Real Data Sources Research Articles

Taking Advantage of Spare Battery Capacity in Cellular Networks to Provide Grid Frequency Regulation

The increasing use of renewable energies places new challenges on the balance of the electricity system between demand and supply, due to the intermittent nature of renewable energy resources. However, through frequency regulation (FR) services, owners of battery storage systems can become an essential part of the future smart grids. We propose a thorough first study on the use of batteries associated with base stations (BSs) of a cellular network, to participate in ancillary services with respect to FR services, via an auction system. Trade-offs must be made among the number of participating BSs, the degradation of their batteries and the revenues generated by FR participation. We propose a large-scale mathematical programming model to identify the best participation periods from the perspective of a cellular network operator. The objective is to maximize profit while considering the aging of the batteries following their usage to stabilize the electrical grid. Experiments are conducted with data sets from different real data sources. They not only demonstrate the effectiveness of the optimization model in terms of the selection of BSs participating in ancillary services and providing extra revenues to cellular network operators, but also show the feasibility of ancillary services being provided to cellular network operators.

Synergistic Strategy of Sustainable Hospital Site Selection in Saudi Arabia Using Spherical Fuzzy MCDM Methodology

Site selection for a hospital is demanding and challenging work. It’s become more complicated when considering various infectious diseases. Multiple criteria and sub-criteria are considered for selecting the most suitable and efficient hospital location. In this paper, we find the most suitable location for multiple disease-related hospitals using the multi-criteria decision-making (MCDM) methodologies. The proposed study is done by the MCDM techniques, namely spherical Entropy and spherical VIekriterijumsko KOmpromisno Rangiranje (VIKOR) methods. The decision-making for site selection is based on multiple experts’ opinions. Also, we use real data sources in spherical fuzzy numbers (SFN) to capture all its uncertainty. We proposed a new score function and an accuracy function of the SFN to evaluate crisp value from fuzzy numbers. The most prioritized criteria and sub-criteria are determined with their weights. We optimized the proposed site based on demand and criterion weight. Finally, sensitivity analysis and comparative analysis are conducted to check the stability and robustness of the result.

Quality-Diversity Generative Sampling for Learning with Synthetic Data

Generative models can serve as surrogates for some real data sources by creating synthetic training datasets, but in doing so they may transfer biases to downstream tasks. We focus on protecting quality and diversity when generating synthetic training datasets. We propose quality-diversity generative sampling (QDGS), a framework for sampling data uniformly across a user-defined measure space, despite the data coming from a biased generator. QDGS is a model-agnostic framework that uses prompt guidance to optimize a quality objective across measures of diversity for synthetically generated data, without fine-tuning the generative model. Using balanced synthetic datasets generated by QDGS, we first debias classifiers trained on color-biased shape datasets as a proof-of-concept. By applying QDGS to facial data synthesis, we prompt for desired semantic concepts, such as skin tone and age, to create an intersectional dataset with a combined blend of visual features. Leveraging this balanced data for training classifiers improves fairness while maintaining accuracy on facial recognition benchmarks. Code available at: https://github.com/Cylumn/qd-generative-sampling.

Assessment of Greenhouse Gas Emissions from Heavy-Duty Trucking in a Non-Containerized Port through Simulation-Based Methods

Seaports are perceived as multimodal hubs of the logistics supply chain where various transport modes intersect to exchange goods shipped by vessels. Increasing trade and capacity constraints are making this area a major contributor to GHG emissions. National and regional decision-makers perceive port sustainability as a concern while planning GHG mitigation projects. However, to plan and conduct successful GHG management programs, it is critical to first develop an appropriate assessment approach that fits well with the operating and geographical context of the given port. For heavy-duty trucking activities taking place within such ports, several models and methodologies for assessing GHG emissions are available, but their generalization is challenging for many reasons, notably because of the specific features of traffic within the port. Therefore, this paper presents an assessment model for heavy-duty trucking emissions within a non-containerized port based on an in-depth study of the traffic per port zone and on parameters drawn from several real data sources. The GHG model based on road traffic profiles by zone is implemented in a simulation model for emission evaluation and prediction. The output shows the pattern of GHG emissions by zone and provides an outlook on how decision-makers could achieve a GHG reduction plan.

