The November 1948 open session of the Institute of Geological Sciences AS USSR was previously unknown,in contrast to the August 1948 session of VASKhNIL.The publication of the transcript of the session of geologists i...The November 1948 open session of the Institute of Geological Sciences AS USSR was previously unknown,in contrast to the August 1948 session of VASKhNIL.The publication of the transcript of the session of geologists is based on the original verified transcript from the Geological Institute and the Archive RAS.It presented reports on the main scientific directions of geology:stratigraphy,the Quaternary geology,lithology,geotectonics,petrography and petrology,mineralogy and geochemistry,and the geology of ore and coal deposits.This thick book details all the Q&A sessions,discussions of theories,methods,and practice among the leading Soviet geoscientists.The session and its resolution describe the situation and development of geology in the USSR in the mid-twentieth century as well as the collateral impact of the Lysenko affair on the earth sciences in the USSR.展开更多
Computer vision,a scientific discipline enables machines to perceive visual information,aims to supplant human eyes in tasksencompassing object recognition,localization,and tracking.In traditional educational settings...Computer vision,a scientific discipline enables machines to perceive visual information,aims to supplant human eyes in tasksencompassing object recognition,localization,and tracking.In traditional educational settings,instructors or evaluators evaluate teachingperformance based on subjective judgment.However,with the continuous advancements in computer vision technology,it becomes increasinglycrucial for computers to take on the role of judges in obtaining vital information and making unbiased evaluations.Against thisbackdrop,this paper proposes a deep learning-based approach for evaluating lecture posture.First,feature information is extracted fromvarious dimensions,including head position,hand gestures,and body posture,using a human pose estimation algorithm.Second,a machinelearning-based regression model is employed to predict machine scores by comparing the extracted features with expert-assigned humanscores.The correlation between machine scores and human scores is investigated through experiment and analysis,revealing a robustoverall correlation(0.6420)between predicted machine scores and human scores.Under ideal scoring conditions(100 points),approximately51.72%of predicted machine scores exhibited deviations within a range of 10 points,while around 81.87%displayed deviationswithin a range of 20 points;only a minimal percentage of 0.12%demonstrated deviations exceeding the threshold of 50 points.Finally,tofurther optimize performance,additional features related to bodily movements are extracted by introducing facial expression recognitionand gesture recognition algorithms.The fusion of multiple models resulted in an overall average correlation improvement of 0.0226.展开更多
文摘The November 1948 open session of the Institute of Geological Sciences AS USSR was previously unknown,in contrast to the August 1948 session of VASKhNIL.The publication of the transcript of the session of geologists is based on the original verified transcript from the Geological Institute and the Archive RAS.It presented reports on the main scientific directions of geology:stratigraphy,the Quaternary geology,lithology,geotectonics,petrography and petrology,mineralogy and geochemistry,and the geology of ore and coal deposits.This thick book details all the Q&A sessions,discussions of theories,methods,and practice among the leading Soviet geoscientists.The session and its resolution describe the situation and development of geology in the USSR in the mid-twentieth century as well as the collateral impact of the Lysenko affair on the earth sciences in the USSR.
基金Supported by the Open Fund of Key Laboratory of Anhui Higher Education Institutes(CS2021-07)the National Natural Science Foundation of China(61701004)the Outstanding Young Talents Support Program of Anhui Province(gxyq2021178)。
文摘Computer vision,a scientific discipline enables machines to perceive visual information,aims to supplant human eyes in tasksencompassing object recognition,localization,and tracking.In traditional educational settings,instructors or evaluators evaluate teachingperformance based on subjective judgment.However,with the continuous advancements in computer vision technology,it becomes increasinglycrucial for computers to take on the role of judges in obtaining vital information and making unbiased evaluations.Against thisbackdrop,this paper proposes a deep learning-based approach for evaluating lecture posture.First,feature information is extracted fromvarious dimensions,including head position,hand gestures,and body posture,using a human pose estimation algorithm.Second,a machinelearning-based regression model is employed to predict machine scores by comparing the extracted features with expert-assigned humanscores.The correlation between machine scores and human scores is investigated through experiment and analysis,revealing a robustoverall correlation(0.6420)between predicted machine scores and human scores.Under ideal scoring conditions(100 points),approximately51.72%of predicted machine scores exhibited deviations within a range of 10 points,while around 81.87%displayed deviationswithin a range of 20 points;only a minimal percentage of 0.12%demonstrated deviations exceeding the threshold of 50 points.Finally,tofurther optimize performance,additional features related to bodily movements are extracted by introducing facial expression recognitionand gesture recognition algorithms.The fusion of multiple models resulted in an overall average correlation improvement of 0.0226.
