Data Objects Research Articles

A comprehensive theoretical framework for sustainable network contracts: Contracting dimensions and Contract classification

Disruptions in the supply chains/networks result in both performance failures and a poor return on investment (ROI) of network assets. We propose that addressing this situation requires the governance of exchange relationships through contracts guided by sustainability principles. Specifically, we refer to these contracts in the supply and distribution context as “sustainable network contracts”, and the overall framework as the “sustainable contracting framework (SCF)/theory”. Despite the critical role of sustainable network contracts for all key stakeholders in the network, we identify a gap in the existing literature regarding the theory of sustainable network contracts. We bridge this gap by extending the extant dimensions of contracts, as described in Transaction Cost Economics and Relational Exchange Theory, to include sustainability dimensions - constituting the ‘what’ of theory building. To inform sustainable contracts, we propose and employ three factors: costs, benefits, and risks (CBR). We present the ‘how’ of the theory building, outlined in a five-step approach along with an analytical tool, to demonstrate the practical application of our framework. Additionally, we provide a rationale for adopting sustainable network contracts (the ‘why’ of the theory building). Our research methodology involves collecting interview-based data from senior executives (CXOs) (secondary and primary), collecting primary and secondary cost data (objective), integrating behavioral elements (subjective), and employing constrained optimization techniques to determine quantity allocation under various contract policies. Further, we map the proposed eight distinct contract types onto the CBR-space, thereby highlight the relevance of the contract types to real-world practices. This mapping considers network externalities, risks, and allows for a coordinated sustainable approach to contracting from the buyer's (retailer's) perspective. For managers, our framework would serve as an important tool that would inform the contract evaluation process, facilitate local versus global decision-making, safeguard network investments, and help align the interests of buyers and suppliers in each contracting cycle.

Putting everything in its place: using the INSDC compliant Pathogen Data Object Model to better structure genomic data submitted for public health applications.

Fast, efficient public health actions require well-organized and coordinated systems that can supply timely and accurate knowledge. Public databases of pathogen genomic data, such as the International Nucleotide Sequence Database Collaboration (INSDC), have become essential tools for efficient public health decisions. However, these international resources began primarily for academic purposes, rather than for surveillance or interventions. Now, queries need to access not only the whole genomes of multiple pathogens but also make connections using robust contextual metadata to identify issues of public health relevance. Databases that over time developed a patchwork of submission formats and requirements need to be consistently organized and coordinated internationally to allow effective searches.To help resolve these issues, we propose a common pathogen data structure called the Pathogen Data Object Model (DOM) that will formalize the minimum pieces of sequence data and contextual data necessary for general public health uses, while recognizing that submitters will likely withhold a wide range of non-public contextual data. Further, we propose contributors use the Pathogen DOM for all pathogen submissions (bacterial, viral, fungal, and parasites), which will simplify data submissions and provide a consistent and transparent data structure for downstream data analyses. We also highlight how improved submission tools can support the Pathogen DOM, offering users additional easy-to-use methods to ensure this structure is followed.

Statistical method for clustering high-dimensional data based on fuzzy mathematical modeling

Abstract In this paper, firstly, the fuzzy mathematical model and its types and algorithms of fuzzy numbers are studied. Then, the concept of fuzzy mathematics is added to cluster analysis, and fuzzy cluster analysis is carried out on samples or objects after data standardization and the construction of a similarity matrix. Then, the fuzzy C-mean FCM algorithm is proposed, and the FCM algorithm is improved by introducing the point density of data objects, the maximum minimum distance method of point density sampling, and the reduction of computation to improve the statistical function of clustering high-dimensional data. Finally, the algorithm of this paper is compared with other algorithms by AC, PR, RE, convergence speed, running time, DBI index and other indexes in order to analyze the advantages of the FCM algorithm in high dimensional data clustering statistics. The results show that the F value of the FCM algorithm in the Zoo dataset reaches 0.976, which improves 61% compared to FKM and nearly 12.6% compared to the IIFKM0 algorithm, with the best clustering effect. The DBI index of the FCM algorithm is the lowest in the four datasets, which is basically stabilized in the range of 0.5-0.6. This is better than other algorithms.

