Similarity-driven and Task-driven Models for Diversity of Opinion in Crowdsourcing Markets

The recent boom in crowdsourcing has opened up a new avenue for utilizing human intelligence in the realm of data analysis. This innovative approach provides a powerful means for connecting online workers to tasks that cannot effectively be done solely by machines or conducted by professional experts due to cost constraints. Within the field of social science, four elements are required to construct a sound crowd - Diversity of Opinion, Independence, Decentralization and Aggregation. However, while the other three components have already been investigated and implemented in existing crowdsourcing platforms, 'Diversity of Opinion' has not been functionally enabled yet. From a computational point of view, constructing a wise crowd necessitates quantitatively modeling and taking diversity into account. There are usually two paradigms in a crowdsourcing marketplace for worker selection: building a crowd to wait for tasks to come and selecting workers for a given task. We propose similarity-driven and task-driven models for both paradigms. Also, we develop efficient and effective algorithms for recruiting a limited number of workers with optimal diversity in both models. To validate our solutions, we conduct extensive experiments using both synthetic datasets and real data sets.Comment: 32 pages, 10 figure

Data-driven train set crash dynamics simulation

© 2016 Informa UK Limited, trading as Taylor & Francis GroupTraditional finite element (FE) methods are arguably expensive in computation/simulation of the train crash. High computational cost limits their direct applications in investigating dynamic behaviours of an entire train set for crashworthiness design and structural optimisation. On the contrary, multi-body modelling is widely used because of its low computational cost with the trade-off in accuracy. In this study, a data-driven train crash modelling method is proposed to improve the performance of a multi-body dynamics simulation of train set crash without increasing the computational burden. This is achieved by the parallel random forest algorithm, which is a machine learning approach that extracts useful patterns of force–displacement curves and predicts a force–displacement relation in a given collision condition from a collection of offline FE simulation data on various collision conditions, namely different crash velocities in our analysis. Using the FE simulation results as a benchmark, we compared our method with traditional multi-body modelling methods and the result shows that our data-driven method improves the accuracy over traditional multi-body models in train crash simulation and runs at the same level of efficiency

Identifying risks in temporal supernetworks: an IO-SuperPageRank algorithm

Abstract Complex systems pose risks characterized by factors such as uncertainty, nonlinearity, and diversity, making traditional risk measurement methods based on a probabilistic framework inadequate. Supernetworks can effectively model complex systems, and temporal supernetworks can capture the dynamic evolution of these systems. From the perspective of network stability, supernetworks can aid in risk identification for complex systems. In this paper, an IO-SuperPageRank algorithm is proposed based on the supernetwork topological structure. This algorithm reveals network instability by calculating changes in node importance, thereby helping to identify risks in complex systems. To validate the effectiveness of this algorithm, a four-layer supernetwork composed of scale-free networks is constructed. Simulated experiments are conducted to assess the impact of changes in intralayer edge numbers, intralayer node numbers, and interlayer superedge numbers on the risk indicator IO value. Linear regression and multiple tests were used to validate these relationships. The experiments show that changes in the three network topological indicators all bring about risks, with changes in intralayer node numbers having the most significant correlation with the risk indicator IO value. Compared to traditional measures of network node centrality and connectivity, this algorithm can more accurately predict the impact of node updates on network stability. Additionally, this paper collected trade data for crude oil, chemical light oil, man-made filaments and man-made staple fibers from the UN Comtrade Database. We constructed a man-made filaments and fibers supply chain temporal supernetwork, utilizing the algorithm to identify supply chain risks from December 2020 to October 2023. The study revealed that the algorithm effectively identified risks brought about by changes in international situations such as the Russia-Ukraine war, Israel–Hamas conflict, and the COVID-19 pandemic. This demonstrated the algorithm’s effectiveness in empirical analysis. In the future, we plan to further expand its application based on different scenarios, assess risks by analyzing changes in specific system elements, and implement effective risk intervention measures

Spatial–Temporal Patterns of Sympatric Asiatic Black Bears (<i>Ursus thibetanus</i>) and Brown Bears (<i>Ursus arctos</i>) in Northeastern China

Studying the spatial and temporal interactions between sympatric animal species is essential for understanding the mechanisms of interspecific coexistence. Both Asiatic black bears (Ursus thibetanus) and brown bears (Ursus arctos) inhabit northeastern China, but their spatial–temporal patterns and the mechanism of coexistence were unclear until now. Camera traps were set in Heilongjiang Taipinggou National Nature Reserve (TPGNR) from January 2017 to December 2017 to collect photos of the two sympatric bear species. The Pianka index, kernel density estimation, and the coefficient of overlap were used to analyze the spatial and temporal patterns of the two sympatric species. Our findings indicated that the spatial overlap between Asiatic black bears and brown bears was low, as Asiatic black bears occupied higher elevations than brown bears. The two species’ temporal activity patterns were similar at sites where only one species existed, yet they were different at the co–occurrence sites. Asiatic black bears and brown bears are competitors in this area, but they can coexist by changing their daily activity patterns. Compared to brown bears, Asiatic black bears behaved more diurnally. Our study revealed distinct spatial and temporal differentiation within the two species in TPGNR, which can reduce interspecific competition and facilitate coexistence between them

