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Peer-reviewed conferences, the main publication venues in CS, rely critically on matching highly qualified reviewers for each paper. Because of the growing scale of these conferences, the tight timelines on which they operate, and a recent surge in explicitly dishonest behavior, there is now no alternative to performing this matching in an automated way. This paper introduces Large Conference Matching (LCM) , a novel reviewer–paper matching approach that was recently deployed in the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), and has since been adopted (wholly or partially) by other conferences including ICML 2022, AAAI 2022-2024, and IJCAI 2022-2024. LCM has three main elements: (1) collecting and processing input data to identify problematic matches and generate reviewer–paper scores; (2) formulating and solving an optimization problem to find good reviewer–paper matchings; and (3) a two-phase reviewing process that shifts reviewing resources away from papers likely to be rejected and towards papers closer to the decision boundary. This paper also describes an evaluation of these innovations based on an extensive post-hoc analysis on real data—including a comparison with the matching algorithm used in AAAI's previous (2020) iteration—and supplements this with additional numerical experimentation.2 [ABSTRACT FROM AUTHOR]

Renewable energy production throughout low-voltage grids has gradually increased in electrical distribution systems, therefore introducing small energy producers - prosumers. This paradigm challenges the traditional unidirectional energy distribution flow to include disperse power production from renewables. To understand how energy usage can be optimized in the dynamic electrical grid, it is important to understand the behavior of prosumers and their impact on the grid's operational procedures. The main focus of this study is to investigate how grid operators can obtain an automatic data-driven system for the low-voltage electrical grid management, by analyzing the available grid topology and time-series consumption data from a real-life test area. The aim is to argue for how different consumer profiles, clustering and prediction methods contribute to the grid-related operations. Ultimately, this work is intended for future research directions that can contribute to improving the trade-off between systematic and scalable data models and software computational challenges. [ABSTRACT FROM AUTHOR]

Objective Friction foot blisters are a common injury occurring in up to 39% of marathoners, the most common injury in adventure racing, and represent more than 70% of medical visits in multi-stage ultramarathons. The goal of the study was to determine whether paper tape could prevent foot blisters in ultramarathon runners. Methods This prospective randomized trial was undertaken during RacingThePlanet 155-mile (250-km), 7-day self-supported ultramarathons in China, Australia, Egypt, Chile, and Nepal in 2010 and 2011. Paper tape was applied prerace to one randomly selected foot, with the untreated foot acting as the own control. The study end point was development of a hot spot or blister on any location of either foot. Results One hundred thirty-six participants were enrolled with 90 (66%) having completed data for analysis. There were 36% women, with a mean age of 40 ± 9.4 years (range, 25–40 years) and pack weight of 11 ± 1.8 kg (range, 8–16 kg). All participants developed blisters, with 89% occurring by day 2 and 59% located on the toes. No protective effect was observed by the intervention (47 versus 35; 52% versus 39%; P = .22), with fewer blisters occurring around the tape on the experimental foot than under the tape (23 vs 31; 25.6% versus 34.4%), yet 84% of study participants when queried would choose paper tape for blister prevention in the future. Conclusions Although paper tape was not found to be significantly protective against blisters, the intervention was well tolerated with high user satisfaction. [ABSTRACT FROM AUTHOR]

Abstract: Recommendation algorithms have been researched extensively to help people deal with abundance of information. In recent years, the incorporation of multiple relevance criteria has attracted increased interest. Such multi-criteria recommendation approaches are researched as a paradigm for building intelligent systems that can be tailored to multiple interest indicators of end-users – such as combinations of implicit and explicit interest indicators in the form of ratings or ratings on multiple relevance dimensions. Nevertheless, evaluation of these recommendation techniques in the context of real-life applications still remains rather limited. Previous studies dealing with the evaluation of recommender systems have outlined that the performance of such algorithms is often dependent on the dataset – and indicate the importance of carrying out careful testing and parameterization. Especially when looking at large scale datasets, it becomes very difficult to deploy evaluation methods that may help in assessing the effect that different system components have to the overall design. In this paper, we study how layered evaluation can be applied for the case of a multi-criteria recommendation service that we plan to deploy for paper recommendation using the Mendeley dataset. The paper introduces layered evaluation and suggests two experiments that may help assess the components of the envisaged system separately. [Copyright &y& Elsevier]

In Fritz John’s famous paper (1979), he discovered that for the wave equation □ u = ∣ u ∣ p , where 1 < p < 1 + 2 and □ denoting the d’Alembertian, there is no global solution for any nontrivial and compactly supported initial data. This paper is intended to simplify his proof by applying a Gronwall’s type inequality. [ABSTRACT FROM AUTHOR]

Abstract: This study investigates the predictive ability of gene-expression programming (GEP) in the estimation of methane yield (Y m ) and effluent substrate (S e ) produced by two anaerobic filters. The modeling study was carried out using the data obtained from two upflow anaerobic filters – one mesophilic (35°C) and one thermophilic (55°C) – operated for the treatment of paper-mill wastewater under varying organic loadings. The GEP model was composed of three inputs, hydraulic retention time (T hr ), organic loading rate (R ol ), and influent substrate (S i ), and one output, either S e or Y m . The Stover–Kincannon model was also used for data analysis and to evaluate the prediction ability. Three statistical criteria, root mean square error (RMSE), determination coefficient (R 2), and Akaike''s information criteria (AIC), were the means used for comparison. The results showed that the GEP approach predicted the performance of both anaerobic filters much better than the Stover–Kincannon model. [Copyright &y& Elsevier]