Multi-round minimum cost consensus model with objectivity-fairness driven feedback mechanism

In traditional group decision-making (GDM) and consensus reaching process (CRP), it may raise the issue of fairness for the moderator to subjectively assign each decision-maker’s (DM’s) weight and unit cost, and it is sometimes difficult to reach consensus with only one round of the CRP. In this paper, the impact of the aforementioned situations on the consensus is first analyzed, and then two specific improvement methods are accordingly provided. Based on which, the definitions of DM’s closeness centrality, affiliated subgroup as well as individual consensus level are given, and some incentives and penalties regarding DM’s weight and unit cost are used during the CRP, so as to improve the applicability and operability of the minimum cost consensus (MCC) in practical GDM problems while ensuring the fairness among DMs and the objectivity of data. Moreover, the single-round MCC is extended into the multi-round MCC by virtue of designing iterative methods on DM’s opinion, weight and unit cost, building the multi-round minimum cost consensus model (MRMCCM) with objectivity-fairness driven feedback mechanism, and presenting a detailed procedure alongside its algorithm of the multi-round CRP. Finally, a comprehensive evaluation case of financial product under the multi-round consensus validates the effectiveness and practicality of the new proposal, while its comparative analysis with other consensus models shows that the MRMCCM and the CRP with feedback mechanism help motivate DMs to reach higher-quality consensus at lower cost.

Robust DTW-based entropy fuzzy clustering of time series

AbstractTime series are complex data objects whose partitioning into homogeneous groups is still a challenging task, especially in the presence of outliers or noisy data. To address the problem of robustness against outliers in clustering techniques, this paper proposes a robust fuzzy C-medoids method based on entropy regularization. In-depth, we use an appropriate exponential transformation of the dissimilarity based on Dynamic Time Warping, which can be computed also for time series of different length. In addition, the fuzzy framework provides the necessary flexibility to cope with the complexity of the features space. It allows a time series to be assigned to more than one group, considering potential switching behaviours. Moreover, the use of a medoids-based approach enables the identification of observed representative objects within the dataset, thus enhancing interpretability for practical applications. Through an extensive simulation study, we successfully demonstrate the effectiveness of our proposal, comparing and emphasizing its strengths. Finally, our proposed methodology is applied to the daily mean concentrations of three air pollutants in 2022 in the Province of Rome. This application highlights its potential, namely the capability to intercept outliers and switching time series while preserving group structures.

Why policy-based authorisation is critical for identity-first security

The enterprise perimeter is changing; it is now about data objects, application programming interfaces (APIs), microservices and applications. In this evolving, decentralised and highly segmented world, security and identity access management (IAM) leaders find themselves struggling with a security methodology to address their concerns — specifically to answer the very basic question: Who has access to what and when? Identity-first security is emerging as the most effective way to answer these concerns, by placing identity at the centre of the security design. This paper will cover the ‘why’ of identity-first security, what is important to know and consider, and then the ‘how’ it can be achieved. The paper argues for this methodology and presents detailed flow of why modernised policy-based authorisation is crucial for identity-first security. The paper is directed to security and IAM professionals and leaders who want to learn more about how security and identity are tightly coupled and the way to get there with policy-based authorisations.

Deep Multimodal Complementarity Learning.

Complementarity plays a significant role in the synergistic effect created by different components of a complex data object. Complementarity learning on multimodal data has fundamental challenges of representation learning because the complementarity exists along with multiple modalities and one or multiple items of each modality. Also, an appropriate metric is needed for measuring the complementarity in the representation space. Existing methods that rely on similarity-based metrics cannot adequately capture the complementarity. In this work, we propose a novel deep architecture for systematically learning the complementarity of components from multimodal multi-item data. The proposed model consists of three major modules: 1) unimodal aggregation for extracting the intramodal complementarity; 2) cross-modal fusion for extracting the intermodal complementarity at the modality level; and 3) interactive aggregation for extracting the intermodal complementarity at the item level. To quantify complementarity, we utilize the TUBE distance metric to measure the difference between the composited data object and its label in the representation space. Experiments on three real datasets show that our model outperforms the state-of-the-art by +6.8% of mean reciprocal rank (MRR) on object classification and +3.0% of MRR on hold-out item prediction. Qualitative analyses reveal that complementarity is significantly different from similarity.

