可以检测到网站的安全,是否存在漏洞等
代码出处:http://www.haoservice.com/docs/19 无 {"resultcode":"0","reason":"Successed!", "result":{ "state":1, "webstate":1, /*网站安全等级 0:安全 1:警告 2:严重 3:危险 其他:未知*/ "msg":"警告", /*网站安全等级说明*/ "data":{ "loudong":{ /*漏
代码出处:http://www.haoservice.com/docs/19
{ "resultcode":"0", "reason":"Successed!", "result":{ "state":1, "webstate":1, /*网站安全等级 0:安全 1:警告 2:严重 3:危险 其他:未知*/ "msg":"警告", /*网站安全等级说明*/ "data":{ "loudong":{ /*漏洞*/ "high":"0", /*高危漏洞*/ "mid":"0", /*严重漏洞*/ "low":"3", /*警告漏洞*/ "info":"9" /*提醒漏洞*/ }, "guama":{ "level":0, /*0说明正常*/ "msg":"没有挂马或恶意内容" }, "xujia":{ "level":0, "msg":"不是虚假或欺诈网站" }, "cuangai":{ "level":0, "msg":"未篡改" }, "pangzhu":{ "level":0, "msg":"没有旁注" }, "score":{ "score":85, "msg":"安全等级打败了全国77%的网站!但略有瑕疵,离五星神站就差一步啦!" }, "google":{ "level":0, "msg":"没有google搜索屏蔽" } } } }

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Today I would like to introduce to you an article published by MIT last week, using GPT-3.5-turbo to solve the problem of time series anomaly detection, and initially verifying the effectiveness of LLM in time series anomaly detection. There is no finetune in the whole process, and GPT-3.5-turbo is used directly for anomaly detection. The core of this article is how to convert time series into input that can be recognized by GPT-3.5-turbo, and how to design prompts or pipelines to let LLM solve the anomaly detection task. Let me introduce this work to you in detail. Image paper title: Largelanguagemodelscanbezero-shotanomalydete

01 Outlook Summary Currently, it is difficult to achieve an appropriate balance between detection efficiency and detection results. We have developed an enhanced YOLOv5 algorithm for target detection in high-resolution optical remote sensing images, using multi-layer feature pyramids, multi-detection head strategies and hybrid attention modules to improve the effect of the target detection network in optical remote sensing images. According to the SIMD data set, the mAP of the new algorithm is 2.2% better than YOLOv5 and 8.48% better than YOLOX, achieving a better balance between detection results and speed. 02 Background & Motivation With the rapid development of remote sensing technology, high-resolution optical remote sensing images have been used to describe many objects on the earth’s surface, including aircraft, cars, buildings, etc. Object detection in the interpretation of remote sensing images

Since the launch of ChatGLM-6B on March 14, 2023, the GLM series models have received widespread attention and recognition. Especially after ChatGLM3-6B was open sourced, developers are full of expectations for the fourth-generation model launched by Zhipu AI. This expectation has finally been fully satisfied with the release of GLM-4-9B. The birth of GLM-4-9B In order to give small models (10B and below) more powerful capabilities, the GLM technical team launched this new fourth-generation GLM series open source model: GLM-4-9B after nearly half a year of exploration. This model greatly compresses the model size while ensuring accuracy, and has faster inference speed and higher efficiency. The GLM technical team’s exploration has not

When implementing machine learning algorithms in C++, security considerations are critical, including data privacy, model tampering, and input validation. Best practices include adopting secure libraries, minimizing permissions, using sandboxes, and continuous monitoring. The practical case demonstrates the use of the Botan library to encrypt and decrypt the CNN model to ensure safe training and prediction.

Produced by 51CTO technology stack (WeChat ID: blog51cto) Mistral released its first code model Codestral-22B! What’s crazy about this model is not only that it’s trained on over 80 programming languages, including Swift, etc. that many code models ignore. Their speeds are not exactly the same. It is required to write a "publish/subscribe" system using Go language. The GPT-4o here is being output, and Codestral is handing in the paper so fast that it’s hard to see! Since the model has just been launched, it has not yet been publicly tested. But according to the person in charge of Mistral, Codestral is currently the best-performing open source code model. Friends who are interested in the picture can move to: - Hug the face: https

To protect your Struts2 application, you can use the following security configurations: Disable unused features Enable content type checking Validate input Enable security tokens Prevent CSRF attacks Use RBAC to restrict role-based access

In the security comparison between Slim and Phalcon in PHP micro-frameworks, Phalcon has built-in security features such as CSRF and XSS protection, form validation, etc., while Slim lacks out-of-the-box security features and requires manual implementation of security measures. For security-critical applications, Phalcon offers more comprehensive protection and is the better choice.

SHIB coin is no longer unfamiliar to investors. It is a conceptual token of the same type as Dogecoin. With the development of the market, SHIB’s current market value has ranked 12th. It can be seen that the SHIB market is hot and attracts countless investments. investors participate in investment. In the past, there have been frequent transactions and wallet security incidents in the market. Many investors have been worried about the storage problem of SHIB. They wonder which wallet is safer for SHIB coins at the moment? According to market data analysis, the relatively safe wallets are mainly OKXWeb3Wallet, imToken, and MetaMask wallets, which will be relatively safe. Next, the editor will talk about them in detail. Which wallet is safer for SHIB coins? At present, SHIB coins are placed on OKXWe