Research on the optimization of interior design of architectural space considering user perception

Abstract This paper integrates the concept of user perception into the presentation of architectural interior spaces. It posits that by employing virtual presentation modes, interior design products are fully demonstrated to users, enhancing their perception of the available information and thereby facilitating the optimization of interior design. The research systematically extracts the influencing factors of user perception by conducting a thorough analysis and synthesis of the literature pertinent to architectural interiors. Additionally, this study combines findings from a questionnaire survey with methods of correlation and regression analysis to further substantiate the identified factors influencing user perception. On this basis, combined with the establishment of a refined model based on real data sources and an indoor modeling method based on tree hierarchy, an indoor 3D virtual scene is constructed with the help of Unity3D software to realize the optimization of architectural space interior design. Overall style of architectural interior space = −2.304 + 0.381*furniture + 0.274*soft furnishing color + 0.205*hard furnishing material + 0.165*lamps + 0.183*hard furnishing color + 0.106*accessories + 0.124*soft furnishing material + 0.115*fabric. Senior and graduate users of higher education had the same attitude towards “personalization” of interior design (both scored 4.33, close to “approved”), while faculty members had the lowest rating (2.6). This study can serve as a guide for the presentation of architectural interiors.

Research on securities price prediction and insurance strategy optimization based on deep learning algorithm

The process of attempting to estimate the future prices of particular stocks by utilizing historical data and various analytical tools, including deep learning algorithms, is called stock price prediction. Insurance providers’ overall approach and decisions to manage their risks, enhance their profitability, and give value to their policyholders are referred to as the insurance strategy. It requires various things to be considered, including underwriting procedures, pricing strategies, product creation, risk analysis, claims administration, and investment choices. This study proposed optimizing an insurance strategy and predicting securities prices using a deep learning algorithm. Initially, the real stock data sources for Microsoft Corporation (MSFT) were gathered from Ping An Insurance Company of China (PAICC) and the Shanghai-based National Association of Securities Dealers Automated Quotation (NASDAQ). Normalization is the procedure used to preprocess data for the raw data. We suggest an Enhanced dragonfly-optimized deep neural network (EDODNN) with stock price forecasting and insurance. The outcomes demonstrate that the proposed model outperforms the current methodology and achieves accuracy, precision, recall, F1 score, R 2, and RMSE. To display the effectiveness of the suggested system, its performance is compared to more established methods to obtain the highest level of efficiency for the research.

A Nested Monte Carlo Simulation Model for Enhancing Dynamic Air Pollution Risk Assessment

The risk assessment of air pollution is an essential matter in the area of air quality computing. It provides useful information supporting air quality (AQ) measurement and pollution control. The outcomes of the evaluation have societal and technical influences on people and decision-makers. The existing air pollution risk assessment employs different qualitative and quantitative methods. This study aims to develop an AQ-risk model based on the Nested Monte Carlo Simulation (NMCS) and concentrations of several air pollutant parameters for forecasting daily AQ in the atmosphere. The main idea of NMCS lies in two main parts, which are the Outer and Inner parts. The Outer part interacts with the data sources and extracts a proper sampling from vast data. It then generates a scenario based on the data samples. On the other hand, the Inner part handles the assessment of the processed risk from each scenario and estimates future risk. The AQ-risk model is tested and evaluated using real data sources representing crucial pollution. The data is collected from an Italian city over a period of one year. The performance of the proposed model is evaluated based on statistical indices, coefficient of determination (R2), and mean square error (MSE). R2 measures the prediction ability in the testing stage for both parameters, resulting in 0.9462 and 0.9073 prediction accuracy. Meanwhile, MSE produced average results of 9.7 and 10.3, denoting that the AQ-risk model provides a considerably high prediction accuracy.

