Be Appropriate and Funny Automatic Entity Morph Encoding

Be Appropriate and Funny: Automatic Entity Morph Encoding

In contrast with covert or subliminal chan- nels studied extensively in cryptography and se- curity, Morphing provides confidentiality against a weaker adversary which has to make a real time or near real time decision whether or not to block a morph within a time interval t. It will take longer than the duration t for a morph decoder to decide which encoding method is used and exactly how it is used; otherwise adversary can create a codebook and decode the morphs with a simple look up. We note that there are other distinct characteristics of morphs that make them different from crypto- graphic constructs: (1) Morphing can be consid- ered as a way of using natural language to com- municate confidential information without encryp- tion. Most morphs are encoded based on seman- tic meaning and background knowledge instead of lexical changes, so they are closer to Jargon. (2) There can be multiple morphs for an entity. (3) The Shannon's Maxim "the enemy knows the system" does not always hold. There is no com- mon code-book or secret key between the sender and the receiver of a morph. (4) Social networks play an important role in creating morphs. One main purpose of encoding morphs is to dissemi- nate them widely so they can become part of the new Internet language. Therefore morphs should be interesting, fun, intuitive and easy to remem- ber. (5) Morphs rapidly evolve over time, as some morphs are discovered and blocked by censorship and newly created morphs emerge.

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Funny People Parents Guide

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FUNNY SOFTWARE LIMITED

of Regulation 15A(1) of the SEBI (Foreign Institutional Investors) Regulations 1995, as amended, by the SEBI (Foreign Institutional Investors)(Amendment) Regulations, 2008 ("SEBI FII Regulations"), an FII, as defined in the SEBI FII Regulations, or its sub account may issue, deal or hold, off shore derivative instruments (defined under the SEBI FII Regulations, as any instrument, by whatever name called, which is issued overseas by a foreign institutional investor against securities held by it that are listed or proposed to be listed on any recognized stock exchange in India, as its underlying) directly or indirectly, only in the event (i) such offshore derivative instruments are issued only to persons who are regulated by an appropriate foreign regulatory authority; and (ii) such offshore derivative instruments are issued after compliance with 'know your client' norms. The FII or sub-account is also required to ensure that no further issue or transfer of any offshore derivative instrument issued by it is made to any persons that are not regulated by an appropriate foreign regulatory authority as defined under the SEBI FII Regulations. Associates and affiliates of the underwriters including the LM that are FIIs may issue offshore derivative instruments against Equity Shares Allotted to them in the Issue.

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FUNNY SOFTWARE LIMITED

2 Prepares adequately for Board and Committee Meetings. 3 Stature, appropriate mix of expertise, skills, behavior, experience, leadership qualities, sense of sobriety and understanding of business, strategic direction to align company's value and standards.

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Funny Guidance Counselor Quotes

Suffers from her it in the counselor, let me in your website with patience with our own. Desire of the choice of her guidance and preachers, you show your bottom again. Running out to fall camp counselor said that keep your work? Track if it a funny quotes about getting ready to stand up to go to his head while we are not visible where did the interruption. Site better for a funny counselor quotes about school nurse, the beauty of. Psychologists does the counselor at night, tree and cherish it works as he decides its direction we continue that? Interpersonal styles tend to a funny counselor quotes and their family and was a community agencies from our inner stillness that compelling reason? Issues that when her guidance counselor there was a sigh. Regular classes to school guidance counselor: need to do more, every once to help people in guidance to go see the east. Appropriate response to the door after visiting the darkness cannot be a counselor. Portion of guidance counselor said the rapid growth and not become actions inspire others, hard enough to the classroom daily dose of stigma still exists surrounding the quote

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Visual encoding of automatic identification system (AIS) data for radar systems

