Era Of Big Data Research Articles

Privacy-Centric AI: Navigating the Landscape with Federated Learning

Abstract: In the era of big data and privacy concerns, federated learning has emerged as a promising approach to training machine learning models while preserving data privacy. This paper explores the principles and applications of federated learning, highlighting its potential to revolutionize privacy-centric AI. We discuss the methodology, significance, and challenges of federated learning, providing insights into its future directions. By leveraging decentralized data and aggregating model updates, federated learning enables the development of powerful AI models without compromising individual privacy. We present real-world applications and cite relevant studies to demonstrate the transformative impact of federated learning across various domains.

Legal Regulation of Transnational Data Flows: International Experience and Chinese Solutions

The arrival of the big data era has made most countries realize the importance of data resources for economic development. On the one hand, data flow promotes trade between countries, and on the other hand, the rapid expansion of the scale of multinational business giants has led to a proliferation of incidents of illegal collection and utilization of user privacy, putting national security at risk.In 2019, WTO members of the e-commerce negotiations put forward proposals for cross-border data flow based on their own positions, and major countries such as China, the United States, Russia and other major countries have all put forward different proposals.It can be seen that data flow regulation has become a key topic in the international arena nowadays, with sufficient necessity and practical significance. As a result, this paper adopts the comparative analysis method, normative analysis method and other research methods to analyze the issue and make suggestions on the direction of China's legislation.

Improved ACOM pattern matching in 4D-STEM through adaptive sub-pixel peak detection and image reconstruction

The technique known as 4D-STEM has recently emerged as a powerful tool for the local characterization of crystalline structures in materials, such as cathode materials for Li-ion batteries or perovskite materials for photovoltaics. However, the use of new detectors optimized for electron diffraction patterns and other advanced techniques requires constant adaptation of methodologies to address the challenges associated with crystalline materials. In this study, we present a novel image-processing method to improve pattern matching in the determination of crystalline orientations and phases. Our approach uses sub-pixel adaptive image processing to register and reconstruct electron diffraction signals in large 4D-STEM datasets. By using adaptive prominence and linear filters, we can improve the quality of the diffraction pattern registration. The resulting data compression rate of 103 is well-suited for the era of big data and provides a significant enhancement in the performance of the entire ACOM data processing method. Our approach is evaluated using dedicated metrics, which demonstrate a high improvement in phase recognition. Several features are extracted from the registered data to map properties such as the spot count, and various virtual dark fields, which are used to enhance the handling of the results maps. Our results demonstrate that this data preparation method not only enhances the quality of the resulting image but also boosts the confidence level in the analysis of the outcomes related to determining crystal orientation and phase. Additionally, it mitigates the impact of user bias that may occur during the application of the method through the manipulation of parameters.

Distributed Implementation of Computing Language Model in Cloud Computing Network

In this paper, the distributed implementation of Computing Language Model (CLM) in cloud computing network is studied and discussed. With the advent of the era of big data, the application of CLM in natural language processing (NLP), machine translation and other fields is increasingly extensive, and the demand for computing resources is also increasing. As an effective way to manage computing resources, distributed computing can make full use of resources in cloud computing environment and realize efficient execution of computing tasks. In this paper, the application of distributed computing methods such as data parallelism, model parallelism and hybrid parallelism in cloud computing environment is studied and analyzed, and the advantages and disadvantages of different methods in CLM implementation are discussed. The experimental results show that the hybrid parallel method can effectively combine the advantages of data parallelism and model parallelism, and improve the training efficiency and performance of CLM. This study provides important theoretical guidance and technical support for the distributed implementation of CLM in cloud computing networks, and is of great significance for further promoting the development of big data and AI technology.

Research on the Path of Financial Accounting Transformation to Management Accounting in the Big Data Era

This paper explores the significance, existing problems, and strategies for addressing the transformation of financial accounting to management accounting in the era of big data. It first elucidates the importance of the transformation, including enhancing decision support, conducting cost-benefit analysis, and improving performance. Subsequently, it analyzes the problems encountered during the transformation process, such as lack of skills and knowledge, low utilization of data resources, and difficulties in system integration. It then proposes countermeasures to address these issues, including technological capacity building, talent training and recruitment, system integration coordination, and enhancement of management accounting decision analysis capabilities. Finally, it points out that as technology and market environments evolve, this transformation will continue to deepen, necessitating continuous skill enhancement for professionals in the financial accounting field and proactive adaptation by enterprises to enhance competitiveness.