Improving service quality management based on call center monitoring solution.

The presented paper investigates common call center challenges ranging from management to software and agents. Service quality is a complex and multifaceted construction that has a major impact on customer acquisition, retention and loyalty. Because there are a few different factors that influence service quality, call center managers must know what they are before they can work to optimize them. As an important part of any business is communication, both within the organization and with customers and clients, a feature-rich, advanced Private Branch Exchange (PBX) phone system is of great benefit. Call Centers are one of the most commonly used implementations of PBX systems in businesses of any type, shape and size. We suggest an efficient and secure solution for collecting, processing, storing, analyzing and visualizing data from call centers. The proposed approach is driven by applying the type of IP PBX engine based on Asterisk. AWS cloud hosted clustering approaches and load balancing system algorithms have been studied in order to mitigate progressively growing load indicators as we add more customers and thus real data sources to the system. The ELK Stack has been chosen as the optimal approach: it is open source and covers a need in the log management and analytics space. The proposed solution will help to automate the call flow process, where it is necessary, based on the analysis of the collected call centers data. The monitoring results obtained along with the particular call center structure knowledges gives us an opportunity for improving call strategies such as ACDs and IVRs, filling agent's knowledge gaps, eliminating the absence of well-defined practices of call handling, etc. It also eliminates the need to hire additional staff or outsource call centers to share the load.

What Could Models of Superorganismal Cognition Offer to Embodied AI?

Superorganisms such as ant or honeybee colonies exhibit extraordinary collective intelligence, such as an ability to identify and choose the best available nest site in an uncertain world. This collective cognition is inextricably reliant on the embodiment of individual agents, specifically their movement through space. We have recently developed models of superorganismal cognition based on a compelling analogy with techniques in Bayesian statistics, which are likewise aimed at grappling with the uncertainty and incompleteness of real data sources. These models foreground some potential lessons for the design of embodied artificial intelligences, such as robot swarms. For example, the spatial distribution of independently judging agents can convey valuable information, relaxing expectations that regular inter-agent (‘inter-neuronal’) communication is necessary for cognition, which points to the potential of minimal field swarm robotics. Meanwhile, the importance of individual heterogeneity to effective and resilient collective cognition in biology suggests great potential in this area for engineering.

Detection of temporality at discourse level on financial news by combining Natural Language Processing and Machine Learning

Finance-related news such as Bloomberg News, CNN Business and Forbes are valuable sources of real data for market screening systems. In news, an expert shares opinions beyond plain technical analyses that include context such as political, sociological and cultural factors. In the same text, the expert often discusses the performance of different assets. Some key statements are mere descriptions of past events while others are predictions. Therefore, understanding the temporality of the key statements in a text is essential to separate context information from valuable predictions. We propose a novel system to detect the temporality of finance-related news at discourse level that combines Natural Language Processing and Machine Learning techniques, and exploits sophisticated features such as syntactic and semantic dependencies. More specifically, we seek to extract the dominant tenses of the main statements, which may be either explicit or implicit. We have tested our system on a labelled dataset of finance-related news annotated by researchers with knowledge in the field. Experimental results reveal a high detection precision compared to an alternative rule-based baseline approach. Ultimately, this research contributes to the state-of-the-art of market screening by identifying predictive knowledge for financial decision making.

