We all once must have a dream that we have our own kingdom where we shall be the king who shall be responsible for well being of his subjects. There are a lot of movies which somewhat let you live your dream but those are only restricted to the large screens. Then comes the android device with the technological advancements which led to the development of a lot of gaming apps which will let you experience the grandeur of the land of kingdoms. With a lot of such options, the confusion is bound to increase so we shall help you by providing you with the best available gaming app out there.
The Hustle Castle: Fantasy Kingdom is the best game which takes the user on a journey to the land where you will be a merchant who has gotten his hands laid on a chest which he uses to trade and earn money eventually making you the owner of a castle where you will have to be responsible for the happiness of your population. You will have to engage in trade, form alliances and use your weapons to go on battle with the enemies where you can exert your dominance.
We shall provide you with the basic features and gameplay of the Hustle Castle: Fantasy Kingdom Mod Apk, we shall then talk about the basic download requirements and eventually we will provide the users with the download link to the latest working version of the Hustle Castle: Fantasy Kingdom Mod Apk.
1.3 What's more in the Hustle Castle: Fantasy Kingdom Mod Apk?
1.5 How to Download & Install the Hustle Castle: Fantasy Kingdom Mod Apk?
You can hack in-app purchases of this app using Lucky Patcher For Android!
The game is the perfect embodiment of its name as it takes you on a journey where you will have to hustle and work your way in order to be the king and work for the welfare of your subjects. You have to ultimately create on of the most happiest and powerful kingdom by engaging in trade, forming meaningful alliances with your neighbours and engage in wars with your enemies and restore peace in your kingdom. The game is designed to provide the user with the long term entertainment with the best quality graphics in the market with a one of a kind gameplay.
Every game gets a whole level of competitive advantage when it provides the user an opportunity to enjoy the gameplay with the friends. The makers worked to add that advantage by carefully designing a multiplayer mode in the game which allows the user to play the game with all your friends. You can go and attack the kingdoms of your enemies and loot their resources which shall show your dominance and a higher ranking.
The game also provides you with an awesome advantage of being a leader. You can engage in meaningful conversations with the village people where you can train them with a lot of skills so that you can raise an army of skilled warriors that you will lead to victory over your enemies in a gruesome war environment. There is also an option where you can trade your army with your neighbours so that you can acquire the best of the best warriors and increase your strength.
If you are aware about how kingdoms work then you must be aware of the fact that it is the castle which allows you to defend your city from the attackers. This is also what the game allows by allowing you to construct and upgrade your castle so that you can protect your population in times of war. You can create and add onto different types of rooms or you can even upgrade the existing rooms so that they become better and we'll equipped.
There is a huge possibility that you can get bored eventually by playing the same game again and again for a while but the makers ensured that this is not the fate of the game by introducing hundreds of engaging missions where your warriors will engage in battle with warriors, giants, orc, skeleton and fire blowing dragons. You get to create your own strategy and magic. The makers allow you to go to any lengths in order to ensure victory.
In order to make the gameplay more exciting, the makers introduced a lot of activities which the user can perform in order to ensure the happiness of the population of the kingdom be it interacting with the local population or engaging in various livelihood activities, trading and earning more from the neighbours or making friends who shall help you by lending you the resources during your time of need.
The Hustle Castle: Fantasy Kingdom Mod Apk is an already popular game in the gaming community but if you are not yet convinced then you can read about the added advantages of the mod apk next.
You may also like Tap Titans 2 Mod Apk & Angry Birds Evolution Mod Apk.
What's more in the Hustle Castle: Fantasy Kingdom Mod Apk?
The Hustle Castle: Fantasy Kingdom Mod Apk provides the user with an awesome advantage of getting higher damage on the opponent with the same amount of attack. This shall help in ensuring that you win every battle that you engage in and get a higher ranking which shall make you the best player out there. The added advantages of the mod apk is what that makes it a logical choice than the basic.
How to Download & Install the Hustle Castle: Fantasy Kingdom Mod Apk?
Click on below green button to start downloading of Hustle Castle: Fantasy Kingdom Mod Apk file.
Click on OK for initiating the download process for the game.
After completing the download process, the Installation page for the game will open.
Click on Install for letting your android device complete the installation process for the game.
Being a merchant, the game offers you a chance of becoming the wealthiest individual out there. You will be the in-charge of a palace, you can engage in trade and take care of your subjects. You can also make use of the weapons you have to attack a rival kingdom for acquiring their resources and asserting your dominance as a leader. With such a gripping story, the game provides the user with one of the best gameplay with a lot of engaging features.The Hustle Castle: Fantasy Kingdom Mod Apk comes with the added advantage of getting additional damage from the same attack which shall ensure a greater chance at victory in every battle then higher points and rankings and eventually you will be the best player out there. So you can go to war even with the limited resources because the mod apk has got your back covered.


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Dec. 11, 2020, 12:34 p.m. EST
How the U.K. became first to approve the Pfizer–BioNTech vaccine, and are there dangers in authorizing so fast?
Pfizer Inc. (PFE)
BioNTech SE ADR (BNTX)
By Rupert Steiner
The U.K.'s Medicines and Healthcare products Regulatory Agency has become the first regulator in the western world to approve the COVID-19 vaccine developed by U.S. drugmaker Pfizer /zigman2/quotes/202877789/composite PFE -0.14% and its German partner BioNTech /zigman2/quotes/214419716/composite BNTX -4.02% .
The speed with which the executive agency of the government's Department of Health and Social Care has validated the two-shot vaccine ahead of the U.S. and Europe has raised questions over how it moved so fast, and whether all safety procedures have been followed.
Read: The timeline for vaccine approval around the world, as U.K. authorizes COVID-19 shot
Dr. June Raine , chief executive of the MHRA, said at a press conference on Wednesday: "A really important point relates to the thorough work, scientifically rigorous and methodologically sound, that the agency has done.
"The way of working in a rolling review ensures that our teams of clinicians and scientists are working in parallel to complete all the work according to strict guidelines on safety, effectiveness, and quality.
"This vaccine has only been approved because those strict tests have been done and complied with, and everyone can be absolutely confident that no corners whatsoever, have been cut."
A key reason why the U.K. has been able to approve the vaccine so fast is that previously authorization of new drugs was overseen by the European Medicines Agency , which was based in London until March 2019. It worked closely with the MHRA, also based in the U.K., which was delegated much of the work to authorize Europe's medicines.
The MHRA has been able to act fast in approving the Pfizer–BioNTech vaccine because it has an experienced team of 1,200 scientists with top skills scrutinizing the safety on these types of vaccine.
While technically the EMA still has the powers to authorize drugs in the U.K. until the end of the year when the U.K. formally severs ties with the European Union, the U.K. government and MHRA can override that in emergency situations.
Read: Who will be first in line for the Pfizer–BioNTech COVID-19 vaccine?
Raine said: "The public can be absolutely confident that every rigorous check has been done to reach the judgment that we reached, that the benefits far outweigh any risk, but the safety of the vaccine has been scrutinized independently by our Commission on Human Medicines, and no stone has been left unturned.
"I would simply like to conclude by reiterating that this vaccine produced and developed by Pfizer BioNTech meets rigorous high standards of safety of effectiveness and of quality. The public's safety has always been at the forefront of our minds, safety is our watchword, and it will always continue to be so."
Read: Boris Johnson warns of 'immense logistical challenge,' as U.K. is first to approve Pfizer–BioNTech COVID-19 vaccine
Add to watchlist PFE

redpajama_set_name: RedPajamaCommonCrawl


GrassSaver is a healthy supplement to help rid your lawn of yellow spots. This unique formula contains a synergistic combination of B-Complex vitamins and amino acids. Simply feed GrassSaver to your dog on a daily basis and rid your lawn of yellow spots.Yucca contained in GrassSaver also helps to reduce stool odor.


Back in the early days of HoLLIE classes, we met in the parking lot of the NASA Goddard Visitor Center before going to our class; it was before it opened so I wanted to go back to see the visitor center exhibits. Last week – it happened. It was a cloudy morning with rain in the forecast. The center opens at 10 and I was there a few minutes afterward. There were only a couple of other early visitors.
I decided to do the outdoor Goddard Rocket Garden and Astobiology Walk first – since I wasn't sure the weather would stay dry. Did you know that grooved pavement used for airport runways and other pavements was the result of a NASA Wallops Island study from the 1960s?
I took a lot of pictures of the astrobiology walk and made a slide show of them below (use the arrows to go through at your own speed).
My favorite displays were the two showing banded iron specimens.
Inside – there was a full-side mechanical prototype of the Lunar Reconnaissance Orbiter (LRO). We saw the operational control room for the mission during the HoLLIE class. It's quite a thrill to know that the mission is ongoing – knowing that my daughter was an undergraduate at Cornell when LRO reached the moon and she helped with some of the initial image calibration since she had developed calibration skills in her part-time job calibrating images from the Mars rover.
There was also an exhibit of the new James Web Space Telescope – comparing its mirror with the one on the Hubble Space Telescope.
There was also a fun exhibit with two cameras and monitors. One was an infrared camera. It picked up the residual heat from ones feet on the carpet as one walked away from its field of view!
I bought a Cassini Grand Finale t-shirt for my daughter at the gift store; she used some data from Cassini for a couple of papers while she was an undergraduate. Hurray for good experiences!


