Dynamic Vector Research Articles

Simultaneous Position and Orientation Planning of Nonholonomic Multirobot Systems: A Dynamic Vector Field Approach

Simultaneous Position and Orientation Planning of Nonholonomic Multirobot Systems: A Dynamic Vector Field Approach

DeepSecure: A computational design science approach for interpretable threat hunting in cybersecurity decision making

Businesses and industries are placing a greater emphasis on information systems for cybersecurity decision-making due to the rising cybersecurity threat landscape and the critical need to protect their digital assets. Threat hunting provides a data-driven and proactive approach to cybersecurity, enabling organizations to efficiently detect, analyze, and respond to cyber threats in real-time. Despite playing a crucial role, these systems face several obstacles, including the manual analysis of technical threat intelligence, the non-Gaussian nature of real-world data, the high rate of false positives produced during threat hunting, and the lack of interpretation and justification for these complex models. This article adopts the computational design science paradigm to develop a novel IT artifact for threat-hunting named DeepSecure. First, to automatically extract latent patterns from multivariate time series datasets, we propose a dynamic vector quantized variational autoencoder technique. Second, a multiscale hierarchical attention bi-directional gated recurrent unit-based threat-hunting mechanism is designed. Finally, we provide the visualization of attention scores to aid in model interpretation. We evaluate the DeepSecure against state-of-the-art benchmarks on two publicly available datasets, namely, ToN-IoT and CSE-CIC-IDS2018. The experimental evaluation proves that our model can efficiently identify threat types. Beyond demonstrating practical utility, the proposed framework can help address the lack of interpretation and justification for complex models in cyber threat detection and will allow organizations to respond to potential security incidents quickly.

Dynamic interactions among exchange rate, natural gas demand, production in manufacturing and battery industry export – evidence from Hungary

In the context of the current global economic transformation, the rapid rise of the electric vehicle industry has made the battery industry a new focus of competition among countries. Despite its relatively small economic and demographic size, Hungary has garnered significant academic interest by emerging as the world’s third-largest battery producer in 2021. The study employed a dynamic vector autoregressive (VAR) model to determine that fluctuations in the Hungarian exchange rate have a notable immediate influ-ence on exports in the battery business，which suggests that changes in exc-hange rates directly affect international competitiveness. The battery sector is experiencing a progressive increase in the influence of fluctuations in na-tural gas demand, particularly in terms of their impact on production costs and supply chains. The battery industry has had substantial advancements in technological innovation and production efficiency as a direct result of the expansion in manufacturing production. And Hungary’s total industrial deve-lopment is significantly influenced by the long-term effects of export expan-sion in the battery industry. JEL Codes: L62, E44, O14, C22

Dynamic Analysis of Customer Demand Based on Intuitionistic Fuzzy Number in Product Planning

To address the fuzziness and variability in determining customer demand importance, a dynamic analysis method based on intuitionistic fuzzy numbers is proposed. First, selected customers use intuitionistic fuzzy numbers to represent the importance of each demand. Then, the preference information is aggregated using customer weights and time period weights through the intuitionistic fuzzy ordered weighted average operator, yielding a dynamic vector of the subjective importance of the demand index. Finally, the feasibility of the proposed method is demonstrated through an application example of a vibrating sorting screen.

Feature differences reduction and specific features preserving network for RGB-T salient object detection

In RGB-T salient object detection, effective utilization of the different characteristics of RGB and thermal modalities is essential to achieve accurate detection. Most of the previous methods usually only focus on reducing the differences between modalities, which may ignore the specific features that are crucial for salient object detection, leading to suboptimal results. To address the above issue, an RGB-T SOD network that simultaneously considers the reduction of modality differences and the preservation of specific features is proposed. Specifically, we construct a modality differences reduction and specific features preserving module (MDRSFPM) which aims to bridge the gap between modalities and enhance the specific features of each modality. In MDRSFPM, the dynamic vector generated by the interaction of RGB and thermal features is used to reduce modality differences, and then a dual branch is constructed to deal with the RGB and thermal modalities separately, employing a combination of channel-level and spatial-level operations to preserve their respective specific features. In addition, a multi-scale global feature enhancement module (MGFEM) is proposed to enhance global contextual information to provide guidance information for the subsequent decoding stage, so that the model can more easily localize the salient objects. Furthermore, our approach includes a fully fusion and gate module (FFGM) that utilizes dynamically generated importance maps to selectively filter and fuse features during the decoding process. Extensive experiments demonstrate that our proposed model surpasses other state-of-the-art models on three publicly available RGB-T datasets remarkably. Our code will be released at https://github.com/JOOOOKII/FRPNet.

