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Before 581 ad thanks to a small farming population, there was no severe soil erosion in the middle basin, and the lower river was quiescent. 30 flood events of which most were “overspill” events causing little harm. During the periods of Sui, Tang, and Five Dynasties (581-960 ad), the farming population in the middle basin recovered and exceeded that of the Western Han Dynasty. Numerous trees in the middle basin were cut down for farming, settlement, fuel, and the construction of palaces and temples. Grasslands in the middle basin were well preserved as ranches until the mid Tang Dynasty, owning to the exceptionally prosperous horse administration in that period. As the Tang Empire declined after the An-Shi Rebellion (755-763 ad), so did these ranches. During the late Tang and Five Dynasties, as the annexation of land intensified, more and more farmers lost their lands. The states then let the abandoned ranches be reclaimed by farmers. For the lower Yellow River, the first levee breach of the Eastern Han River was recorded in 598. After that floods became frequent.
Thus, I will address them together. I began this chapter by identifying values related to intended outcomes of IndieWeb software. Autonomy and self-empowerment are presented as overarching goals, defined and selected from community members’ long held perspectives of the Web, and further articulated in principles for building. 10. Have fun-specify that these values are defined by individuals controlling what data they put online. The overall strategy for this is to use one’s own website instead of relying on a third-party platform. Clearly, this is a fundamental design decision that defines IndieWeb’s overall approach. These values are also articulated through principles about processes for building, with autonomy being particularly salient. 4. Use what you make), and to start building right away rather than waiting for consensus. 10, respectively), with interoperability to be figured out after individuals have been free to develop a variety of approaches. Here, autonomy is employed as a value for defining processes of building. These process-oriented and outcome-oriented principles are closely related, and in fact co-constitute one another.
Click on Block Sender. Outlook can then add this sender's email address to the blocked list and filter out any future messages from them. Junk Email Preferences. Go to the Blocked Senders tab, click on the specific email address, then choose Remove Selected Sender. Make sure the email address of the person you want to block is in the Contacts app. If it isn’t, you need to make an entry for this sender. Threading options. Tap Blocked Sender Options. Click on Blocked, then choose Add New. Tap on the contact you created for the unwanted email sender. Enable mail in iCloud so it syncs this preference across all your Apple devices. Open an email from the unwanted contact. click through the up coming internet page on the name of the sender. TapFrom in the header and choose Block This Contact. Blocked. Find the name of the blocked sender and hit Unblock. Using multi-factor or two-factor authentication means even if a phishing attack compromises your username and password, hackers won't be able to overcome the additional authentication requirements tied to your account. Your email service provider may have its own filter, but using it with anti-spam software can provide an additional layer of cybersecurity. This is because emails travel through two spam filters before they reach your inbox. So if junk gets through one spam filter, the other should catch it. Look for an anti-spam filter that works with your email provider. Suppose you do fall victim to a spam email by clicking on a malicious link or inadvertently downloading malware. In that case, a good antivirus solution such as Kaspersky Total Security will recognize the malware and prevent it from damaging your device or network.
You’ll find two new directories, training and testing. These folders contain the decoded files. Now, we can train our filter by using the labels from the SPAMTrain.label file provided. Files with a label of 0 are spam, while those with a label of 1 are ham. The training might take some time. Now that you’ve trained the filter, it’s now time to test it. Feel free to test this on a few messages. You’ll find that the filter gets it right most of the time. If we train it on a more comprehensive dataset, the results would improve. In addition, you can also play around with the threshold value of 0.5 in predict() and see if it improves things. To keep things easy to understand, we’ve kept our spam filter simple. However, we could add a variety of bells and whistles to make it more accurate. For example, in an actual implementation, we could assume a higher value of if the email originates from an IP address known for sending out spam prolifically.
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