Construction of a Green and Low-Carbon Travel Order Prediction Model Based on Shared Bicycle Big Data

In the era of big data, traditional analysis methods are insufficient to meet the growing demand for green and low-carbon travel orders in shared bicycle systems. To address this issue, a new order demand forecasting model, named the “convolutional neural network (CNN)”—“long short-term memory (LSTM)” model (CNN-LSTM), is proposed by integrating CNN and LSTM techniques. The research further validates the spatiotemporal prediction performance of this model. The experimental results demonstrate that LSTM exhibits favorable prediction performance in terms of time feature analysis, as evidenced by the overlapping of the true value (TV) and predicted value (PV) curves. Notably, LSTM achieves an impressively low mean squared error (MSE) value of 0.0063, which is significantly lower compared to CNN (0.0082) and XGBoost (0.0074). Upon incorporating date and weather characteristics, the predictive performance improves significantly, achieving an outstanding MSE value of 0.0043. However, when it comes to spatial feature analysis, the LSTM algorithm alone proves inadequate, obtaining a MSE value of 0.0084. Thus, by employing the CNN-LSTM combination model, a lower MSE value of 0.0066 is achieved, outperforming the comparison algorithms. Overall, the CNN-LSTM model exhibits strong predictive capabilities regarding the temporal and spatial requirements of shared bicycles. This model plays a key role in accurately forecasting order demands, facilitating urban transportation planning and management, as well as guiding the planning and location of non-motorized vehicle stops.

Toward structure–multiple activity relationships (SMARts) using computational approaches: A polypharmacological perspective

In the current era of biological big data, which are rapidly populating the biological chemical space, in silico polypharmacology drug design approaches help to decode structure-multiple activity relationships (SMARts). Current computational methods can predict or categorize multiple properties simultaneously, which aids the generation, identification, curation, prioritization, optimization, and repurposing of molecules. Computational methods have generated opportunities and challenges in medicinal chemistry, pharmacology, food chemistry, toxicology, bioinformatics, and chemoinformatics. It is anticipated that computer-guided SMARts could contribute to the full automatization of drug design and drug repurposing campaigns, facilitating the prediction of new biological targets, side and off-target effects, and drug-drug interactions.

An Intelligent Financial Investment Decision Model Based on Multi-Agent Reinforcement Learning

Financial investigation decision is a complex affair that highly relies on expert knowledge. In the era of big data, it remains practical to make intelligent financial investigation decisions via computing technology. As a consequence, this work introduces reinforcement learning to search for effective decision schemes that are adaptive to the diverse financial environment. Therefore, an intelligent financial investment decision model based on multi-agent reinforcement learning is proposed in this paper. First, the long short-term memory (LSTM) model is utilized to strengthen financial investment data. Then, the hidden Markov model (HMM) is utilized to make a preliminary judgment on financial investment strategy. Next, a multi-agent deep deterministic policy gradient (MADDPG) is employed to set the goal of agent reinforcement and determine decision indicators based on the financial market environment. Multiple agents (investors) form a cooperative strategy through learning and interaction to achieve the optimal investment decision in the financial market. The experimental results on realistic data indicate that the proposal can play an important role in investment decision-making.

Enhancing Big Data Security through Comprehensive Data Protection Measures: A Focus on Securing Data at Rest and In-Transit

Purpose: This research paper aims to enhance Big Data security by implementing comprehensive data protection measures, focusing on securing data at rest and in transit. In the era of Big Data, organizations handle vast quantities of data characterized by high velocity, volume, and variety, which complicates management and increases security risks. Methodology: The study examines various data protection strategies, including encryption, access control, data masking, immutable storage, tokenization, and physical security for data at rest. For data in transit, it explores encryption protocols, secure transfer methods like SSH and TLS, VPNs, Zero Trust architecture, and secure APIs. These methods are crucial for safeguarding sensitive information and preventing unauthorized access. Findings: The findings highlight common security challenges in Big Data, such as data breaches, unauthorized access, and integrity issues. The study emphasizes the need for robust protection measures and offers a comprehensive view of the data security landscape. Implementing these strategies helps organizations safeguard sensitive information and ensure compliance with international data protection regulations, enhancing their overall security posture. Unique contribution to theory policy and practice: This paper contributes to theory, policy, and practice by advocating comprehensive data protection strategies. It stresses the importance of continuous monitoring and regulatory compliance, providing practical insights into best practices and technologies that protect Big Data. The research supports developing robust data protection policies and practices, advancing knowledge in Big Data security.

