Analysis of Supply Factors of the Migrant Workers Based on Comprehensive Fuzzy Evaluation

The factors affecting the supply of the migrant workers are very complex, which is difficult to use a specific number to demonstrate due to factors such as different groups of people, different time and different degrees of effect. This paper adopts the comprehensive fuzzy evaluation method to simulate the main factors affecting the supply of migrant workers including income, cost, expectancy, having a quantitative analysis of their influence on the labor supply of migrant workers. Key words: Comprehensive fuzzy evaluation; Migrant workers; Supply; Factors analysis Résumé: Les facteurs affectant la fourniture des travailleurs migrants sont très complexes, ce qui est difficile à utiliser c’est de démonter un nombre spécifique en raison de facteurs tels que les différents groupes de personnes, de temps différents et les différents degrés d'effet. Ce document adopte la méthode d'évaluation globale floue pour simuler les principaux facteurs affectant l'offre de travailleurs migrants dont le revenu, le coût, l'espérance, ayant une analyse quantitative de leur influence sur l'offre de travail des travailleurs migrants Mots-clés: L’évaluation floue complète; Les travailleurs migrants; L’approvisionnement; L'analyse des facteur

Adaptive supervisory switching control system design for active noise suppression of duct-like application

Active noise suppression for applications where the controlled system response varies with time is a difficult problem, especially for time varying nonlinear systems with large model error. On the basis of adaptive switching supervisory control theory, an adaptive supervisory switching control algorithm is proposed with a new controller switching strategy for active noise suppression of duct-like application. Real time experimental verification tests show that the proposed algorithm is effective with good noise suppression performance

DWSI: AN APPROACH TO SOLVING THE POLYGON INTERSECTION-SPREADING PROBLEM WITH A PARALLEL UNION ALGORITHM AT THE FEATURE LAYER LEVEL

A dual-way seeds indexing (DWSI) method based on R-tree and the OpenGeospatial Consortium (OGC) simple feature model was proposed to solve the polygon intersection-spreading problem. The parallel polygon union algorithm based on the improved DWSI and the OpenMP parallel programming model was developed to validate the usability of the data partition method. The experimental results reveal that the improved DWSI method can implement a robust parallel task partition by overcoming the polygon intersection-spreading problem. The parallel union algorithm applied DWSI not only scaled up the data processing but alsospeeded up the computation compared with the serial proposal, and it showed ahigher computational efficiency with higher speedup benchmarks in the treatment of larger-scale dataset. Therefore, the improved DWSI can be a potential approach to parallelizing the vector data overlay algorithms based on the OGC simple data model at the feature layer level

Can We `Feel' the Temperature of Knowledge? Modelling Scientific Popularity Dynamics via Thermodynamics

Just like everything in the nature, scientific topics flourish and perish. While existing literature well captures article's life-cycle via citation patterns, little is known about how scientific popularity and impact evolves for a specific topic. It would be most intuitive if we could `feel' topic's activity just as we perceive the weather by temperature. Here, we conceive knowledge temperature to quantify topic overall popularity and impact through citation network dynamics. Knowledge temperature includes 2 parts. One part depicts lasting impact by assessing knowledge accumulation with an analogy between topic evolution and isobaric expansion. The other part gauges temporal changes in knowledge structure, an embodiment of short-term popularity, through the rate of entropy change with internal energy, 2 thermodynamic variables approximated via node degree and edge number. Our analysis of representative topics with size ranging from 1000 to over 30000 articles reveals that the key to flourishing is topics' ability in accumulating useful information for future knowledge generation. Topics particularly experience temperature surges when their knowledge structure is altered by influential articles. The spike is especially obvious when there appears a single non-trivial novel research focus or merging in topic structure. Overall, knowledge temperature manifests topics' distinct evolutionary cycles

DWSI: AN APPROACH TO SOLVING THE POLYGON INTERSECTION-SPREADING PROBLEM WITH A PARALLEL UNION ALGORITHM AT THE FEATURE LAYER LEVEL

A dual-way seeds indexing (DWSI) method based on R-tree and the OpenGeospatial Consortium (OGC) simple feature model was proposed to solve the polygon intersection-spreading problem. The parallel polygon union algorithm based on the improved DWSI and the OpenMP parallel programming model was developed to validate the usability of the data partition method. The experimental results reveal that the improved DWSI method can implement a robust parallel task partition by overcoming the polygon intersection-spreading problem. The parallel union algorithm applied DWSI not only scaled up the data processing but alsospeeded up the computation compared with the serial proposal, and it showed ahigher computational efficiency with higher speedup benchmarks in the treatment of larger-scale dataset. Therefore, the improved DWSI can be a potential approach to parallelizing the vector data overlay algorithms based on the OGC simple data model at the feature layer level

