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Purpose: The focus of industrial cluster innovation lies in the cooperation between enterprises and universities/scientific research institutes to make a theoretical breakthrough in the system and mechanism of industrial cluster network. Under the theoretical framework of cluster network, industrial structure can be optimized and upgraded, and enterprise benefit can be improved. Facing the increasing proliferation and multi-structured enterprise data, how to obtain potential and high-quality innovation features will determine the ability of industrial cluster network innovation, as well as the paper aims to discuss these issues. Design/methodology/approach: Based on complex network theory and machine learning method, this paper constructs the structure of "three-layer coupling network" (TLCN), predicts the innovation features of industrial clusters and focuses on the theoretical basis of industrial cluster network innovation model. This paper comprehensively uses intelligent information processing technologies such as network parameters and neural network to predict and analyze the industrial cluster data. Findings: From the analysis of the experimental results, the authors obtain five innovative features (policy strength, cooperation, research and development investment, centrality and geographical position) that help to improve the ability of industrial clusters, and give corresponding optimization strategy suggestions according to the result analysis. Originality/value: Building a TLCN structure of industrial clusters. Exploring the innovation features of industrial clusters. Establishing the analysis paradigm of machine learning method to predict the innovation features of industrial clusters. [ABSTRACT FROM AUTHOR]

Globally, cyber threats continue to increase in severity, complexity and volume. Cyber intelligence tradecraft requires a deep understanding of the cyber threat landscape and the application of analytical rigour to identify patterns and draw conclusions from a varied and evolving attack surface. To achieve this, adopting tools and frameworks synergises the benefits between the creative human mind with the power of technology under a standardised structure. This improves cyber intelligence practice by enabling analysts to evaluate and compare local trends with global trends; produce meaningful insights; and apply models and solutions to address cyber-specific challenges. Originally heavily rooted in the notion that pieces of data and information equate to intelligence, the practice of cyber intelligence has notably improved. The right tools and frameworks enable intelligence analysts to work more efficiently and effectively, moving beyond the data to achieve a thorough understanding of the threat environment. This enables them to provide more timely, accurate, relevant and actionable intelligence to decision makers. This paper describes how to enhance human-driven intelligence tradecraft through the adoption of technology to improve the efficiency and accuracy of data collection in addition to enriching data analysis and providing actionable insights in the everchanging cyber threat landscape. [ABSTRACT FROM AUTHOR]

According to a preprint abstract, researchers have developed a set of nested models that account for order restrictions in psychological tasks. They used a Bayesian approach to compare models and found that everyone shows the Stroop effect, where congruent colors are named more quickly than incongruent ones. However, they also found that some people violate order constraints in the Simon task, where spatially incongruent responses occur more quickly than congruent ones. The paper includes R-Markdown code for data analysis and the data is freely available. This research has not yet been peer-reviewed. [Extracted from the article]

According to a preprint abstract, researchers have developed a set of nested models that account for order restrictions in psychological tasks. They used a Bayesian approach to compare models and applied it to data from Stroop, Simon, and Eriksen interference tasks. The results showed that while everyone exhibited the Stroop effect, some individuals violated order constraints in the Simon task. The paper includes R-Markdown code for data analysis and the data itself is freely available. Please note that this preprint has not been peer-reviewed. [Extracted from the article]

Purpose: The purpose of this study is to make a prediction of the R&D output of China from the perspective of R&D institutions and put forward a set of policy recommendations for further development of the science and technology in China. Design/methodology/approach: In this paper, an improved discrete grey multivariable model is proposed, which takes both the interaction effects and the accumulative effects into account. As the current research on China's R&D activities is generally based on the perspective of universities or industrial enterprises above designated size while few studies put their focus on R&D institutions, this paper applies the proposed model to carry out an empirical analysis based on the data of China's R&D institutions from 2009 to 2019. The prediction results from the new model are then compared with three existing approaches and the comparison results indicate that the proposed model generally outperforms existing methods. A further prediction of the R&D output in China's R&D institutions is conducted into a future horizon from 2020 to 2023 by using the model. Findings: The results indicate that China's R&D institutions have a good development trend and broad prospects, which is closely related to China's long-term investment in science and technology. Additionally, the R&D inputs of China possess obvious interaction effects and accumulative effects. Originality/value: The paper considers the interaction effects and the accumulative effects of R&D inputs simultaneously and proposes an improved discrete grey multivariable model, which fills the gap in previous studies. [ABSTRACT FROM AUTHOR]

Data Analytics, Diagnostics and Screening, Electroencephalography, Health and Medicine, Information Technology, Software Keywords: Data Analytics; Diagnostics and Screening; Electroencephalography; Health and Medicine; Information Technology; Software EN Data Analytics Diagnostics and Screening Electroencephalography Health and Medicine Information Technology Software 7679 7679 1 06/19/23 20230623 NES 230623 2023 JUN 23 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Week -- New research on data analytics is the subject of a new report. This paper deals specifically with the existing EEG data analysis tools and the processes involved in analyzing the EEG data using these tools.". [Extracted from the article]

Copyright of Nuova Secondaria is the property of Edizioni Studium S.r.l and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

When considering internal data or the results of a study, often business leaders either take the evidence presented as gospel or dismiss it altogether. Both approaches are misguided. What leaders need to do instead is conduct rigorous discussions that assess any findings and whether they apply to the situation in question. Such conversations should explore the internal validity of any analysis (whether it accurately answers the question) as well as its external validity (the extent to which results can be generalized from one context to another). To avoid missteps, you need to separate causation from correlation and control for confounding factors. You should examine the sample size and setting of the research and the period over which it was conducted. You must ensure that you're measuring an outcome that really matters instead of one that is simply easy to measure. And you need to look for—or undertake—other research that might confirm or contradict the evidence. By employing a systematic approach to the collection and interpretation of information, you can more effectively reap the benefits of the ever-increasing mountain of external and internal data and make better decisions. [ABSTRACT FROM AUTHOR]