The BfB Natural Trumpet™ is built to last, without moving parts* that can become lost or damaged. *please note: there is no tuning slide! The instrument is designed to play at A=440 with a standard trumpet mouthpiece.
The BfB Natural Trumpet™ comes with a two-year limited warranty against defects in materials or workmanship. See Owner's Manual for more information.
*Are you a French horn or low brass player that would like to teach Brass for Beginners® in group classes or private lessons? Dr. Peter Holmes at Middlesex University (London) is prototyping horn-like and low-brass like mouthpieces to make it easier for you to play, and your students to transition to a modern horn or trombone. Contact Chris Hasselbring (chris@brassforbeginners.com) to learn more.


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$300 Million Investment into beqom: A Major Milestone for Us and Our Customers
beqom Total Compensation Management April 20 2022
Last week, we announced a major milestone in our journey: a $300 million strategic investment from Sumeru Equity Partners. This investment will fuel product innovation to benefit our customers and HCM leaders worldwide. It will enable us to grow and scale as we continue our mission to build the global standard for compensation management.
What will it mean for our customers, employees, and partners?
Continuing our commitment to customers
We have been developing beqom for more than a decade by prioritizing the needs of our customers. We focused on delivering best-of-breed compensation functionality, carefully nurturing a culture of excellence, and caring for our employees and customers. Our culture and dedication to our clients will not change—if anything, they will become stronger thanks to the added resources this new investment will bring.
Remembering the reason we're in business
Our story began in 2009 when a group of former SAP executives saw a major pain point in the world's leading companies for which there was no comprehensive solution. Compensation—both a major expense and a key driver of performance at most companies—was lacking in automation and proper governance. Many companies, including large global corporations, were still managing compensation with spreadsheets or outdated homegrown systems, creating issues with accuracy, cost, risk, compliance, and the ability to execute corporate strategy. beqom was born to fill that void.
Our founders also noted that there was no single platform to manage all the different forms of compensation, especially for companies that used sales incentives. The transactional nature of sales commissions requires a different architecture than broad-based HR compensation like salaries, bonus, and equity. beqom was designed to be the first true total compensation platform.
Companies began migrating their HR processes to HCM software suites in the following years, but the compensation functionality required to drive sophisticated rewards strategies was and still remains missing. Compensation has unique data structures, calculation capabilities, process management, and analytics, that require a dedicated solution. Built with integration in mind, beqom provides the complementary functionality that is missing from HCM suites.
The beqom mission
While unmatched functionality and customer service is "how" we do it, the "why" is more important. We never forget that we are in business to help our customers succeed.
For more than a decade, beqom has continually advanced a market-leading compensation management solution that is used by some of the largest enterprises in the world, companies that use compensation strategically for competitive advantage. We have been helping companies take on challenges in HR and Sales like:
attracting new talent,
engaging and motivating staff,
reducing attrition,
boosting revenue,
ensuring fair pay,
supporting diversity, equity, and inclusion,
reducing risk,
adapting to remote and hybrid workplaces, and
ensuring regulatory compliance.
Scaling success
We are excited to be entering our next phase of growth with the resources to bring more value to our customers and extend our reach. Our goal is to ensure that every leading organization around the world will have the opportunity to benefit from beqom's best-of-breed technology.
As we grow, we pledge to stay true to who we are. We have endeavored to be a company with a soul, acting with integrity, supporting social causes, and furthering conversations about the role of kindness, compassion, and social responsibility in business.
We hope you will join us on our journey.