Empowering generation z accountants in the era of data complexity and open innovation: Nurturing big data analytics, diagnostic, and forensic accounting skills

This research fills a significant knowledge gap by exploring the development of Big Data Analytics Skills (BAS), Diagnostic Skills (DS), and Forensic Accounting Skills (FAS) among Generation Z accounting students in Thailand. Employing a quantitative research approach, the study conducted an online survey of fourth-year accounting students from this generation, utilizing the convenience sampling technique. The investigation aimed to assess the impact of BAS and DS on FAS within the accounting student cohort. The sample consisted of 465 participants. Path analysis conducted using the SmartPLS program affirmed significant relationships: BAS significantly influences both DS (H1, β=0.817, p<0.001) and FAS (H2, β=0.391, p<0.001), while DS notably impacts FAS (H3, β=0.484, p<0.001), concurrently serving as a mediator between BAS and FAS (H4, β=0.395, p<0.001). These findings suggest the imperative need for curriculum development and tailored training programs aimed at enriching these skills among emerging accountants. Such skill enhancement is vital to equip them to grapple with the challenges inherent in the era of big data and to bolster their competence in the detection and prevention of financial fraud and deceptive practices. The study not only holds significance for the development of skills essential for future accountants but also makes substantial contributions towards enhancing transparency and accountability within the accounting and finance sector. Furthermore, it opens avenues for potential improvements in educational and training curricula in the field of accounting, ensuring alignment with the demands of the big data era and better preparing emerging accountants to effectively address the specter of potential fraud.

Quantitative Analysis and Prediction Methods for Sports Competition Results

In the era of big data, sports performance prediction becomes critical by analyzing accumulated competition data, especially the influence of momentum factors on competition results, although its definition and quantification are controversial in academia. This study explores the application of momentum in predicting the results of sports competitions and proposes a comprehensive research framework combining sports science and sports psychology. By collecting and analyzing the competition data, the feasibility of momentum as an important index in predicting the results of the competition is confirmed. The results show that momentum not only reflects the immediate performance of athletes in the game but also predicts their future results. This finding is of great significance for coaches and athletes to formulate strategies and adjust tactics in the game. Through continuous accumulation and analysis of data, we can more accurately predict the results of the game, and improve the level and fairness of sports competition.

Solving Contextual Stochastic Optimization Problems through Contextual Distribution Estimation

Stochastic optimization models always assume known probability distributions about uncertain parameters. However, it is unrealistic to know the true distributions. In the era of big data, with the knowledge of informative features related to uncertain parameters, this study aims to estimate the conditional distributions of uncertain parameters directly and solve the resulting contextual stochastic optimization problem by using a set of realizations drawn from estimated distributions, which is called the contextual distribution estimation method. We use an energy scheduling problem as the case study and conduct numerical experiments with real-world data. The results demonstrate that the proposed contextual distribution estimation method offers specific benefits in particular scenarios, resulting in improved decisions. This study contributes to the literature on contextual stochastic optimization problems by introducing the contextual distribution estimation method, which holds practical significance for addressing data-driven uncertain decision problems.

Recent Trends in Plastic Surgery: A Network Analysis of the Abstract Titles of the Largest German Plastic Surgery Congress 2023.