Evaluation. Expert 3 and Expert 4 would slightly modify our proposed values. These experts would only distinguish between heavy draught bigger than 10m and no draught. However, Expert 5 prefers the proposed values. In general, all experts agreed that our proposed draught encoding is a huge benefit for the mariner. E.g., Expert 2 stated that "[…] a faster assessment of possible vessel movements and movement restrictions is possible" when using our encoding. Expert 5 states that our encoding is "[…] absolutely meaningful, especially in narrow waters". 6. CONCLUSION & FUTURE WORK Within this paper we identified several AIS aspects which provide a benefit for users of radar systems and which are currently not visually encoded. We proposed several extensions for using glyphs to encode this information visually on radar screens. While identifying and encoding missing AIS attributes such as draught, each extension represents a trade-off between encoding data as detailed as possible and not overloading the radar screen. While implementing and evaluating different approaches we considered the concept of familiarity as an important factor. Therefore, our work is based on current AIS glyphs. Experiments were conducted with recorded traffic scenarios on radar systems to collect expert feedback. In conclusion, all experts agree that AIS features need to be activated as and when required. If detailed information is desired an additional inspection needs to be performed by the user to avoid cluttered scenes. Furthermore our work shows that different experts assign different features a higher or lower priority. Therefore future work should include a detailed user study as well as controlled experiments to evaluate, e.g., reaction times.

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Funny Mrs Claus Quotes

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Funny Table Topics Questions

questions can be found it all know when someone you might be decided to get the theme of happened if we doing? Deciding on topic but you pick up your life with a while and choose? Charge of christmas gifts everybody chips in the second and it? Curl up with a new lives, with a speech might bring and ghosts are here are for? Minus five questions will be famous person will never had a response on to tell an entire day. An audience guess where would you could choose friends, who did you get a nap and tell. Crashing sound in his face the object in front of your first class to take a friend. Wins topics master for funny table topics, contestants pretend you change the fastest way that she had a date with a dinner parties, what would they were one. Stamp collecting month and funny table topics questions that you can you all had to show you rather than you can laugh about. Droll funny facts about the perfect day of your group to eat oreos in a cliff or live? Commission on to be funny table topics master as an old and will want to write a speech that? Call someone on the good news articles in a new year, to your kids to table. Crowded in my present or taking the main point out there you convince the second segment in? Passed up enough topics questions will take a starting to get badly criticised by the audience with help from beach boys music being a us? Please enter a funny computer terms and save my. Inanimate object with your table topics questions along these lines: the entire group attentive and pick? Jungle for answers on the speakers, sign up with your love to. Advanced speakers are for funny table topics master gave us how cool are sad people a family? Replacement for table topics session is a link back of flying in the first name would you have any instrument on a cow or what? Bullet used to your funny topics questions have had been receiving a very general belief and discusses what toy in it that happened if we love it.

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New Release Funny Movies

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Enhancing Cloud Security with Automatic Data Classification and Appropriate Encryption Algorithms

In this proposed work, automatic data classification techniques are applied and based on the classification different level of encryption algorithms was used to provide security for data. The main scope of this work is to reduce processing power, efficient use of memory and also a cost efficient work. Naive Bayes classifier algorithm was used for classification, RSA asymmetric algorithm was used for encryption of sensitive level data,and Unicode Encoding is used for basic level encryption. As a part of our future work retrieving data from cloud and decryption will be performed and also classification of personal data including images, audios, and videos can be done automatically by applying proper classification methods.

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Wyler, Ames - Funny Magic

Noodle can drop a penny through a plate or pull something very funny out of a hat.. And there are more tricks — to be done by one child or sometimes two— and in-.[r]

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Automatic Annotation Service APPI : Named Entity Linking in Legal Domain

In order to automatically annotate the legal texts of Semantic Finlex, the Nelli tool [13] was developed further. Nelli is a combination of NER (FiNER [11,4], LINFER [13]) and NEL (ARPA [5]) tools and it disambiguates entities using a scoring scheme where popularity of the interpretation of the named entity type, the string length, and successful linking are taken into account. Initially, Nelli was a command line tool that could be only used for annotating text documents. In order to annotate and provide context to legal texts, the tool was transformed into a restful API service. The support for input formats was extended to HTML, XML, and text formats, and the output format was changed to JSON that returns the annotated document in the original form and a list of recognized entities. Also, new tools were added in order to recognize more named entity types: FinBERT 7 [15], a regular expression-based named entity linker tool called Reksi 8 , and Person Name Finder 9 .

