似然比证据评价方法的科学性探讨
A Scientific Discussion on the Evidence Evaluation Method of Likelihood Ratio
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- Keywords: Evidence evaluation; Likelihood ratio; Forensic identification; Fact finding; Scientificity证据评价; 似然比; 司法鉴定; 事实认定; 科学性
- Abstract: The evaluation of evidence plays a crucial role in the process of scientific evidence generation, exerting a significant impact on the utilization of scientific evidence within legal proceedings.The likelihood ratio quantifies the probability ratio between two competing claims, exhibiting characteristics of objectivity, transparency, and fault tolerance.Incorporating the application of likelihood ratio assessment in the evaluation of common material evidence, this study examines the scientific approach to evaluating evidence through likelihood ratio analysis from two perspectives.From the perspective of forensic identification, an evaluation method that excessively relies on the subjective judgment of the appraiser exacerbates uncertainty in both the appraisal process and its outcome, thereby perpetuating these uncertainties into the fact determination link. The evaluation of likelihood ratio can effectively enhance the objectivity and validity of scientific evidence, thereby providing a more scientifically rigorous approach for utilizing scientific evidence in fact determination. From the point of view of fact finding, The likelihood ratio evaluation method enables the quantification of evidence’s impact on the fact finder’s belief, reducing judges’ reliance on scientific evidence, enhancing the standardization and rationality of their fact finding process, and facilitating scientifically grounded factual determinations.证据评价是科学证据生成过程中的重要部分，影响法庭科学证据的应用。似然比评价方法描述两种竞争性主张的概率比值，具有客观性、透明性、容错率等特点。结合似然比评价在常见物证评价中的应用，从两个角度对似然比证据评价方法的科学性进行探讨。从司法鉴定的角度来看，过于依赖鉴定人主观判断的评价方法模糊了鉴定过程及结果中存在的不确定性，其影响会延续到事实认定环节。通过似然比评价能够有效提高科学证据的客观、有效性，为科学证据服务于事实认定提供更为科学的手段。从事实认定的角度来看，似然比评价方法能够量化证据对事实认定者的信念影响，弱化法官对科学证据的依赖性，强化法官事实认定的规范性、合理性，推动事实认定科学化。
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基于PHP+MySQL的司法鉴定人继续教育平台设计与实现
Design and implementation of continuing education platform for judicial appraisers based on PHP+MySQL
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- Keywords: Judicial appraisers; Continuing education; Online learning; B/S architecture; PHP+MySQL司法鉴定人; 继续教育; 在线学习; B/S 架构; PHP+MySQL
- Abstract: In order to improve the quality and credibility of judicial expertise, the Ministry of Justice issued Administrative Measures on Judicial expertise Education and Training in 2021, which clearly stipulates the requirements and methods for the job training of judicial appraisers. Under the influence of COVID-19, large-scale offline training will lead to the gathering of people, which violates the epidemic prevention policy and brings the risk of the spread of the virus. In fact, due to the impact of COVID-19, working from home and online learning have become a part of people’s life, while the development of computer and Internet technology has promoted and supported online education. A better solution is to develop an online learning platform for annual continuing education of judicial appraisers. Based on the “National Judicial Appraisers Education and Training Base” of Zhongnan University of Economics and Law, this paper studies the design and implementation of the online learning platform for judicial appraisers’ continuing education based on PHP+MySQL, aiming to promote the development of online training in Hubei province and even in central China. In this paper, design and implementation of the system architecture, key process design of system, background management of website and the site reception are introduced in detail. Through the trial operation of website, the existing problems and the direction of further improvement of the system are summarized.为提升司法鉴定的质量和公信力，2021 年司法部发布了《司法鉴定教育培训工作管理办法》，对司法鉴定人岗位培训的要求和方式做了明确的规定。在新冠肺炎疫情的影响下，大规模的线下培训会引起人员的聚集，违反防疫政策的同时带来病毒传播的风险。事实上，由于疫情的影响，居家办公、在线学习已经成为人们生活的一部分，同时计算机和网络技术的发展又推动和支持了在线教育的开展。开发司法鉴定人年度继续教育在线学习平台，变线下培训为线上培训不失为一个更好的解决办法。本文依托中南财经政法大学“全国司法鉴定人教育培训基地”，研究基于PHP+MySQL 的司法鉴定人继续教育在线学习平台的设计与实现，旨在推动线上培训在湖北省乃至华中地区区域内的发展。本文对系统框架、系统关键流程设计、平台后台管理端到平台前端设计与实现进行了详细的介绍。通过平台试运行情况，总结了系统目前存在的问题与进一步完善的方向。
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基于机器学习的线条痕迹检验研究
Study on Examination of Striated Toolmarks Based on Machine Learning
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- Keywords: Forensic sciences; Striated mark; Artificial intelligence; Machine learning法庭科学; 线条痕迹; 人工智能; 机器学习
- Abstract: The traditional method of manual examination of tool mark is challenged in the court for its subjectivity. With reference to the challenging, the computer-based approach have been studied in the world. The approach mainly focused on extraction of striation feature and statistical examination of striations. The machine learning method was studied, and four groups of experiments were conducted with a 2D image dataset of tool marks made by screwdrivers, cutting pliers and bolt clippers. The four LBP derivatives operators were developed to extract the tool-mark features and then construct the features into a feature vector. The random forest algorithm was adopted to identify the labeled feature vectors by supervised learning. The experimental results show that the proposed method achieved a high-rate of identification of the striated marks generated under identical conditions, and reduced the uncertainty of results examined by traditional method. Furthermore, the proposed method is immune to the unstable illumination when the image data of the striated marks are collected, and avoids the difficulty in mark inspection caused by manually preset parameters in the existing methods.传统的工具痕迹人工检验鉴定方法缺乏客观性受到法庭的强烈挑战。针对此挑战,国内外研究了线条痕迹的计算机处理方法，主要采用了提取线条特征与线条之间的统计检验。研究了基于人工智能的机器学习算法，对三种工具制作线条痕迹的2D图像数据集做了4组实验，通过提取线条痕迹的四种局部二进制模式(LBP)的衍生算子构建痕迹的特征向量，使用随机森林算法对带标签的特征向量进行监督学习。实验结果表明本方法对于在相同条件下制作的线条痕迹具有较高的识别率，且能够有效克服工具痕迹2D图像数据光照不稳定的缺点，也避免了现有方法中因人工预设置参数给痕迹检验带来的困难和检验结果的不确定性。
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国家自然科学基金项...
