Neighborhood Model Research Articles

Another neighbourhood semantics for intuitionistic logic

Abstract In this paper we first introduce a new neighbourhood semantics for propositional intuitionistic logic. We then naturally extend this semantics to first-order intuitionistic logic. We also study bisimulation between neighbourhood models and prove some of their basic properties for both propositional and first-order intuitionistic logic.

A Neighborhood Model with Both Distance and Quantity Constraints for Multilabel Data.

In this paper, a novel distance-based multilabel classification algorithm is proposed. The proposed algorithm combines k-nearest neighbors (kNN) with neighborhood classifier (NC) to impose double constraints on the quantity and distance of the neighbors. In short, the radius constraint is introduced in the kNN model to improve the classification accuracy, and the quantity constraint k is added in the NC model to speed up computing. From the neighbors with the double constraints, the probabilities for each label are estimated by the Bayesian rule, and the classification judgment is made according to the probabilities. Experimental results show that the proposed algorithm has slight advantages over similar algorithms in calculation speed and classification accuracy.

Neighbourhood communication model for enhancing trust and promoting players’ cooperative behavior: a case of iterated n-players prisoner’s dilemma

Human beings collaborate when organized in an unchanging network of social relationships and if the advantage of collaboration surpasses the entire cost of collaboration with all neighbours. Cooperation may be seen in community and natural systems when selfless action is rewarded despite the risk of exclusion. Trust between neighbours is crucial since cooperative behaviour spreads more rapidly and efficiently in environments with a high trust rating. In this paper, we introduce an alternative neighbourhood communication topology to enhance the level of trust between n prisoner’s dilemma players and promote cooperative behaviour. The proposed topology allows players to communicate with their local neighbours and share their experiences within the universe of other players that are not locally connected. To understand the overall players’ behaviour locally and globally, the topology is supported by a knowledge base accessible by all players. Our topology was tested against five other communication topologies over four 1000-game tournaments. The results show that our model outperforms other strategies in promoting cooperative behaviour among participating players in small and large populations.

Deep attributed network representation learning via attribute enhanced neighborhood

Attributed network representation learning aims at learning node embeddings by integrating network structure and attribute information. It is a challenge to fully capture the microscopic structure and the attribute semantics simultaneously, where the microscopic structure includes the one-step, two-step and multi-step relations, indicating the first-order, second-order and high-order proximity of nodes, respectively. In this paper, we propose a deep attributed network representation learning via attribute enhanced neighborhood (DANRL-ANE) model to improve the robustness and effectiveness of node representations. The DANRL-ANE model adopts the idea of the autoencoder, and expands the decoder component to three branches to capture different order proximity. We linearly combine the adjacency matrix with the attribute similarity matrix as the input of DANRL-ANE, where the attribute similarity matrix is calculated by the cosine similarity between the attributes based on the social homophily. Moreover, the sigmoid cross-entropy loss function is extended to capture the neighborhood character, so that the first-order proximity could be well-preserved. We compare our model with the state-of-the-art models, especially, the latest graph autoencoders (GAEs) method, ARGA, and demonstrate the contribution of each module on real-world datasets and two network analysis tasks, i.e., link prediction and node classification. The DANRL-ANE model performs well on various networks, even on sparse networks or networks with isolated nodes when the attribute information is sufficient.

Evaluation of Physical Education Teaching Quality Based on the Random Multivariate Matrix Convolution Neural Network Model

With the non-stop merchandising and improvement of bodily training instructing reform, the institution of a couple of getting to know the assessment and evaluation mannequin of bodily training different educating impact can stimulate the enthusiasm and activity in sports activities and learning, which is useful to domesticate students’ cognizance of lifelong bodily exercise. In the educating process, through the integration technique of varied instructing methods, this paper explores the issues present in the method of bodily training reform, places ahead a diverse bodily training instructing impact assessment mannequin that meets the wants of customized intelligence education and construction, and makes use of the technique of empirical evaluation to affirm the comparison model. In order to reflect the effect of physical training effectively and accurately, a convolution neural neighbourhood model based totally completely on the random multivariate matrix is proposed to reflect on consideration on the excellent of bodily education. The overall performance of the prediction accuracy assessment mannequin is evaluated via the simulation test and in contrast with the standard approach model. The experimental information is that the average assessment accuracy of the single convolution neural community mannequin is 82.15%, whilst the common comparison accuracy of the convolution neural community mannequin based totally on the random multivariate matrix is 97.58%, and the prediction accuracy is increased by means of 15.43%. The common prediction error of the single convolution neural community mannequin is 0.97, and the common error is 0.91, the common error is decreased by using 0.05. It shows that the random multivariate matrix convolution neural neighbourhood model can efficaciously realize the evaluation of instructing quality.