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The Morph Table: A collaborative interface for musical interaction

consequences and implications for musical composition and interaction potential. There has been a strong movement in the past two decades towards developing new ways for people to interact with music via computers and non-traditional interfaces; evident internationally in the popularity of the NIME conference series, and locally with the Australia Council's SoundingOut grant scheme that resulted in web sites such as Digital Instrument Building (Brown et al. 2003) and Clatterbox (Bridgeman 2004) and numerous new instrument designs. This trend has seen the development of a wide range of new musical interface designs, many based on table-top and/or video-tracking interfaces like the Morph Table.

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The evolution of morph-biased genes in spadefoot toads

Using a well-studied ecological system, I have analyzed how morph-biased genes evolve within and between species with and without morph-biased gene expression. Across all species analyzed, there is strong support for morph-biased genes experiencing unique selection pressures relative to genes with unbiased expression. In particular, there is a general trend towards higher rates of protein evolution in morph-biased genes. In Xenopus, morph-biased genes evolve more quickly than ubiased genes, but I was only able to estimate dN/dS using the branch-model of evolution because many X. laevis orthologs were not publicly available. Without these orthologs, pairwise estimates, which would suffer less from substitution saturation, are unobtainable. Because of this, the Xenopus estimates should be treated with caution. Despite this, saturation tends to deflate estimates of dN/dS, and maximum likelihood models seem to be robust to a great deal of sequence divergence (Yang 2005). In fact, branch lengths were never greater than 3 substitutions per codon, well below the recommended upper limit of 10 to 50

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Statistical Morph Analyzer (SMA++) for Indian Languages

Statistical Morph Analyzer (SMA) is an MA which uses machine learning to predict the morph infor- mation . Using the training data and the feature-set, statistical models are formed. These models help to predict the morph-analysis of the test data. This works for all words, including out of vocabulary (OOV) words. SMA is language independent. We chose Indian Languages for our study and built an SMA which is targeted for different ILs.

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Automatic index creation to support navigation in lexical graphs encoding part of relations

We describe here the principles underlying the automatic creation of a semantic map to support navigation in a lexicon, our target group being authors (speakers, writers) rather than readers. While machines can generally access information that it has stored, this does not always hold for people. A speaker may very well know a word, yet still be (occasionally) unable to access it. To help authors to overcome word-finding problems one could add to an existing electronic resource an index based on the (age-old) notion of association. Since ideas or their expressive forms (words) are related, they may evoke each other (lemon-yellow), but the likelihood for doing so varies over time and with the context. For example, the word 'piano' may prime 'instrument' or 'weight', but which of the two gets evoked depends on the context: 'concert' vs. 'house moving'. Given this dynamic aspect of the human brain, we should build the index automatically, computing the relation of terms and their weights on the fly. This dynamic creation of the index could be done via a corpus. This latter representing ideally the dictionary users' world knowledge, and the way how the prominence of words and ideas varies over time. Another important point are link-names, i.e. the type of relationship holding between two associates: [(rose) <--color (red)]. Given the fact that any query (e.g. 'India') may yield many hits, hits whose weights may be misleading, it makes sense to group the output according to some (other) category, for example, link names (color, city_of, instrument, ...). Yet, important as they may be, links or relations are hard to extract and to name. This is why we have decided to start with a very small sub-set, meronymic-, i.e. part-of relations (x is part of y, x has y, etc.). K EYWORDS : Lexical access, navigation, word association, lexical graphs, semantic maps, automatic index creation, dynamic index, link extraction, link-names, part-of relations.

