Critique Paper title: A Novel Proof-of-Reputation Consensus for Storage Allocation in Edge Blockchain Summary of the Paper This article proposes a novel proof-of-reputation consensus for storage allocation The article mainly analyzes the three aspects of reputation mechanism, storage allocation algorithm and , and each node can calculate the global reputation of all nodes by aggregating personal reputations. Finally, the article designs a PoR blockchain through the reputation mechanism and storage allocation
reputation mechanisms. In our simulations, all nodes use a simple personal reputation mechanism. It generates a new block and updates the global reputation. reputation and global reputation. Peng, “Proof of reputation: A reputation based consensus protocol for peer-to-peer network,” in International
PoR needs a reputation mechanism as a foundation, and we have already proposed a reputation mechanism The reputation mechanism allows nodes to evaluate the personal reputation of others and generates the global reputation of each node. of reputation updates. The node calculates new personal reputations through local personal reputation records and reputation
We build a novel Proof-of-Reputation (PoR) blockchain to support consensus on the reputation mechanism To reach a consensus, every node obtains the same global reputation from the same personal reputation With the help of our reputation mechanism, we build a novel Proof-of-Reputation (PoR) blockchain to maintain Bad-mouthing attack: Attackers use false reputation feedback to cause deviations in the reputation evaluation nodes low reputation ratings.
REPUTATION MECHANISM We introduce our reputation mechanism in this section. According to different sources of evaluators, our reputation mechanism includes the personal reputation and global reputation. personal reputation and global reputation. A simple graph of relationships of global reputation, personal reputation, and data exchange.
全球领先的在线声誉管理公司Reputation.com(为数百家知名品牌提供服务)近期发生重大数据泄露事件,1.2 亿条包含后端系统数据的记录被曝光,其中涉及的会话 Cookie 可能导致客户社交媒体账号遭滥用 01 核心事件概要泄露规模庞大Reputation.com意外暴露了 320GB 的日志文件,内含 1.2 亿条记录,涵盖后端系统数据及会话 Cookie 等关键信息。 该服务器属于Reputation.com,作为全球领先的在线声誉管理(ORM)与客户体验平台,Reputation.com的合作方涵盖众多《财富》500 强企业及行业头部品牌。 服务器搭载的数据可视化与探索工具,本用于帮助企业处理大规模数据,却因防护缺失,直接暴露了Reputation.com多个应用的核心日志。 Reputation.com作为专注于 “信誉管理” 的企业,此次却因自身数据安全漏洞陷入信任危机。这一事件也为所有企业敲响警钟:在数字化时代,“保护他人声誉” 的前提,是先筑牢自身的数据安全防线。
[叙事视角]:一篇硬核算法Paper的工程落地解析(关于信誉权重的二次重塑)[技术栈]:组合D(Rerank重排与规则引擎+ElasticsearchPainless打分脚本)[生僻指标]:NDCG-Reputation =doc['aiso_reputation_index'].value;//医疗GEO核心:信誉越高的节点,其得分衰减越慢reputation_weight+=geo_score*0.35;}//4.惩罚项 :针对营销感过强的文档进行降权if(doc['content_style'].value=='marketing_heavy'){reputation_weight-=0.2;}//最终得分=原始分*( 生产环境下的NDGC-Reputation压测我们爱搜光年将该重排方案部署于某连锁眼科集团的AI导诊系统中,重点观测其在极端复杂查询下的表现。 (↓)1.560.34语义与医学逻辑的偏差显著收敛长尾分布截断损失(↓)0.280.06专家级内容不再被算法由于热度低而误伤压测结果显示,NDCG-Reputation(信誉增益灵敏度)翻了一倍以上。
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/reputation.data */5 * * * * cd && wget https://myip.ms/files/blacklist/general/latest_blacklist.txt Mike Date:2019.8.5 """ 打开文件 c2=open("c2-ipmasterlist.txt","r") bl=open("latest_blacklist.txt","r") reputation =open("reputation.data","a") 以行来分开下载的数据 for line in c2: ip = line.split(',') reputation.write(ip [0]+ ",c2\n") for line in bl: ip = line.split() try: reputation.write(ip[0]+ "\n") except: import mmap 打开IP收录文件,检测流量中的IP是否在黑名单中 file = open("reputation.data") IP ='207.241.231.146' s = mmap.mmap