Purpose: Time analysis and institution analysis as well as journal analysis allow the study to show literature distribution in this research area. Research hotspots and trending among different times are revealed by network-structural properties and network-temporal property. The study aims to shed light on international cluster research progress on strategic niche management (SNM). Design/methodology/approach: Using searched literature data on SNM from 1991 to 2018 from the database of Web of Science (WOS), the article maps the citation network and completes the citation analysis based on bibliometric citation analysis. Findings: These eight research streams reveal the development of SNM from theoretical description to target-oriented study and finally diversification analysis. Originality/value: The paper identifies eight continuous research streams in SNM: sustainable transition, dynamical diversity, complexity, social-technical system, social innovation, social-cognitive evolution, emerging market and policy mix. [ABSTRACT FROM AUTHOR]

According to a preprint abstract, researchers have developed a set of nested models that account for order restrictions in psychological tasks. They used a Bayesian approach to compare models and found that everyone shows the Stroop effect, where congruent colors are named more quickly than incongruent ones. However, they also found that some people violate order constraints in the Simon task, where spatially incongruent responses occur more quickly than congruent ones. The paper includes R-Markdown code for data analysis and the data is freely available. This research has not yet been peer-reviewed. [Extracted from the article]

A recent study conducted by researchers at Shandong First Medical University and Shandong Academy of Medical Sciences has found that the G-protein signaling regulator 16 (RGS16) is associated with cancer, neurodegenerative diseases, and cardiovascular diseases. The study also revealed that RGS16 can be used as a biomarker for cancer diagnosis and prognosis. The researchers used data visualization analysis to examine 290 publications on RGS16 and found that the United States had the largest number of publications, with the Research Center of Allergy and Infectious Diseases (NIAID) publishing the most papers. The study concluded that RGS16 research is currently focused on various biological applications, including inflammation, cancer, ulcerative colitis, metabolic acidosis, platelet activation, and thrombosis. This research provides an overview of current and potential future research hotspots in the field of RGS16 and can be a valuable resource for interested researchers. [Extracted from the article]

Starting with Barnard (1945, 1947), many papers have shown that exact unconditional tests outperform Fisher's Exact Test in 2 × 2 tables with independent binomial data. Less has been published about unconditional tests with multinomial data. However, in many multinomial 2 × 2 analyses, a binomial-like comparison of proportions is of interest rather than inference in terms of odds ratios. Thus, this paper proposes using a partially conditional binomial analysis with data that are actually multinomially distributed. This partially conditional analysis, conditioning on the row totals and then using the unconditional binomial analysis, is more powerful than the fully conditional Fisher's Exact Test, has good power comparable to the fully unconditional multinomial analysis, and provides exact confidence intervals for the difference of proportions. Also, the partially conditional binomial analysis requires considerably less computation than the fully unconditional analysis. [ABSTRACT FROM AUTHOR]

In this paper, an interval type-2 evolving fuzzy Kalman filter (IT2EFKF) is designed for processing unobservable spectral components of uncertain experimental data. The primary objective of the methodology proposed in the present research is to provide a novel mathematical tool for interval and spectral processing of uncertain experimental data in order to deal with real-world filtering, tracking, and forecasting problems. The adopted methodology consider the following steps: an initial model of the interval type-2 fuzzy Kalman filter, which is off-line identified from an initial window of the experimental data; the updating of antecedent proposition of interval type-2 fuzzy Kalman filter by using an interval type-2 formulation of evolving Takagi–Sugeno (eTS) clustering algorithm and the updating of consequent proposition by using a type-2 fuzzy formulation of Observer/Kalman Filter Identification (OKID) algorithm, taking into account the multivariable recursive Singular Spectral Analysis of the experimental data. Computational results for tracking the Mackey-Glass chaotic time series demonstrate the effectiveness of the proposed approach when compared to related methodologies from the literature, with superior results for RMSE of 0.0026. Experimental results for tracking a 2DoF helicopter illustrate its applicability, with superior results for RMSE and VAF of 0.00156 and 99.9874% for yaw angle, and of 0.00702 and 99.8863% for pitch angle, respectively. [ABSTRACT FROM AUTHOR]

The continuous Teissier distribution was proposed by the French biologist Georges Teissier in 1934. This paper introduces the one-parameter discrete analogue distribution and studies some of its statistical properties, focussing the attention on the computer generation of pseudo-random data and the parameter estimation problem. More precisely, the new discrete distribution is suitable to deal with both underdispersed and overdispersed count data, the quantile function can be expressed in closed form in terms of the Lambert W function and the failure rate function is increasing. Monte Carlo simulation experiments revealed that estimation methods such as maximum likelihood, least squares and quantile least squares may produce estimates far away from the true parameter value. This drawback can be overcome due to the existence of an analytical expression for the quantile function and then accurate estimates are obtained by the maximum likelihood method even for small sample sizes. Additionally, the new distribution is also used to derive a first-order integer-valued autoregressive process. Different real data sets are used to illustrate that the proposed distribution provides a better fit than other alternative models and also outperforms other competitive processes when time series of counts are considered. [ABSTRACT FROM AUTHOR]

