攻防世界--Time_losing

Time_losing

下载文件后打开是一大堆 txt文件

一个一个看太麻烦了 直接放kali里面一起查看一下

提示说这里面并没有flag 好吧另起思路

题目描述说

2033-05-18 11:33:20似乎是个好时间。

想到了时间戳的思路 查看一下这个时间的时间戳

时间戳在线转换地址:时间戳(Unix timestamp)转换工具 - 在线工具

再查看一下那些txt文件的时间戳

直接去看文件属性中的时间 修改时间是不对的 都到2033年去了

转换成时间戳

看到这串时间戳 比题目描述的时间戳多了88

多了88? 这代表啥呢

看了一眼ASCII表

88对应了X 那会不会每个文件的时间戳都会和源时间戳有一个差值 而且这差值都对应一个字符 连起来就是flag

ok先按照这个思路试试看

写个脚本 脚本代码如下

import os,time  #查找处理文件要调用os库 先用import 导入
​
oldtime=2000000000
a=""
​
for i in range(0,47): #有0~46个txt文件 就是循环47次
   file = r"C:\Users\12275\Desktop\stego\{0}.txt".format(i)  #文件路径
   newtime = int(os.path.getmtime(file))  #获取最近修改的时间
   s = newtime - oldtime
   a = a+chr(s)   #将差值转为ASCII码
print(a)

脚本详细编写过程可以参考文章: python小记--攻防世界Time_losing解题脚本编写_tzyyyyyy的博客-优快云博客

运行一下

flag出现

XMan{seems_to_be_related_to_the_special_guests}

### Retrieval-Augmented Generation (RAG) in NLP #### Definition of RAG Retrieval-Augmented Generation combines the strengths of retrieval-based models with generative models to improve conversational systems' performance. Traditional retrieval methods excel at finding relevant information but lack flexibility when generating responses that require synthesis or creativity. Generative models can produce novel text but may suffer from hallucinations—generating content not grounded in factual knowledge. By integrating both approaches, RAG leverages external databases or corpora as a source of evidence during generation, ensuring outputs are more accurate and contextually appropriate while maintaining natural language fluency[^1]. #### Implementation Details The architecture typically consists of two main components: - **Retriever**: Responsible for fetching documents most pertinent to user queries using techniques like dense passage retrieval. ```python class Retriever: def __init__(self): pass def retrieve(self, query): # Implement document search logic here pass ``` - **Generator**: Utilizes retrieved contexts alongside input prompts to craft coherent replies via transformer architectures such as BART or T5. ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer class Generator: def __init__(self): self.tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large") self.model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large") def generate(self, prompt, context): inputs = self.tokenizer(prompt + " " + context, return_tensors="pt", max_length=512, truncation=True) output_ids = self.model.generate(inputs["input_ids"]) response = self.tokenizer.decode(output_ids[0], skip_special_tokens=True) return response ``` To enhance traditional RAG further, Graph RAG introduces graph structures into the mix, allowing better representation of relationships between entities within stored knowledge bases compared to vector representations alone[^3]. This approach facilitates richer contextual understanding across diverse domains including healthcare, finance, etc., where interconnected data points play crucial roles. #### Use Cases One prominent application area lies in customer service automation through virtual assistants capable of providing precise answers based on vast amounts of structured/unstructured textual resources without losing personal touch[^4]. Another potential field is legal research assistance; lawyers could benefit greatly by having access to case law summaries generated dynamically according to specific needs rather than manually sifting through countless precedents. --related questions-- 1. How does Cross-Attention mechanism contribute to improving RAG's effectiveness? 2. What challenges might one encounter when implementing custom retrievers tailored towards specialized industries? 3. Can you provide examples illustrating how Graph RAG outperforms conventional RAG implementations regarding entity relationship handling? 4. In what ways has pre-training large-scale language models impacted advancements made within this domain over recent years?
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