English Writing Feedback Based on Online Automatic Evaluation in the Era of Big Data

Under the influence of the continuous development of modern information technology, the online automatic evaluation system for English writing teaching was born in response to demand. The rise of the online automatic assessment and correction system has broken through the limitations of the campus, making English writing teaching no longer restricted by time and space. Adapting to the needs of English writing development in the era of big data, making full use of modern information technology to improve writing effect, further promoting the development of educational informatization and the transformation of writing teaching, and realizing the mutual blending and innovative development of education and information technology have become the inevitable trend of future development. This paper applies the combination of teacher feedback and automatic feedback to the teaching of English writing in senior high school and discusses whether the combination of the two can change the disadvantages of the automatic writing evaluation system and solve the problem of traditional single teacher evaluation. In this paper, the text feedback system is introduced into the context information, and the context information is used to filter the difference between the active context and the active context, so as to further reduce the number of participants in collaborative filtering and improve the real-time computing efficiency of the algorithm. By distinguishing dynamic text quality indicators from stable text quality indicators, this paper proposes a similar context-aware neighborhood model. Based on experimental analysis, the accuracy of feedback results is 45.2% higher than that of the original online automatic evaluation system.

An integrated care systems model approach for speech and language therapy head and neck cancer services in England: service development and re-design in Cheshire and Merseyside.

The incidence of head and neck cancer (HNC) is increasing globally and changes in treatment mean that patients are living longer with the condition. It is recognised that while there have been improvements at the diagnostic phase of the pathway, follow-up and on-going care can be fragmented and inequitable. Integrated care models (ICMs) are acknowledged as beneficial. The National Health Service in England is moving to a model whereby services are being re-organised to integrated care systems. This paper reviews the literature and discusses potential models of care to enhance speech and language therapy (SLT) provision for patients with HNC in line with the emerging ICS. The COVID-19 pandemic has provided an opportunity to review service provision and SLT teams quickly adapted to offering remote support. Discussions are currently on-going to explore the potential for patient initiated follow-up via the PETNECK 2 trial and the Buurtzorg 'neighbourhood model' holds promise. ICMs put the patient at the centre of care and have reported benefits for experience of care and clinical outcomes. Navigating organisational structures is complex. The Buurtzorg model provides a practical and theoretical framework to support organisational change.

In preprints: the problem of producing precise patterns.

In preprints: the problem of producing precise patterns.

Bathymetric LiDAR and multibeam echo-sounding data registration methodology employing a point cloud model

Airborne LiDAR bathymetry (ALB) and multibeam echosounders (MBESs) are the most commonly used technologies for underwater topographical measurements. However, ALB cannot obtain underwater information in deep water. Due to concerns about the safety of the survey vessels, it is difficult to take measurements in shallow water using an MBES. To maximize the complementary advantages of these two technologies, this paper proposes a point cloud registration algorithm for ALB and MBES data based on azimuth angle features. First, the topographical curvature features of ALB and the MBES point cloud data are extracted by using a cylindrical neighborhood model and a quadric surface fitting algorithm. Then, the azimuth angle similarity of the curvature feature points is used to match the corresponding points. Finally, a truncated least-squares-based iterative nearest-adjacent point (TrICP) method is utilized to realize multisource data registration. To verify the performance of the proposed registration algorithm, four areas with typical ALB and MBES point cloud data from Xisha Island, South China Sea, are used as the study areas. Compared to the traditional and typical curvature-based registration (CR) algorithm for point cloud data registration, the proposed algorithm obtains average RMSE values of 0.278 m, 0.252 m, 0.214 m and 0.177 m in the four typical study areas. The corresponding registration accuracy is improved by 91%, 73%, 84% and 49%, respectively. The experimental results show that the proposed algorithm is more reliable and effective for ALB and MBES point cloud registration than the previous algorithms.

