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    Researchers' Work from Nanjing University of Aeronautics and As- tronautics Focuses on Robotics (Synergy Between Soft Feet and an Active Tail To Enhance the Climbing Ability of a Bio-inspired Climbing Robot)

    11-12页
    查看更多>>摘要:Research findings on Robotics are discussed in a new report. According to news reporting from Nanjing, People's Republic of China, by NewsRx journalists, research stated, "Lizards use the synergy between their feet and tail to climb on slopes and vertical terrains. They use their soft adhesive feet with millions of small hairs to increase their contact area with the terrain surface and press their tails against the terrain to actively maintain stability during climbing." Funders for this research include The National Key R &D Program of China, Topic 4-NUAA, National Key R &D Program of China.

    Chonnam National University Researcher Has Provided New Study Findings on Artificial Intelligence (University students' perceptions of artificial intelligence-based tools for English writing courses)

    11-11页
    查看更多>>摘要:Research findings on artificial intelligence are discussed in a new report. According to news reporting from Gwangju, South Korea, by NewsRx journalists, research stated, "This research explores the perceptions of Korean university students regarding artificial intelligence (AI)-based writing tools that include tools guided by machine learning, such as Google Translate and Naver Papago, and generative AI tools, such as Grammarly." Our news editors obtained a quote from the research from Chonnam National University: "A mixed methodology was used, including both quantitative and qualitative data. Among students who have taken English writing courses, 80 Korean university students volunteered for the online survey. After the survey, the research team recruited interview participants, and five volunteered participants joined the focus group interview. The study results indicate that these AI-based writing tools could improve English language learners (ELLs) writing skills. ELLs also noted the strengths and weaknesses of each AI-based tool, including the accessibility of translation machine learning and the error-checking capabilities of generative AI."

    New Machine Learning Study Findings Recently Were Reported by Researchers at Aristotle University of Thessaloniki (Truthful Meta- explanations for Local Interpretability of Machine Learning Models)

    13-14页
    查看更多>>摘要:Investigators publish new report on Machine Learning. According to news reporting from Thessaloniki, Greece, by NewsRx journalists, research stated, "Automated Machine Learning-based systems' integration into a wide range of tasks has expanded as a result of their performance and speed. Although there are numerous advantages to employing ML-based systems, if they are not interpretable, they should not be used in critical or high-risk applications." Funders for this research include HEAL-Link Greece, European Union (EU). The news correspondents obtained a quote from the research from the Aristotle University of Thessa- loniki, "To address this issue, researchers and businesses have been focusing on finding ways to improve the explainability of complex ML systems, and several such methods have been developed. Indeed, there are so many developed techniques that it is difficult for practitioners to choose the best among them for their applications, even when using evaluation metrics. As a result, the demand for a selection tool, a meta-explanation technique based on a high-quality evaluation metric, is apparent. In this paper, we present a local meta-explanation technique which builds on top of the truthfulness metric, which is a faithfulness-based metric."

    Department of Engineering Researchers Target Machine Learning (A Benchmarking on Optofluidic Microplastic Pattern Recognition: A Systematic Comparison Between Statistical Detection Models and ML-Based Algorithms)

    13-13页
    查看更多>>摘要:2024 FEB 22 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New study results on artificial intelligence have been published. According to news reporting from the Department of Engineering by NewsRx journalists, research stated, "Microplastics, small particles of plastic found in the environment, have become an increasingly worrying topic in recent years." Financial supporters for this research include Pon "ricerca E Innovazione". Our news correspondents obtained a quote from the research from Department of Engineering: "This paper compares a statistical detection model to classifiers from various supervised learning paradigms in order to detect microplastics. The objective of this paper is to present a benchmark for detecting microplastics using statistical and machine learning models. The main goal is to assess and compare their performance when the defined parameters deviate from the optimal solution of the respective model. Results are presented in terms of probability error, comparing the performance of the machine learning techniques to the statistical model. The study considers a range of signal-to-noise ratios and a priori event probabilities, focusing on the classifiers' ability to handle amplitude variability and threshold variation." According to the news reporters, the research concluded: "Results show that as the number of analyzed particles in the flow increases, the detection performance improves, with Support Vector Machine, Linear Discriminant Analysis and Naive Bayes standing out from the other methods."