In this study, the relationship between freezing point and suction of four types of chloride loess samples (CaCl 2 , MgCl 2 , NaCl and KCl) was investigated by freezing point tests and filter paper method-based suction tests. Break points were observed from the freezing point versus water content curves near the plastic limit, which decline at higher salt contents. The freezing point declines roughly linearly with salt content for samples with 2–1 type chloride salts (CaCl 2 and MgCl 2), while decreases attenuatedly for those with 1–1 type chloride salts (NaCl and KCl). The higher the cation content, the more significant the freezing point depression, and the ranking of Mg2+ > Ca2+ > Na+ > K+ was roughly noted, which is linked with the cation hydration capacity. In addition, the relationship between the osmotic suction of samples h 0soil and that of chloride solution h 00solution was suggested to be described by a linear relationship. The freezing point of samples depends on both matric suction and osmotic suction. The limitations of the Mizoguchi's model were noticed through data analysis and by retaining its basic construct, a suction-based model for freezing point of chloride loess was established and verified. • Relationship between freezing point and suction of chloride loess was revealed. • Effect of cations on freezing point of chloride loess depends on hydration degree. • Osmotic suction of chloride loess is fitted as a function of that of chloride solution. • A modified Mizoguchi model for chloride loess considers the influence of suction. [ABSTRACT FROM AUTHOR]

Big data has become the mainstream of information dissemination today. Big data plays an important role in the field of intelligent network monitoring. How to improve the hidden danger identification of intelligent network monitoring system and improve its accuracy is a hot issue to be solved. Therefore, in the context of big data, this paper uses intelligent analysis algorithm to identify and analyze the hidden dangers of intelligent network monitoring system. This paper mainly applies the methods of experimental setup, fuzzy analysis and data comparison to carry out relevant research on intelligent analysis of hidden danger identification of intelligent network monitoring system. The experimental data shows that the accuracy of the data obtained by the proposed method in the hidden danger identification is more than 50%, but less than 80%. Therefore, the application of background difference method needs further improvement. [ABSTRACT FROM AUTHOR]

The applications of clustering in biomedical is pervasive and ubiquitous. A typical example is gene expression data analysis, where clustering is emerging as a powerful solution for uncovering cancer-related insights. Neutrosophic c -means (NCM) clustering has advantages over other conventional clustering methods in characterizing the uncertainty and imprecision caused by cluster overlap and identifying outliers. Nonetheless, NCM and its derivatives equally treat the contribution of each attribute to the cluster. In biomedical applications, genes (i.e. attributes) should take different importance in identifying different clusters. In this paper, we first propose a self-adaptive attribute-weighted neutrosophic c -means (AWNCM) clustering method to overcome the above defects. Moreover, a new objective function is designed to obtain optimal neutrosophic partition, cluster centers and attribute weights. Since AWNCM tends to be more effective against spherical data, we further develop a kernelized version of AWNCM, called KAWNCM, in order to better satisfy the clustering of some complex data (i.e. non-spherical data). We employ the iterative optimization strategy to obtain the optimal solutions for AWNCM and KAWNCM. The advantage of AWNCM and KAWNCM is to improve performance by learning the importance of each attribute to the cluster while maintaining an efficient solution to cluster overlap and outliers. Extensive experimental results using synthetic data and gene expression data demonstrate the feasibility and effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

In a society based on data-driven, data inclusion and data access play a significant role in societal development. A called democratization of data through open access, Open Data, must be nurtured by countries to empower their citizens, entrepreneurs, companies, industries, academics, and organizations, in general. Open Data Scoring System is an evaluation system that ranks countries in 22 categories of openness in data, divided into the 3 pillars of sustainability. In this paper, we will present the importance of Industry 4.0 and its relation to sustainability and the role of Data Science in Industry 4.0 assuming an Open Design approach. Then, an analysis is made considering the Gross Domestic Product (GDP) of the most relevant countries worldwide, the USA and China, concerning the six (6) higher ranked categories of openness data of these countries, supported by the Open Data Scoring System from 2015 to 2020. Our findings reveal that in the USA and China the main categories are seven (7), five (5), and 2 (two) categories of economic, social, and environmental sustainability, respectively. Through a correlations and co-occurrences analysis of the open data scoring worldwide reveals that the most significant categories are four (4) economic, one (1) social, and two (2) environmental. [ABSTRACT FROM AUTHOR]

This paper aims to demonstrate the findings obtained through the analysis and application of an unsupervised K-means algorithm on the SINAN database from 2001 to 2022 in Brazil, with the objective of understanding which states have the highest number of tuberculosis cases and identifying similarities among them that may contribute to a higher case rate relative to the local population. We will begin with a brief historical introduction, followed by an overview of the characteristics related to tuberculosis transmission. Subsequently, we will discuss the results obtained from the year-to-year analysis of the collected data. [ABSTRACT FROM AUTHOR]

Driver behavior is one of the principal factors associated with road accidents. Much research to date focusing on Machine learning technology has been successfully applied to identifying driving styles and recognizing unsafe behaviors. In this paper, the development of an android mobile application (Driver Alert) is described, with the aim of collecting data from mobile phone sensors data to identify certain patterns and understand drivers' behaviors. Additional information was recorded regarding weather and traffic information, using public API's to complement the data directly collected from the vehicle. Four machine learning models (K-Means, Algorithm Agglomerative Hierarchical, Random Forest and Support Vector Machines) were tested and compared to identify different driver profiles. A native mobile application named DriverAlert was developed to support collect data and make it available, through an online dashboard, to drivers and researchers. Due to the available tools and libraries, it possesses, Python language was used, as it is a powerful programming language for workloads in data science, machine learning, and scientific computing. [ABSTRACT FROM AUTHOR]