Modern technologies for agriculture: how far Pakistani farmers are from the developed world?

In order to accomplish the objective of the current study household data was collected from one district of Sindh province. Main aim of this study is to find out that which modern technologies are being used at various form level and suggest police measure for technical advancement. For this study a purposive sample selection method was adopted to select Hyderabad, because it was considered that farmers of this location might be more aware about modern technologies due to Sindh Agriculture University and Directorate General of Agriculture extension are situated in this district. Those institutes are organizing awareness seminars/training for farmers of the province. Diversified stakeholders are presenting this region, that is why, we selected the respondents from there. At the second stage, through stratified random sampling method 80 households were selected 50 small, 20 medium and 10 large based on proportion of population size in each category of small medium and large farmers. Stratification was made on the bases of small (farmers own land from 1 to 12 acres), medium (farmers own land from 13 to 25 acres) and large (farmers own land from 26 to 46 acres) farmers. Results revealed that the small farmers predominantly rely on farming for their livelihood. They have limited educational attainment, with the majority completing only primary education. While they exhibit a strong commitment to farming, their relatively small landholding of 3.9 acres and the prevalence of financial challenges, as indicated by total household expenditure exceeding income, suggest potential vulnerabilities. Medium farmers, strikes a balance between farming and education, with a more diverse educational background. With a larger landholding of 16 acres and stable income and expenditure patterns, this household appears to be in a relatively secure financial position. Large farmers exhibit the most favorable economic profile. With a more educated and experienced demographic, extensive land ownership, and strong financial stability, they are positioned for prosperity

Surveying and mapping of large-scale 3D digital topographic map based on oblique photography technology

In the fields of geographic information systems and cartography, accurate topographic maps are crucial. Although traditional topographic mapping methods have certain accuracy, they have limitations in capturing features and building details. Therefore, this article proposes a large-scale 3D digital terrain mapping method based on oblique photography technology. Firstly, the UAV route is planned and image control points are arranged, to collect large-scale geographical object data by using UAV oblique photography technology. Then Smart 3D software is used to complete the 3D modeling of real large-scale topographic map through data preprocessing, air-to-air survey, control point import, 3D modeling, texture mapping and other steps based on the obtained geographical object data. The 2D and 3D linkage integrated mapping mode is adopted to obtain geomorphic information and elevation information, complete the data collection of indoor features, confirm the features that cannot be read on both sides of the indoor industry, complete the field supplementary mapping and adjustment, and realize the mapping of large-scale 3D digital topographic map. The experimental results show that this method can use oblique photography technology to collect data and establish a three-dimensional model of large-scale 3D digital terrain maps. The mean square error of accuracy detection is 0.202m, the mean square error of elevation is 0.257m, the mean square error of plane is 0.201m, and the mean square error of elevation is 0.197m. It has high drawing accuracy and excellent aerial triangulation performance.

Geospatial aspects of building information modelling (BIM) based utilities management

The concept of Building Information Modelling (BIM) which was originally used for construction design has developed with increasing BIM dimensions from 3D, 4D, 5D, 6D, 7D to 10D and with the addition of semantics to 3D objects so that BIM allows it to be developed in broader management such as utility and assets management. In a utility management system, object data acquisition process will face some problem in varying object’s dimensions and location require the merging of several 3D data which is collected from different equipment with different accuracy and precision which for this research using the High Definition Surveying (HDS) method from Terrestrial Laser Scanning (TLS) and Handheld Laser Scanning (HLS) instruments. The problem that arises is in point cloud data merging process with different point densities, in this paper merging data will use Helmert 3D transformation method. Another problem is in the objects georeferencing into the Earth coordinate system. A georeferenced system is needed to integrate all of utility object into a utility management system in a building. This process is necessary if the development of utility database management will be linked with the creation of 3D models in the bigger scopes such as smart cities or narrower scopes such as smart buildings. The end result is a georeferenced BIM-based utility database management system, which allows for spatial analysis or automation using coordinate systems embedded in 3D objects.