Does economic globalization promote civil peace in developing countries?

This study questions the widely supported claim that economic globalization benefits developing countries by enhancing their prospects for civil peace. Drawing on ideas from across the social sciences, this study questions the nature of causation between economic globalization and civil peace and how it should be conceptualized and modeled. Instead of assuming a straightforward and static relationship whereby economic globalization directly increases the likelihood of civil peace, a range of alternative hypotheses are considered, including those about reverse and reciprocal causation and about dynamic and path-dependent effects. Using dynamic panel regression and vector autoregression, the study empirically examines how trade openness and foreign direct investment affect civil violence in a sample of all countries classified by the United Nations as developing. The results find positive links between economic globalization and civil peace, as much of the literature predicts, but show that this relationship mainly arises from complex patterns of causation rather than from the pacifying effects of economic globalization. These results are robust to various model specifications and sampling strategies. The study ends by considering whether economic globalization may indirectly promote civil peace by fostering long-term economic development.

Nonseparability of credit card services within Divisia monetary aggregates

Abstract We use the New Keynesian Dynamic Stochastic General Equilibrium (DSGE) framework and Vector Autoregressive (VAR) to investigate the usefulness and relevancy of monetary services, augmented to include credit card transaction services. We use the new credit-card-augmented Divisia monetary aggregates in the models to further the existing research on their usefulness and relevancy. In this research, we compare three different monetary aggregates within the New Keynesian framework: (1) the aggregation-theoretic “true” monetary aggregate, (2) the credit-card-augmented Divisia monetary aggregate, and (3) the simple sum monetary aggregate. We acquire the following primary results. (1) The credit-card-augmented Divisia monetary aggregate tracks the theoretical (true) monetary aggregate, while simple sum does not. Although this result would be expected from the theory in classical economic models, the result is not an immediate implication of the theory in New Keynesian models and therefore needs empirical confirmation. (2) Under the recursive VAR framework, the credit-card-augmented Divisia monetary aggregate serves as a preferable monetary policy indicator compared to the traditional federal funds rate. (3) On theoretical grounds, we find that the separability condition for existence of a monetary aggregator function could fail, if credit card deferred payment services were excluded from the monetary services block, unless all markets are perfect.

Micro Multiobjective Evolutionary Algorithm With Piecewise Strategy for Embedded-Processor-Based Industrial Optimization.

In some industrial applications, it is required to do off-line multiobjective optimization in embedded systems. Due to their limited computing and memory capability, embedded processor may not be able to run conventional multiobjective optimization evolutionary algorithms (MOEAs). This article proposes a micro MOEA with piecewise strategy (μ MOEA) for industrial optimization in embedded processor. μ MOEA introduces an improved piecewise strategy based on the MOEA/D framework, which serially optimizes subclusters to be compatible with embedded processor under limited computing power. For the purpose of further enhancing μ MOEA, a dynamic and flexible weight vector update trigger mechanism is proposed, so that the algorithm can save and utilize the computing resources of the embedded processor as much as possible. Abundant artificial test problems are carrying out to test the performance of μ MOEA. Through various experiments, it can be found that μ MOEA has outstanding performance in ZDT, DTLZ, SMOP, and MaF problems. Last and most importantly, μ MOEA is successfully applied to two specific application scenarios of industrial optimization on embedded processor for simulation, such as two different types of semi-autogenous grinding optimization problems and micro-grid energy optimization problem, which prove the feasibility of applying MOEA to embedded processor.

Iron Loss and Temperature Rise Analysis of a Transformer Core Considering Vector Magnetic Hysteresis Characteristics under Direct Current Bias.