• An DBSCAN extension for online structural clustering is proposed. • Information granules are constructed to describe structures of shaped clusters. • Granular computing and rule-based modeling are applied for online clustering. • Good performances are achieve on accuracy and time in online structural clustering. In online structural clustering, general density-based clustering algorithms have problems of low scalability and high computation cost, especially in big data analysis, this paper proposed a DBSCAN extension algorithm with consideration of granule computing to handle these problems. This algorithm mainly makes use of advantages of DBSCAN and granular descriptors to realize effective and efficient structural online clustering. Frist, to extract structural clusters effectively, DBSCAN is considered as the basic clustering algorithm in this research. Second, since DBSCAN's results are not numerical for online testing, this paper proposes to apply granule computing (GrC) to construct information granules describing arbitrarily-shaped clusters from DBSCAN. Third, to realize an efficient online structural clustering, especially in big data analysis, a series of granular fuzzy models are built with consideration of structural information, then a rule-based model is formed for guiding online clustering of new testing data. Through the proposed method, the online clustering ability of DBSCAN is developed with reduced computation cost, meanwhile the structural clustering ability is also retained in online testing. Experiments on synthetic data, publicly available data and real-world data are discussed, online testing accuracy and computation time are evaluated to validate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The article explore the usage and potential of ChatGPT plugins and data analysis. It discusses the evolution of ChatGPT, from its early limitations in generating inaccurate information to its improvements through the introduction of plugins. It explains that plugins provide ChatGPT with the ability to access real-time information, enhance its functionality, and reduce the generation of inaccurate data.

Location factor of recommender systems has been extensively studied in the past decade. However, there is no research thoroughly analyzing location's role in news recommendation. In this paper, a comprehensive exploration on role of location in news recommendation is presented. First of all, based on analysis of real news datasets, we find that news recommendation differs from spatial item recommendation. Location affects news consumption behaviors of users with two-fold aspects including geographic feature and semantic feature. Regarding geographic feature, location influences news recommendation according to region rather than latitude-longitude level. Furthermore, interesting news topics are also impacted by semantic feature of location. Semantic feature may play a more positive role than geographic feature. The novel findings consistently manifest that, as non-spatial items, news differ from spatial items in that location influences users' selection in terms of different pattern and degree. In summary, geographic and semantic features influence reading preference through mapping locations into special topics. Changing of location topics leads to varying of reading preference. The news datasets in this paper belong to check in data. NewsREEL dataset is from a company, and it is provided by German researcher. The location data in Twitter dataset is also check in data. NetEase news dataset are collected from NetEase news websites, and the type of location data is city or region. [ABSTRACT FROM AUTHOR]

In this paper, we utilize machine learning algorithms for intelligent analysis of large-scale electronic medical records to explore and develop general methods and tools suitable for electronic medical record analysis in medical databases. Keywords: Data Analytics; Electronic Medical Records; Health and Medicine; Information Technology; Medical Records; Records as Topic EN Data Analytics Electronic Medical Records Health and Medicine Information Technology Medical Records Records as Topic 1265 1265 1 06/26/23 20230630 NES 230630 2023 JUN 30 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Week -- New study results on data analytics have been published. Data Analytics, Electronic Medical Records, Health and Medicine, Information Technology, Medical Records, Records as Topic. [Extracted from the article]

The article examines the rise of ubiquitous computing. Describes the proliferation of smart phones, mobile computing devices and immersive technologies that produce "anywhere and everywhere data" and how such devices are becoming increasing entwined. Describes the local, temporal and contextual nature of generated data. Applies the post-cognitive framework from social science to information science in order to describe how information is transformed through interactions between media sources. Discusses how the various devices should complement each other as ubiquitous analytics works to leverage the shared data.

Concept evolution detection is an important but difficult task in streaming data analysis, and further the noise may seriously limit the detection performance gains. This paper proposed a concept evolution detection method based on noise reduction soft boundary (NRSB). To reduce the negative effect of noise samples near category boundary, the noise reduction soft boundaries are defined based on the introducing of extension distance, intension distance and corresponding noise reduction process. Then the sample space is divided into in-class, mixed-class and out-of-class areas corresponding to each category. For online new coming samples, three strategies of automatic labeling by model, active labeling by expert and multi-class collaborative labeling will be adopted respectively according to different areas they located in. Automatic model labeling does not need to update the model, which may improve the learning efficiency. Expert active labeling will help with difficult samples in mixed-class area. Multi-class collaborative labeling can change the sample label dynamically during online learning. The experimental results show that the proposed method can reflect the character of dynamic evolution of streaming data well and detect the concept evolution effectively. • Noise reduction soft boundary is proposed and then the category distribution can be described reasonably. • The negative effect of noise sample located near category boundary will be reduced effectively. • The proposed can effectively reflect the evolution character well and detect the concept evolution effectively. [ABSTRACT FROM AUTHOR]

Clustering is an important data analysis technique. However, due to the diversity of datasets, each clustering algorithm is unable to produce satisfactory results on some particular datasets. In this paper, we propose a clustering optimization method called HIAC (H ighly I mproving the A ccuracy of C lustering algorithms). By introducing gravitation, HIAC forces objects in the dataset to move towards similar objects, making the ameliorated dataset more friendly to clustering algorithms (i.e. , clustering algorithms can produce more accurate results on the ameliorated dataset). HIAC is independent of clustering principle, so it can optimize different clustering algorithms. In contrast to other similar methods, HIAC is the first to adopt the selective-addition mechanism, i.e. , only adding gravitation between valid-neighbors, to avoid dissimilar objects approaching each other. In order to identify valid-neighbors from neighbors, HIAC introduces a decision graph, from which the naked eye can observe a clear division threshold. Additionally, the decision graph can assist HIAC in reducing the negative effects that improper parameter values have on optimization. We conducted numerous experiments to test HIAC. Experiment results show that HIAC can effectively ameliorate high-dimensional datasets, Gaussian datasets, shape datasets, the datasets with outliers, and overlapping datasets. HIAC greatly improves the accuracy of clustering algorithms, and its improvement rates are far higher than that of similar methods. The average improvement rate is as high as 253.6% (except maximum and minimum). Moreover, its runtime is significantly shorter than that of most similar methods. More importantly, with different parameter values, the advantages of HIAC over similar methods are always maintained. The code of HIAC is available at https://github.com/qiqi12/HIAC. [ABSTRACT FROM AUTHOR]