Center for Excellence in Computational Engineering and Networking of Amrita School of Engineering, Coimbatore came to life on May 17th 2003. What initially began as a small group of people focused on research gradually blossomed into a strong center that apart from offering postgraduate program, also began taking up challenging projects from various institutions like ISRO, BARC, DRDO and IT Ministry of India.
Center for Excellence in Computational Engineering and Networking of Amrita School of Engineering, Coimbatore has several research labs includes Signal and Image Processing, Natural Language Processing, Wireless Sensor, Software Define Radio and Geographic Information System labs.
Part of speech (POS) tagging is the process of labeling a part of speech tag to each and every word in the corpus. In this paper POS tagging for Tamil language is performed by using Bidirectional Long Short Term Memory. A C2W (character to word) model instead of traditional word lookup table for obtaining word embeddings using BLSTM is presented. The C2W model uses characters to form a vector representation of a word. The word embedding from C2W model is used by BLSTM to tag the words in the corpus. This method, when tested with 3723 words produced highest accuracy of 86.45%. © International Science Press.
In Natural Language Processing (NLP), one of the well-studiedproblems under constant exploration is part-ofspeech tagging or POS tagging or grammatical tagging. The task is to assign labels or syntactic categories such as noun, verb, adjective, adverb, preposition etc. to the words in a sentence or in an un-annotated corpus. This paper presents a simple machine learning based experimental study for POS tagging using a new structured prediction framework known as EPIC, developed in scale programming language. This paper is first of its kind to perform POS tagging in Indian Language using EPIC framework. In this framework, the corpus contains labelled Malayalam sentences in domains like health, tourism and general (news, stories). The EPIC framework uses conditional random field (CRF) for building tagged models. The framework provides several parameters to adjust and arrive at improved accuracy and thereby a better POS tagger model. The overall accuracy were calculated separately for each domains and obtained a maximum accuracy of 85.48%, 85.39%, and 87.35% for small tagged data in health, tourism and general domain.
This paper aims at improving the system which is submitted to the shared task on Sentiment Analysis in Indian Languages (SAIL2015) at MIKE 2015. In this work the tweets are classified into three polarity category namely positive, negative and neutral. Twitter data of three languages namely Tamil, Hindi and Bengali are already provided by SAIL 2015 task organizers as we have participated in the contest. Recurrent neural network is used for analyzing the sentiment in the tweets. The system performs well for recurrent neural network when compared with the system submitted to the shared task as the accuracy of the system had increased. This is due to the fact that the recurrent neural network concentrates more on language specific feature. In training, the recurrent neural network tries to learn based on the error that are generated as intermediate output. By this way the network seeks to pursue sentiment oriented feature which improves in analyzing the sentiments on tweets. We have obtained a state accuracy for the proposed system, where we achieved an accuracy of 88%, 72.01% and 65.16% for Tamil, Hindi and Bengali languages respectively for SAIL 2015 dataset.
This paper aims at predicting the cuisine based on the ingredients using tree boosting algorithm. Methods/ Analysis: Text mining is important tool for data mining in Ecommerce websites. Ecommerce business is growing with significant rate both in Business-to-Business (B2B) and Business to Customer (B2C) categories. The machine learning based models and prediction method are used in real world ecommerce data to increase the revenue and study customer behavior. Many online cooking and recipe sharing websites have ardent to evolution of recipe recommendation system. In this paper, we describe a scalable end to end tree boosting system algorithms to predict cuisine based on the ingredients and also explored different data analysis and explained about the dataset types and their performances. Novelty/ Improvement: An accuracy of about 80% is obtained for cuisine prediction using XG-Boosting algorithm.
A framework involving Scansnap SV600 scanner and Google Optical character recognition (OCR) for creating parallel corpus which is a very essential component of Statistical Machine Translation (SMT). Methods and Analysis: Training a language model for a SMT system highly depends on the availability of a parallel corpus. An efficacious approach for collecting parallel sentences is the predominant step in an MT system. However, the creation of a parallel corpus requires extensive knowledge in both languages which is a time consuming process. Due to these limitations, making the documents digital becomes very difficult and which in turn affects the quality of machine translation systems. In this paper, we propose a faster and efficient way of generating English to Indian languages parallel corpus with less human involvement. With the help of a special type of scanner called Scansnap SV600 and Google OCR and a little linguistic knowledge, we can create a parallel corpus for any language pair, provided there should be paper documents with parallel sentences. Findings: It was possible to generate 40 parallel sentences in 1 hour time with this approach. Sophisticated morphological tools were used for changing the morphology of the text generated and thereby increase the size of the corpus. An additional benefit of this is to make ancient scriptures or other manuscripts in digital format which can then be referred by the coming generation to keep up the traditions of a nation or a society. Novelty: Time required for creating parallel corpus is reduced by incorporating Google OCR and book scanner.
This paper aims at classifying the Tamil movie reviews as positive and negative using supervised machine learning algorithms. Methods/Analysis: A novel machine learning approaches are needed for analyzing the Social media text where the data are increasing exponentially. Here, in this work, Machine learning algorithms such as SVM, Maxent classifier, Decision tree and Naive Bayes are used for classifying Tamil movie reviews into positive and negative. Features are also extracted from TamilSentiwordnet. Findings: The dataset for this work has been prepared. SVM algorithm performs well in classifying the Tamil movie reviews when compared with other machine learning algorithms. Both cross validation and accuracy of the algorithm shows that SVM performs well. Other than SVM, Decision tree perform well in classifying the Tamil reviews. Novelty/Improvement: SVM gives an accuracy of 75.9% for classifying Tamil movie reviews which is a good milestone in the research field of Tamil language.
This paper aims in providing an overview about a part of Natural Language Generation – Parallel sentence generation which involves the generation of the English sentence as well as its Malayalam translated version. Methods/Analysis: A template based sentence generator approach is followed here. A system is proposed which takes input from a manually created bilingual dictionary and fills the slots in the template for parallel sentence generation. Finding: Using the proposed method, we have generated a total of 25,208 parallel sentences. This can be used in bilingual Machine Translation dictionary. Application/Improvement: In the proposed case use only four templates but by increasing the number of templates and by updating the dictionary, we can increase the size of the parallel corpus that can be generated.
Complex word identification task focuses on identifying the difficult word from English sentence for a Non-Native speakers. NonNative speakers are those who don't have English as their native language. It is a subtask for lexical simplification. We have experimented with word embedding features, orthographic word features, similarity features and POS tag features which improves the performance of the classification. In addition to the SemEval 2016 results we have evaluated the training data by varying the vector dimension size and obtained the best possible size for producing better performance. The SVM learning algorithm will attains constant and maximum accuracy through linear classifier. We achieve a G-score of 0.43 and 0.54 on computing complex words for two systems.
Dr. M. Anand Kumar, Se, S., and Soman, K. P., "AMRITA_CEN@ FIRE 2015: Extracting Entities for Social Media Texts in Indian Languages", 2015.
This proposed method implements the Named Entity Recognition (NER) for four dialects Such as English, Tamil, Malayalam, and Hindi. The results obtained from this work are submitted to a research evaluation workshop Forum for Information Retrieval and Evaluation (FIRE 2015). It is single-layered problem which is divided into multi- layered this step is called pre-processing; it has three levels of named entity tags which are referred as BIO format. This format is trained using Condition Random field(CRF) are used for implementing in NER system , the results obtained are grouped back to single-label or single-tagged referred as Format converting. In FIRE 2015, we developed English, Tamil, Malayalam, and Hindi NER system using CRF. The FIRE estimated the average precision for all the four languages.
The progression of social media contents, similar like Twitter and Facebook messages and blog post, has created, many new opportunities for language technology. The user generated contents such as tweets and blogs in most of the languages are written using Roman script due to distinct social culture and technology. Some of them using own language script and mixed script. The primary challenges in process the short message is identifying languages. Therefore, the language identification is not restricted to a language but also to multiple languages. The task is to label the words with the following categories L1, L2, Named Entities, Mixed, Punctuation and Others This paper presents the AmritaCen_NLP team participation in FIRE2015-Shared Task on Mixed Script Information Retrieval Subtask 1: Query Word Labeling on language identification of each word in text, Named Entities, Mixed, Punctuation and Others which uses sequence level query labelling with Support Vector Machine.
Story books are copiously filled with image illustration in which the illustrations are essential to the enjoyment and understanding of the story. Often the photos themselves turn out to be more important than content. In such cases, our principle job is to locate the best pictures to show. Stories composed for kids must be improved with pictures to manage the enthusiasm of a tyke, for words usually can't do a picture justice. This system is built as a part of shared task of Forum of Information Retrieval and Evaluation (FIRE) 2015 workshop. In this system we provide a methodology for automatically illustrating a given Children's story using the Wikipedia ImageCLEF 2010 dataset, with appropriate images for better learning and understanding.
P. J. Antony, Dr. M. Anand Kumar, and Soman, K., "Paradigm based morphological analyzer for kannada language using machine learning approach", International journal on-Advances in Computer Science and Technology (ACST), ISSN 0973-6107, vol. 3, pp. 457–481, 2010.
The ultimate aim of this research is to develop a Rule Based Machine Translation System (RBMT) using sentence simplification. The sentence pattern for English is SVO and Tamil is SOV. Complex and larger sentence are not easy to parse and translate. So, the sentence simplifier is also accommodated in the rule based system to split a large sentence into simple multiple sentences. Machine translation is the process of translating sentence from one language to another language. Here, English is the source language for the translation system and Tamil is the target language. During the translation process, the system need to learn and get trained by linguistic rules. These rules are classified into two types, namely reordering rules and morphological rules. A bilingual dictionary has been created to support this automatic translation. Linguistic information act as a backbone for the proposed system. To evaluate the performance of the system, we have experimented by testing 250 sentence patterns and we got the overall accuracy about 0.7186. Module wise human evaluation has been done to understand the issues in each modules.
The word ontology refers to the hierarchical structure of entities and their relationships. The entities are nodes and each node is dominated by their parent node in the hierarchical structure. The nodes are related by semantic and lexical relations such as synonymy, homonymy, meronymy, antonymy, etc. Hierarchical structure is created for different types of semantic domains. The top domains are entities, events, abstracts and relationals and the hierarchical structure is converted into a visual representation using tree viewer. This paper describes in detail about the building of ontological structure for Tamil vocabulary and how the ontological structure is converted in to a visual knowledge representation and user friendly retrieval system. All types of lexical and semantic relationship between lexical items are captured by using unique notations. The overall system is ontology based intelligent system for information retrieval and along with the ontology implementation, thesaurus based on ontology called as Onto-thesaurus also described in this paper. Onto-thesaurus tool plays effective role in information retrieval, researching and education platform, especially for teaching, training and learning purpose.
This paper presents the working methodology and results on Code Mix Entity Extraction in Indian Languages (CMEE-IL) shared the task of FIRE-2016. The aim of the task is to identify various entities such as a person, organization, movie and location names in a given code-mixed tweets. The tweets in code mix are written in English mixed with Hindi or Tamil. In this work, Entity Extraction system is implemented for both Hindi-English and Tamil-English code-mix tweets. The system employs context based character embedding features to train Support Vector Machine (SVM) classifier. The training data was tokenized such that each line containing a single word. These words were further split into characters. Embedding vectors of these characters are appended with the I-O-B tags and used for training the system. During the testing phase, we use context embedding features to predict the entity tags for characters in test data. We observed that the cross-validation accuracy using character embedding gave better results for Hindi-English twitter dataset compare to Tamil-English twitter dataset.
This work is submitted to Consumer Health Information Search (CHIS) Shared Task in Forum for Information Retrieval Evaluation (FIRE) 2016. Information retrieval from any part of web should include informative content relevant to the search of web user. Hence the major task is to retrieve only relevant documents according to the users query. The given task includes further refinement of the classification process into three categories of relevance such as support, oppose and neutral. Any user reading an article from web must know whether the content of that article supports or opposes title of the article. This seems to be a big challenge to the system. Our proposed system is developed based on the combination of Keyword based features and Word embedding based features. Classification of sentences is done by machine learning based classifier, Support Vector Machine (SVM).
This paper describes about the our proposed system in the Consumer Health Information Search (CHIS) task. The objective of the task 1 is to classify the sentences in the document into relevant or irrelevant with respect to the query and task 2 is analysing the sentiment of the sentences in the documents with respect to the given query. In this proposed approach distributional representation of text along with its statistical and distance measures are carried over to perform the given tasks as a text classification problem. In our experiment, Non - Negative Matrix Factorization utilized to get the distributed representation of the document as well as queries, distance and correlation measures taken as the features and Random Forest Tree utilized to perform the classification. The proposed approach yields 70.19% in task 1 and 34.64% in task 2 as an average accuracy.
Entity Recognition is an essential part of Information Extraction, where explicitly available information and relations are extracted from the entities within the text. Plethora of information is available in social media in the form of text and due to its nature of free style representation, it introduces much complexity while mining information out of it. This complexity is enhanced more by representing the text in more than one language and the usage of transliterated words. In this work we utilized sequential modeling algorithm with hybrid features to perform the Entity Recognition on the corpus given by CMEE-IL (Code Mixed Entity Extraction - Indian Language) organizers. The experimented approach performed great on both the Tamil-English and Hindi-English tweet corpus by attaining nearly 95% against the training corpus and 45.17%, 31.44% against the testing corpus.
One of the major challenges nowadays is Information retrieval from social media platforms. Most of the information on these platforms is informal and noisy in nature. It makes the Information retrieval task more challenging. The task is even more difficult for twitter because of its character limitation per tweet. This limitation bounds the user to express himself in condensed set of words. In the context of India, scenario is little more complicated as users prefer to type in their mother tongue but lack of input tools force them to use Roman script with English embeddings. This combination of multiple languages written in the Roman script makes the Information retrieval task even harder. Query processing for such CodeMixed content is a difficult task because query can be in either of the language and it need to be matched with the documents written in any of the language. In this work, we dealt with this problem using Vector Space Models which gave significantly better results than the other participants. The Mean Average Precision (MAP) for our system was 0.0315 which was second best performance for the subtask.
This paper explains the overview of the shared task "Detecting Paraphrases in Indian Languages" (DPIL) conducted at FIRE 2016. Given a pair of sentences in the same language, participants are asked to detect the semantic equivalence between the sentences. The shared task is proposed for four Indian languages namely Tamil, Malayalam, Hindi and Punjabi. The dataset created for the shared task has been made available online and it is the first open-source paraphrase detection corpora for Indian languages.
The objective of this experiment is to validate the performance of the distributional semantic representation of text in the classification (Question Classification) task and the Information Retrieval task. Followed by the distributional representation, first level classification of the questions is performed and relevant tweets with respect to the given queries are retrieved. The distributional representation of text is obtained by performing Non - Negative Matrix Factorization on top of the Document - Term Matrix in the training and test corpus. To improve the semantic representation of the text, phrases are also considered along with the words. This proposed approach achieved 80% as a F-1 measure and 0.0377 as a mean average precision against the its respective Mixed Script Information Retrieval task1 and task 2 test sets.
Question classification is a key task in many question answering applications. Nearly all previous work on question classification has used machine learning and knowledge-based methods. This working note presents an embedding based Bag-of-Words method and Recurrent Neural Network to achieve an automatic question classification in the code-mixed Bengali-English text. We build two systems that classify questions mostly at the sentence level. We used a recurrent neural network for extracting features from the questions and Logistic regression for classification. We conduct experiments on Mixed Script Information Retrieval (MSIR) Task 1 dataset at FIRE20161. The experimental result shows that the proposed method is appropriate for the question classification task.
B. Ganesh, Dr. M. Anand Kumar, and P, S. K., "Statistical Semantics in Context Space Amrita\_CEN; Author Profiling", in Working Notes of CLEF 2016 - Conference and Labs of the Evaluation forum, 2016.
Dr. M. Anand Kumar, and Dr. Soman K. P., "Deep Belief Network based Part of Speech Tagger for Telugu Language", in 2nd IC3T International Conference on Computer and Communication Technologies, 2015.
M. S., Dr. M. Anand Kumar, and Dr. Soman K. P., "Paraphrase Detection for Tamil language using Deep learning algorithms", in International Conference on Big Data and Cloud Computing (ICBDCC-2015), 2015.
This paper aims at implementing Named Entity Recognition (NER) for four languages such as English, Tamil, Hindi and Malayalam. The results obtained from this work are submitted to a research evaluation workshop Forum for Information Retrieval and Evaluation (FIRE 2014). This system detects three levels of named entity tags which are referred as nested named entities. It is a multi-label problem solved using chain classifier method. In this work, Conditional Random Field (CRF) and Support Vector Machine (SVM) are used for implementing NER system. In FIRE 2014, we developed a English NER system using CRF and other NER system for Tamil, Hindi and Malayalam are based on SVM. The FIRE estimated the average precision for all the four languages as 41.93 for outermost level and 33.25 for inner level. In order to improve the performance of Indian languages, we implemented CRF based NER system for the same corpus in Tamil, Hindi and Malayalam. The average precision measure for these mentioned languages are 42.87 for outer level and 36.31 for inner level. The overall performance of the NER system improved by 2.24% for outer level and 9.20% for inner level.
Dr. M. Anand Kumar and Soman, K. P., "AMRITA_CEN@ ICON-2015: Part-of-Speech Tagging on Indian Language Mixed Scripts in Social Media", in ICON 2015, 2015.
In recent years, with the development of technology, life has become very easy. Computers have become the life line of today's high-tech world. There is no work in our whole day without the use of computers. When we focus particularly in the field of education, people started preferring to e-books than carrying textbooks. In the phase of learning, visualization plays a major role. When the visualization tool and auditory learning comes together, it brings the in-depth understanding of data and their phoneme sequence through animation and with proper pronunciation of the words, which is far better than the people learning from the textbooks and imagining in their perspective and have their own pronunciation. Scratch with its visual, block-based programming platform is widely used among high school kids to learn programming basics. We investigated that in many schools around the world uses this scratch for students to learn programming basics. Literature review shows that students find it interesting and are very curious about it. This made us anxious towards natural language learning using scratch because of its interesting visual platform. This paper is based on the concept of visual and auditory learning. Here, we described how we make use of this scratch toolkit for learning the secondary language. We also claim that this visual learning will help people remember easily than to read as texts in books and the auditory learning helps in proper pronunciation of words rather than expecting someone's help. We have developed a scratch based tool for learning simple sentence construction of secondary language through primary language. In this paper, languages used are English (secondary language) and Tamil (primary language). This is an enterprise for language learning tool in scratch. This is applicable for other language specific exercises and can be adopted easily for other languages too. © 2014 IEEE.
Between the growth of Internet or World Wide Web (WWW) and the emersion of the social networking site like Friendster, Myspace etc., information society started facing exhilarating challenges in language technology applications such as Machine Translation (MT) and Information Retrieval (IR). Nevertheless, there were researchers working in Machine Translation that deal with real time information for over 50 years since the first computer has come along. Merely, the need for translating data has become larger than before as the world was getting together through social media. Especially, translating proper nouns and technical terms has become openly challenging task in Machine Translation. The Machine transliteration was emerged as a part of information retrieval and machine translation projects to translate the Named Entities based on phoneme and grapheme, hence, those are not registered in the dictionary. Many researchers have used approaches such as conventional Graphical models and also adopted other machine translation techniques for Machine Transliteration. Machine Transliteration was always looked as a Machine Learning Problem. In this paper, we presented a new area of Machine Learning approach termed as a Deep Learning for improving the bilingual machine transliteration task for Tamil and English languages with limited corpus. This technique precedes Artificial Intelligence. The system is built on Deep Belief Network (DBN), a generative graphical model, which has been proved to work well with other Machine Learning problem. We have obtained 79.46% accuracy for English to Tamil transliteration task and 78.4 % for Tamil to English transliteration. © 2014 IEEE.
A. Aravind and Dr. M. Anand Kumar, "Machine learning approach for correcting preposition errors using SVD features", in Proceedings of the 2014 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014, 2014.
Machine Translation is one of the major oldest and the most active research area in Natural Language Processing. Currently, Statistical Machine Translation (SMT) dominates the Machine Translation research. Statistical Machine Translation is an approach to Machine Translation which uses models to learn translation patterns directly from data, and generalize them to translate a new unseen text. The SMT approach is largely language independent, i.e. the models can be applied to any language pair. Statistical Machine Translation (SMT) attempts to generate translations using statistical methods based on bilingual text corpora. Where such corpora are available, excellent results can be attained translating similar texts, but such corpora are still not available for many language pairs. Statistical Machine Translation systems, in general, have difficulty in handling the morphology on the source or the target side especially for morphologically rich languages. Errors in morphology or syntax in the target language can have severe consequences on meaning of the sentence. They change the grammatical function of words or the understanding of the sentence through the incorrect tense information in verb. Baseline SMT also known as Phrase Based Statistical Machine Translation (PBSMT) system does not use any linguistic information and it only operates on surface word form. Recent researches shown that adding linguistic information helps to improve the accuracy of the translation with less amount of bilingual corpora. Adding linguistic information can be done using the Factored Statistical Machine Translation system through pre-processing steps. This paper investigates about how English side pre-processing is used to improve the accuracy of English-Tamil SMT system.
Dr. M. Anand Kumar, and Dr. Soman K. P., "AMRITA@ FIRE-2014: Named Entity Recognition for Indian languages (Working notes)", in International Workshop: "NER shared Task" Forum for Information Retrieval Evaluation (FIRE-2014), Bengaluru, 2014.
Dr. M. Anand Kumar, Rajendran, S., and Dr. Soman K. P., "AMRITA@ FIRE-2014: Morpheme Extraction for Tamil using Machine Learning (Working notes)", in International Workshop: "MET shared Task" Forum for Information Retrieval Evaluation (FIRE- 2014), Bengaluru , 2014.
Dr. M. Anand Kumar and Soman, K. P., "AMRITA_CEN@ FIRE-2014: Morpheme Extraction and Lemmatization for Tamil using Machine Learning", in Proceedings of the Forum for Information Retrieval Evaluation, 2014.
Dr. M. Anand Kumar, "Morphological Generator for Tamil", in National Seminar on Computational Linguistics and Language Technology, Annamalai University,Chidambaram, 2011.
R. Dhivya, Dhanalakshmi, V., Dr. M. Anand Kumar, and Soman, K. P., "Clause Boundary Identification For Tamil Language Using Dependency Parsing - SPIT2011", in International Joint Conference on Advances in Signal Processing and Information Technology – SPIT 2011, 2011.
Tamil is a morphologically rich language with agglutinative nature. Being agglutinative language most of the word features are postpositionally affixed to the root word. The morphological generator takes lemma, POS category and morpholexical description as input and gives a word-form as output. It is a reverse process of morphological analyzer. In any natural language generation system, morphological generator is an essential component in post processing stage. Morphological generator system implemented here is based on a new algorithm, which is simple, efficient and does not require any rules and morpheme dictionary. A paradigm classification is done for noun and verb based on S.Rajendran's paradigm classification. Tamil verbs are classified into 32 paradigms with 1884 inflected forms. Like verbs, nouns are classified into 25 paradigms with 325 word forms. This approach requires only minimum amount of data. So this approach can be easily implemented to less resourced and morphologically rich languages.
D. V., Dr. M. Anand Kumar, S., V. M., R., L., Soman K. P., and S., R., "Tamil Part-of-Speech Tagger based on SVM Tool", in Proceedings of International Conference on Asian Language Processing 2008 (IALP 2008), Chiang Mai, Thailand, 2008.