Direct current (DC) bias induced by the DC transmission and geomagnetically induced current is a critical factor in the abnormal operation of electrical equipment and is widely used in the field of power transmission and distribution system state evaluation. As the main affected component, the vector magnetization state of a transformer core under DC bias has rarely been studied, resulting in inaccurate transformer operation state estimations. In this paper, a dynamic vector hysteresis model that considers the impact of rotating and DC-biased fields is introduced into the numerical analysis to simulate the distribution of magnetic properties, iron loss and temperature of the transformer core model and a physical 110 kV single-phase autotransformer core. The maximum values of B, H and iron loss exist at the corners and T-joint of the core under rotating and DC-biased fields. The corresponding maximum value of the temperature increase is found in the main core limb area. The temperature rise of the 110 kV transformer core under various DC-biased conditions is measured and compared with the FEM (Finite Element Method) results of the proposed model and the model solely based on the magnetization curve B||H. The calculation error of the temperature rise obtained by the improved model is approximately 3.76-15.73% and is much less than the model solely based on magnetization curve B||H (approximately 50.71-66.92%).

A Computational Fluid Dynamics and Support Vector Regression Combined Method for Predicting the Flow Performance of the Five‐Strand Tundish

To optimize the flow field of a multistrand tundish and improve the steel quality, a prediction model integrating the techniques of computational fluid dynamics (CFD) and support vector regression (SVR) is proposed. A total of 125 cases with different structure parameters, i.e., the diameter and inclination angle of the orifices, are designed. The flow field of liquid steel in the tundish is calculated by CFD, and a sample dataset is established. Then, the SVR, combining the genetic algorithm with K‐fold, is employed to train and predict the sample dataset. In the results, it is shown that the SVR model demonstrates good learning performance on the training dataset. The average absolute errors of predictions for dead volume fraction and consistency are 0.50% and 0.71%. In addition, the SVR model takes much less computational cost than the CFD method to predict the flow performance of tundish in a given baffle structure.

A novel deforestation risk and baseline allocation model for the next generation of nested REDD+ projects

Nature-based solutions that use a counterfactual scenario depend heavily on the methodology used to determine the business as usual (BAU) case, i.e., the “baseline.” Reducing emissions from deforestation and forest degradation (REDD+) projects traditionally set baselines using a “reference area” as a control for estimating BAU deforestation and emissions in the treatment (project) area. While the REDD+ market is shifting from project-based to nested approaches as countries increase their efforts to meet nationally determined contributions (NDCs) to the Paris agreement’s global climate target, methodologies for allocating national baselines are not yet formalized and tested, despite an urgent need to scale the market. We present a novel method for mapping deforestation risk and allocating national forest reference emission levels (FREL) to projects: baseline allocation for assessed risk (BAAR). This approach provides a spatial predictor of future deforestation using a dynamic vector, and a method for allocating a FREL to differentiated risk areas at the project level. Here, we present BAAR using 34 REDD+ projects in the Democratic Republic of the Congo (DRC). We demonstrate the importance of risk-based FREL allocations to balance fitness for purpose and scientific rigor. We show how BAAR can be used by governments to focus voluntary carbon market finance in areas at highest risk of imminent deforestation, while maintaining alignment with nationally determined contribution (NDC) goals.

¿Partisanos latinofoucaultinos? Elementos para una historia del saber crítico al poder securitario-punitivo en Argentina

In a situation of advance and consolidation of the ultra-right in Latin America, in which security claims and government interventions in the name of the “combat against insecurity” have been one of its most dynamic vectors, this work is aimed at exploring the contours of another approach to the question of crimes and punishments. During the seventies, in the heat of dictatorships and exiles, a critical, combative, partisan criminological model was forged in Argentina. This other model is tributary of a strategic assembly between the “Foucault effect” in Argentina and Latin American critical criminology. Specifically, the traces of this process are covered through three strong ideas: 1) the “Foucault effect” in Argentina and critical criminology are the ties of a certain critical attitude; 2) that history is realized in a topological structure: it unfolds in the ups and downs experienced by intellectuals here in the dictatorship and there in forced exile; 3) these imports, readings and uses of Foucault overflow and dislocate Foucauldian analysis itself.

Conway-Bromage-Lyndon (CBL): an exact, dynamic representation of k-mer sets.