    Researchers at University of Illinois Have Reported New Data on Machine Learning (Physics-informed Machine Learning Method With Space-time Karhunen-loeve Expansions for Forward and In- verse Partial Differential Equations)

    14-15页
    查看更多>>摘要:New research on Machine Learning is the subject of a report. According to news originating from Urbana, Illinois, by NewsRx correspondents, research stated, "We propose a physics - informed machine -learning method based on space -time -dependent Karhunen-Loeve expansions (KLEs) of the state variables and the residual least -square formulation of the solution of partial differential equations. This method, which we name dPICKLE, results in a reduced -order model for solving forward and inverse time -dependent partial differential equations." Funders for this research include U.S. Department of Energy (DOE) Advanced Scientific Computing (ASCR) program, United States Department of Energy (DOE). Our news journalists obtained a quote from the research from the University of Illinois, "By conditioning KLEs on data, dPICKLE seamlessly assimilates data in forward and inverse solutions. KLEs are linear in unknown parameters. Because of this, and unlike physicsinformed deep -learning methods based on the residual least -square formulation, for well -posed partial differential equation (PDE) problems, dPICKLE leads to linear least -square problems (directly for linear PDEs and after linearization for nonlinear PDEs), which guarantees a unique solution."

    Studies from University of Agder in the Area of Artificial Intelli- gence Described (Discursive Framing and Organizational Venues: Mechanisms of Artificial Intelligence Policy Adoption)

    15-16页
    查看更多>>摘要:New research on Artificial Intelligence is the subject of a report. According to news reporting out of Kristiansand, Norway, by NewsRx editors, research stated, "The purpose of this article is twofold: to theoretically assess ideational and organizational explanatory factors in the adoption of artificial intelligence policies; and to examine the extent to which the European Union has managed to facilitate a coordinated artificial intelligence policy in the Nordic countries. The study utilizes a mixed-methods approach based on systematic web searching, systematic policy document analysis and key informant semi-structured interviews." Our news journalists obtained a quote from the research from the University of Agder, "The study finds that the European Union has utilized framing-based strategies to set an agenda for a coordinated European artificial intelligence policy. Moreover, the strategy has affected member-state artificial intelligence policies to the extent that key tenets of European Union artificial intelligence discourse have penetrated Nordic public documents. However, the extent to which the Nordic countries incorporate European Union artificial intelligence policy discourse diverges at the national level. Differentiated national organizational capacities among Nordic countries make the adoption of artificial intelligence policies divergent. This observation is theoretically accounted for through a conversation between organizational theory of public governance and discursive institutionalism."

    Data from Nelson Mandela African Institution of Science and Tech- nology Provide New Insights into Machine Learning (Prediction of SACCOS Failure in Tanzania using Machine Learning Models)

    16-17页
    查看更多>>摘要:New study results on artificial intelligence have been published. According to news orig- inating from the Nelson Mandela African Institution of Science and Technology by NewsRx correspondents, research stated, "Savings and Credit Co-Operative Societies (SACCOS) are seen as viable opportunities to promote financial inclusion and overall socioeconomic development." Our news reporters obtained a quote from the research from Nelson Mandela African Institution of Science and Technology: "Despite the positive outlook for socioeconomic progress, recent observations have highlighted instances of SACCOS failures. For example, the number of SACCOS decreased from 4,177 in 2018 to 3,714 in 2019, and the value of shares held by SACCOS members in Tanzania dropped from Tshs 57.06 billion to 53.63 billion in 2018. In particular, there is limited focus on predicting SACCOS failures in Tanzania using predictive models. In this study, data were collected using a questionnaire from 880 members of SACCOS, using a stratified random sampling technique. The collected data was analyzed using machine learning models, including Random Forest (RF), Logistic Regression (LR), K Nearest Neighbors (KNN), and Support Vector Machine (SVM). The results showed that RF was the most effective model to classify and predict failures, followed by LR and KNN, while the results of SVM were not satisfactory."