Differential privacy (DP) is a general approach to defend against inference attacks, but hard to balance the privacy-utility trade-off for some complex data analysis tasks. To improve the utility of data analysis, a weaker privacy definition with a more accurate estimate of privacy risk may be a straightforward and effective solution. Total variation distance (TVD) privacy is an appropriate tool for this issue, but it has not been adequately studied. In this paper, we systematically study TVD privacy and explore its applications. We provide a comprehensive theoretical analysis of TVD privacy and demonstrate its advantage in measuring privacy risks with the example of membership inference attacks. Our work indicates that TVD privacy is a helpful tool in estimating privacy risks and has the potential to be widely used as a general privacy definition. [ABSTRACT FROM AUTHOR]

• A method for representing node performance based on multi-attribute expression. • A TOPSIS-based algorithm for evaluating the node performance. • A replica placement algorithm based on the entropy weight TOPSIS method. Performance optimization based on node attributes is of profound significance in the replica placement algorithm used in Hadoop distributed file system (HDFS). Currently, most researchers studying replica placement algorithms consider only a single attribute or a few multiple attributes. However, a single attribute cannot accurately express the performance of a node. Therefore, this paper proposes a replica placement algorithm based on the entropy weight TOPSIS (technique for order preference by similarity to ideal solution) method, called TS-REPLICA. First, a multi-attribute matrix that comprehensively reflects the performance and load of nodes is defined. Then, a TOPSIS-based algorithm is proposed to calculate the performance score of each data node. In addition, the entropy weight method is introduced to derive the weights of attributes for balancing the influence of weights of multiple attributes. Next, the comprehensive load score of each data node in the Spark cluster, the average comprehensive load score of each rack, and the average comprehensive load score of the entire cluster are calculated, and replica placement is performed based on the obtained scores. Finally, the effectiveness of the proposed algorithm is verified on various datasets and test cases. The experimental results show that the TS-REPLICA algorithm outperforms the better comparison algorithm in execution number in Spark cluster. [ABSTRACT FROM AUTHOR]

Data quality is an important consideration in many engineering applications and projects. Data collection procedures do not always involve careful utilization of the most precise instruments and strictest protocols. As a consequence, data are invariably affected by imprecision and sometimes sharply varying levels of quality of the data. Different mathematical representations of imprecision have been suggested, including a classical approach to censored data which is considered optimal when the proposed error model is correct, and a weaker approach called interval statistics based on partial identification that makes fewer assumptions. Maximizing the quality of statistical results is often crucial to the success of many engineering projects, and a natural question that arises is whether data of differing qualities should be pooled together or we should include only precise measurements and disregard imprecise data. Some worry that combining precise and imprecise measurements can depreciate the overall quality of the pooled data. Some fear that excluding data of lesser precision can increase their overall uncertainty about results because lower sample size implies more sampling uncertainty. This paper explores these concerns and describes simulation results that show when it is advisable to combine fairly precise data with rather imprecise data by comparing analyses using different mathematical representations of imprecision. Pooling data sets is preferred when the low-quality data set does not exceed a certain level of uncertainty. However, so long as the data are random, it may be legitimate to reject the low-quality data if its reduction of sampling uncertainty does not counterbalance the effect of its imprecision on the overall uncertainty. [ABSTRACT FROM AUTHOR]

A recent study conducted at the University of California Riverside has developed an algorithm for searching optimal variance component estimators in linear mixed models. The study focuses on the estimation of variance components, which is crucial in data analysis based on a linear mixed model. The algorithm aims to derive strictly positive unbiased estimators with minimum variance by modifying the method-of-moments estimator using a class of square non-singular regularization matrices. The proposed method allows for the convenient study of the theoretical and numerical properties of the estimator, even in small samples. The research concludes by illustrating the use of the algorithm in two illustrative examples. [Extracted from the article]

Genetics, Health and Medicine, Medical Ethics Our news reporters obtained a quote from the research from Iran University of Medical Sciences: "In this paper, we address a common ethical dilemma of clinicians regarding whether to perform genetic tests on a deceased patient's sample upon a request from first-degree relatives against the patient's wishes in the last days of life. Keywords: Genetics; Health and Medicine; Medical Ethics EN Genetics Health and Medicine Medical Ethics 2023 FEB 10 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Week -- Current study results on medical ethics have been published. [Extracted from the article]

TempWeb focuses on investigating infrastructures, scalable methods, and innovative software for aggregating, querying, and analyzing heterogeneous data at Web scale. Emphasis is given to data analysis along the time dimension for web data that has been collected over extended time periods. A major challenge in this regard is the sheer size of the data it exposes and the ability to make sense of it in a useful and meaningful manner for its users. It is worth noting that this trend of using big data to make inferences is not specific to Web content analytics, so work presented here might be useful in other areas, too. TempWeb has been created for this purpose and is the ideal venue to discuss all its facets. Date: 25 May, 2022. Website: http://temporalweb.net/. [ABSTRACT FROM AUTHOR]