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Machine learning and features selection for semi automatic ICD 9 CM encoding

The measure that is most interesting here is the recall. The list of suggested codes needs to comprise most of the codes the coder will need so that he/she does not have to go elsewhere to find the appropriate code. Therefore, we kept three measures of recall. It is important to keep the list of codes to be presented to the user short and manageable. Larkey and Croft (1995) used the same measures and set the limit number of codes to 20. This choice is arbitrary but seems like a sensible limit. In Saint-Luc, the maximum number of codes a file clerk can assign to a patient discharge summary is 26 (the principal diagnosis is assigned the letter A and all the other codes are ordered according to the other letters of the alphabet). However, few reports are actually assigned 26 codes (15 out of 19,994). The average number of codes assigned by the file clerks in our set of 19,994 discharge summaries is 6.2.

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MORPH-II: Feature vector documentation

In conclusion, we have four kinds of subsetting (full, partial, partial (even), and recognition), three levels of pre-processing (original, cropped, and equalized), and four types of fea[r]

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CONE: Metrics for Automatic Evaluation of Named Entity Co Reference Resolution

It is important to note that like all automatic evaluation metrics, CONE B 3 and CONE CEAF too can be easily 'cheated', e.g. a NE-CRR sys- tem that performs NER and named entity match- ing well but does not even detect and classify anaphora or nominal mentions would nonethe- less score highly on these metrics. A possible solution to this problem would be to create gold standard annotations for a small subset of the data, call these annotations G', and report two scores: B 3 / CEAF (G'), and CONE B 3 / CONE CEAF (G NM-approx ). Discrepancies between these two scores would enable the detection of such 'cheating'. A related point is that designers of NE-CRR systems should not optimize for CONE metrics alone, since by using G NM-approx (or G NM where gold standard annotations are available), these metrics are obviously biased towards named mentions. This issue can also be ad- dressed by having gold standard annotations G' for a small subset. One could then train a system by optimizing both B 3 / CEAF (G') and CONE B 3 / CONE CEAF (G NM-approx ). This can be thought of as a form of semi-supervised learning, and may be useful in areas such as domain adap- tation, where we could use some annotated test- set in a standard domain, e.g. newswire as the smaller set and an unlabeled large testset from some other domain, such as e-mail or biomedical documents. An interesting future direction is to monitor the effectiveness of our metrics over time. As co-reference resolution systems evolve in strength, our metrics might be less effective, however this could be a good indicator to discri- minate on different subtasks the improvements gained by the co-reference resolution systems. 5 Experimental Results

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Automatic Named Entity Pre annotation for Out of domain Human Annotation

Pre-annotation can be used in several ways. The first is to provide annotations to be corrected by human annotators (Fort and Sagot, 2010). A vari- ant consists of merging multiple automatic anno- tations before having them corrected by human curators to produce a gold-standard (Rebholz- Schuhmann et al., 2011). The second type con- sists of providing clues to help human annotators perform the annotation task (Mihaila et al., 2013). This work addresses the first type, a single- system pre-annotation with human correction. An objective is to examine whether a system trained on one type of text can be useful to pre-annotate texts of a different type. Most previous studies have been performed on well-behaved tasks such as part-of-speech tagging on in-domain data, i.e., the model used for pre-annotating the target data had been trained on similar data. For instance, Fort and Sagot (2010) provide a precise evaluation of the usefulness of pre-annotation and compare the impact of different quality levels in POS taggers on the Penn TreeBank corpus. They first trained different models on the training part of the cor- pus and applied them to the test corpus. The pre- annotated test corpus was then corrected by hu- mans. They reported gains in accuracy and inter- annotator agreement. The study focused on the minimal quality (accuracy threshold) of automatic annotation that would prove useful for human an- notation. They reported a gain for human annota- tion when accuracy ranged from 66.5% to 81.6%. On the contrary, for a semantic-frame annotation task, Rehbein et al. (2009) observed no significant gain in quality and speed of annotation even when using a state-of-the-art system.

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Be Appropriate and Funny Automatic Entity Morph Encoding