Spatial-temporal patterns of human-wildlife conflicts under coupled impact of natural and anthropogenic factors in Mt. Gaoligong, western Yunnan, China

Human-wildlife conflicts (HWC) are major conservation concerns that threaten both wildlife and rural livelihoods, and can vary spatially and temporally in diverse environments. A better understanding of HWC enhances mitigation strategies and promotes human-wildlife coexistence. We gathered HWC incidents from 2012 to 2021 in Longyang District, Yunnan Province, China, to assess their spatial-temporal patterns and determinants. We found that HWC were distributed unevenly, with more occurring near the protected area and away from densely populated areas. Poisson regression indicated that forests and croplands were the key factors influencing the spatial patterns of HWC for Asiatic black bear (Ursus thibetanus), but shrubs for rhesus monkey (Macaca mulatta). Furthermore, the HWC occurrences fluctuated across time, peaking in August-October. These conflicts intensified between 2012 and 2021, mainly involving wild boars (Sus scrofa) and rhesus monkeys, while HWC involving Asiatic black bears declined slightly. We suggest a multi-faceted strategy with compensation, prevention, and conservation awareness to mitigate HWC in this region

MODELING THE COMPRESSIVE STRENGTH OF ULTRA-HIGH PERFORMANCE CONCRETE (UHPC) BY A MACHINE LEARNING TECHNIQUE OPTIMIZED BY NOVEL ALGORITHM

The Ultra-High Performance Concrete (UHPC) as an efficient material in constructional projects needs to be investigated in terms of ingredients and their magnitudes to compute the compressive strength of concrete. Empirical determination of relationships between constituents can demand more energy and cost. At the same time, the intelligent systems have enabled us to appraise the compressive strength based on ingredients' composition. Also, choosing eco-friendly materials in concrete as one of the widely-used items worldwide should be encouraged. This study has attempted to model the compressive strength of UHPC. Support Vector Regression (SVR), as a machine learning technique aligned with the Particle Swarm Optimization (PSO) and Henry's Gas Solubility Optimization (HGSO), have been used to simulate the compressive strength of concrete calculated based on different materials used in the present article. Eight constituents were tested to generate the compressive strength values. Various metrics were used to evaluate the modeling process. In this regard, the R2 of test phase modeling for SVR-HGSO was obtained 0.960 while for SVR-PSO, 0.925. In the training stage, the correlation rate was computed 0.902 for SVR-HGSO, which is 1.7 percent higher than SVR-PSO with R2 0.887

A 4.1 GHz–9.2 GHz Programmable Frequency Divider for Ka Band PLL Frequency Synthesizer

High speed divider is highly desired in the millimeter wave (mmW) frequency synthesizer design. A high operating frequency, low power consumption 90-nm CMOS programmable pulse swallow multi-modulus-divider is presented in this paper. High speed true-single-phase-clock D-flip-flop (TSPC DFF) is used in the counter in order to obtain a high operating frequency. It can operate at a frequency range from 4.1 GHz to 9.2 GHz, with a division ratio of 101&ndash;164. It has a power efficiency of 3.1 GHz/mW, and it can be used to provide a high quality reference frequency in the mmW phase-locked loop

Six New Species of Leucoagaricus (Agaricaceae) from Northeastern China

Six new species, Leucoagaricus albosquamosus, Leucoagaricus atroviridis, Leucoagaricus aurantioruber, Leucoagaricus candidus, Leucoagaricus centricastaneus and Leucoagaricus virens, collected from northeastern China are described based on morphological characters and molecular evidence. Illustrations of fresh basidiomata and line drawings of key anatomical characters are provided. A phylogenetic tree inferred from internal transcribed spacer (ITS) region and large subunit ribosomal RNA gene (LSU) sequences shows that three of the new taxa are nested within the section Leucoagaricus and two of the new taxa are in the subgenus Sericeomyces, whereas the other new taxus is clustered with Leucoagaricus viriditinctus and Leucoagaricus irinellus, forming a clade that does not fit in any known section

Six New Species of <i>Leucoagaricus</i> (Agaricaceae) from Northeastern China

Six new species, Leucoagaricus albosquamosus, Leucoagaricus atroviridis, Leucoagaricus aurantioruber, Leucoagaricus candidus, Leucoagaricus centricastaneus and Leucoagaricus virens, collected from northeastern China are described based on morphological characters and molecular evidence. Illustrations of fresh basidiomata and line drawings of key anatomical characters are provided. A phylogenetic tree inferred from internal transcribed spacer (ITS) region and large subunit ribosomal RNA gene (LSU) sequences shows that three of the new taxa are nested within the section Leucoagaricus and two of the new taxa are in the subgenus Sericeomyces, whereas the other new taxus is clustered with Leucoagaricus viriditinctus and Leucoagaricus irinellus, forming a clade that does not fit in any known section