浅析本雅明的“韵味”与“机器复制”

文章以本雅明美学中的"韵味"与"机器复制"等关键词为对象,阐释其主要特征与意义。从而说明本雅明在古典艺术和自现代社会以来产生的艺术之间确定了一条界线,即随着机器时代的到来,传统艺术的韵味消失于机器复制时代的艺术中了。     

把集邮文化融入先进文化建设大潮

集邮以其广泛的群众基础和深厚的人文魅力,是构建先进文化的有机载体,把集邮文化融入我国先进文化建设大潮,具有重要的现实意义。     

Automated Classification of Epiphyses in the Distal Radius and Ulna using a Support Vector Machine

The aim of this study was to automatically classify epiphyses in the distal radius and ulna using a support vector machine (SVM) and to examine the accuracy of the epiphyseal growth grades generated by the support vector machine. X‐ray images of distal radii and ulnae were collected from 140 Chinese teenagers aged between 11.0 and 19.0 years. Epiphyseal growth of the two elements was classified into five grades. Features of each element were extracted using a histogram of oriented gradient (HOG), and models were established using support vector classification (SVC). The prediction results and the validity of the models were evaluated with a cross‐validation test and independent test for accuracy (P_A). Our findings suggest that this new technique for epiphyseal classification was successful and that an automated technique using an SVM is reliable and feasible, with a relative high accuracy for the models.     

How to Cautiously Uncover the “Black Box” of Machine Learning Models for Legislative Scholars

Machine learning models, especially ensemble and tree-based approaches, offer great promise to legislative scholars. However, they are heavily underutilized outside of narrow applications to text and networks. We believe this is because they are difficult to interpret: while the models are extremely flexible, they have been criticized as “black box” techniques due to their difficulty in visualizing the effect of predictors on the outcome of interest. In order to make these models more useful for legislative scholars, we introduce a framework integrating machine learning models with traditional parametric approaches. We then review three interpretative plotting strategies that scholars can use to bring a substantive interpretation to their machine learning models. For each, we explain the plotting strategy, when to use it, and how to interpret it. We then put these plots in action by revisiting two recent articles from Legislative Studies Quarterly.     

Deep learning for sex determination: Analyzing over 200,000 panoramic radiographs

The objective of this study is to assess the performance of an innovative AI-powered tool for sex determination using panoramic radiographs (PR) and to explore factors affecting the performance of the convolutional neural network (CNN). The study involved 207,946 panoramic dental X-rays and their corresponding reports from 15 clinical centers in São Paulo, Brazil. The PRs were acquired with four different devices, and 58% of the patients were female. Data preprocessing included anonymizing the exams, extracting pertinent information from the reports, such as sex, age, type of dentition, and number of missing teeth, and organizing the data into a PostgreSQL database. Two neural network architectures, a standard CNN and a ResNet, were utilized for sex classification, with both undergoing hyperparameter tuning and cross-validation to ensure optimal performance. The CNN model achieved 95.02% accuracy in sex estimation, with image resolution being a significant influencing factor. The ResNet model attained over 86% accuracy in subjects older than 6 years and over 96% in those over 16 years. The algorithm performed better on female images, and the area under the curve (AUC) exceeded 96% for most age groups, except the youngest. Accuracy values were also assessed for different dentition types (deciduous, mixed, and permanent) and missing teeth. This study demonstrates the effectiveness of an AI-driven tool for sex determination using PR and emphasizes the role of image resolution, age, and sex in determining the algorithm's performance.     

Classification of firing pin impressions using HOG-SVM

Crimes, such as robbery and murder, often involve firearms. In order to assist with the investigation into the crime, firearm examiners are asked to determine whether cartridge cases found at a crime scene had been fired from a suspect's firearm. This examination is based on a comparison of the marks left on the surfaces of cartridge cases. Firing pin impressions can be one of the most commonly used of these marks. In this study, a total of nine Ruger model 10/22 semiautomatic rifles were used. Fifty cartridges were fired from each rifle. The cartridge cases were collected, and each firing pin impression was then cast and photographed using a comparison microscope. In this paper, we will describe how one may use a computer vision algorithm, the Histogram of Orientated Gradient (HOG), and a machine learning method, Support Vector Machines (SVMs), to classify images of firing pin impressions. Our method achieved a reasonably high accuracy at 93%. This can be used to associate a firearm with a cartridge case recovered from a scene. We also compared our method with other feature extraction algorithms. The comparison results showed that the HOG-SVM method had the highest performance in this classification task.     

对文件绝对形成时间的综合检验

在理论上,文件一定能反映出某种时闻信息,这为人们鉴定文件形成时间提供了可能性。文检工作者通常采用理化方法进行文件形成时间的检验,但该方法常受制于纸质、色料、保管条件等,因此在实践中并非每份文件都能够检验出形成时间。对于不具备理化检验条件的案件,应该分析文件时间特征,进行综合检验,以确定文件的绝对形成时间。     

92式9毫米与勃朗宁9毫米手枪射击弹痕研究

QSZ92式9毫米手枪和勃朗宁9毫米手枪是发射同一种枪弹的两种枪支,文章通过实验观察这两种枪支射击弹头上的进膛痕迹、拔弹痕迹、坡膛痕迹和线膛痕迹的形态特征,统计分析了痕迹出现的位置和长度及宽度,对这两种枪支的射击弹头痕迹作了比较研究。     

自含墨印章印文盖印时间受老化条件的影响

目的探索利用气相色谱法较精确、定量测定自含墨印章印文盖印时间受人为老化条件的影响。方法分别在恒温烘烤、紫外线照射、纯水浸泡等人为老化条件下,测定自含墨印章印油中所含有的主要相关溶剂成份的含量变化。结果得出了主要溶剂成份在人为老化条件下其含量的变化规律以及人为老化与自然老化的对应关系。结论利用气相色谱法可以对不同保存条件下自含墨印章印文的盖印时间进行测定,为实际相关案件的检验提供了依据。