In this article, we introduce the Conway-Bromage-Lyndon (CBL) structure, a compressed, dynamic and exact method for representing k-mer sets. Originating from Conway and Bromage's concept, CBL innovatively employs the smallest cyclic rotations of k-mers, akin to Lyndon words, to leverage lexicographic redundancies. In order to support dynamic operations and set operations, we propose a dynamic bit vector structure that draws a parallel with Elias-Fano's scheme. This structure is encapsulated in a Rust library, demonstrating a balanced blend of construction efficiency, cache locality, and compression. Our findings suggest that CBL outperforms existing dynamic k-mer set methods. Unique to this work, CBL stands out as the only known exact k-mer structure offering in-place set operations. Its different combined abilities position it as a flexible Swiss knife structure for k-mer set management. https://github.com/imartayan/CBL.

Research on decomposition-based multi-objective evolutionary algorithm with dynamic weight vector

In recent years, multi-objective evolutionary algorithm based on decomposition has gradually attracted people's interest. However, this algorithm has some problems. For example, the diversity of the algorithm is poor, and the convergence and diversity of the algorithm are unbalanced. In addition, users don't always care about the entire Pareto front. Sometimes they may only be interested in specific areas of entire Pareto front. Based on the above problems, this paper proposes a decomposition-based multi-objective evolutionary algorithm with dynamic weight vector (MOEA/D-DWV). Firstly, a weight vector generation model with uniform distribution or preference distribution is proposed. Users can decide which type of weight vector to generate according to their own wishes. Then, two combination evolution operators are proposed to better balance the convergence and diversity of the algorithm. Finally, a dynamic adjustment strategy of weight vector is proposed. This strategy can adjust the distribution of weight vector adaptively according to the distribution of solutions in the objective space, so that the population can be uniformly distributed in the objective space as much as possible. MOEA/D-DWV algorithm is compared with 9 advanced multi-objective evolutionary algorithms. The comparison results show that MOEA/D-DWV algorithm is more competitive. Data availabilityData will be made available on request.

Volatility Spillover from the Chinese Stock Market to the G20 Stock Markets in the Wake of the Pandemic COVID-19

We analyze the volatility spillover effect from the Chinese stock market to different stock markets in the G20 countries. We employ dynamic conditional correlation and vector autoregression (VAR) to analyze adjusted daily closing stock indices extending from 1st October 2019 to 30th June 2020. The result reveals that there is short-run volatility in sample stock return except Australia and South Korea. Similarly, there is long-term volatility persistence in sample countries’ stock exchange except Australia, Saudi Arabia, Russia, and France. However, Australia is only the country where there is no short- and long-run information transmission derived from China. Therefore, there is a portfolio diversification opportunity in this country during COVID-19. Overall, this paper shows significant interdependencies between the Chinese and the G20 markets which furnish momentous implications to the stakeholders of markets.

Microblog sentiment analysis method using BTCBMA model in Spark big data environment

Abstract Microblogs are currently one of the most well-liked social platforms in China, and sentiment analysis of microblog texts can help further analyze the realization of their media value; however, the current task of sentiment analysis based on microblog information suffers from low accuracy due to the large size and high redundancy of microblog data, a microblog sentiment analysis method using Bidirectional Encoder Representation from Transformers (BERT)–Text Convolutional Neural Network (TextCNN)–Bidirectional Gate Recurrent Unit (BiGRU)–Multihead-Attention model in Spark big data environment is proposed. First, the Chinese pre-trained language model BERT is used to convert the input data into dynamic character-level word vectors; then, TextCNN is used to effectively obtain local features such as keywords and pool the filtered features; then, BiGRU is introduced to quickly capture more comprehensive semantic information; finally, a multi-headed attention mechanism is implemented to emphasize the most significant features in order to accomplish the sentiment classification of microblog information task precisely. By comparing the existing advanced models, the proposed model demonstrates an improvement of at least 4.99% and 0.05 in accuracy and F1-score evaluation indexes, respectively. This enhancement significantly enhances the accuracy of microblog sentiment analysis tasks and aids pertinent authorities in comprehending the inclination of individual’s attitude toward hot topics. Furthermore, it facilitates a prompt prediction of topic trends, enabling them to guide public opinion accordingly.

