1, Σexp(ct)) models for the natural gas consumption of China and Chongqing City, China, respectively. Results show that the models built have high simulation precision and prediction precision.

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    Bo LI, Kai HUANG, Junhui LI, Yufu LIAO
    Journal of Systems Science and Information. 2023, 11(6): 776-794. https://doi.org/10.21078/JSSI-2022-0004

    With the advancement of society and science and technology, the demand for detecting small objects in practical scenarios becomes stronger. Such objects are only represented by relatively small coverage of pixels, and the features are degraded severely after being extracted by a deep convolutional neural network, which is detrimental to the detection performance for small objects. Therefore, an intuitive solution is to increase the resolution of small objects by cropping the original image. In this paper, we propose a simple but effective object density map guided region localization module (DMGRL) to locate and crop the regions of interest where small objects may exist. Firstly, the density map of the objects is estimated by object density map estimation network, and then the coordinates of the small object regions are calculated; Secondly, the continuous differentiable affine transformation is utilized to crop these regions so that the detector with DMGRL can be trained end-to-end instead of two-stage training. Finally, the all prediction results of input image and cropped region images are merged together to output the final detection results by non maximum suppression (NMS). Extensive experiments demonstrate the superior performance of the detector incorporated DMGRL.

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    Qinghua DONG, You ZHANG, Xin ZHANG
    Journal of Systems Science and Information. 2023, 11(6): 745-760. https://doi.org/10.21078/JSSI-2023-0145

    A three-dimensional boundary-spanning technology search model including search depth, scope and height is established, and a quantitative calculation method is proposed to dynamically describe an organisation's technology search behaviour and demand characteristics. Organisations are clustered by types as technical, comprehensive, or professional using k-means based on technology search behaviour. Recommendation strategies for various types of organisations are proposed based on this, and the search and supply libraries of each organisation are built by considering their type and search contents. The semantic similarity between patents in different libraries is calculated using a Word2Vec and TextRank model to achieve patent recommendations. An empirical study of the robotics field shows a recommendation accuracy of 0.751, and the accuracy of the technical, comprehensive, and professional types is 0.8282, 0.5389 and 0.7723, respectively. This study considers an organisation's dynamic search behaviour and makes class-based recommendations, with a low computational complexity and strong interpretability.

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    Liwen HUANG
    Journal of Systems Science and Information. 2023, 11(6): 761-775. https://doi.org/10.21078/JSSI-E2022056

    This paper introduces the related concepts of the hybrid spherical-shaped dataset and proposes a new discriminant analysis method based on the spherical-shaped dataset (SDAM), then SDAM is further improved by the idea of the class cover and presents the nonlinear discriminant analysis method (NDAM). To demonstrate the effectiveness of these two methods, this work constructs seven hybrid spherical-shaped datasets and uses nine UCI datasets. Numerical experiments on these examples indicate that SDAM can preferably solve the discriminant problem for the hybrid sphericalshaped dataset, but this method does not always work well for real datasets; NDAM overcomes the drawbacks of SDAM and better solves the discriminative problem of real datasets. Besides, it has better stability.