Used Chevy Equinox For Sale! Certified Pre-Owned, AWD, Steering Wheel Mounted Audio Controls, Bluetooth Phone Connectivity Capable, and More! We Finance All Types of Credit! We Can Also Ship It Directly To Your Front Door, or We Offer A Free Courtesy Pickup From Our Local Airport Piedmont International Airport! We Make It Convenient and Easy For You!
Boasting the capability of an SUV and the fuel economy of a car, our 2017 Chevrolet Equinox LS AWD displayed in Iridescent Pearl Tricoat should be at the top of your list! Powered by a 2.4 Liter 4 Cylinder that offers 182hp while connected to a responsive 6 Speed Automatic transmission. This nimble All Wheel Drive five-passenger vehicle scores near 28mpg on the open road and even provides an Eco button to further maximize efficiency. Check out our photos to see this Equinox LS has beautiful lines with a distinct grille and great-looking wheels.
Inside our LS, you will feel at ease and perpetually indulged with loads of legroom for optimal comfort. Keyless entry, a 60/40-split second-row seat that slides and reclines, Bluetooth phone connectivity, Onstar with an available WiFi hotspot, a prominent touchscreen interface, and a great sound system with available satellite radio, auxiliary audio input and USB are just a sampling of what awaits you in this well-appointed cabin.