Processing distortion models: A comparative study

When dealing with sets of probabilities, distortion or neighbourhood models are convenient practical tools, as they rely on very little parameters. In this paper, we study their behaviour when such models are combined and processed through some reasoning tools. More specifically, we study their behaviour when merging different distortion models quantifying uncertainty on the same quantity, and when manipulating distortion models defined over multiple variables.

Public Announcements, Public Lies and Recoveries.

The paper gives a formal analysis of public lies, explains how public lying is related to public announcement, and describes the process of recoveries from false beliefs engendered by public lying. The framework treats two kinds of public lies: simple lying update and two-step lying, which consists of suggesting that the lie may be true followed by announcing the lie. It turns out that agents’ convictions of what is true are immune to the first kind, but can be shattered by the second kind. Next, recovery from public lying is analyzed. Public lies that are accepted by an audience cannot be undone simply by announcing their negation. The paper proposes a recovery process that works well for restoring beliefs about facts but cannot be extended to beliefs about beliefs. The formal machinery of the paper consists of KD45 models and conditional neighbourhood models, with various update procedures on them. Completeness proofs for a number of reasoning systems (converse belief logic, public lies logic, lying and recovery logic, conditional neighbourhood logic, plus its dynamic version) are included.

Impact of green walls on ventilation and heat removal from street canyons: Coupling of thermal and aerodynamic resistance

Green walls are considered a sustainable and environmental technology for improving the local urban microclimate. The presented wind tunnel measurements reveal the coupling effect of aerodynamic and thermal resistance of green walls in flat and steep street canyons in a simplified urban neighborhood model. The building and ground surfaces are set to thermal conditions at a series of Richardson numbers (Ri) ranging from 0 to 2.07. Green walls induce flow resistance due to their high skin friction and large volume-specific surface areas, which leads to the formation of a thick boundary layer near green walls, where hot air is trapped. These boundary layers weaken the convective heat transfer from building surfaces to the canyon, leading to an air temperature reduction in the canyon. The lower temperature reduces the strength of buoyancy, which, together with the aerodynamic resistance, results in the reduction of air flow speed, turbulence kinetic energy (TKE), and air ventilation in the canyon. The reduction of air ventilation is more significant in non-isothermal conditions, particularly for the steep canyon, where the air entrainment at the roof level is reduced by 32.4% in the isothermal condition, and air removal is reduced by up to 91.5% in the non-isothermal conditions (Ri of the steep canyon, Ris = 2.07). Green walls alter the flow structures by suppressing the strength of the downward flow, canyon-wide vortical flow, and ascending buoyant flow.

Modeling Buildings in CityGML LOD1: Building Parts, Terrain Intersection Curve, and Address Features

3D city models integrate heterogeneous urban data from multiple sources in a unified geospatial representation, combining both semantics and geometry. Although in past decades they have predominantly been used for visualization, today they are used in a large range of tasks related to exploration, analysis, and management across multiple domains. The complexity of urban processes and the diversity of urban environments bring challenges to the implementation of 3D city models. To address such challenges, this paper presents the development process of a 3D city model of a single neighborhood in the city of Sofia based on the CityGML 2.0 standard. The model represents the buildings in LOD1, focusing on CityGML features, including building parts, the terrain intersection curve, and the address. Similar building models of 18 cities provided as open datasets are explored and compared to extract good modeling practices. As a result, workflows for the generation of 3D building models in LOD1 are elaborated on and improvements in the feature modeling are proposed. Two building model options are examined: (1) the modeling of a building as a single solid with differentiated rooftops and (2) the modeling of a building with separate building parts. The developed 3D building model contains 471 buildings and 198 addresses in both cases of building parts modeling. In addition, the terrain intersection curve (TIC) is modeled and a method for removing the duplicating TIC features is applied. Consequently, the overall count of vertices of the TIC is lowered from 12,112 to 8042. Finally, the possibilities for visualization of the model in popular platforms such as ArcGIS Pro and Cesium Ion are explored.

Development of transferable neighborhood land use regression models for predicting intra-urban ambient nitrogen dioxide (NO2) spatial variations.