Data cleaning, a prerequisite to subsequent data analysis, has always been the focus of data science research. Datasets with errors can severely detract from the quality of downstream analytical results. Unfortunately, despite the proliferation of various data cleaning methods, it remains a time-consuming problem and frequently entails considerable labor expenses. In reality, errors are often heterogeneous and require different solutions. As a result, stand-alone methods often inadequate for addressing dirty data with multiple types of errors, while studies have demonstrated that combining such methods always require human intervention and the result remains unsatisfactory. In this paper, we propose an unsupervised integrated data cleaning framework, namely RLclean. Based on deep reinforcement learning, RLclean takes advantages of multiple data cleaning techniques, enabling it to effectively clean multiple types of errors and achieve satisfactory results. Additionally, it eliminates the need for costly human involvement, as the cleaning strategy is learned by data-driven, which further allows the framework to self-adapt to diverse domains. RLclean mainly consists of two parts: (i) an integrated error detection model that unites multiple techniques to detect different types of errors from multiple views; and (ii) an integrated data repair model that learns the optimal repair operations and repairs dirty data in an unsupervised manner. Extensive experiments on benchmark datasets have demonstrated the superiority of RLclean over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Researchers from Southern Cross University in Lismore, Australia, have conducted a study on nature play in early childhood education, focusing on the use of visual diaries as a pedagogical tool. The study utilized participatory cartography to create visual and verbal mappings of nature play pedagogies by educators and academics, as well as visual mappings of nature play experiences by young children. The research concluded that visual diaries are a valuable tool for educators, facilitating collaborative artmaking moments with children and documenting nature play pedagogies effectively. This peer-reviewed study provides insights into the benefits of engaging visual diaries in early childhood education. [Extracted from the article]

The article discusses the introduction of psychTestR, an R package that allows users to design and conduct behavioral psychological experiments. The software enables users to combine data analysis and data collection, facilitating complex experiment designs. It also supports a modular approach to experiment design and provides tools for automated testing. The resulting experiments can be deployed for lab testing or online data collection. The article emphasizes that the preprint has not been peer-reviewed and provides a link for more information on the research. [Extracted from the article]

Multi-view graph clustering (MVGC) is a technique that combines information from multiple views to perform clustering analysis on graph data. However, the consensus information between the different views is not fully utilized. Additionally, the influence of noise is inevitable, leading to insufficient robustness of the algorithm. To address these issues, this paper proposes coupled double consensus multi-graph fusion for multi-view clustering method (CDCMGF). Specifically, we first utilize the self-expressive property of the original data to obtain similarity graphs. Next, we further integrate the fusion of multiple similarity graphs into a consensus graph. However, the consensus information from different views is still not fully utilized, and there is some noise. Then, we utilize the self-expressive property of the consensus graph to obtain a much cleaner consensus graph. Fourth, we stack the two consensus graphs into a tensor, which is subjected to the constraint of the tensor nuclear norm (TNN). Then, the two consensus graphs reinforce each other, allowing for the comprehensive utilization of the consensus information from different views and reducing the influence of noise. Ultimately, by utilizing the augmented Lagrange multiplier method (ALM), the four steps outlined above are unified into a framework. The CDCMGF achieves a performance improvement of up to 64.86%, and the experimental results from various public datasets indicate that the CDCMGF algorithm outperforms the state-of-the-art algorithms. In other words, these experimental results validate the importance of fully utilizing the consensus information among the different views. The code is publicly available at https://github.com/TongWuahpu/CDCMGF. [ABSTRACT FROM AUTHOR]

This paper investigates differential privacy Kalman filtering for graphical dynamic systems. An enhanced differential privacy Kalman filter is initially developed by leveraging the topology, a unique characteristic of graphical dynamic systems, to improve the filtering performance. And then upper and lower error bounds of the enhanced filter are established to evaluate the recipient's ability to infer sensitive data. Based on these error bounds, a privacy calibration guideline is provided to balance privacy and usability of sensitive data. A notable feature of the enhanced filter is that its Kalman gain matrix is a diagonal matrix, which facilitates the independent update of information at each node by reducing the coupling between nodes. This feature diminishes the accumulation of filtering errors, thereby improving the data availability for the recipient. Finally, the effectiveness of the proposed approach is verified through a simulation of epidemiological data analysis, and the results show that the enhanced filter performs well in systems with high privacy levels. [ABSTRACT FROM AUTHOR]

As the Industrial Internet-of-Things (IIoT) evolves, a growing number of industrial control systems (ICSs) are connecting to the Internet, making them more vulnerable to malicious attacks. This paper addresses the detection of false data injection (FDI) attacks, a prevalent threat to open ICSs. We introduce an innovative anomaly detection technique using isomorphic analysis to safeguard ICSs against FDI attacks. Isomorphic analysis involves comparing transmitted signals with their expected values, which are derived from mathematical models or isomorphic components. For a comprehensive defense mechanism, we incorporate three specific detectors: the control signal detector, the actuating signal detector, and the sensor reading detector. Designed to detect FDI attacks across various parts of the ICS, these detectors ensure the integrity of all transmitted signals throughout the physical control system. While the control signal detector adopts a threshold method, the other two rely on statistical approaches. If an attack is detected, the detectors can correct tampered signals before they reach downstream components, enhancing the system's overall resilience and fault tolerance. The effectiveness of these detectors is supported by rigorous mathematical proofs. Moreover, our experimental findings further reveal the superiority of the isomorphic strategy over prior work in terms of detection rate, detection time delay, and system resilience. [ABSTRACT FROM AUTHOR]