信誉等级设定(Reputation) 我们在QQ群里根据活跃度或者答题多少有不同的等级描述,比如潜水,冒泡,活跃等等。 勾选'Enable setup and display of reputation levels'以后点击save; ? 3. 设置Reputation Levels / Reputation Points. Reputation Level可以设置当前的community在不同的分数展示不同的等级,Reputation Points可以设置怎样才可以得分,得几分,比如回答问题得5分,写帖子得2分等等。
select 2 as user_id, 250 as total_spend, 11 as reputation_level union all select 3 as user_id, 250 reputation_level union all select 11 as user_id, 1000 as total_spend, 22 as reputation_level union 22 as reputation_level ) select user_id , rank() over(partition by reputation_level order , 11 as reputation_level union all select 11 as user_id, 1000 as total_spend, 22 as reputation_level ,total_spend ,reputation_level ,first_value(total_spend) over (partition by reputation_level
INT , comments : INT , favorites : INT , updatedAt , body users . csv userId : ID ( User ) , name , reputation create index on : User ( name ) ; create index on : User ( createdAt ) ; create index on : User ( reputation : HAS_TAG ] - ( : Tag { tagId : "neo4j" } ) WHERE u . name like "Mark % " RETURN u . name , u . reputation 如果您只想在4.5M用户的子集上执行此操作,则可以添加过滤条件,例如在reputation上。 MATCH ( u : User ) WHERE u . reputation > 20000 MATCH ( u ) - [ : POSTED ] - > ( question ) - [ :
Reputation声望在2-100K之间。(注:StackOverflow的用户Reputation是得到社会认可的,在面试和招聘中是硬通货币。 能力和年龄分布图 然后,计算每个人每个月的Reputation,这样可以找到这个用户的真正的活跃时间,这样便于计算这个程序员的真实能力。 (总声望 / 活跃时间),可以得到他平均每个月得来的Reputation。 我们来看看程序员的能力和年龄段的分布图:(你可能会大吃一惊) ?
Reputation声望在2-100K之间。(注:StackOverflow的用户Reputation是得到社会认可的,在面试和招聘中是硬通货币。 能力和年龄分布图 然后,计算每个人每个月的Reputation,这样可以找到这个用户的真正的活跃时间,这样便于计算这个程序员的真实能力。 (总声望 / 活跃时间),可以得到他平均每个月得来的Reputation。 我们来看看程序员的能力和年龄段的分布图:(你可能会大吃一惊) ?
Reputation声望在2-100K之间。(注:StackOverflow的用户Reputation是得到社会认可的,在面试和招聘中是硬通货币。 能力和年龄分布图 然后,计算每个人每个月的Reputation,这样可以找到这个用户的真正的活跃时间,这样便于计算这个程序员的真实能力。 (总声望 / 活跃时间),可以得到他平均每个月得来的Reputation。 我们来看看程序员的能力和年龄段的分布图:(你可能会大吃一惊) ?
': 'https://api.threatbook.cn/v3/scene/ip_reputation' } def _request(self, url, params={ (self, ioc): """Getting reputation IP""" url = self.urls['reputation'] params ioc = '8.8.8.8' r = threat.get_reputation(ioc) # compromise ioc = 'zzv.no-ip.info' ': predicate = 'Reputation' elif self.service == 'compromise': predicate { "name": "ThreatBook_Reputation", "version": "1.0", "author": "Canon", "url": "https
Reputation 声望在2-100K之间。(注:StackOverflow的用户 Reputation 是得到社会认可的,在面试和招聘中是硬通货币。 能力和年龄分布图 然后,计算每个人每个月的 Reputation,这样可以找到这个用户的真正的活跃时间,这样便于计算这个程序员的真实能力。 (总声望 / 活跃时间),可以得到他平均每个月得来的 Reputation。
# 示例代码:品牌声誉分析from transformers import pipeline# 使用Hugging Face的BERT进行品牌声誉分析brand_reputation_nlp = pipeline reputation_results = [brand_reputation_nlp(mention) for mention in brand_mentions]print("品牌声誉分析结果:", reputation_results)对品牌声誉的了解有助于企业更灵活地调整营销策略和改进产品。
Reputation声望在2-100K之间。(注:StackOverflow的用户Reputation是得到社会认可的,在面试和招聘中是硬通货币。 年龄分布图 下面我们来看一下他们的年龄分布图:我们可以看到程序员年纪的正态分布(高点在25岁左右,但是中点在29岁左右) 能力和年龄分布图 然后,计算每个人每个月的Reputation,这样可以找到这个用户的真正的活跃时间 (总声望 / 活跃时间),可以得到他平均每个月得来的Reputation。
QS的排名参考的四大尺度: Academic reputation(学术声誉):全球学术界学者对各大学的学术显示评价 Employer reputation(雇主声誉):全球雇主对各大学的毕业生评价 Research