Data ownership in training artificial intelligence in healthcare

"In the framework of this article, we research whether, according to the national regulation of Latvia, the data used in the training of artificial intelligence (AI) in medicine can be considered an object of property rights. An analytical and descriptive method will be used for the research. The analytical method will help analyse the data as a property term and determine its principles. Meanwhile, the descriptive method will describe the AI technology definition and processes. The work will clarify whether Latvia should provide a more transparent and fairer approach to patient data and their right to fully manage it, based on the examples of Finland and Belgium. We cannot consider all personal data objects of property rights in civil law because it is difficult to determine the beginning and end of personal data. However, personal data obtained from an organisation and stored in the personal cloud are considered an object of property rights. At the same time, by analogy, it can be concluded that ownership rights to data may also exist in other circumstances, depending on the type of data and its storage location. Keywords: personal data, property rights, civil law, medicine law."

Velocity Estimation from LiDAR Sensors Motion Distortion Effect

Many modern automated vehicle sensor systems use light detection and ranging (LiDAR) sensors. The prevailing technology is scanning LiDAR, where a collimated laser beam illuminates objects sequentially point-by-point to capture 3D range data. In current systems, the point clouds from the LiDAR sensors are mainly used for object detection. To estimate the velocity of an object of interest (OoI) in the point cloud, the tracking of the object or sensor data fusion is needed. Scanning LiDAR sensors show the motion distortion effect, which occurs when objects have a relative velocity to the sensor. Often, this effect is filtered, by using sensor data fusion, to use an undistorted point cloud for object detection. In this study, we developed a method using an artificial neural network to estimate an object’s velocity and direction of motion in the sensor’s field of view (FoV) based on the motion distortion effect without any sensor data fusion. This network was trained and evaluated with a synthetic dataset featuring the motion distortion effect. With the method presented in this paper, one can estimate the velocity and direction of an OoI that moves independently from the sensor from a single point cloud using only one single sensor. The method achieves a root mean squared error (RMSE) of 0.1187 m s−1 and a two-sigma confidence interval of [−0.0008 m s−1, 0.0017 m s−1] for the axis-wise estimation of an object’s relative velocity, and an RMSE of 0.0815 m s−1 and a two-sigma confidence interval of [0.0138 m s−1, 0.0170 m s−1] for the estimation of the resultant velocity. The extracted velocity information (4D-LiDAR) is available for motion prediction and object tracking and can lead to more reliable velocity data due to more redundancy for sensor data fusion.

A Preliminary Study on the Effectiveness of K-Nearest Neighbor Algorithm Based on Local Mean for Classification of Bioinformatics Data.

The preliminary classification of biological class data is of great importance for bioinformatics. One can quickly classify object data by comparing their existing features with known traits. k-nearest neighbor algorithm is easy to apply in this field, but its drawbacks make it less meaningful to improve the efficiency of the algorithm by simply changing the distance model, so this study uses a local mean-based k-nearest neighbor classifier and compares the accuracy of the predicted classification of six different distance models used. The prediction accuracies in the experimental results were all greater than 70%, and the highest accuracy was achieved in different data sets for all distance models with K=2; the prediction accuracy of Minkowski distance with different parameters had the highest volatility in the test.and the experimental results can be used as a reference for related practitioners.

Development of an innovative model for the inter-integration of the architecture of the intelligent computer environment of critical infrastructure facilities of the railway

The object of research is the processes of intelligent management of the computer environment of data objects of the critical infrastructure of railway transport. When developing an innovative model of deep mutual integration of the architecture of an intelligent computer environment for the management of objects in the power supply system of railway transport, the only urgent task is to solve the problems of efficient and reliable power supply of electricity for train traction to ensure the transportation process. The approaches, mathematical models and methods that became the basis for the creation of an innovative model and a new structure of the management system, the integration of the architecture of the computer environment of data of critical objects of the infrastructure of railway transport, meet the requirements of modernity and the strategy of sustainable development of the transport infrastructure. Management of an intelligent computer environment in the context of the railway power supply system is a complex process that includes the use of various technologies and strategies to optimize the functioning of systems, increase reliability, ensure efficient use of electricity, and ensure the transportation process. The basis of the creation of an intelligent computer environment is the principle of a single informational and synchronous space for the formation of primary information, which is an important concept in the development of intelligent control systems for the power supply system of railway transport. This principle requires that all parameters and data that are collected from different systems of power supply facilities should be combined in a single information space and be available for analysis and management in real time. This principle creates the basis for innovative solutions in the field of railway power supply system management, and the use of the Internet of Things, artificial intelligence, machine learning and deep learning allows the development of systems that meet modern requirements for the efficiency and reliability of energy systems. The use of the approach of deep mutual integration of the architecture of the computer environment is key in the possibility of automating the processes of data collection and analysis, as well as in improving the interaction between the components of the systems of power supply facilities of railway transport, the reliability and efficiency of the system, which makes it more flexible and adaptive to changes in load and working conditions. The research presented in the work can be used in practice in organizations, structural units and at the levels of the management system of critical infrastructure objects in railway transport, transport sector enterprises, which will allow a quick response to an emergency situation and switch to backup modes, ensuring reliability and availability of power supply in conditions of challenges.