This paper proposes a fuzzy cluster-scaled principal component analysis (fuzzy cluster-scaled PCA) for mixed data, which consists of both numerical and categorical data with respect to quantitative and qualitative variables. The fuzzy cluster-scaled PCA has been proposed for high-dimension, low-sample size (HDLSS) data in which the number of variables (or dimensions) is much larger than the number of objects (or samples). In this case, the target data is only for the numerical data. The proposed fuzzy cluster-scaled PCA in this paper can be applied to the mixed type of HDLSS data by utilizing the feature of the fuzzy cluster-scaled correlation, which is decomposed into two parts: the first part is the correlation of classification structures between variables and the second part is the correlation between variables. Then, the first part can be adapted to the categorical data, and the second part can be used for the numerical data through the same objects. Several numerical examples used data from a survey concerned with the relationship between students' choice of devices and scores of examination/class marks to measure the educational effect show a better performance of this proposed method, and insightful, important information on educational effectiveness is clarified. [ABSTRACT FROM AUTHOR]

Small data and big data are distinct approaches to data analysis and utilization in various applications. While big data has been the focus of many research and business efforts for more than ten years, small data is increasingly being recognized as having potential value in certain settings. We systematically review literature and conclude that small data can be indeed valuable in certain scenarios. This paper incorporates the data value perspective of small data within various application areas. For this, we apply the data-information-knowledge-wisdom (DIKW) hierarchy to categorize papers and findings, and discuss the papers from the view point of "data value". Our review identifies various contexts where small data can be used to create value, such as data pre-processing, classification tasks, anomaly detection, forecasting and decision support. We also highlight industries that may be particularly promising areas for practitioners and researchers focused on small data. In addition, we provide an overview of methods and tools for small data analysis, including statistical techniques, visualization, and machine learning algorithms. Finally, based on our results, we suggest, that further research should focus on small data analysis. [ABSTRACT FROM AUTHOR]

this paper reviews the results obtained from recent studies mentioning the crustal architecture in Cameroon. It also discusses the study of [1] "Estimation of Curie depth, geothermal gradient and near-surface heat flow from spectral analysis of aeromagnetic data in the Loum-Minta area (Centre-East Cameroon)". The paper published by [1] is an application of the spectral method for the determination of Curie depths, followed by interpretations on the local thermal structure (thermal gradient, heat flux) of the crust. However, some outliers are remarkable and depend at first analysis on a bad mesh of the data following a windows of 27.75 * 27.75 km. Similarly, the failure to take into account the various parameters (seismic, geological, tectonic) which would help to better calibrate the results. with this communication, we would like to mention and clarify some errors in the paper published by [1] based on a global review of thermal behaviour of the crust. It is strongly recommended that information on regional geology and seismicity be used to estimate the Curie depths and that other independent data be integrated to determine the thermal and mechanical behaviour of the crust in Cameroon. [ABSTRACT FROM AUTHOR]

In a recent contribution G. Shanmugam (2018) discusses and neglects the importance of hyperpycnal flows and hyperpycnites for the understanding of some sediment gravity flow deposits. For him, the hyperpycnal flow paradigm is strictly based on experimental and theoretical concepts, without the supporting empirical data from modern depositional systems. In this discussion I will demonstrate that G. Shanmugam overlooks growing evidences that support the importance of hyperpycnal flows in the accumulation of a huge volume of fossil clastic sediments. Sustained hyperpycnal flows also provide a rational explanation for the origin of well sorted fine-grained massive sandstones with floating clasts, a deposit often wrongly related to sandy debris flows. [ABSTRACT FROM AUTHOR]

Collecting and structuring data on which to perform specific data analyses is a long and complicated process. It requires substantial advanced planning of what data and what data characteristics will be needed for the study. It is important to specify in what format they should be collected. On the other side, there are huge amounts of data stored in a variety of applications. This data is clean and consistent. In many cases, it can be used for new analyses, different from the original purposes for which the information was collected. This is particularly true for data in healthcare. Medical information accumulated in multiple systems can be used for further research and analysis. This paper presents a methodology, appropriate data models, and relevant software decisions that support the reuse and explorations of medical data for specific treatment assessments based on data routinely collected from clinical systems. Some initial results are presented and discussed. [ABSTRACT FROM AUTHOR]

With the increasing volume, velocity, and variety of data generated by various sources, big data has become a critical challenge for many organizations. Cloud computing provides an efficient and cost-effective solution to address the challenges of big data analysis. In this paper, we review the state-of-the-art cloud computing technologies for big data analysis, including cloud storage, cloud computing platforms, and cloud-based big data analytics tools. We also identify the major challenges and solutions for cloud-based big data analysis. [ABSTRACT FROM AUTHOR]

Recurrent neural networks (RNNs) and long short-term memory (LSTM) models have demonstrated tremendous effectiveness in modeling time series data due to the ability they possess to capture temporal dependencies. Stock market data is an example of time series data. Stock price data is very volatile and dynamic therefore one of the most turbulent areas to invest in might be the stock market. The choice to buy or sell stocks is heavily influenced by external circumstances and statistical analysis of previous stock performance. Stock price index forecasting has been a major area of research for many years and many machine learning and deep learning methods have been proposed to simplify this hard process, but little success has been discovered so far. In this paper, the application of RNNs and LSTMs on a stock price dataset to predict future values is explored. We start with Recurrent neural networks (RNNs) to predict the values but one of the major challenges with it is the "vanishing gradient" problem, which makes it difficult for the network to learn long-term dependencies in the data. To overcome this long short-term memory (LSTM) was used which eliminates the vanishing gradient problem. Data were normalized and divided into time steps to determine the relationship between past values and future values to make accurate predictions. The outcomes show that the MLS LSTM method significantly outperforms existing algorithms, achieving a prediction accuracy of 98.1% on the training data set and a 91.97% accuracy on the testing data set. [ABSTRACT FROM AUTHOR]