Automatic generation of textual descriptions in data-to-text systems using a fuzzy temporal ontology: Application in air quality index data series

In this paper we present a model based on computational intelligence and natural language generation for the automatic generation of textual summaries from numerical data series, aiming to provide insights which help users to understand the relevant information hidden in the data. Our model includes a fuzzy temporal ontology with temporal references which addresses the problem of managing imprecise temporal knowledge, which is relevant in data series. We fully describe a real use case of application in the environmental information systems field, providing linguistic descriptions about the air quality index (AQI), which is a very well-known indicator provided by all meteorological agencies worldwide. We consider two different data sources of real AQI data provided by the official Galician (NW Spain) Meteorology Agency: (i) AQI distribution in the stations of the meteorological observation network and (ii) time series which describe the state and evolution of the AQI in each meteorological station. Both application models were evaluated following the current standards and good practices of manual human expert evaluation of the Natural Language Generation field. Assessment results by two experts meteorologists were very satisfactory, which empirically confirm that the proposed textual descriptions fit this type of data and service both in content and layout.

A novel metamodel-based framework for large-scale dynamic origin–destination demand calibration

Calibrating dynamic traffic demand for stochastic traffic simulators is one of the big challenges due to computational burden. This paper proposes a novel framework to calibrate a dynamic car origin–destination matrix of large-scale networks, which has high computational efficiency. The proposed framework is relied on the metamodel optimization technique, based on the reference of aggregated traffic flow dynamics as expressed by the recent advance in traffic flow theory, namely the bi-modal macroscopic fundamental diagram (MFD).We validate our proposed approach with the Stochastic Perturbation Simultaneous Approximation (SPSA) algorithm which is widely used for the same purpose in the literature. Our result confirms that our approach can facilitate the demand calibration effectively. We also show that the traffic conditions at a link level are also reproduced realistically from our network-level calibration, and that our approach outperforms the traditional link-level OD calibration by comparing with the Aimsun OD adjustment in addition to SPSA.Furthermore, we demonstrate via different realistic network studies that the proposed approach is computationally efficient compared to the existing state-of-the-art approach. Our result shows that the proposed approach can be applied effectively regardless of the topology of the network, and that the model parameters do not have significant influence on the optimization results. Our calibration results show that just a few iterations are needed to calibrate the OD demand in the proposed approach even for the large-scale complex network underpinned by a bi-modal MFD derived from multiple real data sources.

Missing Data Reconstruction Based on Spectral k-Support Norm Minimization for NB-IoT Data

An effective fraction of data with missing values from various physiochemical sensors in the Internet of Things is still emerging owing to unreliable links and accidental damage. This phenomenon will limit the predicative ability and performance for supporting data analyses by IoT-based platforms. Therefore, it is necessary to exploit a way to reconstruct these lost data with high accuracy. A new data reconstruction method based on spectral k-support norm minimization (DR-SKSNM) is proposed for NB-IoT data, and a relative density-based clustering algorithm is embedded into model processing for improving the accuracy of reconstruction. First, sensors are grouped by similar patterns of measurement. A relative density-based clustering, which can effectively identify clusters in data sets with different densities, is applied to separate sensors into different groups. Second, based on the correlations of sensor data and its joint low rank, an algorithm based on the matrix spectral k-support norm minimization with automatic weight is developed. Moreover, the alternating direction method of multipliers (ADMM) is used to obtain its optimal solution. Finally, the proposed method is evaluated by using two simulated and real sensor data sources from Panzhihua environmental monitoring station with random missing patterns and consecutive missing patterns. From the simulation results, it is proved that our algorithm performs well, and it can propagate through low-rank characteristics to estimate a large missing region’s value.

A Bi-directional Fuzzy C-Means Clustering Ensemble Algorithm Considering Local Information

The classic Fuzzy C-means (FCM) algorithm has limited clustering performance and is prone to misclassification of border points. This study offers a bi-directional FCM clustering ensemble approach that takes local information into account (LI_BIFCM) to overcome these challenges and increase clustering quality. First, various membership matrices are created after running FCM multiple times, based on the randomization of the initial cluster centers, and a vertical ensemble is performed using the maximum membership principle. Second, after each execution of FCM, multiple local membership matrices of the sample points are created using multiple K-nearest neighbors, and a horizontal ensemble is performed. Multiple horizontal ensembles can be created using multiple FCM clustering. Finally, the final clustering results are obtained by combining the vertical and horizontal clustering ensembles. Twelve data sets were chosen for testing from both synthetic and real data sources. The LI_BIFCM clustering performance outperformed four traditional clustering algorithms and three clustering ensemble algorithms in the experiments. Furthermore, the final clustering results has a weak correlation with the bi-directional cluster ensemble parameters, indicating that the suggested technique is robust.