AI-Assisted Chinese Language Teaching System in Distributed Sensor Networks

The Chinese learning system gained enormous importance to support the population of China with recent technologies. The AI and ML-assisted classes are useful for learners. However, it can cut down the employment of teachers. Various planning and act was passed to reform the Chinese education system with artificial intelligence and machine language. On the other hand, this education process is interesting, customized, and unsupervised based. It was observed that the human touch study process is more satisfying and clears doubts easily. The key object of data gathering sensor-based network is accumulating all the data from the nodes and sending those sensed data to the base station. These nodes are basically working while focusing on one thought: all the nodes present in the sensor-based system are well aware of all the other nodes currently working in the same network and transmitting data in the base station. Although AI comes with a few challenges like lack of resources, training, and various security issues, if these are overcome with proper strategies, AI can be the equipment to reign in the 21${}^{st}$ century. Additionally, LLMs and NLP modeling are described in the results that helped to understand the use of different models. Moreover, the Benefits of LLMs and NLP for Chinese language training are discussed in the study.

Application of the K-Means Algorithm for Clustering Production of Capture Fisheries in Maluku Province

Maluku Province has large natural resources with various potentials, from the ocean floor to the mainland. Capture fishery products are one of the leading sectors that contribute greatly to the GRDP of Maluku Province. The K-Means clustering algorithm is a suitable algorithm for grouping data objects that have the same identity. The purpose of this study is to cluster districts/cities in Maluku Province based on capture fishery products. The type of data in this study is secondary data sourced from the Maluku Province Central Bureau of Statistics (BPS) Publication in 2022. The result is that there are 3 districts/cities clusters in Maluku Province based on capture fishery products. Cluster 1 with the category of sufficient capture fisheries products, namely the Districts of Tanimbar Islands, Buru, East Seram, West Seram, South Buru, Southwest Maluku, Ambon City and Tual City. Furthermore, Cluster 2 with the category of many capture fishery products, namely the Aru Islands Regency and Southeast Maluku Regency. Furthermore, for Cluster 3, the category of capture fishery products is very large, namely Central Maluku Regency.

A clustering method based on multi-positive–negative granularity and attenuation-diffusion pattern

As an important part of machine learning, clustering methods have been continuously paid attention to. Current clustering methods divide data objects usually based on Euclidean metric, which is a basic and effective metric method. However, with the high dimensionality of data and the diversification of data representation, the complexity of the spatial structure of real-world data continues to rise. Classical clustering methods face many challenges such as insufficient clustering effectiveness, the sensitivity of clustering method parameters, and lack of stability of clustering results. Aiming at the above problems, this paper designs a non-Euclidean metric and constructs a multi-granularity staged clustering method based on the metric. First of all, this paper uses the sequential relationship of each feature of the data to construct a similarity measure between objects from the perspective of positive and negative granularity to improve the clustering algorithm’s understanding of complex spatial structure data. Secondly, this paper designs the attenuation-diffusion pattern divides and conquers according to the distribution characteristics of data objects in different patterns, and uses the heuristic idea to effectively cluster the data in stages from local to global. Again, based on the above, this paper proposes a clustering method based on multi-positive-negative granularity and attenuation-diffusion pattern, which can effectively deal with the challenges brought by complex spatial structure data to clustering methods. Finally, the effectiveness and robustness of the proposed method and advanced clustering methods are compared and analyzed on UCI real data sets. Experimental results show that the method proposed in this paper has obvious advantages in clustering results on complex spatial structure data. In addition, in the two directions of non-Euclidean metrics and multi-granularity clustering, the method proposed in this paper provides a new perspective for effectively dealing with the design of clustering methods on complex spatial structure data.