This paper examines the relationship among Research and Development (R&D) expenditure, government effectiveness, and voice accountability in 15 Latin American countries. R&D expenditure is a crucial driver of innovation and economic growth, and effective governance and voice accountability are essential for promoting transparency and citizen participation in decision-making processes. The study utilizes a panel dataset from 2002 to 2021 and employs computer data analysis to estimate the effects of changes in government effectiveness and voice accountability on R&D expenditure. The findings reveal a positive impact of government effectiveness and voice accountability on R&D expenditure. A 1% increase in government effectiveness is associated with a 0.618% increase in research development expenditure, while a 1% rise in voice accountability corresponds to a 0.625% increase. These results highlight the importance of effective governance and citizen engagement in fostering R&D investment. [ABSTRACT FROM AUTHOR]

The metaheuristics approach is important for solving many engineering applications, including healthcare. In the healthcare sector, many problems related to patients' treatments are not covered, and it is one of the issues for healthcare services across the globe. Healthcare support functions like clinical decision support, patient profiling, customized care, and health tracking could be the area where data analysis effectively provides end-user support. This paper focused on the metaheuristics algorithm concept with data mining technologies to provide a healthcare sector solution mechanism. This paper identifies the data analysis problem of the healthcare sector. It reviews various recent technologies used in healthcare and related sectors. It focused on the importance of metaheuristics algorithms, artificial intelligence, and implementation methodologies. [ABSTRACT FROM AUTHOR]

The ultra-short-term electricity demand forecast is very crucial to the operation of power grid. This paper analyses the hourly electricity demand in a functional data framework and uses the functional nonparametric prediction method to forecast the hourly electricity demand of the next day. The results indicate functional nonparametric prediction method performs better than traditional time series model (seasonal-ARMA). This paper expands the application field of functional data analysis. This paper also introduces some future research directions under this topic. [ABSTRACT FROM AUTHOR]

The impact of COVID-19 on the Global scale is tremendously drastic. There are several types of research going on across the world simultaneously to understand and overcome this dire pandemic outbreak. This paper is purely a statistical study on a distinct set of datasets regarding COVID-19 in India. The motivation of this study is to provide an insight into the rapid growth of confirmed COVID-19 cases in India. The rapid growth of COVID-19 cases in India started in March 2020. The main objective of this paper is to provide a solid statistical model for the policymaker to handle this kind of pandemic situation in the near future with nonlinear data. In this paper, the data was got from 1st April to 29th November 2020. To come up with a solid statistical model, various nonlinear data such as confirmed COVID-19 cases, maximum temperature, minimum temperature, the total population (state-wise), the total area in km2 (state-wise), and the total rural and urban population count (state-wise) have been analyzed. In this paper, six different Generalized Additive Models (GAM) was identified after a thorough analysis of other researchers' (Xie and Zhu, 2020; Prata et al., 2020) findings. In all perspectives, the results were identified and analyzed. The GAM model regarding total COVID-19 confirmed cases, total population, and the total rural population provides the best average fit of R2 value of 0.934. As the population value is quite high, the author has concise it using logarithm to provide the best p -value of 0.000542 and 0.001407 for a relation between the total number of COVID-19 cases regarding the total population and total rural population respectively. [ABSTRACT FROM AUTHOR]

Semi-Supervised Graph Clustering (SSGC) has emerged as a pivotal field at the intersection of graph clustering and semi-supervised learning (SSL), offering innovative solutions to intricate data analysis problems. However, despite its significance and wide-ranging applications, there exists a notable void in the literature—a comprehensive survey specifically dedicated to SSGC techniques and their diverse applications remains conspicuously absent. Addressing this gap, this paper presents a systematic and comprehensive review of SSGC methodologies, spanning from well-established approaches to cutting-edge developments. Through a meticulous categorization, critical examination, and insightful discussion of these techniques, this survey not only illuminates the current landscape of SSGC but also identifies unexplored avenues for exploration and innovation. In this paper we present a comprehensive survey of conventional graph construction, graph clustering, SSL methods, evaluation metrics and their primary development process. We then provide a taxonomy of SSGC techniques based on their structures and principles and thoroughly analyze and discuss the related models. Furthermore, we review the applications of SSGC in various fields. Lastly, we summarize the limitations of current SSGC algorithms and discuss the future directions of the field. [ABSTRACT FROM AUTHOR]

Expert testimony is only admissible in common-law systems if it will potentially assist the trier of fact. In order for a forensic-voice-comparison expert's testimony to assist a trier of fact, the expert's forensic voice comparison should be more accurate than the trier of fact's speaker identification. "Speaker identification in courtroom contexts – Part I" addressed the question of whether speaker identification by an individual lay listener (such as a judge) would be more or less accurate than the output of a forensic-voice-comparison system that is based on state-of-the-art automatic-speaker-recognition technology. The present paper addresses the question of whether speaker identification by a group of collaborating lay listeners (such as a jury) would be more or less accurate than the output of such a forensic-voice-comparison system. As members of collaborating groups, participants listen to pairs of recordings reflecting the conditions of the questioned- and known-speaker recordings in an actual case, confer, and make a probabilistic consensus judgement on each pair of recordings. The present paper also compares group-consensus responses with "wisdom of the crowd" which uses the average of the responses from multiple independent individual listeners. • Forensic voice comparison based on automatic-speaker-recognition technology. • Speaker identification by groups of collaborating lay listeners. • "Wisdom of the crowd" from independent individual-listener responses. • The forensic-voice-comparison system outperformed most groups of listeners. [ABSTRACT FROM AUTHOR]

With the fast-moving deployment of IoT (Internet of Things) applications, a large amount of data is generated and the data is in need for processing to be able contribute to the user's requirements. All of the IoT applications require low latency and certain bandwidth to provide us with new features which are Time-Dependent. Thus, it would be advantageous to redesign the current IoT architecture by understanding the unique properties of communications and computational power to support intelligent processing of IoT data and its analysis. The paper is focused on topics such as characteristics of IoT data, trends in IoT network architecture, problems in IoT data analysis and their solutions. Thus, in particular, the paper is a discussion that Edge software-defined computing and CEP (Complex Event Processing) based fog computing as a architecture that promises to support the different requirements of IoT data analytics. And also, the paper is a discussion about how machine learning, deep learning are useful in removing data processing anomalies. The leader node election for data processing at cloud is also a time consuming task, the proposed genetic based Genetic based Leader Election Algorithm (GLEA) algorithm in the paper show promising results for the solution. [ABSTRACT FROM AUTHOR]