A Hybrid Model to Ensure Biosecurity during Pandemic Situations Using OpenCV

This paper presents a hybrid model consisting of hardware, software subsystems and publicly available trained feature sets. The developed hybrid model is useful in automated contactless collection and analysis of employees’ and visitors’ data in an organization especially during pandemic situations to ensure biosecurity. Such data include temperature and face mask status. If the set norms are not satisfied, the entry into the premises will be restricted or denied. The status is also updated in the corresponding record in the organization database. The hardware subsystem includes an arduino nano, sensors and audio visual alarms. The software subsystem was developed using OpenCV in Python and VSCode editor. Both offline and real time implementations were carried out. The model was validated using real time images and online data sources. The system was tested and found to work satisfactorily under practical input conditions.

OpenRank – a novel approach to rank universities using objective and publicly verifiable data sources

PurposeAcademic rankings are facing various issues, including the use of data sources that are not publicly verifiable, subjective parameters, a narrow focus on research productivity and regional biases and so forth. This research work is intended to enhance creditability of the ranking process by using the objective indicators based on publicly verifiable data sources.Design/methodology/approachThe proposed ranking methodology – OpenRank – drives the objective indicators from two well-known publicly verifiable data repositories: the ArnetMiner and DBpedia.FindingsThe resultant academic ranking reflects common tendencies of the international academic rankings published by the Shanghai Ranking Consultancy (SRC), Quacquarelli Symonds (QS) and Times Higher Education (THE). Evaluation of the proposed methodology advocates its effectiveness and quick reproducibility with low cost of data collection.Research limitations/implicationsImplementation of the OpenRank methodology faced the issue of availability of the quality data. In future, accuracy of the academic rankings can be improved further by employing more relevant public data sources like the Microsoft Academic Graph, millions of graduate's profiles available in the LinkedIn repositories and the bibliographic data maintained by Association for Computing Machinery and Scopus and so forth.Practical implicationsThe suggested use of open data sources would offer new dimensions to evaluate academic performance of the higher education institutions (HEIs) and having comprehensive understanding of the catalyst factors in the higher education.Social implicationsThe research work highlighted the need of a purposely built, publicly verifiable electronic data source for performance evaluation of the global HEIs. Availability of such a global database would help in better academic planning, monitoring and analysis. Definitely, more transparent, reliable and less controversial academic rankings can be generated by employing the aspired data source.Originality/valueWe suggested a satisfying solution for improvement of the HEIs' ranking process by making the following contributions: (1) enhancing creditability of the ranking results by merely employing the objective performance indicators extracted from the publicly verifiable data sources, (2) developing an academic ranking methodology based on the objective indicators using two well-known data repositories, the DBpedia and ArnetMiner and (3) demonstrating effectiveness of the proposed ranking methodology on the real data sources.

From a source of real data to a brief news report: Introducing first‐year preservice teachers to the basic cycle of learning from data

AbstractStatistical data is used in the media and on online platforms, often with an interpretation that serves some commercial or political purpose. Primary preservice teachers must be able to understand statistical data and analyze it critically. Only in this way will they be able to contribute as future teachers to the development of a critical spirit in the community. We present the process of construction on a teacher‐training program of an activity for first‐year students (aged 19) based on data published by Eurostat. The activity, structured around the basic cycle of learning from data, provided real data on different topics in the form of tables, graphs and infographics, and its final goal required students to generate a brief news report based on what they had learned from the data. Finally, we note that this activity can be used at various levels, from secondary education to the introductory tertiary.

