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2.虽然网络类型的划分标准各种各样,但是从地理范围划分是一种大家都认可的通用网络划分标准。按这种标准可以把各种网络类型划分为局域网、城域网、广域网和互联网四种。局域网(Local Area Network,LAN)通常我们常见的“LAN”就是指局域网,这是我们最常见、应用最广的一种网络。局域网随着整个计算机网络技术的发展和提高得到了充分的应用和普及,几乎每个单位都有自己的局域网,甚至有的家庭中都有自己的小型局域网。很明显,所谓局域网,就是在局部地区范围内的网络,它所覆盖的地区范围较小。局域网在计算机数量配置上没有太多的限制,少的可以只有两台,多的可达几百台。城域网(Metropolitan Area Network,MAN)这种网络一般来说是在一个城市,不在同一地理小区范围内的计算机互联。这种网络的连接距离可以在10~100公里,它采用的是IEEE 802.6标准。城域网多采用ATM技术做骨网。ATM是一种用于数据、语音、视频以及多媒体应用程序的高速网络传输方法。ATM包括一个接口和一个协议,该协议能够在一个常规的传输信道上,在比特率不变及变化的通信量之间进行切换。广域网(Wide Area Network,WAN)这种网络也称为远程网,所覆盖的范围比城域网(MAN)更广,它一般是在不同城市之间的LAN或者MAN网络互联,地理范围可从几百公里到几千公里。

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2. Many recent models in software engineering introduced deep neural models based on the Transformer architecture or use transformer based Pre-trained Language Models (PLM) trained on code. Although these models achieve the state of the arts results in many downstream tasks such as code summarization and bug detection, they are based on Transformer and PLM, which are mainly studied in the Natural Language Processing (NLP) field. The current studies rely on the reasoning and practices from NLP for these models in code, despite the differences between natural languages and programming languages. There is also limited literature on explaining how code is modeled.Here, we investigate the attention behavior of PLM on code and compare it with natural language. We pre-trained BERT, a Transformer based PLM, on code and explored what kind of information it learns, both semantic and syntactic. We run several experiments to analyze the attention values of code constructs on each other and what BERT learns in each layer. Our analyses show that BERT pays more attention to syntactic entities, specifically identifiers and separators, in contrast to the most attended token [CLS] in NLP. This observation motivated us to leverage identifiers to represent the code sequence instead of the [CLS] token when used for code clone detection. Our results show that employing embeddings from identifiers increases the performance of BERT by 605% and 4% F1-score in its lower layers and the upper layers, respectively. When identifiers’ embeddings are used in CodeBERT, a code-based PLM, the performance is improved by 21%-24% in the Fl-score of clone detection. The findings can benefit the research community by using code-specific representations instead of applying the common embeddings used in NLP, and open new directions for developing smaller models with similar performance.

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Ideally, people would like to know when an earthquake is going to happen and how bad it will be. People have long believed that earthquakes can be forecast. In Japan, scientists have wired the Earth and sea to detect movements. The Chinese have traditionally watched animals and plants for warning signs of earthquakes. (1) Certain birds, like pigeons, also seemed to be especially noisy, and were reported to be flying in unusual patterns before the earthquake. Perhaps most interesting, and most easily measured, is a chemical change in ground water before a quake. Experimental data seem to indicate that the amount of radon (氡) in the water under the surface of the Earth waxes before an earthquake.Every family needs to have earthquake emergency plans. How will family members leave the area during the chaos following and earthquake? Everyone should agree on a meeting point outside the area—perhaps in a town several miles away. (2) Also important is an arrangement for family member to communicate if there is an earthquake in case that an earthquake happens in a large city, many of the telephone lines within the city are likely to be down. The few remaining working lines will be busy with the calls that naturally occur after a disaster and it will be difficult to call from one part of the city to another. It might, however, be possible to call outside the city. (3) A sensible arrangement is to have all of the members of the family call to check in with a friend or relative who lives more than a hundred miles away.(4) Although scientists still cannot predict earthquakes, they are learning a great deal about how the large plates in the earth’s crust move, the stresses between plates, how earthquakes work, and the general probability that a given place will have an earthquake. Someday soon it may actually become possible to predict earthquakes with accuracy. (5) However, even if prediction becomes possible, people who live in areas where earthquakes are a common occurrence will still have to do their best to prevent disasters by building structures that are resistant to ground movement and by being personally prepared. These precautions can make a great difference in saving lives and preventing the loss of homes. Education concerning bow to survive an earthquake should be a major emphasis for all government programs and earthquake-related research projects.

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