Visual querying of location-based data assists users in expressing query requirements, investigating query results and making inferences. However, directly accessing data records exposes individual location information and may cause privacy issues. Conventional aggregation-based methods can preserve location-relevant privacy but may lead to the loss of detailed information and failure of analysis. Visualization aids users in gaining a deeper comprehension of the query process and the variation of information concerning privacy-preservation. In this paper, we present a privacy-aware visual query approach for location-based data. We propose a graph-based privacy-preserving scheme to protect location privacy in the visualization, and two visual metaphors to enhance understandings of information-variation in the privacy-preserving process. We design and implement a visual interface that supports a progressive process of query conditions specification and query results exploration. Experiments on real-world urban datasets demonstrate that our approach is capable of making a fair balance between location privacy and data analysis. [Display omitted] • Directly query location data may lead to privacy issues. • Anonymizing data preserves data privacy but limits data utility. • Graph-based anonymization sanitizes data privacy effectively and dynamically. • Interactive visual query interface enables a fair balance between privacy and utility. [ABSTRACT FROM AUTHOR]

Powered by the rapid progress of analytics techniques and the increasing availability of healthcare data, artificial intelligence (AI) is bringing a paradigm shift to healthcare applications. AI techniques offer considerable advantages for the evaluation and assimilation of large amounts of complex healthcare data. However, to effectively use AI tools in healthcare, key issues need to be considered and several limitations must be addressed, such as privacy-preserving and authentication of the healthcare data for analysis in training and inference procedures. Although various techniques ranging from cryptographic tools to obfuscation mechanisms have been proposed to provide privacy guarantees for data in AI-based services, none of them is applicable to online AI-driven healthcare applications. For they require a heavy computational cost on protecting privacy without offering authentication services for third parties. In this paper, we present RASS, an efficient privacy-preserving and authentication scheme for securing analyzed data in an AI-driven healthcare system. The security proofs of our construction indicate that its unforgeability and multi-show unlinkability can defend against the tempering and collusion attacks respectively. Finally, we conduct sufficient efficiency analysis, and the results show that RASS achieves the above security demands without introducing complex computation and communication costs. • Privacy-preserving and authentication are crucial for AI based healthcare systems. • RASS supports multiple redactions without invaliding the authenticity. • The security analysis show RASS defends arbitrary-replace and linking attacks. • RASS performs well in securing privacy and authenticity of healthcare data. [ABSTRACT FROM AUTHOR]

• Gas turbine life management monitors and assesses turbine status and conditions. • Inlet and exit air passages impact gas turbine performance, not physical attributes. • Study gap: An incomplete gas-turbine model limits big data's predictive analysis. • Big data analytics drive optimized informed integrated health monitoring decisions for gas turbines. • Data mining extends gas turbine life by forecasting faults for the future. In addition to power generation, gas turbines (GT) are crucial components in many engineering applications, including those for aircraft, ships, and other machinery that needs a prime mover. One of the main concerns was predicting a comprehensive GT model, including intake, exit, and power-generator characteristics. Hence, this study provides a comprehensive review of GT's main components, parameters, faults, and predicting models using machine learning and data mining applications. Forty-four publications between 2014 and 2023 were chosen for this review study based on the primary keywords associated with the paper title. This study focuses solely on the GT model prediction aspects, considering the significance of field distribution, applications of data mining, research goals, benefits, constraints, and contributions to aviation, the oil and gas industry, and marine propulsion fields. Based on the examined studies, ANN, ELM, and KF applications show promise for potential GT life management. The typical limitations of GT life prediction, as observed in 93.2% of the review papers, involved the absence of a comprehensive GT model that incorporates all vibration factors and the use of substantial amounts of real data for predictive modelling. Another notable finding that underscores the knowledge gap in this area is the utilization of complete GT units to monitor unit life and facilitate life management. Apparently, the development of GT life management is still riddled with several limitations, which call for more improvement. [ABSTRACT FROM AUTHOR]

Mobile data offloading is a highly promising approach in mobile networks that tackles network congestion at Base Stations (BSs) and greatly improves both the Quality of Service (QoS) and Quality of Experience (QoE) for users. It presents significant business opportunities for operators, particularly in light of the exponential growth in mobile data traffic and the ongoing digital transformation. To effectively uphold the desired levels of QoS and QoE in the elevation of escalating digitalization and the unprecedented surge in data traffic, this paper presents offloading through a diverse range of technologies such as data offloading through Small Cell Networks (SCNs), Wi-Fi offloading, Device-to-Device (D2D) offloading, and data offloading through Vehicular Ad-Hoc Networks (VANETs). The SCNs and Wi-Fi offloading involve migrating data traffic to the alternative infrastructure i.e. the small BS and the Wi-Fi Access Points (AP), respectively while D2D focuses on transferring data through the device without transversing the BSs. VANETs is the process of offloading data in vehicular scenarios that consist of Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Everything (V2X). Additionally, mobile data offloading from cellular BS is categorized into four main factors: energy consumption or energy awareness, economic considerations, user satisfaction, and network congestion. These factors play a crucial role in the ongoing adoption and implementation of mobile data offloading strategies. Different technologies utilize diverse techniques to tackle the challenge of offloading, aligning with their specific research objectives. This paper delves into the challenges and outlines future research directions in the field of mobile traffic offloading. [ABSTRACT FROM AUTHOR]