Land use regression (LUR) models have been extensively used to predict air pollution exposure in epidemiological and environmental studies. The lack of dense routine monitoring networks in big cities places increased emphasis on the need for LUR models to be developed using purpose-designed neighborhood-scale monitoring data. However, the unsatisfactory model transferability limits these neighborhood LUR models to be then applied to other intra-urban areas in predicting air pollution exposure. In this study, we tackled this issue by proposing a method to develop transferable neighborhood NO2 LUR models with comparable predictive power based on only micro-scale predictor variables for modeling intra-urban ambient air pollution exposure. Taking Auckland metropolis, New Zealand, as a case study, the proposed method was applied to three neighborhoods (urban, central business district, and dominion road) and compared with the corresponding counterpart models developed using pools of (a) only macro-scale predictor variables and (b) a mixture of both micro- and macro-scale predictor variables (traditional method). The results showed that the models using only macro-scale variables achieved the lowest accuracy (R2: 0.388-0.484) and had the worst direct (R2: 0.0001-0.349) and indirect transferability (R2: 0.07-0.352). Those models using the traditional method had the highest model fitting R2 (0.629-0.966) with lower cross-validation R2 (0.495-0.941) and slightly better direct transferability (R2: 0.0003-0.386) but suffered poor model interpretability when indirectly transferred to new locations. Our proposed models had comparable model fitting R2 (0.601-0.966) and the best cross-validation R2 (0.514-0.941). They also had the strongest direct transferability (R2: 0.006-0.590) and moderate-to-good indirect transferability (R2: 0.072-0.850) with much better model interpretability. This study advances our knowledge of developing transferable LUR models for the very first time from the perspective of the scale of the predictor variables used in the model development and will significantly benefit the wider application of LUR approaches in epidemiological and environmental studies.

Urban growth trends in city of Taif Analyzed with geographical information systems: اتجاهات النمو العمراني بمدينة الطائف باستخدام نظم المعلومات الجغرافية

This study sought to determine trends in urban growth between (1380 Ah-1440 Ah) of Taif city as one of the main provinces of mecca. The city of Taif is also witnessing rapid urban and population growth, which led to the expansion of the city of Taif significantly in the north-east direction, so this study aimed to determine the direction of this expansion and its proportion through the use of modern technologies GIS and remote sensing in the process of processing, analysis and interpretation of space visuals obtained from King Abdul Aziz City of Science and Technology and digital and paper maps from the Ministry of Municipal and Rural Affairs and then to reach the desired results. By highlighting the results, the results showed a 39-kilometer urban extension to the northeast of the city center due to the easy nature of the land and the remoteness of the rugged mountainous areas. One of the most prominent recommendations of the study is to take care of roads and link new neighborhoods to the old in new ways, as the population is increasing and the roads are still the same as they have not kept pace with this expansion. Work to develop neighborhood's and model schemes in the north-east and provide key services in order to create.

Tree decline and mortality following pathogen invasion alters the diversity, composition and network structure of the soil microbiome

The loss of tree health is a global concern with many unknown cascading impacts on the diversity and function of forest ecosystems. Specifically, it is uncertain how the process of tree decline and mortality driven by exotic pathogens might alter the soil microbiome. Here we combined high-throughput sequencing, neighborhood models, and network analysis to explore the impacts of the decline of a Mediterranean tree species, Quercus suber, on the diversity, composition and network structure of soil fungal and bacterial communities in forests invaded by the exotic pathogen Phytophthora cinnamomi. The belowground footprint of pathogen-driven tree decline implied an increase in the taxonomic and phylogenetic diversity of both bacteria and fungi, but also a severe reduction of tree-symbiotic fungi and Proteobacteria known to have positive effects on plant growth and disease suppression. Moreover, we detected alterations of the topology of soil microbial networks in declining tree neighborhoods (lower connectivity, higher modularity), with implications for ecosystem function. Our findings reveal the large impacts that moderate levels of tree decline can have on the soil microbiome of invaded forests, and highlight the recovery of a functionally diverse and highly connected soil microbiome as a key target in the restoration of these disturbed systems.

