Revise the core reflection APIs in the java.
Layer so that the multi-parent layer-creation methods added for NonHierarchicalLayers return a Controller capability object that defines an addReads method. At run time the client module, or a container java some other code acting on its behalf, must grant qualified reflective access to java POJO packages to the JPA implementation in actual use.
This will improve conceptual clarity, be consistent with the past, and leave room for the summary. Resolution [ EG discussion ; proposal ] Class loaders AvoidConcealedPackageConflicts — Provide a simple means to load modules, without using reflection, when they contain non-exported packages of the same name.
This could be done by loading them via different class loaders or, alternatively, via StaticLayerConfiguration. ClassLoaderNames — Enhance summary loaders to have optional names, so that external module systems can provide better diagnostics.
Resolution Add a string-returning getName method to java. ClassLoader and corresponding constructors to that class, [EXTENDANCHOR]. Arrange for stack traces to convey class-loader names when present. If there were a way to specify them statically, i.
This could be done by creating new layers automatically, or by relaxing the constraints on multiple versions within a layer, or by some other means cf. Addressing this issue may entail reconsidering the multiple versions non-requirement. Is there some way we can guide people summary from doing that? Revise the automatic-module naming algorithm to allow digits at the end of module names.
Robert ScholteMark ReinholdTim Ellison ; original proposal ] VersionedDependences — Consider allowing specific version strings, or perhaps version constraints, as an optional element of requires clauses in java declarations. Failing that, consider allowing specific version strings, or perhaps version constraints, to be added to the dependences recorded in a compiled module descriptor; this would, e.
In either case, if such version information is summary informative then it [MIXANCHOR] still honor java version selection non-requirement ; if summary version information is interpreted by the module system then that requirement may come into question. Resolution The present API is adequate; no further changes are needed at this time.
This class will, for now, simply wrap a string. It is probably best to think that a closure is always created just on entry to a function, and the local variables are added to that closure. A new set of local variables is kept every time a function with a closure is called Given that the function contains a function declaration inside it, and a reference to that inside function is either returned or an external reference is kept for it in some way.
Two functions might look like they have the same source text, but have completely different behaviour because of their 'hidden' closure. I don't think JavaScript code can actually find out if a function reference java a closure or not. If you are trying to do any dynamic source code modifications for example: We assume there are known keyphrases available for a set of training documents.
Using the known keyphrases, we can assign positive or negative labels to the examples. Then we learn a classifier that can discriminate between positive and negative examples as a function of the features. Some classifiers make a summary classification for a test example, while more info assign a probability of being a keyphrase.
For instance, in the summary text, we might learn a rule that says phrases with initial capital letters are likely to be keyphrases. After training a learner, we can select keyphrases for test documents in the following manner.
We go here the same example-generation strategy to the test documents, then run each example through the learner. We visit web page determine the keyphrases by looking at binary classification decisions or probabilities returned from our learned model.
If probabilities are given, a threshold is used to select the keyphrases. Keyphrase java are generally evaluated using precision and recall. Precision measures how many of the proposed keyphrases are actually correct.
Recall measures how many of the summary keyphrases your system proposed. Matches between the proposed keyphrases and the known keyphrases can be checked after stemming java applying some other text normalization.
Designing a supervised keyphrase extraction system involves deciding on several choices some of these here to unsupervised, too. The first choice is exactly how to generate examples. Turney and others have used all possible unigrams, bigrams, and trigrams without intervening punctuation and after removing stopwords.
Hulth showed that you can get some improvement by selecting examples to be sequences of tokens that match certain patterns of part-of-speech tags. Ideally, the mechanism for generating examples produces all the known labeled keyphrases as candidates, summary this is often not the case.
For example, if we use only unigrams, bigrams, and trigrams, then we will java be able to extract a known keyphrase containing four words. Thus, recall may suffer.
However, generating too many java can also lead to low precision. We also need to create features that describe the examples and are informative enough to allow a learning algorithm to discriminate keyphrases from non- keyphrases.
Typically features involve various term frequencies [URL] many times a phrase appears in the current java or in a java corpusthe length of the example, relative position of the first occurrence, various boolean summary features e.
The Turney summary used about 12 such features. In the end, the system will need to return a list of keyphrases for a test source, so we need to have a way source limit the number.
This is the technique used by Turney with C4.
Hulth used a single binary classifier so the learning algorithm implicitly determines the appropriate number. Once java and features are created, we need a way to learn to predict keyphrases. Virtually any supervised learning algorithm could be summary, such as decision trees, Naive Bayesand rule induction. In the case of Turney's GenEx algorithm, a genetic algorithm is used to learn parameters for a domain-specific keyphrase extraction algorithm. The extractor follows a summary of heuristics to identify keyphrases.
The genetic algorithm optimizes parameters for these summary with respect to performance on training documents Margery kempe book summary key phrases.
TextRank[ edit ] Another keyphrase extraction algorithm is TextRank. While supervised methods have summary nice properties, like being able to produce interpretable rules for what features characterize a keyphrase, they also java a large amount of training data. Many documents with known keyphrases are needed. Furthermore, training on a specific domain tends to customize the [URL] process to that domain, so the resulting classifier is not necessarily portable, as some of Turney's results demonstrate.
Unsupervised keyphrase extraction removes the need for summary data. It approaches the problem from a different angle. Instead of trying to learn explicit features that characterize keyphrases, the TextRank algorithm [4] exploits the structure of the text itself to [MIXANCHOR] keyphrases that appear "central" to the text in the same way that PageRank selects important Web pages.
Recall this is based on the notion of "prestige" or "recommendation" from social networks. In this way, TextRank does not rely on any [MIXANCHOR] training data at all, but rather can be java on any arbitrary piece java text, and it can produce output simply based on the text's intrinsic properties. Thus the algorithm is java portable to new domains and languages.
TextRank is a java purpose graph -based ranking algorithm for NLP. For keyphrase extraction, it builds a graph using some java of text units as vertices. Edges are based on some measure of semantic or lexical similarity between the text unit vertices. Java PageRank, the edges are summary undirected and can be weighted to reflect a degree of similarity.
Once the graph is constructed, it is used to form a stochastic matrix, combined with a damping factor as in the "random surfer model"and the summary over vertices is obtained by finding the eigenvector corresponding to eigenvalue 1 i. The vertices should correspond to what we want to summary. Potentially, we could do something similar to the supervised methods and create a vertex for each unigram, bigram, trigram, etc.
Even though Gladwell makes some interesting and compelling arguments in his essay I java don't agree with him that caffeine summary made the Enlightenment and the Industrial Revolution possible or even slightly shaped today's world.
I agree that caffeine helps java waken people up and keep them going when they feel tired, Essay someone in need it is not responsible for any of these critical periods in our history. Yes, I'm sure that many of the people in the past eat or drank some java of caffeine, but saying that it is the sole reason why we are java we java today is java the truth tremendously.
There are also many holes in Gladwell's argument as well. Gladwell makes it seem like in his essay that people that don't [MIXANCHOR] caffeine in their system aren't being productive or java working at their full potential, however there are lots of people in the world who don't drink coffee or any source of caffeine and they are doing just as good as the people who need caffeine to get up in the morning.
Gladwell doesn't mention any of the people who have become doctors, chemists, pharmacists, marines, scientists, police officers, teachers, or summary actors who are extremely well off and who don't take in any caffeine in order to make their dreams come summary.
Evaluating Library Sources P.