This paper presents a novel application of three-way correspondence analysis as a technique to analyse three-way contingency tables with abundance scores of several species. The example data analysis presented was taken from a previous mesocosm experiment and consists of a two-factor experimental design with physical disturbance and organic enrichment as factors, applied to sediment collected from the Oslofjørd, Norway. The focus of the original research was to evaluate the influence of the two factors and their interactions on the abundance of the species present in the sediment. In the current paper we demonstrate that by using a three-way correspondence approach it is possible to undertake simultaneous analysis of the species, identifying and evaluating their relative sensitivity to the environmental factors thus adding additional insight than was possible in the original analysis. In particular, this new approach allowed even relatively scarce species to be included in the analysis and evaluated together with abundant species. This paper demonstrates how three-way correspondence analysis can be a useful analytical tool in teasing out effects and interactions from multi-factorial studies. • Benthic species are differently sensitive to disturbances and organic enrichments. • This sensitivity becomes evident through their relative abundances. • A two-factor design can be used to investigate this abundance variability. • Three-way correspondence analysis is an ideal technique for the analysis. • Biological explanations could be formulated on the basis of the analysis outcomes. [ABSTRACT FROM AUTHOR]

The paper is devoted to a specific analysis of data, originating from a sample of opinions collected from the university students, concerning their learning experiences and the use of modern IT enabled media. The analysis is performed using the 'reverse clustering' approach. We are primarily interested in this paper in the differentiation of the opinions along the lines of the potential cultural backgrounds, extending our previous investigations of this matter. This is of very high importance from the perspective of the potential use of such opinions in the attempts of crowdsourcing or similar use of the web-based collection of concepts and ideas. In the present paper we treat the aspects of results, provided by the 'reverse clustering' approach, with main emphasis on the role of specific variables in shaping of the apparent cultural divides in the sets of opinions. Thus, both the nature of these divides is assessed and the potential use of the reverse clustering is evaluated. [ABSTRACT FROM AUTHOR]

Monitoring ionospheric scintillation on a global scale requires introducing a network of widely distributed geodetic receivers, which call for a special type of scintillation index due to the low sampling rate of such receivers. ROTI, as a scintillation index with great potential being applied in geodetic receivers globally, lacks extensive verification in the high-latitude region. Taking the phase scintillation index (σ ϕ) provided by ionospheric scintillation monitoring receivers as the reference, this paper analyses data collected at 8 high-latitude GNSS stations to validate the performance of ROTI statistically. The data is evaluated against 4 parameters: 1, the detected daily scintillation occurrence rate; 2, the ability to detect the daily occurrence pattern of ionospheric scintillation; 3, the correlation between the detected scintillation and the space weather parameters, including the 10.7 cm solar flux, Ap, the H component of longitudinally asymmetric and polar cap north indices; 4, the overall distribution of the scintillation magnitude. Results reveal that the scintillation occurrence rates, the occurrence patterns of ionospheric scintillations and the correlations provided by ROTI are generally consistent with those given by σ ϕ , particularly in the middle-high-latitude region. However, the analysis on the distribution of σ ϕ for different ranges of ROTI shows ROTI cannot achieve accurate scintillation monitoring at the epoch level in all selected stations. The main outcomes of this paper are of importance in guiding the reasonable application area of ROTI and developing a high-latitude ionospheric scintillation model based on geodetic receivers. [ABSTRACT FROM AUTHOR]

• We introduce eight years' worth of New Zealand forestry incident data. • We analyse forestry incidents from the perspective of worker-failure and fatigue. • 70% of incidents in this data are attributed, at least in part, to worker-failures. • 78% of worker-failure based causes show indications of fatigue. Reports of incidents in dangerous work environments can be analysed to identify common hazards, in turn aiding in the prevention of future accidents. Whilst studies exist that do this, most focus on causes that involve physical risks. In this paper we propose an alternative approach, and illustrate causes of forestry incidents from the perspective of worker-failure and fatigue. This paper outlines the analysis of eight years' worth of New Zealand forestry incident data, with a focus on the cause of, and time that, incidents occur. This has resulted in two main findings. First, 70% of incidents can be attributed, at least in part, to worker-failures. Second, 78% of worker-failure based causes show indications of fatigue. This indicates that a significant number of forestry incidents are caused by worker-fatigue. Finally, this dataset showed inconsistencies in data quality, similar to those that exist in other datasets. This did not affect our analysis. However, these types of errors have the potential to affect the data quality in the national reporting system. The results from this study will be used in a larger project on detecting fatigue in forestry workers for injury and incident prevention. It is also our hope that other researchers may find these results of interest for further fatigue prevention research in hazardous industries. [ABSTRACT FROM AUTHOR]

The paper examines teacher pragmatic acts (verbal and embodied) in response to children's crying in a Russian preschool. It focuses on crying episodes associated with children's inability or unwillingness to follow the norms and expectations surrounding children's conduct (e.g., crying when not allowed to do something). The paper employs discourse analysis; the data are 40 h of video- and audio-recorded interactions. The analysis shows that teachers used directives and other pragmatic acts (e.g., assessments, explanations) that encouraged children to stop crying. The directives were produced in multi-party participation frameworks where peers were referred to or recruited as active participants. Teachers used positive and negative assessments of crying. Crying was presented in a negative light in contrast with positive non-crying behaviors; positive assessments (praise and affectionate touch) were offered when crying stopped. The consequences of crying were presented as negatively affecting others. It is argued that such discursive treatment of crying contributes to socializing children to conformity. The paper contributes to our understanding of how through the use of pragmatic acts, child crying is discursively treated in ways that invoke norms of pragmatic conduct (e.g., not crying when asked to participate in a planned activity) and social values regarding group membership. • Analysis examines teachers' pragmatic acts (verbal and embodied) in response to crying in a Russian preschool. • Methods utilize discourse analysis. • Findings explore teachers' use of directive trajectories and other pragmatic acts that encouraged children to stop crying. • Conclusions are discussed in relation to norms of pragmatic conduct and social values. [ABSTRACT FROM AUTHOR]