Enhanced Design of a Tai Chi Teaching Assistance System Integrating DTW Algorithm and SVM

Physical education using technology has enabled traditional practices like Tai Chi, a martial art known for its multiple health benefits and meditative aspects, to set coordinated goals. This research presents an intelligent Tai Chi Teaching Assistance System supported by the integration of the Dynamic Time Warping algorithm and Support Vector Machine, in which can practitioners providing real-time feedback to improve Tai Chi learning and quality. In the system, the DTWA Dynamic Time Warping Algorithm was used to accurately compare a practitioner’s complex body movements with the Tai Chi standard movements dataset, taking into account execution speed deviations and others. Meanwhile, the SVM was employed to classify the movement as to quality and correctness, thereby being able to provide precise, individual feedback. This hybrid approach ensures a high-motion recognition accuracy rate while also adhering to nuanced Tai Chi requirements. The system was evaluated through detailed testing with various levels of Tai Chi experience. Evaluation showed that the students’ performance and understanding of most Taijiquan movements and related physical exercises improved significantly. It indicates the system has a practical application value for also beginners and intermediate and last expert, respectively. It also shows the effectiveness of combining DTW and SVM to support learners ‘body movement trajectory in a physical learning environment, opening them up to additional technology-assisted physical training applications. This provides implications for a more promising generation of future physical education involving the incorporation of complex AI technology.

Smart Meter Tampering Detection Using IoT Based Unsupervised Machine Learning

Abstract: This work presents a novel smart grid tampering detection system re-engineered for end user monitoring and pipeline automation. The research focused on distributed energy resources. In context, the end user load profile, and generation capacity were processed in the cloud environment for tampering management. Computational pipelined methodology was adopted using baseline data from an independent electricity consumption data from 2018-2021 Abuja. First, a smart grid (SG) survey was carried using existing home estate at Abuja to ascertain tampering procedures in distributed energy resource domain. From the energy survey, the system architecture was developed and implemented based on computational model curve for dynamic attack vector mitigation.

ESG practices mitigating geopolitical risks: Implications for sustainable environmental management

As climate change and geopolitical conflicts intensify, understanding how geopolitical risks affect companies prioritizing Environmental, Social, and Governance (ESG) practices is crucial. This study investigates the dynamic relationship between global geopolitical risks and the performance of Environmental, Social, and Governance (ESG) and non-ESG companies, particularly their influence on green markets. Utilizing a robust methodological framework, including the dynamic time-varying parameters vector autoregression (TVP-VAR) model, and causal impact modeling, we analyze daily financial data from 2021 to 2024. The results reveal a substantial negative impact of geopolitical risks on non-ESG companies, contrasting with the resilience of ESG-committed counterparts. This suggests that ESG-committed companies demonstrate better resilience against geopolitical risks, emphasizing the protective role of ESG practices amid uncertainties. Additionally, the inclusion of ESG companies in green markets diminishes the severity of the negative impact of geopolitical risks, underlining the transformative role of ESG commitment in shaping investor behavior towards sustainable investments. Our findings offer insights for policymakers and investors navigating geopolitical risks and ESG performance, with a focus on environmental management, and provide guidance for effective risk mitigation and investment policies to enhance environmental sustainability.

Language model based on deep learning network for biomedical named entity recognition

Biomedical Named Entity Recognition (BioNER) is one of the most basic tasks in biomedical text mining, which aims to automatically identify and classify biomedical entities in text. Recently, deep learning-based methods have been applied to Biomedical Named Entity Recognition and have shown encouraging results. However, many biological entities are polysemous and ambiguous, which is one of the main obstacles to the task of biomedical named entity recognition. Deep learning methods require large amounts of training data, so the lack of data also affect the performance of model recognition. To solve the problem of polysemous words and insufficient data, for the task of biomedical named entity recognition, we propose a multi-task learning framework fused with language model based on the BiLSTM-CRF architecture. Our model uses a language model to design a differential encoding of the context, which could obtain dynamic word vectors to distinguish words in different datasets. Moreover, we use a multi-task learning method to collectively share the dynamic word vector of different types of entities to improve the recognition performance of each type of entity. Experimental results show that our model reduces the false positives caused by polysemous words through differentiated coding, and improves the performance of each subtask by sharing information between different entity data. Compared with other state-of-the art methods, our model achieved superior results in four typical training sets, and achieved the best results in F1 values.