Every year, German-speaking experts in plastic, reconstructive, and aesthetic surgery gather to discuss the latest developments at Germany's largest conference for plastic surgery, the joint annual meeting of the German Society of Plastic, Reconstructive and Aesthetic Surgery (DGPRÄC) and the Association of German Aesthetic Plastic Surgeons (VDÄPC). Since the topics of the conference have a lasting impact on the practice and research of plastic surgery, an examination of the presented content provides insight into the driving developments in plastic surgery in Germany. We conducted a retrospective network analysis of all abstract titles from the DGPRÄC and VDÄPC annual meeting in 2023. Data were extracted regarding titles, language, author, and place of origin, and the titles were categorized into the four pillars of the specialty. The titles were standardized and subjected to network analysis. A total of 299 titles from 281 lectures and 18 instructional courses were analyzed. After preprocessing the data, 2463 words with 9384 connections qualified for network analysis. The most frequently mentioned keywords throughout the congress were 'Surgery', 'Breast', 'Reconstruction', 'Flap', 'Patient', 'Tissue', and 'Therapy'. Locations contributing the highest number of abstracts were Ludwigshafen, Hanover, Leipzig, and Munich. In the era of big data, network analysis provides the ability to identify underlying structures and nodes in multidimensional, complex datasets. This study demonstrates the useful application of network analysis to identify thematic focuses and connections at the current DGPRÄC and VDÄPC annual meeting. Sites of intensified research could thus be identified.

Simulation Learning and Optimization: Methodology and Applications

Due to the rapid advancement of computational power and the Internet of Things (IoT) together with recent developments in digital-twin and cyber-physical systems, the integration of big data analytics techniques (BDAT) (e.g., data mining, machine learning (ML), deep learning, data farming, etc.) into traditional simulation methodologies, along with the enhancement of past simulation optimization techniques, has given rise to an emerging field known as simulation learning and optimization (SLO). Unlike simulation optimization, SLO is a data-driven fusion approach that integrates conventional simulation modeling methodologies, simulation optimization techniques, and learning-based big data analytics (BDA) to enhance the ability to address stochastic and dynamic decision-making problems in the real-world complex system and quickly find the best outcomes for the decision support in both real-time and nonreal-time analyses to achieve the data-driven digital twin capability. Although some literature in the past has mentioned similar applications and concepts, no paper provides a structural explanation and comprehensive review of relevant literature on SLO from both methodological and applied perspectives. Consequently, in the first part of this paper, we conduct a literature review on a novel SLO methodology emerging from the fusion of traditional simulation methodology, simulation optimization algorithms, and BDAT. In the second part of this paper, we review the applications of SLO in various contexts, with detailed discussions on manufacturing, maintenance, redundancy allocation, hybrid energy systems, humanitarian logistics, and healthcare systems. Lastly, we investigate potential research directions for SLO in this new era of big data and artificial intelligence (AI).

Reflections on the Use of Big Data Analysis to Enhance the Level of Grassroots Supervision Work in the Construction of Smart Customs -- Research on Supervision Scenario Innovation in Digital Government Construction

In the construction of smart customs, how to use the idea of digital government to strengthen the supervision and control of grassroots power has become a new mode indispensable for customs to improve its governance capacity in the era of big data. I study the significance of big data analysis on supervision in the construction of smart customs, the main structural characteristics, the current problems and measures, so as to make a better combination of big data analysis and grass-roots supervision, so as to establish a more efficient and more perfect supervision system, so that the supervision mechanism can run better, and to build a good cage of power.

The Reform and Practice of Innovative Talents Training Mode of Statistics Major in the Big Data Era

With the advent of the era of big data, the importance of data resources has become increasingly prominent, and the demand for innovative data analysis talents is growing. As a major of training data analysis talents, statistics is facing new requirements and challenges. To adapt to the development of the times, it is imperative to reform the talent training mode. Through the analysis of the present situation of the training mode of statistics specialty in colleges and universities, it is found that there are some problems in the current statistics specialty education, such as outdated curriculum system, weak teachers, and traditional teaching methods. Combined with the characteristics of the big data era and statistics majors in colleges and universities, some constructive suggestions are put forward to construct the training mode for innovative talents in statistics majors.