In recent years, combining web scraping techniques with Natural Language Processing (NLP) has emerged as a powerful approach to unlock deeper insights from unstructured textual data. This research study presents a detailed exploration of web scraping using Natural Language Processing (NLP) techniques, demonstrating how these methodologies can be synergistically integrated to extract and analyze unstructured text from diverse web sources. This research study analyzes the challenges posed by unstructured data on the web and how NLP can play a pivotal role in converting this text into structured and actionable information. The first part of the paper covers an overview of web scraping methods, including rule-based parsing, XPath queries, and the use of web scraping libraries such as BeautifulSoup and Scrapy. The second part of this research work focuses on applying NLP techniques to process and analyze the extracted textual data. Further, the preprocessing steps such as tokenization, stemming, and stop word removal, are analyzed followed by more advanced techniques like Named Entity Recognition. [ABSTRACT FROM AUTHOR]

Nowadays, the continuous development of big data technology makes all walks of life try to apply big data technology for analysis, and literature research is certainly no exception. On the surface, literature, as a form of culture and art, does not seem to intersect with big data, but due to the continuous improvement of artificial intelligence technology and the exhaustive ability brought by big data, as well as the fact that both are only tools to assist people in literature research, the application of big data analysis in literature research is becoming more and more extensive and effective. How to use the big data analysis to conduct literature research and in which direction to conduct, have become the focus of the current literature research field. This paper will illustrate the module development of big data technology analysis in literature research through specific examples. [ABSTRACT FROM AUTHOR]

Statistical analysis plans describe the processes of data handling and analysis in clinical trials; by doing so they increase the transparency of the analysis and reporting of studies. This paper reports the planned statistical analysis plan for the Whiplash ImPaCT study. For individuals with whiplash injury, Whiplash ImPaCT aims to assess the effectiveness of a guidelines-based clinical pathway of care compared with usual care. We report the planned procedures, methods, and reporting for the primary and secondary analyses of the Whiplash ImPaCT study. The primary outcomes are Global Recovery and Neck Disability Index at 3 months post-randomisation. Outcomes will be analysed according to the intention to treat principle using linear mixed models. A cost-utility analysis will be conducted to compute the incremental cost-effectiveness of the intervention to usual care. We describe data handling, our analytical approach, assumptions about missing data, and our planned methods of reporting. This paper will provide a detailed description of the planned analyses for the Whiplash ImPaCT trial. • a statistical plan of a randomised controlled trial of a clinical pathway of care for whiplash. • The clinical trial aims to improve outcomes after whiplash injury. • Primary outcomes are global recovery and neck disability. [ABSTRACT FROM AUTHOR]

Oscillations in control loops are one of the most prevalent problems in industrial processes. Due to their adverse effect on the overall process performance, finding how oscillations propagate through the process units is of major importance. This paper presents a method for integrating process causality and topology which ultimately enables to determine the propagation path of oscillations in control loops. The integration is performed using a dedicated search algorithm which validates the quantitative results of the data-driven causality using the qualitative information on plant connectivity. The outcome is an enhanced causal model which reveals the propagation path. The analysis is demonstrated on a case study of an industrial paperboard machine with multiple oscillations in its drying section due to valve stiction. [ABSTRACT FROM AUTHOR]

In transportation engineering, spatio-temporal data including traffic flow, speed, and occupancy are collected from different kinds of sensors and used by transportation engineers for analysis. However, the missing data influence the analysis and prediction results significantly. In this paper, Denoising Autoencoders are used to impute the missing traffic flow data. In our earlier research, we focused on a more general situation and used three kinds of Denoising Autoencoders: "Vanilla", CNN, and Bi-LSTM, to impute the data with a general missing rate of 30%. The Autoencoder models are used to train on data with a high missing rate of about 80% in this paper. We demonstrate that even under extreme loss conditions, and Autoencoder models are very robust. By observing the hyper-parameter tuning process, the changing prediction accuracy is shown and in most cases, the three models maintain the original accuracy even under the worst situations. Moreover, the error patterns and trends concerning different sensor stations and different hours on weekdays and weekends are also visualized and analyzed. Finally, based on these results, we separate the data into weekdays and weekends, train and test the model respectively, and improve the accuracy of the imputation result significantly. [ABSTRACT FROM AUTHOR]

Maternal and fetal morbidity and mortality can be prevented through careful monitoring during pregnancy. Using artificial intelligence doctors can establish a more accurate and a much faster diagnosis. In this paper, we propose a merger between different Deep Learning algorithms and three types of voting classifiers, unweighted and weighted hard voting, and soft voting for differentiating the abdomen view planes of a second trimester fetal morphology ultrasound. The deep learning algorithms that were applied on our dataset were ResNet50, InceptionV3, EfficientNetV2S, and MobileNet3Large, [19–22]. Our goal was to determine which voting classifier method better suits our data; hence we have made a quantitative comparison between unweighted and weighted hard voting applied on the committee of deep learning algorithms and soft voting applied on the same committee. We have also performed a thorough statistical benchmarking process to validate our findings. The obtained results showed that soft voting and hard voting with unequal weights outperform the vote of hard voting with equal weights classifier and the stand-alone algorithms. [ABSTRACT FROM AUTHOR]

In this study, we aimed to construct a system to perform statistical analyses for educational improvement while preserving privacy. Various probabilistic statistical algorithms have been developed for data agitation. However, one of the major challenges in using these algorithms is determining appropriate parameters. To address this issue, our previous research proposed the "α-criterion" as a criterion that must be satisfied by appropriate parameters. This paper outlines a system that calculates the parameters that satisfy the α-criterion while performing real-time data agitation on the given aggregate data. To verify that our implementation actually works, we show a use case with sample data on kibana-Elastic log analysis provided by Elastic N. V. [ABSTRACT FROM AUTHOR]

