Vô hiệu hóa cảnh báo Error Reporting cho Windows
Cảnh báo Error Reporting là một cảnh báo được thiết lập theo mặc định và luôn gây cảm giác khó chịu cho người dùng khi muốn tiến hành sửa chữa hoặc cài đặt một ứng dụng nào đó trên máy tính bởi nó sẽ yêu cầu người dùng gửi thông tin đến Microsoft. Và để cho việc sử dụng máy tính một cách thuận lợi hơn, việc vô hiệu hóa cản báo Error Reporting là một việc khá cần thiết.
The delivery of a packet to a host or a router requires two levels of addressing: logical and physical. We need to be able to map a logical address to its corresponding
physical address and vice versa. This can be done by using either static or dynamic mapping.
Loại bỏ hộp thoại Error Reporting trong windows XP Trên Windows XP khi một ứng dụng nào đó gặp lỗi đã và sẽ chuẩn bị đóng lại, Hệ điều hành sẽ đưa ra một "bố cáo" về lỗi vừa xảy ra và hỏi bạn có muốn gửi báo cáo lỗi này cho Microsoft hay không, sau đó lại hỏi bạn có muốn cập nhật vá lỗi hay không.
Nếu bạn đồng ý cập nhật thì có khi sau khi Update xong lỗi còn nặng hơn và có thể treo cả máy tính.
Aims of the study: Common errors of reported speech made by grade 11 students at Doc Binh Kieu high school, Kien Giang province to find out grade 11 students common errors in using reported speech; to suggest some solutions to help the students avoid these errors.
Metabolizable energy (ME) energy is used to denote the energy value of feed for poultry. ME can be expressed as values of apparent metabolizable energy (AME). AME is used to determine the exchange energy food for birds. Vietnam Nehring methods used to estimate the AME value of the raw material fed to chickens. This method is usually the error is large and ...
This paper reports on the recognition component of an intelligent tutoring system that is designed to help foreign language speakers learn standard English. The system models the grammar of the learner, with this instantiation of the system tailored to signers of American Sign Language (ASL). We discuss the theoretical motivations for the system, various difficulties that have been encountered in the implementation, as well as the methods we have used to overcome these problems. Our method of capturing ungrammaticalities involves using malrules (also called 'error productions'). ...
As a consequence of the increasing importance of tritium resulting
from nuclear fission and neutron activation, from its use in accelerators,
from its use in research and industry, and from its use in the
investigation of the environment and its distribution in the environment,
the NCRP designated a scientific committee to prepare a report
on the currently acceptable methods of measuring tritium. This
report is particularly aimed in assisting an individual to select a
procedure suitable to the problem at hand....
Nowadays, digital terrain models (DTM) are an important source of spatial data for various applications in many scientific disciplines. Therefore, special attention is given to their main characteristic ‐ accuracy. At it is well known, the source data for DTM creation contributes a large amount of errors, including gross errors, to the final product.
The search in patent databases is a risky business compared to the search in other domains. A single document that is relevant but overlooked during a patent search can turn into an expensive proposition. While recent research engages in specialized models and algorithms to improve the effectiveness of patent retrieval, we bring another aspect into focus: the detection and exploitation of patent inconsistencies. In particular, we analyze spelling errors in the assignee ﬁeld of patents granted by the United States Patent & Trademark Ofﬁce.
This paper proposes a method of correcting annotation errors in a treebank. By using a synchronous grammar, the method transforms parse trees containing annotation errors into the ones whose errors are corrected. The synchronous grammar is automatically induced from the treebank. We report an experimental result of applying our method to the Penn Treebank.
This work introduces a new approach to checking treebank consistency. Derivation trees based on a variant of Tree Adjoining Grammar are used to compare the annotation of word sequences based on their structural similarity. This overcomes the problems of earlier approaches based on using strings of words rather than tree structure to identify the appropriate contexts for comparison. We report on the result of applying this approach to the Penn Arabic Treebank and how this approach leads to high precision of error detection. ...
Faced with the problem of annotation errors in part-of-speech (POS) annotated corpora, we develop a method for automatically correcting such errors. Building on top of a successful error detection method, we ﬁrst try correcting a corpus using two off-the-shelf POS taggers, based on the idea that they enforce consistency; with this, we ﬁnd some improvement. After some discussion of the tagging process, we alter the tagging model to better account for problematic tagging distinctions.
We evaluate measures of contextual ﬁtness on the task of detecting real-word spelling errors. For that purpose, we extract naturally occurring errors and their contexts from the Wikipedia revision history. We show that such natural errors are better suited for evaluation than the previously used artiﬁcially created errors. In particular, the precision of statistical methods has been largely over-estimated, while the precision of knowledge-based approaches has been under-estimated.
In this paper, we propose a practical method to detect Japanese homophone errors in Japanese texts. It is very important to detect homophone errors in Japanese revision systems because Japanese texts suffer from homophone errors frequently. In order to detect homophone errors, we have only to solve the homophone problem. We can use the decision list to do it because the homophone problem is equivalent to the word sense disambiguation problem.
The Constituent Likelihood Automatic Word-tagging System (CLAWS) was originally designed for the low-level grammatical analysis of the million-word LOB Corpus of English text samples. CLAWS does not attempt a full parse, but uses a firat-order Markov model of language to assign word-class labels to words. CLAWS can be modified to detect grammatical errors, essentially by flagging unlikely word-class transitions in the input text.
High-quality quantitative data generated under standardized conditions is
critical for understanding dynamic cellular processes. We report strategies
for error reduction, and algorithms for automated data processing and for
establishing the widely used techniques of immunoprecipitation and immu-noblotting as highly precise methods for the quantification of protein levels
A collection of 3208 reported errors of Chinese words were analyzed. Among which, 7.2% involved rarely used character, and 98.4% were assigned common classifications of their causes by human subjects. In particular, 80% of the errors observed in writings of middle school students were related to the pronunciations and 30% were related to the compositions of words. Experimental results show that using intuitive Web-based statistics helped us capture only about 75% of these errors.
We propose a new method for detecting errors in "gold-standard" part-ofspeech annotation. The approach locates errors with high precision based on n-grams occurring in the corpus with multiple taggings. Two further techniques, closed-class analysis and finitestate tagging guide patterns, are discussed. The success of the three approaches is illustrated for the Wall Street Journal corpus as part of the Penn Treebank.
Building on work detecting errors in dependency annotation, we set out to correct local dependency errors. To do this, we outline the properties of annotation errors that make the task challenging and their existence problematic for learning. For the task, we deﬁne a feature-based model that explicitly accounts for non-relations between words, and then use ambiguities from one model to constrain a second, more relaxed model. In this way, we are successfully able to correct many errors, in a way which is potentially applicable to dependency parsing more generally. ...
This paper presents a conditional random ﬁeld-based approach for identifying speaker-produced disﬂuencies (i.e. if and where they occur) in spontaneous speech transcripts. We emphasize false start regions, which are often missed in current disﬂuency identiﬁcation approaches as they lack lexical or structural similarity to the speech immediately following. We ﬁnd that combining lexical, syntactic, and language model-related features with the output of a state-of-the-art disﬂuency identiﬁcation system improves overall word-level identiﬁcation of these and other errors. ...