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Shuanglin Li 发布的文章
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验证码变得越来越简单，甚至无感背后的思考 背景作为曾经的一枚网站开发人员，为防止机器人或者自动化程序频繁访问或者暴力破解用户信息，就会在用户登录等场景增加验证码。自此以后，验证码与机器人就开始斗智斗勇，验证码开始变得越来越复杂，甚至到了机器能够快速识别，而人反而无法读取的状态，严重违背了验证码的初衷。reCAPTCHA 2.0这时reCAPTCHA 2.0出现了，即通过我不是机器人按钮（如图1所示），搜集用户鼠标路径来判断不是机器人的代理点击，其工作原理是通过搜集点击前用户鼠标移动的随机性，判断是人的行为而不是机器人在操作。{mtitle title="图1 reCAPTCHA 2.0"/}reCAPTCHA 3.02018年，Google再次升级了reCAPTCHA，在这个被称为V3的版本中，用户已经连【我不是机器人】的复选框也看不到了，系统会在背后悄悄分析用户浏览网站的方式，并根据其行为的恶意程度给出一个风险评分。如果用户评分过低，网站可能会要求用户输入更多的信息来证明自己的身份。目前，已经有65万个网站使用了最新的reCAPTCHA 3.0，而使用reCAPTCHA的网站超过了450万，包括Top 1000网站中的25%。在Google看来，这是最好的体验，用户不需要任何输入，而且它很难被破解程序学习。唯一的问题是，Google掌握了越来越多的用户隐私。据FastCompany报道，两位研究者对reCAPTCHA 3.0 进行了测试后发现，用户是否使用Google Cookies是决定评分的一个重要因素。也就是说，如果用户选择让Google记住登录信息的话，会得到更高的分数，没有登录Google帐号，或者使用VPN或者浏览器通常会被提示高风险。{mtitle title="图2 机器人自动化识别"/}背后的思考验证码的本质，是让你证明自己是人，而不是机器。但随着技术越来越发达，ChatGPT等的出现，人的行为不段被学习，识别人的行为的门槛也越来越高。这时候，虽然reCAPTCHA 3.0通过收集人的更多行为信息数据，判断人的行为，是不是就是正确的方向呢？如何实现自动化对人的独特行为的识别仍然是未来的发展方向。如：人的随机性行为、人的不确定性行为，但是如何去刻画和存储这种行为呢？
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元宵节（中国传统节日）——祝大家元宵节快乐，兔年吉祥 基本信息中文名称：元宵节英文名称：Lantern Festival别称：上元节、上元佳节、春灯节、灯节、小正月、元夕节日时间：农历正月十五节日类型：中国传统节日流行地区：中国节日起源：原始崇拜、信仰节日活动：赏灯、猜灯谜、耍龙灯节日饮食：元宵、汤圆节日意义：农历新年的第一个月圆之夜设定时间：汉朝遗产编号：Ⅹ-71类别：民俗批准时间：2014年12月3日节日简介元宵节，又称上元节、小正月、元夕或灯节，是春节之后的第一个重要节日，是中国亦是汉字文化圈的地区和海外华人的传统节日之一。正月是农历的元月，古人称夜为"宵"，所以把一年中第一个月圆之夜正月十五称为元宵节。中国古俗中，上元节(天官节、元宵节)﹑中元节(地官节、盂兰盆节)﹑下元节(水官节)合称三元。元宵节始于2000多年前的秦朝。汉文帝时下令将正月十五定为元宵节。汉武帝时，"太一神"祭祀活动定在正月十五(太一:主宰宇宙一切之神)。司马迁创建"太初历"时，就已将元宵节确定为重大节日。传统习俗出门赏月、燃灯放焰、喜猜灯谜、共吃元宵、拉兔子灯等。此外，不少地方元宵节还增加了耍龙灯、耍狮子、踩高跷、划旱船、扭秧歌、打太平鼓等传统民俗表演。2008年6月，元宵节选入第二批国家级非物质文化遗产。
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Freshness-driven vehicle routing problem: Modeling and application to the fresh agricultural product pick-storage-transportation 该论文主要针对生鲜农产品从生产基地到收购站，再转运到加工厂过程中导致生鲜农产品新鲜度降低，使得运输成本增高的问题，建立考虑新鲜度驱动的两阶段车辆路径问题优化模型，并分别设计遗传算法和粒子群算法进行求解，并以湖南炎陵黄桃为实例，验证模型和算法的可靠性，并通过敏感性分析给出管理建议，论文发表在《Journal of Industrial and Management Optimization》。论文摘要The fresh agricultural product (FAP) has highly perishable, hard to storage, and huge cost during the picking, storage, and transportation stage. In this paper, we aim to develop a multi-objective optimization model to minimize the total cost of collecting the FAP, where from the acquisition points aside the planting areas to the collection center, and the cost of transferring them to the processing factories (demand points) as well as to minimize the freshness decay during these processes. A epsilon constraint algorithm is used to convert the objective, freshness decay into constraints, and the model is transformed into a single-objective optimization model. According to the characteristics of the model, a hybrid algorithm based on genetic algorithm is developed. In order to verify the effectiveness of the model and algorithm, an example of yellow peach in Yanling, Hunan, China is constructed. And a comparison algorithm, simulated annealing algorithm, is presented. The results show that the effectiveness and efficiency of the hybrid algorithm based on genetic algorithm is better than that of simulated annealing algorithm. The insights of the sensitivity analysis indicate that the model and algorithm presented in this paper can be extended to other FAPs.论文全文{anote icon="fa-download" href="http://lishuanglin.cn/blog/usr/uploads/2023/01/2463324662.pdf" type="secondary" content="论文PDF下载"/}
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A novel min–max robust model for post-disaster relief kit assembly and distribution 该论文是针对灾后应急物资配送过程中的模块化装配问题，建立两阶段多目标鲁棒优化模型，并采用$\epsilon$-约束算法对模型进行求解，最后用云南地震相关数据对模型和算法进行验证，并通过敏感性分析得到相应管理启示。文章发表在《Expert Systems with Applications》。论文摘要In disaster response phase, different types of emergency relief materials are prepared simultaneously. Assorting and packing a proportion of relief items into relief kits will benefit in improving relief distribution agility and efficiency. This study focuses on the relief kit assembly and distribution problem, which includes two stages. The first stage solves the facility location and relief kit assembly problem with the minimum operation cost. The second stage optimizes the relief kit distribution plan with the minimum distribution cost and maximum demand satisfaction, in which an epsilon-constraint method is adopted to transfer the bi-objective model into a single one with the minimum total cost. Then, a min–max robust model is developed to cope with the uncertain demand and travel time. Computational experiments are provided to validate the effectiveness of the min–max robust model compared with deterministic model and two-stage stochastic model. A realistic case study based on earthquakes in Yunnan Province is provided to illustrate the applicability of the proposed min–max robust model. Some managerial insights are obtained by sensitivity analyses as follows. Assembling relief kits in the distribution centers is more effective than that in the demand points. Specifically, the average cost and 95% percentile of the former are 19.45% and 20.52% lower than those of the latter respectively. The vehicle loading capacity has a greater influence on the optimal solution than that of the available working time. Decision makers can balance the total cost and uncertainty budget by adjusting the conservatism level under expected demand satisfaction.论文全文{anote icon="fa-download" href="http://lishuanglin.cn/blog/usr/uploads/2023/01/2835826775.pdf" type="secondary" content="论文PDF下载"/}
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