Highlights: [•] Carbon-fiber based electrodes are investigated in a zero-gap flow field cell configuration. [•] Charge–discharge curves are carried out in single cell and short stack for VRB application. [•] Three electrode half-cell data are corroborated both in single cell and short stack for VRB. [ABSTRACT FROM AUTHOR]

• This paper investigates the role of various data security techniques for 5G networks. • A comparative analysis of symmetrical and asymmetrical cryptographic algorithms. • Analysis of various encryption algorithms and schemes vs attacks. • It has a SWOT analysis of various data security techniques. • It helps to conquer various issues such as heterogeneity, security and scalability. With the increasing number of emerging robust networks, the challenges to design new security protocols and techniques are never ending. With the enlargement of 5G paradigm, there is a remarkable makeshift in how the distributed devices perform to achieve a common goal. The 5G technology has been significantly revolutionized by the advent of the Internet of Things, and its quick and pervasive evolution. However, it remains imperative that all these devices work in a sequential manner to execute a collective and secure operation. Despite computing being focused around cloud infrastructures in the last two decades, the 5G networks and the huge amount of data it generates is presently inverting this trend, shifting computing power back to where data is generated from. To encompass different autonomous and collaborative systems with functions of data sensing and authentication between such systems in a predictive and adaptive manner is a challenging task. Wireless network design in 5G is expected to emphasize broadly on three facets: security, privacy and energy efficiency. Therefore, this paper investigates the role of various data security and privacy techniques for different generation networks, but emphasis is on 5G. It addresses encryption schemes with the focus on the challenges faced such as side channel and ciphertext attacks, inherent key generation, sharing and distribution, key escrow, analysis of various overheads, and potential techniques, and solutions to address such challenges by leveraging 5G network devices. With the proliferation of ubiquitous computing and wide adoption of hybrid encryption techniques, various attacks will be circumvent in an intelligent manner. [ABSTRACT FROM AUTHOR]

Nowadays, system-on-chip (SoC) integrates more processing cores and IPs with network-on-chip (NoC) for satisfying today's demands, including low power consumption and high performance, without affecting the elasticity and scalability. When the on-chip processing elements (PEs) are increased, burst traffic will be generated from memory and cache accesses. As a result, the packet transmission latency is increased, reducing the system's speed performance. This paper proposes a new hybrid Base delta Neighbourhood Indexing Sequence (BΔ-NIS) based compression method for compressing the network traffic. The previous solutions for data compression often contain incompressible packets for certain data patterns. The proposed method uses Neighbourhood Indexing Sequence (NIS) algorithm to determine the optimal codeword for uncompressible chunks of delta compression approach using zeros and ones based traversals. Because of the dynamic nature of BΔ-NIS, it is highly effective and compresses all chunks in the packets efficiently. Moreover, the concept of dynamic header flit extension is introduced in the proposed compression method, which provide unlimited scalability. The proposed BΔ-NIS algorithm is robust to packet losses and leads to significant power saving. The simulation results show that the proposed BΔ-NIS reduces the hardware overhead and overtakes the existing data compression techniques by attaining a mean compression ratio of 0.73. In addition, BΔ-NIS accomplishes 51% latency reduction compared to the baseline router (without any compression). It also minimizes the power consumption of the de/compression modules by 70.14% and 20.8% as compared to the NoΔ and FlitZip respectively. • To propose an effective data compression technique for on-chip communication network, which reduces the data traffic overhead, loss of data packet, delay, and energy consumption by enhancing the delta compression method using NIS. Due to reduced operations, the proposed BΔ-NIS compressor utilizes the router's bandwidth and links without consuming more power. • To reduce the network latency by exploiting the neighbourhood information on the BΔ-NIS compressor through the consideration of zero's and one's traversals. • To increase the compression ratio by identifying data patterns within a flit and an optimal codeword for uncompressible packets of the delta compression method. • To give high level of confidence in terms of compression ratio by storing the metadata of the proposed BΔ-NIS compressor in the unused portion of the header flit. [ABSTRACT FROM AUTHOR]

Email marketing is the practice of sending commercial emails to a targeted audience. Email marketing currently routinely produces a significant ROI (return on investment) in the marketing sector. However, a significant research question in email marketing is how to gather subscriber data and deliver emails just to interested customers. Faced with the explosion in the volume of information, companies are becoming increasingly aware of the structural challenges of Big Data. Collecting and processing large amounts of heterogeneous data in real time is indeed invaluable. In the era of Big Data, the analysis and exploitation of data, by email marketing specialists, is now essential to remain competitive. In this paper, we review the impact of Big Data on email marketing activities and we identify the techniques, frameworks and data types that were adopted. Therefore, our contribution to this work is the proposed conceptual framework for email marketing campaign practice based on big data analytics. [ABSTRACT FROM AUTHOR]

In recent times, cloud computing is being utilized largely for storage and information sharing purposes in several established commercial segments, particularly those where online businesses are prevalent, such as Google, Amazon, etc. Cloud system presents several benefits to users in terms of easy operations, low implementation, and maintenance expenses. However, significant risks are encountered in the data security procedures of cloud systems. Although the area is frequently being analyzed and reformed, the concern of cloud data protection and user reliability remains under uncertainty due to growing cyber-attack schemes as well as cloud storage system errors. To deal with this risk and contribute to the endeavor of providing optimal data security solutions in cloud data storage and retrieval system, this paper proposes a Symmetric Searchable Encryption influenced Machine Learning based cloud data encryption and retrieval model. The proposed model enhances data security and employs an effective keyword ranking approach by using an Artificial Neural Network. The comparative assessment of the proposed model against multiclass SVM and Naïve Bayes has established the better operational potentiality of the model. The effectiveness of the proposed work is justified by the association between high TPR and low FPR. Further, a low CCR of 0.6973 adds up to the success of the proposed work. [ABSTRACT FROM AUTHOR]