Neighborhood-based and density-based clustering methods are applied in various data analysis applications. However, most of them have low performance in mixed heterogeneous datasets, and high computational cost. To mitigate these challenges, in this paper, a geometric-based clustering method is proposed which uses the concept of natural neighborhoods to extract the local density of data points. At the first stage of the algorithm, primary clusters are formed by identifying dense points that have a higher number of natural neighbors. Then, the heads of the clusters are extracted in dense points, based on the local natural density criterion. Afterward, a novel natural neighborhood-based method is applied to identify the points having a low number of natural neighbors, which called weak or noise data points. Finally, the desired clusters are obtained by ignoring the weak/noise points. The computational complexity of the proposed method is O (n l o g n). The proposed method is independent of any specific parameter setting. The results of experiments on benchmark datasets confirm that it achieves a higher performance compared to the competitors. [ABSTRACT FROM AUTHOR]

With the improving capacity to collect massive amounts of data, distribution-valued data are increasingly used in many applications, where they are presented in a clustered, summarized, or aggregated form to provide detailed information, as opposed to single-valued data. Most of the existing models for distribution-valued data are subject to limitations attributed to the inherent constraints caused by the special expressions of probability distributions. This makes the practical usage of distribution-valued data highly challenging. This paper introduces a novel feature embedding method to characterize a probability distribution, and on this basis, an effective linear regression model that does not contain additional constraints is proposed. Unlike previous models with nonnegative constraints on coefficients, our model is capable of addressing negative coefficients. The detailed parameter estimation procedure applying partial least squares for this model is presented to guarantee more stable results, especially in the presence of a relatively small sample size or multicollinearity among variables. Overall, the proposed method fundamentally facilitates distribution-valued data regression analysis. Extensive simulation experiments and empirical PM 2.5 concentration modeling not only verify the effectiveness of our regression method for distribution-valued data but also demonstrate the advantages of the proposed method compared with existing approaches. [ABSTRACT FROM AUTHOR]

Film reviews contain rich and complex linguistic information that can reflect the opinions and emotions of the reviewers. However, existing methods for emotion classification of film reviews rely on quantifying qualitative evaluations numerically. This approach can lead to difficulties in interpretation, information loss, and performance degradation under massive data. In this paper, we propose a novel method that utilizes a probabilistic linguistic term set (PLTS) and graph attention network (GAT) to classify films based on their emotional content in long reviews. Firstly, the Bi-directional long short-term memory (Bi-LSTM) method is used to convert film reviews into distributed emotional probabilities. This approach not only captures the emotional information in reviews, but also avoids the limitations of numerical quantification. Secondly, using PLTS to represent emotional information not only considers the relationships of linguistic features but also captures multiple emotional information simultaneously. Finally, we utilize multiple GATs to learn and aggregate the distributed emotional probabilities, enabling our method to fully perceive multiple emotional information in the reviews. Experimental results demonstrate that our method outperforms other models in classification accuracy on the IMDB film review dataset. Our method emulates human thinking to analyze emotional information in reviews and uses a human-like attention mechanism to learn the interrelationship between emotions in film reviews. Therefore, our method exhibits significant improvements in both accuracy and interpretability compared to current models, making it applicable to diverse domains that necessitate the analysis of linguistic data. Overall, the proposed method in this paper presents a novel and effective approach to analyzing and classifying films based on linguistic reviews. [ABSTRACT FROM AUTHOR]

A report titled "Data for Environmentally Sustainable and Inclusive Urban Mobility" highlights the benefits of data sharing for cities' mobility improvement projects. By sharing data, cities can achieve goals such as increasing efficiency, prioritizing sustainable transport, and transitioning to low-emission vehicles. For example, Chicago used ridership data from ride-hailing companies to introduce a tax structure and reduce traffic congestion. Real-time data also allows cities to respond swiftly in emergencies. However, the report emphasizes the need for privacy protections and regulatory systems to monitor data collection and usage. It also acknowledges the potential for bias against disadvantaged populations in ride-hailing and micromobility data. The report recommends creating standards for data sharing, improving data usage within cities, and giving cities regulatory power over private mobility service providers. Despite the challenges, the report concludes that leveraging mobility data is essential for achieving sustainable and inclusive urban mobility. [Extracted from the article]

This article presents a discussion with textbook authors Alfred Spector, Peter Norvig, Chris Wiggins and Jeanette Wing with venture partner Ben Fried and software engineer Michael Tingley about data science. Topics include the potential problems when data science is not utilized properly, establishing and executing an analysis rubric, and the authors’ upcoming book, Data Science in Context.

The application of multisource information fusion in real-world scenarios is an emerging practice because it effectively uses consistent and complementary data to optimize decision-making. Dempster-Shafer (D-S) evidence theory is prevalent because it competently handles uncertainty problems by assigning basic probability assignments (BPAs) to multielement subsets. However, a counterintuitive result may be obtained when the evidence is highly conflicting. To overcome this flaw, this paper defines a new divergence measurement to quantify the differences between BPAs; we name this new metric the belief Rényi divergence. The belief Rényi divergence takes the number of possible hypotheses into consideration, which makes it a more rational and effective difference measurement in the realm of evidence theory. Additionally, some important properties of the belief Rényi divergence are extensively explored and proven, in which the belief Rényi divergence also connects to Kullback-Leibler divergence, Hellinger distance and χ 2 divergence. Moreover, a novel multisource information fusion method is devised based on the proposed belief Rényi divergence and belief entropy. Our proposed belief Rényi divergence can efficiently model the differences between evidence, and the belief entropy is used to calculate the information volume of evidence. Thus, the proposed method can sufficiently exploit the relationships among evidence and the information volume of the evidence itself. Two case studies are illustrated to verify the effectiveness and practicality of the proposed method. Also, an experiment on an iris dataset classification is presented to verify the performance of the proposed method. In addition, an EEG data analysis application demonstrates that the proposed method can be effectively used in real-world applications. [ABSTRACT FROM AUTHOR]