Innovative Research on Curriculum Reform in the Context of Four New Discipline Construction -- Taking Probability Theory and Mathematical Statistics Course as an Example

With the massive emergence of new technologies empowered by big data and technology, the four original disciplines of engineering, agriculture, medicine and liberal arts have to keep up with the times and integrate big data, artificial intelligence and other related contents. New engineering, new agriculture, new medicine and new liberal arts came into being, the four new disciplines focus on professional cross-cutting practical [1], but also to improve the quality of talent training creative exploration, in the near future the layout of higher education disciplines or a major reshuffle will come. Industrial transformation and upgrading in the context of the national development of the economy is often heard by us, people need to study, digest, reform and innovate their own technology system on the basis of the introduction of advanced technology. Similarly the transformation and upgrading of disciplines in the context of the big data era is also imperative, various disciplines will develop in the direction of convergence and intersection, and disciplines can no longer be siloed disciplines, they all need to cross-fertilise with new big data-related knowledge to keep up with the times. Probability Theory and Mathematical Statistics is one of the required courses for undergraduates of statistics, big data and computer science majors, which are all related engineering and science majors under the construction of new engineering disciplines, while computers, the Internet, and artificial intelligence are driving educational changes and innovations, so the probability theory and mathematical statistics course should be integrated into the theories of the new engineering discipline to carry out innovation and reform. Through the restructuring of the teaching content to add the big data background of some of the methods to deal with the problem, so that students can use the idea of statistical modelling to analyze the economic and social stochastic phenomena, and can develop a pragmatic spirit based on data [3]. This paper will be from the reform of the teaching mode, teaching content innovation and restructuring, reform of the assessment method and the promotion of the four aspects of the teaching model to develop the probability theory and mathematical statistics in the four new disciplines under the background of the reform and innovation of the curriculum research.

Design Strategy of Network Security Defense System Based on Big Data and Artificial Intelligence Technologies

In the context of the big data era, the Internet has become a crucial tool in people s daily life and work, and continuously changing their way of life, which is of great significance. However, with the rapid development of big data technologies and artificial intelligence tech-nology network security issues have become increasingly severe, the design of network security defense system based on big data technologies and artificial intelligence technologies has become a prominent research area. In this paper, we will analyze the design strategy of network se-curity defense system based on big data technologies and artificial intelligence technologies, so that big data technologies and artificial intelligence technologies can play a vital role in network security defense and ensure the security and stability of the network system.

Nociceptor‐Enhanced Spike‐Timing‐Dependent Plasticity in Memristor with Coexistence of Filamentary and Non‐Filamentary Switching

AbstractIn the era of big data, traditional computing architectures face limitations in handling vast amounts of data owing to the separate processing and memory units, thus causing bottlenecks and high‐energy consumption. Inspired by the human brain's information exchange mechanism, neuromorphic computing offers a promising solution. Resistive random access memory devices, particularly those with bilayer structures like Pt/TaOx/TiOx/TiN, show potential for neuromorphic computing owing to their simple design, low‐power consumption, and compatibility with existing technology. This study investigates the synaptic applications of Pt/TaOx/TiOx/TiN devices for neuromorphic computing. The unique coexistence of nonfilamentary and filamentary switching in the Pt/TaOx/TiOx/TiN device enables the realization of reservoir computing and the functions of artificial nociceptors and synapses. Additionally, the linkage between artificial nociceptors and synapses is examined based on injury‐enhanced spike‐time‐dependent plasticity paradigms. This study underscores the Pt/TaOx/TiOx/TiN device's potential in neuromorphic computing, providing a framework for simulating nociceptors, synapses, and learning principles.

Data-Driven Mechanisms for a Newsvendor Problem: A Case Study

Reducing food waste is paramount for a sustainable future as its implications are important to achieve sustainable development goals set by the United Nations. In many industry groups, the public awareness of reducing food waste that may potentially emerge along firms’ operations has grown. In the era of Big Data, one of the most pursued exercises of this escalating attention on reducing food waste is to utilize artificial intelligence techniques to incorporate sustainability concerns into the decision framework. Many firms embrace machine learning methods to build effective decision mechanisms that help make efficient and sustainable decisions. In this study, we analyze the impact of blending machine learning approaches with demand forecasting and order quantity decisions for a firm operating in a setting where the market demand is random, and the demand structure is not observable to the firm. The performance of the methodology is evaluated on sunflower seed demand data taken from Tadım company. Our results suggest that the joint consideration of forecasting and ordering decisions using the quantile regression approach can lead the firm to decrease its operational cost by 6% on average.