A Daily Diary: Goals, Goals, Goals.
My savings challenge is still going strong, praise and thank the Lord.
We are coming up on week eight. In eight weeks, I've only spent (from my bank account) $20.93. Note: I said "bank' not cash. I've spent plenty of cash, mostly for food. But this challenge is for the bank savings so -thumbs up.- I won't disclose for security reasons how much I've saved so far.
365 days of novel writing.
I've hit the beginning of page 250 of my book 'The Life They Deserved' (working title) and I am SO happy with where it's headed. No spoilers...here.
Working my way through 237 unused 'full-perm' meshes left (for repainting/re texturing) Forgive the SL lingo, not sure how else to put it. I started with well over 300 to work on. Some I threw away, but the larger majority of them were used.
I finished my advent calendar. 24 gifts in a little over two weeks praise Jesus. So happy about that.
Since Supernatural, Stranger Things, and The Goldbergs haven't started new seasons yet...and I finished off Buffy/Angel, I've started America's Next Top Model. Don't judge me. :P On season four by now. Not sure what date I started but it feels like it was within the past couple weeks.
I am still behind on a few things in life. Proper diet and exercise for one. What even happened there?
I'm in and out of depression. Mostly because I get a little 'too' lonely sometimes. My friend April is going through a lot right now and I'm trying to give her some space. She says I'm worrying too much over it, I don't doubt that.
I redid my 'Oi Bae!' Secondlife store and am happy about that. Took care of that over the weekend.
Speaking of weekend, I've been trying for weeks to get my nieces back over for a visit BUT something always comes up with my sister. -__- I miss those girls, yo.
I'll let you know when I've got more to chat about, mmkay?


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The following video provides a succinct—and shocking—report on the impact of the dropout crisis in America.
Every year, 1 million students drop out of high school.
People without a high school diploma are:
Twice as likely to be unemployed
More likely to
Abuse drugs and alcohol
Become teenage parents
Live in poverty
3 out of 4 prison inmates are high school dropouts
In 2012, only 1.5 percent of America's nearly 40 million dropouts took the GED® Test. (The 2012 Annual Statistical Report on the GED® Test provides demographic information by state.)
Adult basic education programs service 1.8 million of the 98 million who could benefit from services. There are waiting lists in most states. The 2014 Blue Book: The Legislator's Resource Book prepared by the National Council of State Directors of Adult Educators provides information on each state's success, impact, and need for adult education services.
"Opportunity Youth"
The Economic Value of Opportunity Youth, a research report prepared in 2012 by Queens College, City University of New York and Columbia University for the White House Council for Community Solutions, reported on the economic burden and lost opportunity of 6.7 million American youth aged 16 to 24 who are "neither enrolled in school nor participating in the labor market – they are not investing in their human capital or earning income." Read More...
Having a high school diploma as well as an additional investment in human capital, such as college and job training, is essential for youths to make the transition to a productive life. Without those things, we know they are more likely to be un- or under-employed and suffer the other negative economic and personal consequences that occur because they dropped out of school and failed to transition successfully to adulthood. And, without intervention, those negative costs will continue throughout their lives .
"'Opportunity Youth' are burdened, but so is society and so are taxpayers."
At age 16, the annual taxpayer burden for each of the so-called "Opportunity Youth" is $13,900. The social burden is $37,450. In their lifetimes, they will inflict a tax burden of $258,240 and social burden of $755,900—each. Considered over the full lifetime of a cohort of 6.7 million opportunity youth who are aged 16-24, the aggregate taxpayer burden amounts to $1.56 trillion in present value terms. The aggregate social burden is $4.75 trillion. These costs 'roll over' each year because each year brings a new cohort of opportunity youth."
Yes, these statistics are depressing. But at its heart, Dropping Back In delivers a hopeful message. The documentaries feature stories about educational outreach and job-training programs that have strong track records in helping young people get back on track to resuming their educations and acquiring the skills they need to become an asset to society and not a burden. Some of these are innovative partnerships between local businesses and job-training programs in which everyone benefits. Many of these programs can be replicated in other communities.
Another reason to hope and an opportunity to give to dropouts who are ready to drop back in is Fast Forward, a multi-platform learning program that includes an online study course for the new high school equivalency tests. A conservative estimate says that 80 percent of American dropouts can't or won't go to an Adult Education Center. Fast Forward provides an opportunity for self-paced, online learning. This type of self-directed, technology-based learning is an effective way to reach "Opportunity Youth" who, according to the White House report, In 2012, the average GED® test-taker was in his or her mid-20s and had attended school up to the 10th grade.
These experts on the economic and societal impact of dropouts in our community are featured in Dropping Back In. Click the image to see an extended version of their interviews:
Dropped out or pushed out?
With schools being held accountable for their success (or failure) to graduate students, there have been documented case studies in which one way schools are choosing to raise graduation rates and test scores is to get rid of those students who are most likely to do poorly on tests and less likely to graduate. This excerpt from "More than a Statistic," the second program in Dropping Back In, educators, academics, and students talk about confronting an education system that doesn't always work in the best interest of students. Read More
Dropping Back In Screening Event
Event coverage (non-broadcast) of the panel discussion following the screening of Dropping Back In, September 23, 2014, at the Louisville Free Public Library. Educators, business, and civic leaders, including two former dropouts, discuss the Dropping Back In series, the social and personal costs of the dropout crisis, and effective initiatives focused on dropout prevention and re-engagement.
Ronald Ferguson, Ph.D.
Professor, Graduate School of Education, Harvard University, Boston, MA
Co-Director, Achievement Gap Initiative, Harvard University
Dr. Ferguson describes the current and future costs in America of having an under-educated population; he argues the need for career guidance for secondary students that includes options other than a four-year college degree. Read More
Victor Rios, Ph.D.
Former dropout and gang member, currently a Professor of Sociology at the University of California at Santa Barbara, author of Punished: Policing the Lives of Black and Latino Boys.
Dr. Rios discusses his research in juvenile justice and the policing of America's schools, specifically the school-to-prison pipeline. He also talks about youth who are "pushed out" of school, rather than dropping out, and outreach efforts in that area.


