The diversity of Samoa’s freshwater macroinvertebrates remains largely unexplored, with past studies focusing on specific species without comprehensive cataloguing. This research evaluated the health of Upolu Island...The diversity of Samoa’s freshwater macroinvertebrates remains largely unexplored, with past studies focusing on specific species without comprehensive cataloguing. This research evaluated the health of Upolu Island’s rural rivers through macroinvertebrate analysis, particularly in the Le Afe and Mulivaifagatoloa Rivers. Collaborating with Samoa’s Water Resources Division in the Ministry of Natural Resources and Environment (MNRE), three sites along each river were sampled, representing a gradient from pristine to anthropogenically impacted areas. A total of 2953 macroinvertebrates were collected and classified into five categories using established identification keys. The Macroinvertebrate Community Index (MCI) and Quantitative Macroinvertebrate Community Index (QMCI) were applied for analysis. The results showed no clear pattern of pollutant-sensitive species prevalence or decline in less disturbed rivers. High MCI scores with low QMCI values indicated numerous low-scoring species, while the opposite suggested a richness of high-scoring taxa. Although MCI and QMCI are tools for monitoring freshwater health, this study lays the groundwork for future research to categorize Samoan macroinvertebrates and assign tolerance scores based on their presence in varying river conditions. .展开更多
Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human resources.Tra-...Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human resources.Tra-ditional machine learning techniques have been broadly employed for personality trait identification;nevertheless,the development of new technologies based on deep learning has led to new opportunities to improve their performance.This study focuses on the capabilities of pre-trained language models such as BERT,RoBERTa,ALBERT,ELECTRA,ERNIE,or XLNet,to deal with the task of personality recognition.These models are able to capture structural features from textual content and comprehend a multitude of language facets and complex features such as hierarchical relationships or long-term dependencies.This makes them suitable to classify multi-label personality traits from reviews while mitigating computational costs.The focus of this approach centers on developing an architecture based on different layers able to capture the semantic context and structural features from texts.Moreover,it is able to fine-tune the previous models using the MyPersonality dataset,which comprises 9,917 status updates contributed by 250 Facebook users.These status updates are categorized according to the well-known Big Five personality model,setting the stage for a comprehensive exploration of personality traits.To test the proposal,a set of experiments have been performed using different metrics such as the exact match ratio,hamming loss,zero-one-loss,precision,recall,F1-score,and weighted averages.The results reveal ERNIE is the top-performing model,achieving an exact match ratio of 72.32%,an accuracy rate of 87.17%,and 84.41%of F1-score.The findings demonstrate that the tested models substantially outperform other state-of-the-art studies,enhancing the accuracy by at least 3%and confirming them as powerful tools for personality recognition.These findings represent substantial advancements in personality recognition,making them appropriate for the development of user-centric applications.展开更多
Security has recently become a major concern in distributed geo-infrastructures for spatial data provision.Thus,a lightweight approach for securing distributed low-power environments such as geo-sensor networks is nee...Security has recently become a major concern in distributed geo-infrastructures for spatial data provision.Thus,a lightweight approach for securing distributed low-power environments such as geo-sensor networks is needed.The first part of this article presents a survey of current security mechanisms for authentication and authorisation.Based on this survey,a lightweight and scalable token-based security infrastructure was developed,which is tailored for use in distributed geo-web service infrastructures.The developed security framework comprises dedicated components for authentication,rule-based authorisation and optimised storage and administration of access rules.For validation purposes,a prototypical implementation of the approach has been created.展开更多
文摘The diversity of Samoa’s freshwater macroinvertebrates remains largely unexplored, with past studies focusing on specific species without comprehensive cataloguing. This research evaluated the health of Upolu Island’s rural rivers through macroinvertebrate analysis, particularly in the Le Afe and Mulivaifagatoloa Rivers. Collaborating with Samoa’s Water Resources Division in the Ministry of Natural Resources and Environment (MNRE), three sites along each river were sampled, representing a gradient from pristine to anthropogenically impacted areas. A total of 2953 macroinvertebrates were collected and classified into five categories using established identification keys. The Macroinvertebrate Community Index (MCI) and Quantitative Macroinvertebrate Community Index (QMCI) were applied for analysis. The results showed no clear pattern of pollutant-sensitive species prevalence or decline in less disturbed rivers. High MCI scores with low QMCI values indicated numerous low-scoring species, while the opposite suggested a richness of high-scoring taxa. Although MCI and QMCI are tools for monitoring freshwater health, this study lays the groundwork for future research to categorize Samoan macroinvertebrates and assign tolerance scores based on their presence in varying river conditions. .
基金This work has been partially supported by FEDER and the State Research Agency(AEI)of the Spanish Ministry of Economy and Competition under Grant SAFER:PID2019-104735RB-C42(AEI/FEDER,UE)the General Subdirection for Gambling Regulation of the Spanish ConsumptionMinistry under the Grant Detec-EMO:SUBV23/00010the Project PLEC2021-007681 funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR.
文摘Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human resources.Tra-ditional machine learning techniques have been broadly employed for personality trait identification;nevertheless,the development of new technologies based on deep learning has led to new opportunities to improve their performance.This study focuses on the capabilities of pre-trained language models such as BERT,RoBERTa,ALBERT,ELECTRA,ERNIE,or XLNet,to deal with the task of personality recognition.These models are able to capture structural features from textual content and comprehend a multitude of language facets and complex features such as hierarchical relationships or long-term dependencies.This makes them suitable to classify multi-label personality traits from reviews while mitigating computational costs.The focus of this approach centers on developing an architecture based on different layers able to capture the semantic context and structural features from texts.Moreover,it is able to fine-tune the previous models using the MyPersonality dataset,which comprises 9,917 status updates contributed by 250 Facebook users.These status updates are categorized according to the well-known Big Five personality model,setting the stage for a comprehensive exploration of personality traits.To test the proposal,a set of experiments have been performed using different metrics such as the exact match ratio,hamming loss,zero-one-loss,precision,recall,F1-score,and weighted averages.The results reveal ERNIE is the top-performing model,achieving an exact match ratio of 72.32%,an accuracy rate of 87.17%,and 84.41%of F1-score.The findings demonstrate that the tested models substantially outperform other state-of-the-art studies,enhancing the accuracy by at least 3%and confirming them as powerful tools for personality recognition.These findings represent substantial advancements in personality recognition,making them appropriate for the development of user-centric applications.
基金This work has been funded by the European Commission(FP7 project GENESIS,reference No.223996)the Austrian Federal Ministry for Science and ResearchThe au。
文摘Security has recently become a major concern in distributed geo-infrastructures for spatial data provision.Thus,a lightweight approach for securing distributed low-power environments such as geo-sensor networks is needed.The first part of this article presents a survey of current security mechanisms for authentication and authorisation.Based on this survey,a lightweight and scalable token-based security infrastructure was developed,which is tailored for use in distributed geo-web service infrastructures.The developed security framework comprises dedicated components for authentication,rule-based authorisation and optimised storage and administration of access rules.For validation purposes,a prototypical implementation of the approach has been created.