PaaS platform security enhancement using fuzzy based access control and trust based signature

AbstractPlatform‐as‐a‐Service (PaaS) is one of the cloud computing services which will be offered to the clients through the virtualization platform. The problem is constructed in the PaaS platform effectively excluding internet service suppliers which do not meet highly secure standards, and so security issues can be a significant a hurdle to online computation. To address the aforementioned issues, the suggested method primarily concentrated proceeding the safety of PaaS infrastructure by using a novel fuzzy based access control technique, in which the information source is realized by using CDO security feature. To further enhance the security, security using trust based signature has been used in which the class and packet filter for each specified authorization were mapped to our class by modifying the security feature code. This public key will be utilized for authenticate that transaction relevant signature if the identity provider (IdP) has provided public safety data about this same two major components represents this little token on whose trusted trademark components are built put, that is, the entire item or even the claims contained. The suggested technique outperforms other current methods in terms of several performance parameters, with a high throughput of 14, CPU utilization of 12.5, and a low execution time of 5.2 s. The proposed ‘PaaS platform security enhancement with the use of fuzzy and trust‐based signatures’ provides high throughput and low execution time. This research work proposes a novel approach to enhancing the security of PaaS infrastructure, which is offered to clients through the virtualization platform in cloud computing. The current problem with PaaS is that it excludes internet service providers that do not meet high‐security standards, which poses a significant challenge to online computation. The suggested approach focuses on improving the safety of the PaaS platform by employing a fuzzy‐based access control technique, using the connectionless data objects security feature as the information source. To further strengthen security, the approach utilizes a trust‐based signature technique where the class and packet filter for each authorization are mapped to the class of the proposed method by modifying the security feature code. The proposed public key is then used to authenticate the transaction relevant signature, provided the IdP has given public safety data about these two components of the token on which Trusted Trademark components are built, that is, the entire item or even the claims contained within it. The proposed approach outperforms current methods in terms of several performance parameters, such as a high throughput of 14, a high CPU utilization of 12.5, and a low execution time of 5.2 s. In summary, the proposed PaaS platform security enhancement using fuzzy and trust‐based signatures provides an effective way to achieve high throughput and low execution time while ensuring platform security.

Efficient Heart Disease Prediction Using PSO Optimized LSTM Classifier

Accurate diagnosis and prognosis of cardiovascular disease are essential in the medical field, enabling cardiologists to administer appropriate treatments. Machine learning has gained prominence in the healthcare sector due to its ability to identify patterns in data. Leveraging machine learning for classifying cardiovascular disease occurrences, significantly minimize misdiagnosis. This research focuses on developing a robust model capable of accurately predicting cardiovascular diseases, ultimately aiming to decrease the mortality rates associated with these conditions. To find clusters of data objects in a dataset, K-means clustering algorithm is implemented. The technique known as particle swarm optimization (PSO) is used to estimate solutions to extremely challenging or unsolvable numerical minimization and maximization problems. LSTM classifier, a deep learning technology is employed to categorize the attacks under various scenarios. In addition to greatly enhancing the healthcare system, this research gives medical practitioners access to an invaluable diagnostic tool. The outcomes of proposed work is examined using programs written in Python Jupyter software tool.

Optimization Model for Timing of Preventive Maintenance of Asphalt Pavement Based on Decision Trees

The timing of preventive maintenance for asphalt pavement determines the effectiveness and cost-effectiveness of preventive maintenance measures. Firstly, a weighted average method combining subjective and objective factors is used to evaluate the performance index of road sections and select preventive maintenance measures. Secondly, a decision tree model for preventive maintenance of highways in Gansu Province is established using field measurement data to determine the timing of preventive maintenance for road sections. Finally, the model is validated using the example of the G22 Qingdao-Lanzhou Expressway by evaluating the pavement service performance index and the timing of preventive maintenance. The results show that compared to directly establishing decision trees based on uniform standards, the decision tree established using field measurement data reflects the differences in the importance of various decision indicators and respects the objectivity of road data. It improves the poor portability of the original decision tree model and enables more accurate determination of the timing of preventive maintenance.