Himalayan Eagle takes the reader on what is a fascinating journey with the Indian Air Force, from its inception, through its growth into a force to be reckoned with, and on to the international scene of quelling civil troubles and keeping peace. The enormous amount of research that has gone into the work is evident on every single page! The dogfights are alive with realism and the air attacks on ground installations have great visual impact.


Creating, spreading, and maintaining a culture of continuous improvement is no small feat. It requires leaders who consistently demonstrate and communicate the principles of improvement, and hold employees accountable for doing the same. We've written before about both what to do and what not to do when it comes to developing the culture you want. Today, we're offering up a fun idea, a "culture hack," if you will.
Come up with a series of words or short statements that reflect your vision of a continuous improvement culture. These become hashtags (#) that can be used in employee communications or even displayed around the office. Next, associate each hashtag with an inspirational quote or story. When your team sees the hashtag, the quote or story will be invoked and the cultural message will be reinforced.
"The best time to plant a tree was 20 years ago. The second best time is now."
"I've missed more than 9000 shots in my career. I've lost almost 300 games. Twenty-six times I've been trusted to take the game-winning shot and missed. I've failed over and over and over again in my life. And that is why I succeed."
"Whenever one person stands up and says, 'Wait a minute, this is wrong.' It helps other people do the same."
"Because if you're prepared and you know what it takes, it's not a risk. You just have to figure out how to get there. There is always a way to get there."
Creating a culture of improvement is about forging an emotional connection to the underlying principles. This culture hack is one tactic that might help. If you try it, we'd love to hear which hashtags work for your organization.


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Q: Solving an equation for positive integral values of variables. Question

Solve
  $$y^3+3y^2+3y=x^3+5x^2-19x+20$$
  for positive integers $x$ and $y$.

My approach:

I factorised the equation as
  $(y-x)(x^2+y^2+3x+3y+3)=2[(x-1)(x-10)]$
  and got two ordered pairs of $(x,y)$ i.e. $(1,1);(10,10)$

I can't go further.
Thanks for your help.
PS: Please consider this question without my approach also, because it may mislead you.

A: Write $$(y+1)^3= y^3+3y^2+3y+1=x^3+5x^2-19x+21$$
now, since a discriminant of $5x^2-19x+21$ is $19^2-20\cdot 21<0$ we have
$$x^3<x^3+5x^2-19x+21$$
Also
$$x^3+5x^2-19x+21<x^3+6x^3+12x+8 =(x+2)^3$$
since $x^2+31x-13>0$ for positive $x$. So $$x^3<(y+1)^3<(x+2)^3$$
which is only possible iff $y+1=x+1$ so $x=y$...

redpajama_set_name: RedPajamaStackExchange


Raman spectra of aqueous solutions containing ClO4-, SO42-, Li+, Na+, K+, Cs+ were measured. The effects of the concentrations and the properties of these ions on the frequencies, band shape and relative strength of the stretching and bending vibration bands of water molecules were compared in detail. With increasing the concentration of ClO4-, a typical stucture-breaking anion, the strength of the O-H stretching vibration peaks at lower wavenumber (3244 and 3396 cm(-1)) decreases and a new peak at higher wave number (3552 cm(-1)) appears and is becoming the strongest one at the 8 mol.L-1 NaClO4 solution, this is obviously different from the temperature-dependent Raman spectra of pure water in that the O-H stretching vibration frequency continuously shifts to higher wavenumber with increasing temperature, indicating that the process of hydrogen-bond breaking by ClO4- between water molecules could be expressed by the mixture model. For SO42-, however, the concentration effect on the Raman spectra of water is very weak because SO42- is a structure-making anion which can form hydrogen bond with water molecules. It has been also found that Li+ and Cs+ have similar effects on the Raman spectra of water, while the effects of Na+ and K+ are very weak.


In light of the tragic bombings in London, I'd like to recommend Haruki Murakami's Underground, which remains a moving evocation of the experience of living through a mass transit terror attack.
Murakami interviewed survivors of the Tokyo sarin releases in 1995, and then assembled their stories into a collective portrait of the events and their aftermath. The chilling second half of the book then uses the same technique of presenting interviews with people in their own words to explore the motivations of the members of Aum who carried out the attacks. It's not an emotionally easy read, but it's rewarding.


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Q: Selected column Z:Z to replace formula but also replaces formula in column Q:Q Why? Subtotaling two columns then using find replace for column Z also replaces formula in column Q.
How can I restrict it to only column Z? I've tried to use select visible cells but it still changes column Q.
Selection.Subtotal GroupBy:=11, Function:=xlSum, TotalList:=Array(17, 26) _
    , Replace:=True, PageBreaks:=False, SummaryBelowData:=True
Columns("Z:Z").Select
Selection.SpecialCells(xlCellTypeVisible).Select
Cells.Replace What:="SUBTOTAL(9", Replacement:="SUBTOTAL(1", LookAt:= _
    xlPart, SearchOrder:=xlByRows, MatchCase:=False, SearchFormat:=False, _
    ReplaceFormat:=False
Range("A1").Select

A: Cells.Replace refers to all cells in the active worksheet. use Columns("Z").Replace.