Web Scraping in the Statistics and Data Science Curriculum: Challenges and Opportunities

Best practices in statistics and data science courses include the use of real and relevant data as well as teaching the entire data science cycle starting with importing data. A rich source of real and current data is the web, where data are often presented and stored in a structure that needs some wrangling and transforming before they can be ready for analysis. The web is a resource students naturally turn to for finding data for data analysis projects, but without formal instruction on how to get that data into a structured format, they often resort to copy-pasting or manual entry into a spreadsheet, which are both time consuming and error-prone. Teaching web scraping provides an opportunity to bring such data into the curriculum in an effective and efficient way. In this article, we explain how web scraping works and how it can be implemented in a pedagogically sound and technically executable way at various levels of statistics and data science curricula. We provide classroom activities where we connect this modern computing technique with traditional statistical topics. Finally, we share the opportunities web scraping brings to the classrooms as well as the challenges to instructors and tips for avoiding them.

Introduction of ITER CODAC relevant technologies on JET and MAST

Abstract The JET control and data acquisition system (CODAS) is an integrated system that provides all the pulse based and continuous data acquisition, real time and slow control and control room interfaces for JET. It has a long history, dating back to the beginning of JET in 1980. It utilises both commercially available hardware along with many in-house modules. The software has grown up and evolved largely independently of other developments in big science. Similarly, the data acquisition system on MAST has a long history of evolution from previous facilities at Culham (COMPASS). It utilises commercially available and in-house hardware (some shared with JET CODAS) and software that has developed largely independently of JET and other external developments. More recently, we have begun to adopt some ITER CODAC relevant technologies on both JET and MAST, in part, to also introduce some standardisation between the two facilities. This started with a pilot project to create a cubicle and environment monitoring system using commercial hardware and EPICS monitoring and HMI. We have since gone on to implement several camera and spectrometer filter controllers, several types of turbomolecular pump controller, and various radiation protection monitors in EPICS on JET. We are also implementing a central information display system for MAST that links the OPC interface on the machine control through to several display screens showing the machine state using EPICS. We have several MARTe based real time applications on JET and are now developing an application to provide real time processing of high-resolution Thomson scattering data using MARTe V2 (an ITER/F4E initiative to improve the robustness of this real-time framework). We are also considering upgrading the existing MARTe applications to this version. On JET we have also started to use another ITER CODAC technology – SDN to supplement the ATM based real time control network on JET. Initially, as a proof of principle, a real time plasma profile display was implemented. This system is now being extended to include several real time data sources which will feed their data back into the ATM network and on to the real time controllers. Looking forward, we anticipate extending the JET real time network with a purely ITER CODAC/SDN connected real time control system and provide a richer ITER CODAC interface to the JET CODAS to accommodate the possibility of ITER diagnostics testing and provide a real stress test for ITER archiving technologies.

When Urban Safety Index Inference Meets Location-Based Data

Information about urban safety, e.g., the safety index of a position, is of great importance to protect humans and support safe walking route planning. Despite some research on urban safety analysis, the accuracy and granularity of safety index inference are both very limited. The problem of analyzing urban safety to predict safety index throughout a city has not been sufficiently studied and remains open. In this paper, we propose U-Safety, an urban safety analysis system to infer safety index by leveraging multiple cross-domain urban location-based data. We first extract spatially-related and temporally-related features from various urban location-based data, including urban map, housing rent and density, population, positions of police stations, point of interests (POIs), crime event records, and taxi GPS trajectories. Then, these features are fed into a novel sparse auto-encoder (SAE) framework with feature correlation constraint to obtain the final discriminative feature representation. Finally, we design a new co-training-based learning method, which consists of two separated classifiers, to calculate safety index accurately. We implement U-Safety and conduct extensive experiments by utilizing various real data sources obtained in New York City. The evaluation results demonstrate the advantages of U-Safety over other methods.