    University of Debrecen Reports Findings in Machine Learning (En- hancing HLA-B27 antigen detection: Leveraging machine learning algorithms for flow cytometric analysis)

    17-18页
    查看更多>>摘要:2024 FEB 22 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Machine Learning is the subject of a report. According to news reporting out of Debrecen, Hungary, by NewsRx editors, research stated, "As the association of human leukocyte antigen B27 (HLA-B27) with spondylarthropathies is widely known, HLA-B27 antigen expression is frequently identified using flow cytometric or other techniques. Because of the possibility of cross-reaction with off target antigens, such as HLA-B7, each flow cytometric technique applies a 'gray zone' reserved for equivocal findings." Our news journalists obtained a quote from the research from the University of Debrecen, "Our aim was to use machine learning (ML) methods to classify such equivocal data as positive or negative. Equivocal samples (n = 99) were selected from samples submitted to our institution for clinical evaluation by HLA- B27 antigen testing. Samples were analyzed by flow cytometry and polymerase chain reaction. Features of histograms generated by flow cytometry were used to train and validate ML methods for classification as logistic regression (LR), decision tree (DT), random forest (RF) and light gradient boost method (GBM). All evaluated ML algorithms performed well, with high accuracy, sensitivity, specificity, as well as negative and positive predictive values. Although, gradient boost approaches are proposed as high performance methods; nevertheless, their effectiveness may be lower for smaller sample sizes. On our relatively smaller sample set, the random forest algorithm performed best (AUC: 0.92), but there was no statistically significant difference between the ML algorithms used. AUC values for light GBM, DT, and LR were 0.88, 0.89, 0.89, respectively."

    Researchers from University of Pompeu Fabra Detail Findings in Artificial Intelligence (Retraining Fact-checkers: the Emergence of chatgpt In Information Verification)

    18-19页
    查看更多>>摘要:Data detailed on Artificial Intelligence have been presented. According to news reporting originating from Barcelona, Spain, by NewsRx correspondents, research stated, "The open launch of new artificial intelligence tools such as ChatGPT-3.5 (Generated Pre-trained Transformer) in November 2022 by the company OpenAI-and then its update to version GPT-4 in March 2023-poses new opportunities and challenges for journalism, and especially for professionals specifically focused on information verification. This research aims to understand and analyze the perceptions generated by the irruption of ChatGPT among fact-checking professionals in Spain with the aim of identifying disadvantages and advantages in its use, professional implications and desired functionalities." Funders for this research include "Instruments of accountability in the face of disinformation: impact of fact-checking platforms as accountability tools and curricular proposal" project, Spanish Government, Generalitat de Catalunya.

    Findings from Central Institute of Technology Provide New Insights into Machine Translation (Word Sense Disambiguation applied to Assamese-Hindi Bilingual Statistical Machine Translation)

    19-19页
    查看更多>>摘要:Investigators discuss new findings in machine translation. According to news reporting from the Central Institute of Technology by NewsRx journalists, research stated, "Word Sense Disambigua- tion (WSD) is concerned with automatically assigning the appropriate sense to an ambiguous word. WSD is an important task and plays a crucial role in many Natural Language Processing (NLP) applications." The news correspondents obtained a quote from the research from Central Institute of Technology: "A Statistical Machine Translation (SMT) system translates a source into a target language based on phrase-based statistical translation. MT plays a crucial role in a WSD system, as a source language word may be associated with multiple translations in the target language. This study aims to apply WSD to the input of the MT system to enhance the disambiguation output. Hindi WordNet was used by selecting the most frequent synonym to obtain the most accurate translation. This study also compared Naive Bayes (NB) and Decision Tree (DT) to test and build a WSD model. NB was more appropriate for the WSD task than DT when evaluated in the Weka machine learning toolkit. To the best of our knowledge, no such work has been carried out yet for the Assamese Indo-Aryan language. The applied WSD achieved better results than the baseline MT system without embedding the WSD module. The results were analyzed by linguist scholars."