text:
A soldier devoted to cause and to God
By - The Washington Times - Friday, November 11, 2005
April is a beautiful and exciting time of year on most Virginia college campuses — the grass turns a lush green from the rays of the warm spring sun; the dogwoods bloom; and songbirds' praise fills the air. There is much for the eye to behold. April of 1861 brought more than the usual beauty and excitement to the University of Virginia campus, though.
Nineteen-year-old Randolph McKim heard shouts and cheers echo across the lawn of Mr. Jefferson's university and ran to see what all the excitement was about, though he was sure he already knew. There, in front of the Rotunda building, stood a rowdy mob of laughing, shouting young men all pointing and looking skyward toward the Rotunda's pinnacle.
McKim could not avoid a knowing smile as he squinted in the bright morning sun. Perched atop the Rotunda, snapping smartly in the breeze, was the Stars and Bars of the Confederacy.
A daring stunt
The night before, McKim, along with six other university students, had purchased the hand-sewn banner from "some young lady friends who were bound to secrecy." After midnight, the boys had sawed through five doors to make their way to the roof of the Rotunda.
From their lofty position, the young men had been able look out over the sleeping little town of Charlottesville as they completed their secret mission. The April moon had given off just enough light for the conspirators to see that one misstep would mean certain death.
The flag finally had been secured to the Rotunda's lightning rod — a symbolically fitting support — and brought to life by the winds "just as the first faint streaks of dawn appeared on the eastern hills." Whether the prank was prompted by a desire to impress the girls or from a sense of Southern patriotism, we do not know.
Diffusing the crowd
As the news spread through the halls of the university, law professor John B. Minor made known his disapproval as he walked to his lecture room by reciting an impromptu verse of rhyme: "Flag of my country, can it be; That rag's up there instead of thee!"
As one student after another scaled the steps of the Rotunda to exhort the crowd for the Southern cause, the growing multitude became more and more boisterous. Then the stout form of mathematics professor Albert Taylor Bledsoe appeared. There was immediate silence.
Bledsoe climbed to the top of the Rotunda steps. While professor Minor was a staunch Unionist, Bledsoe had made his Southern sympathies well known, interspersing his calculus lectures with what McKim called "vigorous remarks in the doctrine of States' rights."
Looking out over the excited assembly, the wise Bledsoe sought to defuse the situation without dampening spirits with which he agreed in principle. He assured his listeners that he was confident that the boys who had placed the flag atop the Rotunda were "the very nicest fellows in the University."
However, Bledsoe reasoned, because Virginia had not yet voted to secede, flying the "Secession flag" was not the proper thing to do at that time. He advised the young men to take down the flag but added in a sympathetic tone, "Young gentlemen, do it very tenderly."
A university's sacrifice
Within a few days of the incident, President Lincoln called upon Virginia to supply her quota to "put down the rebellion." Virginia answered with a call for secession. The Old Dominion and cradle of liberty that had given birth to such men as George Washington, Thomas Jefferson and Patrick Henry would not stand for such heavy-handed oppression.
Figuratively speaking, and in the collective memory of Virginians, her soil was still moist with the blood of the British and, if necessary, in the words of Jefferson, the spilling of additional blood would be fitting: "The tree of liberty must be refreshed from time to time with the blood of patriots and tyrants."
Much of the blood would come from the veins of University of Virginia alumni.
Anyone visiting the university today will notice two large bronze plaques on the wall near the doors on the Rotunda porch. Upon those tablets are the names of 503 men who died for what McKim would later call "the God-implanted instinct which impels a man to defend his own hearthstone." By comparison, the much larger student body of Harvard would sacrifice 117 for the Union.
A painful argument
After Virginia's secession, Randolph McKim left with one of the university's voluntary military companies, the "Southern Guard," for Harpers Ferry. A few weeks later, the university boys were "very properly ordered back to their studies."
En route back to Charlottesville, McKim decided to visit his parents in Baltimore. Upon arriving, he was surprised to learn that his father disapproved of his Southern loyalty.
A heated discussion ensued, and when the elder McKim had made his condemnation clear, the young man turned, faced his father, and with a great deal of emotion said, "Well, father, I comfort myself with the promise, 'When my father and my mother forsake me, then the Lord will take me up.' "
Thus, the two went their separate ways, never to see each other again. McKim later wrote that their "difference was, by reason of our great mutual attachment, very painful to us both."
Though McKim would never again see his father, he wrote to his parents often during the war. One particularly poignant letter, written just before McKim was to be ordained into the ministry, included the following request: "My father, I ask to be remembered at the family altar, that God may prepare me for the responsible office which I am about tremblingly to undertake after seven months' study." One year later, McKim's father would be dead.
A close call
McKim returned to the University of Virginia and completed his studies with five "diplomas" — mathematics, moral philosophy, French, Greek and Latin. McKim would then, like many Southern boys, serve four long years in America's bloodiest conflict — first as a private, then as a staff officer and finally as a chaplain.
McKim saw a fair amount of action and was no stranger to close calls. He wrote of one such incident in "A Soldier's Recollections: Leaves From the Diary of a Young Confederate," published in 1910 and reprinted by Sprinkle Publications of Harrisonburg, Va.
"Later in the battle my beautiful black was shot under me. The ball went right through his head. I heard the 'thud' as it struck, and then the noble animal tumbled and fell … as he fell over on one side, I sprang off on the other."
McKim later made the following entry in his diary about the incident:
"Last Sunday I had a horse shot under me, but my life has been graciously spared … in a perfect hail of shell, cannon-balls, and bullets, I was deeply impressed with my entire dependence on God's care, and in gratitude for my preservation, I inwardly resolved to devote myself more perfectly to his service, and especially to urge my fellow men to repent and turn to God."
An army chaplain
McKim's resolve was unwavering. Having already decided at the age of 16 to enter the ministry, McKim submitted his resignation as first lieutenant aide-de-camp in the autumn of 1863. He was careful to note in his resignation that he intended to "re-enter the service in the capacity of chaplain."
McKim was ordained an Episcopal priest and was licensed by Bishop John Johns "to perform the service … of the Protestant Episcopal Church in the Confederate States" on Feb. 10, 1864, at Trinity Church in Staunton, Va.
During the war, Virginia Theological Seminary had moved from Union-occupied Alexandria to Trinity Church to hold classes. Trinity is the same church to which Patrick Henry and the Virginia Legislature had fled to avoid being captured by the British in June 1781.
McKim was delighted. "I was now happy in the belief that I had achieved my ambition — I was commissioned and equipped for my work as a minister of Jesus Christ in the army that I loved so well … and could now go forward and, by God's blessing, preach and live the Gospel to good purpose."
Lifetime of purpose
McKim's "good purpose" was lifelong. After the war, he served a number of distinguished churches as rector: St. John's in Portsmouth, Va. (1866-67); Christ Church in Alexandria (1867-75) — the same church that both Robert E. Lee and George Washington attended; Holy Trinity in New York City (1875-86); Trinity Church in New Orleans (1886-88); and Church of the Epiphany in Washington (1888-1920).
McKim was also a prolific writer and, in addition to his "Recollections" and numerous articles and pamphlets, he wrote a book concerning the spiritual side of Robert E. Lee titled "The Soul of Lee."
McKim died while playing golf on July 15, 1920, at Bedford Springs, Pa. He was buried at Green Mount Cemetery in Baltimore.
Richard G. (Rick) Williams Jr. is a businessman and writer living in Virginia's Shenandoah Valley and a frequent contributor to this page. He is the author of "The Maxims of Robert E. Lee for Young Gentlemen" (Pelican 2005).


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Q: Android BottomSheetDialog can't show title even though I set a title It's my very first time to use BottomSheetDialog,
the code is like this:
final BottomSheetDialog bottomSheetDialog = new BottomSheetDialog(this);
bottomSheetDialog.setTitle("title");
//bottomSheetDialog.setContentView(...)
bottomSheetDialog.show();

When I ran this code, the title didn't show.
Can you explain to me what happened to this code? Thank you ~

A: If you look into the source code of android.support.design.widget.BottomSheetDialog you will find, that inside it's constructor there is a line:
// We hide the title bar for any style configuration. Otherwise, there will be a gap
// above the bottom sheet when it is expanded.
supportRequestWindowFeature(Window.FEATURE_NO_TITLE);

which clearly states, that Bottom sheet will not have title (with reason). (As of support library version 23.4.0)
Suggestion:
Make a title of the view inside the layout of bottom sheet.