Exploring the Limits of Historical Information for Temporal Knowledge Graph Extrapolation

Temporal knowledge graphs, representing the dynamic relationships and interactions between entities over time, have been identified as a promising approach for event forecasting. However, a limitation of most temporal knowledge graph reasoning methods is their heavy reliance on the recurrence or periodicity of events, which brings challenges to inferring future events related to entities that lack historical interaction. In fact, the current state of affairs is often the result of a combination of historical information and underlying factors that are not directly observable. To this end, we investigate the limits of historical information for temporal knowledge graph extrapolation and propose a new event forecasting model called Contrastive Event Network (CENET) based on a novel training framework of historical contrastive learning. CENET learns both the historical and non-historical dependency to distinguish the most potential entities that best match the given query. Simultaneously, by launching contrastive learning, it trains representations of queries to probe whether the current moment is more dependent on historical or non-historical events. These representations further help train a binary classifier, whose output is a boolean mask, indicating the related entities in the search space. During the inference process, CENET employs a mask-based strategy to generate the final results. We evaluate our proposed model on five benchmark graphs. The results demonstrate that CENET significantly outperforms all existing methods in most metrics, achieving at least 8.3% relative improvement of Hits@1 over previous state-of-the-art baselines on event-based datasets.Comment: Extended version of AAAI paper arXiv:2211.1090

Exploring and Verbalizing Academic Ideas by Concept Co-occurrence

Researchers usually come up with new ideas only after thoroughly comprehending vast quantities of literature. The difficulty of this procedure is exacerbated by the fact that the number of academic publications is growing exponentially. In this study, we devise a framework based on concept co-occurrence for academic idea inspiration, which has been integrated into a research assistant system. From our perspective, the fusion of two concepts that co-occur in an academic paper can be regarded as an important way of the emergence of a new idea. We construct evolving concept graphs according to the co-occurrence relationship of concepts from 20 disciplines or topics. Then we design a temporal link prediction method based on masked language model to explore potential connections between different concepts. To verbalize the newly discovered connections, we also utilize the pretrained language model to generate a description of an idea based on a new data structure called co-occurrence citation quintuple. We evaluate our proposed system using both automatic metrics and human assessment. The results demonstrate that our system has broad prospects and can assist researchers in expediting the process of discovering new ideas.Comment: Accepted by ACL 202

Automatic mapping aquaculture in coastal zone from TM imagery with OBIA approach

IEEE GRSS; The Geographical Society of China<span class="MedBlackText">Aquaculture area monitoring is of great importance for coastal zone sustainable management and planning. This paper focuses on the development and assessment of an automatic approach for aquaculture mapping in coastal zone from TM imagery. The contribution mainly consists of three aspects: first, utilizes the Multi-scale segmentation/object relationship modeling (MSS/ORM) strategy on the object based image analysis (OBIA) of TM imagery; second, evaluates the effectiveness GLCM homogeneity texture feature on pond aquaculture area information extraction; third, compares the analysis results from three different approaches, namely pixelbased maximum likelihood classifier (MLC), One-step supervised OBIA with stand nearest neighbor (SNN) and MSS/ORM OBIA strategy. The final result shows that the MSS/ORM OBIA approach greatly improves the classification accuracy and has good potential for automatic pond aquaculture land mapping in coastal zone from TM imagery.</span

Accelerating Spatial Clustering Detection of Epidemic Disease with Graphics Processing Unit

IEEE GRSS; The Geographical Society of China<span class="MedBlackText">The statistics of disease clustering is of interest to epidemiologists. In order to detect spatial clustering of disease in all the regions of China, we adopted a likelihood ratio based method which utilizes Monte Carlo simulation and spatial exploring to analyze the real time updating data stored in database. However, large number of random tests for Monte Carlo simulation and large scale of the data set had made the speed of analysis too slow to detect and monitor potential public health hazards. Therefore, we explored to adopt graphics processing unit (GPU) and compute unified device architecture (CUDA) to accelerate the spatial exploring and analyzing process. The algorithm has been implemented efficiently on GPU and the access pattern to memory has been optimized to exploit the computing power of GPU. As a result, the GPU based spatial exploring and likelihood ratio test program performed more than forty times faster then the CPU implementation. The Monte Carlo simulation on GPU performed around thirty times faster than the counter part on CPU. By using GPU and CUDA, the usage of our application is changed from verification after the event to early warning.</span