Clustering is one of the most important tasks in the field known as 'Exploratory Data Analysis' (EDA). It explores the dependencies hidden in individual data attributes, dividing them from one set into smaller subsets. In this paper, a Parallel Complete Gradient Clustering Algorithm (PCGCA) is proposed. The Complete Gradient Clustering Algorithm (CGCA) provides a natural interpretation combined with no need for assumptions regarding the number of clusters, making it an appealing choice. Moreover, in CGCA, internal optimization procedures point out the parameters influencing the size of clusters. Algorithms based on kernel density estimation can, therefore, be applied for diverse practical scenarios. Another very useful usage is outlier detection – which is especially important in the currently fast-growing data industry. The described algorithm has been validated in terms of both the speed of calculation and the quality of the obtained solution. The quality of the solution was evaluated with the use of eleven clustering indexes calculated on six data sets. In addition, the obtained result was compared with several classical well-known methods of clustering. [ABSTRACT FROM AUTHOR]

Lately companies have come to recognize the limitations of the view that they must create value only for shareholders. Recognizing that every stakeholder has an impact on other stakeholders—engaged employees improve customer satisfaction, which in turn spurs growth, and so on—many CEOs are pledging to generate benefits for all their constituents: customers, workers, suppliers, communities, and investors. But few leaders have explicit strategies for doing so; most seem to rely on intuitive approaches. The good news is, firms can use data to design and implement effective stakeholder strategies. They should start by exploring outside perspectives of the value they produce—specifically, the ratings of agencies like the Drucker Institute and Just Capital. Firms must then bolster data from such third parties with inside insights and gain an understanding of the interdependencies among their particular stakeholders. Armed with that, they can develop a clear description of their purpose, establish criteria for evaluating progress toward it, set priorities among stakeholders, and start measuring value creation for each group. The last step is sustaining the new strategy through cultural change and by developing supporting processes and organizational structures. [ABSTRACT FROM AUTHOR]

This paper presents a three-dimensional chaotic map with five equilibrium points, which, unlike the classic Lorenz system, possesses a symmetric four-wing structure. Through detailed dynamical analysis and verification, the excellent chaotic properties of this map have been proven. Based on this system, an improved lightweight unmanned surface vehicle (USV) image information encryption system is designed. Given the constraints of USV equipment, the system first compresses images to enhance their storage capacity and transmission efficiency. Subsequently, an enhanced lightweight Feistel network encryption method is developed, utilizing a diagonal interpolation permutation model to ensure the security of the encryption process and reduce its complexity through a block structure. Simulation experiments and data analysis demonstrate that this algorithm effectively ensures the security of image information within the USV system. The use of compressed sensing and a block encryption structure reduces the complexity of the encryption process, thereby enhancing both the encryption efficiency and the transmission efficiency. • A new three-dimensional chaotic map is proposed and a detailed dynamical analysis is conducted. • A novel scrambling and diffusion scheme is designed based on Feistel block cipher structure. • A new chaotic image encryption algorithm is implemented based on compressed sensing and chaotic mapping. [ABSTRACT FROM AUTHOR]

Community detection is a fundamental problem in complex network data analysis. Though many methods have been proposed, most existing methods require the number of communities to be the known parameter, which is not in practice. In this paper, we propose a novel goodness-of-fit test for the stochastic block model. The test statistic is based on the linear spectral of the adjacency matrix. Under the null hypothesis, we prove that the linear spectral statistic converges in distribution to N (0 , 1). The proof uses some recent results in generalized Wigner matrices to prove the main theorem. Numerical experiments and real world data examples illustrate that our proposed linear spectral statistic has good performance. [ABSTRACT FROM AUTHOR]

• A differential privacy-based nonnegative matrix factorization is proposed. • An objective perturbation is designed to limit the effect of noise. • Robustness of items with few ratings is improved by an imputation-based scheme. • The effectiveness of the proposed schemes on large datasets is theoretically analyzed. Nonnegative matrix factorization (NMF)-based models have been proven to be highly effective and scalable in addressing collaborative filtering (CF) problems in the recommender system (RS). Since RS requires tremendous user data to provide personalized information services, the issue of data privacy has gained prominence. Although the differential privacy (DP) technique has been widely applied to RS, the requirement of nonnegativity makes it difficult to successfully incorporate DP into NMF. In this paper, a differentially private NMF (DPNMF) method is proposed by perturbing the coefficients of the polynomial expression of the objective function, which achieves a good trade-off between privacy protection and recommendation quality. Moreover, to alleviate the influence of the noises added by DP on the items with sparse ratings, an imputation-based DPNMF (IDPNMF) method is proposed. Theoretic analyses and experimental results on several benchmark datasets show that the proposed schemes have good performance and can achieve better recommendation quality on large-scale datasets. Therefore, our schemes have high potential to implement privacy-preserving RS based on big data. [ABSTRACT FROM AUTHOR]

• Propose a feasible paradigm for using multidomain data in big data analysis. • Apply multigranularity analysis to small-scale low-dimensional data sets. • Transform prediction into a classification problem using deep forest computation. When addressing analysis and prediction problems in a specific domain based on big data processing, the following problems often arise: only relationships between features in the domain itself are considered, and existing methods are not effective for training models on small sample data sets. The traditional approach usually obtains the relationships between single-domain features. Analysis and forecasting in the problem domain alone can quickly achieve good accuracy, but due to the limitations of the analysis domain, it becomes increasingly difficult to further improve the prediction accuracy. This paper proposes a novel data analysis approach compatible with small sample sets called multidomain data depth analysis (MODE). In contrast to traditional approaches, MODE emphasizes multidomain data and considers the relationships among feature domains in the original data. The features in each domain are orthogonally extracted, and feature dimensions are expanded in accordance with the characteristics of small data sets. A better prediction model can be obtained by using the expanded and strengthened features. We apply this approach to real big data from the field of sociology to predict annual income based on census data in experiments. The experimental results show that MODE offers a better prediction effect based on small multidomain samples. [ABSTRACT FROM AUTHOR]