Neighborhood Rough Set Approach With Biometric Application

This paper provides a new approach for human identification based on Neighborhood Rough Set (NRS) algorithm with biometric application of ear recognition. The traditional rough set model can just be used to evaluate categorical features. The neighborhood model is used to evaluate both numerical and categorical features by assigning different thresholds for different classes of features. The feature vectors are obtained from ear image and ear matching process is performed. Actually, matching is a process of ear identification. The extracted features are matched with classes of ear images enrolled in the database. NRS algorithm is developed in this work for feature matching. A set of 20 persons are used for experimental analysis and each person is having six images. The experimental result illustrates the high accuracy of NRS approach when compared to other existing techniques.

Гуцульська «Весна народів» Каєтана Абгаровича: дискурс пограниччя як стану свідомості культури

This literary study of the Carpathian manifestation of The Springtime of Nations (1848-1849) is based of the short story “At the Hunting Campfire” from the collection “Rusini” [The Rusins] by Kajetan Abgarowicz (Abgar-Soltan), a Polish writer of Armenian descent of the late 19th - early 20th century. The imagological reflection of public sentiments of the 19th century is analysed through the lens of the discourse of the borderland as a state of culture awareness: the Polish (“ours” from the perspective of the Polish author) and “theirs” (i.e. Hutsul). The authors determine the Carpathian society before The Springtime of Nations as cultural borderlands of the Polish, Hutsul, German, Jewish, and Gypsy ethnic groups coexisted in daily life, with their fundamental values unchanged. They argue that the Hutsul community unconditionally accepted the imperative the Austrian highlanders, the model of neighborhood with the Poles, and trusting relations with the gypsies, but infernally mythologized of the Germans in the person of the local officer and his entourage. The focus is placed on the peculiarities of the Hutsul reception of news about the uprising against the Habsburgs outside the Carpathians and the development of the revolutionary events of the local “strange spring” in the contezt of the confrontation between the highlanders and the local authorities during the Carpathian stay of the Hungarian army, which rebelled against the Austrian emperor. The impact of revolutionary events on the HutsuL community is analyzed within their cultural identity awareness as of representatives of a separate ethnic group with its own history of resistance in in the context of opryshoks, an unusual, non-Polish cultural landscape, axiology, and ontology. In his “At the Hunting Campfire”, Kajetan Abgarowicz creates a literary version of the The Springtime of Nations in the Carpathians, with a new Look on the dialogue in the relationship between “our” and “their” cultural universes.

Hierarchical Multiobjective Distributed Deep Learning for Residential Short-Term Electric Load Forecasting

Short-term load forecasting plays an essential role in appliance control in households and demand response at the neighborhood or community level. When load forecasting is simultaneously performed at different levels in a smart community, a central method that directly aggregates raw household data may incur a security issue. However, an individual method that builds separate models for each level requires additional data collection. We propose a distributed load forecasting framework that is secure, data efficient, and resource efficient. Our proposed method shares the latent variables, the reduced expression of the input data, of the household models without revealing the raw data. The latent variables are aggregated and fed to the neighborhood model, and during inference, input data collection can be abbreviated in the neighborhood. Because household models provide informative latent variables, the number of model parameters for the neighborhood model can be reduced. Our proposed method is evaluated using real electricity consumption data for six different models under 24 h and 1 h ahead forecasting. Our proposed method with the long short-term memory, multi-layer perceptron, and convolution neural network-based models presents the degradation in average mean absolute percentage error 3.68% and 5.16% at most for household and neighborhood forecasting, respectively, compared to the individual method. At the same time, it reduces the neighborhood’s model size by approximately 53%.

Interpretable Neighborhood Deep Models for Online Total Transfer Capability Evaluation of Power Systems

Deep learning is considered to be a promising method for total transfer capability (TTC) evaluation due to its high speed and accuracy for nonlinear and complicated problems. However, the operating scenarios of power systems are complex and ever changing. It is difficult to train a single deep model using an overly complex dataset containing operating scenarios with high variability. In addition, making the deep model transparent to validate for operators is another challenge to its application in the power system. To enable the practical application of deep models in TTC evaluation, the operating sample space is clustered into simpler spaces via a two-stage clustering method, and the idea of neighborhood model for the deep TTC evaluation network (DTEN) is proposed. Then, the local post hoc interpretability for the DTEN based on first-order control variable sensitivity is presented. During the interpretability analysis, the quasi-steady state is introduced to assess the coupling effects among different inputs. Case studies on the IEEE 39-bus system and a real-world regional system in China validate the effectiveness of the proposed method.