Hyaluronic acid is composed of repeating sugar units, glucuronic acid and N-acetylglucosamine, which are often associated with increased tumor progression. Urtica dioica agglutinin is a potential component that exhibits a high affinity for binding to N-acetylglucosamine. This study aimed to investigate U. dioica Agglutininʼs potential to inhibit the proliferation and migration of prostate cancer cells with high expression of hyaluronic acid through molecular docking and in vitro studies. The expression of hyaluronan synthase genes in prostate tissue and cell lines was checked by an in silico study, and the interaction between hyaluronic acid with both CD44 transmembrane glycoprotein and U. dioica agglutinin was analyzed through molecular docking. U. dioica Agglutininʼs effect on cell viability (neutral red uptake assay), migration (scratch wound healing assays), and both CD44 and Nanog expression (quantitative real-time polymerase chain reaction) were assessed in vitro. The results showed that in prostate cancer cell lines, the PC3 cell line has the highest expression of hyaluronan synthase genes. U. dioica agglutinin exhibits an interaction of six specific residues on CD44 compared to hyaluronic acidʼs singular residue. While U. dioica agglutinin alone effectively reduced cell viability and wound closer (≥ 150 µg/mL), combining it with hyaluronic acid significantly shifted the effective concentration to a higher dose (≥ 350 µg/mL). These results, together with low Nanog and high CD44 gene expression, suggest that U. dioica agglutinin may impair the CD44-HA pathway in PC3 cells. This possibility is supported by U. dioica Agglutininʼs ability to compete with hyaluronic acid for binding to CD44. Based on this, U. dioica agglutinin as a plant lectin shows promise in inhibiting cancer proliferation and migration by targeting its dependence on hyaluronic acid. [ABSTRACT FROM AUTHOR]

The carrot-made LTB-Syn antigen (cLTB-Syn) is a vaccine candidate against synucleinopathies based on carrot cells expressing the target antigen LTB and syn epitopes. Therefore, the development of an efficient production process is required with media culture optimization to increase the production yields as the main goal. In this study, the effect of two nitrogen sources (urea and glutamate) on callus cultures producing cLTB-Syn was studied, observing that the addition of 17 mM urea to MS medium favored the biomass yield. To optimize the MS media composition, the influence of seven medium components on biomass and cLTB-Syn production was first evaluated by a Plackett–Burman design (PBD). Then, three factors were further analyzed using a central composite design (CCD) and response surface methodology (RSM). The results showed a 1.2-fold improvement in biomass, and a 4.5-fold improvement in cLTB-Syn production was achieved at the shake-flask scale. At the bioreactor scale, there was a 1.5-fold increase in biomass and a 2.8-fold increase in cLTB-Syn yield compared with the standard MS medium. Moreover, the cLTB-Syn vaccine induced humoral responses in BALB/c mice subjected to either oral or subcutaneous immunization. Therefore, cLTB-Syn is a promising vaccine candidate that will aid in developing immunotherapeutic strategies to combat PD and other neurodegenerative diseases without the need for cold storage, making it a financially viable option for massive immunization. [ABSTRACT FROM AUTHOR]

Tropaeolum majus (garden nasturtium) is a plant with relevance in phytomedicine, appreciated not only for its pharmaceutical activities, but also for its beautiful leaves and flowers. Here, we investigated the phytochemical composition of senescent nasturtium leaves. Indeed, we identified yellow chlorophyll catabolites, also termed phylloxanthobilins, which we show to contribute to the bright yellow color of the leaves in the autumn season. Moreover, we isolated and characterized the phylloxanthobilins from T. majus , and report the identification of a pyro-phylloxanthobilin, so far only accessible by chemical synthesis. We show that the phylloxanthobilins contribute to bioactivities of T. majus by displaying strong anti-oxidative effects in vitro and in cellulo , and anti-inflammatory effects as assessed by COX-1 and COX-2 enzyme inhibition, similar to other bioactive ingredients of T. majus , isoquercitrin, and chlorogenic acid. Hence, phylloxanthobilins could play a role in the efficacy of T. majus in the treatment of urinary tract infections, an established indication of T. majus. With the results shown in this study, we aid in the completion of the phytochemical profile of T. majus by identifying additional bioactive natural products as relevant components of this medicinal plant. [ABSTRACT FROM AUTHOR]

Time-frequency methods, which allow us to observe the seismic signal in time and frequency domains simultaneously, can efficiently discern the seismic response features of geological bodies in different frequency bands. In this paper, a newly developed time-frequency method named second order multi-synchrosqueezing transform (SMSST) is introduced to analyze seismic data. SMSST combines the second-order instantaneous frequency estimation and the iterative idea of multi-synchrosqueezing transform to approximate the true instantaneous frequency trajectory. It solves the problems of non-reassignment points and the sensitivity to noise at the same time. Synthetic example is employed to validate the superiority of SMSST in time-frequency analysis. Results obtained from four different methods show that SMSST offer the best performance in terms of energy-concentration and noise robustness. Applications on field data further demonstrate that SMSST-based method is suitable and effective to highlight geological information and stratigraphic features compared to the previous techniques. The capabilities of SMSST for accurate reservoir localization with high resolution, making it a promising and potential tool in seismic interpretation. [ABSTRACT FROM AUTHOR]