text:
HRWiki:STUFF/Archive/candy product
These HRWiki:STUFF items are preserved here as an archive. Do not add new votes.
1 (She Was A) Morphing Detective
2 I STUFF 'em, you vote on 'em
3 Song about Sibilance
4 Chocopantsurglar
5 Crappyshack
6 Junkmail Filta... sorta
7 I betcha I will by the end of this voting
8 Daily dose of sibilance
Instructions · Help
[edit] (She Was A) Morphing Detective
The scene with morphing candy bar shapes is reminiscent of the music video for "(She Was A) Hotel Detective" by They Might Be Giants, in which a revolver morphs into a keychain, a noose, an ashtray, a doorknob, a bloody knife, and a bust of Lincoln.
From: candy product
Posted on: 09:47, 9 May 2006 (UTC)
Arguments for:
A visual comparison
The two sequences are staged and timed similarly.
Some of the content is thematically similar; cf. chainsaw and syringe with knife and ashtray.
Both sequences end with a bust of a famous U.S. president.
TBC have referenced TMBG frequently.
Arguments against:
The bust of Van Buren is a running gag that has been around since Monday, July 14, 2003, long before any recorded collaboration between TBC and TMBG. Not a reference to the video.
They aren't even the same images at all. If they wanted it to be a reference, they probably would have used the same images.
Mentally morphing from one object to another searching for the right image is present in many videos and shows, and is a case of TTATOT.
This has even happened in other places in other cartoons.
Let us not forget that the order of the images isn't remotely similar no matter how you look at it.
The bust of Van Buren being an older reference does not preclude this being a reference to the video...
Yes, it does. If not for it being an older reference, it doesn't even look like Lincoln. It's just a running gag, not a reference. Any connection to the video is completely coincidental.
Well, actually, it doesn't. Assume this *is* a reference and you wanted to end this sequence with a president. Which president is most closely associated with HSR? That's right, Old Kinderhook himself!
But the fact is that it's a bust of Lincoln (the fun fact, that is), and the fact as written is patently false.
As written, the fun fact appears to posit that the one sequence is reminiscent of the other. I don't understand why you feel it claims Van Buren is equal to Lincoln. Can you suggest a revision to avoid this confusion?
The bust of Lincoln doesn't look like Lincoln? C'mon ... does the bust of Van Buren look like Van Buren? I'm sorry, but beard+stovepipe hat=Abraham Lincoln.
Um, I didn't say the bust of Lincoln doesn't look like Lincoln. I said it didn't look like Van Buren. Read more carefully before you flame, thank you.
Oh! I have to admit that I could not imagine anyone positing that point as an argument against. Of course the bust of Van Buren doesn't look like Lincoln. But they're both presidents.
But that's NOT the fact! The fact says that the bust in the email is similar to Lincoln. Just because they're both presidents doesn't mean anything. To reiterate, this fact is false.
Again, I don't see where the fun fact states that. Please, suggest a better way to word it if that's what you infer.
Just to clarify: grammatically, the fact just says that "The scene ... is reminiscent of the music video." Although the correlation between the busts is implied, it certainly isn't stated (VanBuren's never even mentioned). And for that matter, it doesn't try to say that the chainsaw looks like a knife.
Hmmmm ... both sequences reference a weapon, an addiction, and a president. Not sure that I'm convinced yet, but verrrrrry interesting.
That argument is a matter of perspective. The Chainsaw is easily seen as a tool rather than a weapon, and the needle is easily associated with healing rather than an addiction to drugs.
Lets see....knife with chainsaw...both can injure (the knife seems its been used for that purpose already!), a cigarette and a needle...both can be forms of drugs (needle can be for meds), and a lincoln and van buren bust....both busts....wow.....close.....ok.
By itself, the fact sounds like a rather lame connection. Having the visual comparison, though, makes this seem more compelling. If we could include the comparison image with this fact, then I'd be okay with accepting it. Otherwise, I feel people will take it as too much of a stretch.
In spite of trying to compare knife with chainsaw and so forth, let's try comparing the images in the order they appear:
Revolver - "piece of crap"-shaped candy bar (no connection)
Keychain - Chainsaw (well, they both have "chain" in the name...)
Noose - Dollar Sign (no connection)
Ashtry - Thumbs up (no connection)
Doorknob - Syringe (no connection)
Knife - Campfire (any connection here would be very tenuous)
The numbers don't even match, so Indiana and the moustache have nothing to compare to.
And the presidents are last in both cases.
This fact has now transformed into a comparison between two "chains" and two presidents. Only maybe the presidents are left with a significant enough comparison left.
Votes to accept:
Votes to decline:
Mayosolo
The Mu
--TheThingé
The Lyrical Wondermind
Stonecold5987 (talk·edits)
Homsar7(Hom · Sar)
Jnelson09
Spell4yr
Qduk
Ilko Skevüld's Teh C
Vash Lupin VI
DorianGray
⇔Thunderbird⇔
Heimstern Läufer
- Qermaq - (T/C)
— Lapper (talk)
— Bassbone (TALK CONT)
ISlayedTheKerrek
— Has Matt? (talk)
- Collin - (T/C)
Zachio
Fourth of Jay (Fireworks!)
—FireBird|Talk
— GWR 2004 TalkContribs
[ Back to STUFF index ]
[edit] I STUFF 'em, you vote on 'em
Grizzly Chomps, a Hostess-brand chocolate cupcake introduced in 1992, similarly featured a "built-in bite". The package bore a cartoon bear saying, "I start 'em, you finish 'em."
Posted on: 17:46, 22 March 2006 (UTC)
TBC are constantly referencing stuff from their childhood.
Especially rare and little-known phenomena. Also, the way Strong Bad says it implies that he is familiar with junk food products that use similar marketing ploys.
No other treat I know of has a bite-mark shape taken out of it.
SBLOUNSKCHED! would likely be a consumer flop, just like Grizzly Chomps were.
Too much of a stretch; the reference so old and obscure that it's likely just a coincidence.
The secondary details, such as why the bite was taken, do not match; if this truly were a reference there would be some supporting evidence.
Grizzly Chomps aren't even candy; they're cupcakes.
An argument could be made that SBLOUNSKCHED!, containing pepperoni, is not candy either.
It's supposed to be candy, though.
Even if pepperoni isn't candy, marshmallows, licorice, nougat, and chocolate certainly are.
I've never heard of the Grizzly Chomps, but I find this information sufficiently interesting, even if it's a coincidence.
If it's not reasonably certain that TBC put the bite in the candy in reference to the Chomps, then it shouldn't be mentioned.
I agree that it shouldn't go into "Real-World Reference", but is there a place for interesting information like this in "Remarks"?
If you looked hard enough, I bet you could find a handful of remarks similar to this one. On the other hand, including this item in the article puts us on a slippery slope, and I just don't see enough evidence to warrant that.
I would vote for it if it were under "remarks", because it's interesting and I didn't know it.
Of course, no matter what the result of this vote is, it will be listed on the talk page. Sometimes that's the best place for interesting coincidences.
Compdude
Trelawney
Ppk01
— Kilroy / talk
♥ teeeffoh!
~ Laramie, Why? ~
LePorello / T / C
Kookykman (t)(c)(r)
SaltyTalk!
James Craven
- Sluggy42(Talk|Cont.)
--Adun12
Bondfool
— It's dot com
—THE PAPER PREEEOW
D2htornado
Mycroft Holmes
I R F
Joey (talk·edits)
Crystallina
— KieferSkunk (talk) —
[edit] Song about Sibilance
"Sibilance," a word describing the "s" sound, was used by Tom Hanks in a "Wayne's World" sketch on Saturday Night Live in a soundcheck for Aerosmith.
Sibilance is NOT a common word.
The way that Strong Bad says it is almost exactly a direct quote, including intonation of the way Hanks' character says it.
As stated below, this word is commonly used to test microphones. It probably doesn't reference anything but that fact.
Although quite true, this fact has little importance if the second one can't be proven true. If the Jon Stewart one is true, it would be very significant to this email; this fact would then only be necessary to explain Jon Stewart's. On its own, though, it really says nothing more than "There is at least one pop-culture use of 'sibilance' as a microphone check."
I have seen many a sound guy check a mic with the word "sibilance." The SNL and Jon Stewart sketches are merely making light of that fact.
THIS vote is for the other part of the item below.
It's not just sibilance, the "checks" were also used.
So what? The word "check" is even more common in a mic check. Something should be said about sibilance as a mic check word in general, but the direct pop-culture references aren't necessary because we could probably find hundreds of them.
Strong Bad saying it sent me an hour long Google search to figure out where it came from. It was immediately recognizable, but not immediately placeable. Why not just put that it is POSSIBLY a reference? Tom Hanks Wayne's World skit transcript.
Here are the relevant portions of the transcripts:
BARRY: ...and then, the most important thing—I gotta do a sound check. {pulls the mike forward} Check. Check. Check one. Sibilance. Sibilance. Check. Check. Check two. Sibilance. Sibilance. And that's pretty much what I do.
STRONG BAD: Check one, check two. Sibilance. Sibilance. SBEmail.
Both this and the one below are inherently the same issue. Since they were originally together, wouldn't it make more sense to STUFF them together?
There is a distinction. THe SNL reference is more well-known, for example, and The Daily Show is more current. Let's allow them to develop independently.
Bluebry
Pranicbald
--Dacheatbot · Communicate
TheGreatLogini
TheNicestGuy
Ju Ju Master
AbdiViklas
phlip TC
Benabik
[edit] Chocopantsurglar
Homestar's attire in the SBLOUNSKCHED! bar Easter Egg is similar to that of McDonald's spokesperson Hamburglar.
The resemblance IS striking.
It's general burglar attire.
It is striking, but that's because Hamburglar is the most famous example of someone in this attire. That doesn't mean it's a reference to him.
He's missing the hat, tie, and jacket. What's more, the mask is different. Only the shirt matches.
If the email actually had something to do with fast food or a McDonald's style restaurant, this argument may have had more weight, but as it stands, it just looks like a run-of-the-mill burglar outfit.
Check it against the King of Town's outfit in 3 Times Halloween Funjob for comparison, if you like.
Please compare
On the one hand, he is a burglar stealing food. On the other, he's dressed very generically.
His actions provide more of a resemblance than the outfit itself.
TaylorR
--Jeff J. W.(Talk·Contribs)
— Elcool (talk)(contribs)
CaptainPhlegmatic
[edit] Crappyshack
...Strong Bad's later allusion to the Baby Ruth's scatological appearance is most likely a reference to a like-minded gag in the film Caddyshack.
Caddyshack is such an important film, a reference to it must be acknowledged.
This fact is also mentioned on the Wikipedia page for Baby Ruth, giving it significance.
TBC have made references to Caddyshack to the past; for example, Homestar's costume in Halloween Fairstival.
Caddyshack is the most famous example of this particular juxtaposition.
This fact is sort of a stretch. Couldn't it be that they are just referencing the candy's shape itself and nothing else?
I can attest that this has been done before: it was a joke among my 12-year-old friends long before any of us had seen or heard of Caddyshack.
It isn't a reference to anything.
Baby Ruth's appearance is a relatively common joke, but Caddyshack is where it all started.
Its been years since I've seen Caddyshack, can someone state what in Caddyshack this is supposedly referencing?
A candy bar was thrown in the pool and everyone thought it was, well, not a candy bar.
Then they evacuated the pool. They were cleaning, and one of the pool guys ate it. Gree-ose. And the snobby rich woman fainted.
Maybe it's not necessary, or in poor taste, but I'd be more inclined to explain why he singled out Baby Ruth than to name-drop Caddyshack.
Caddyshack is simply the most well known instance of the Baby Ruth being mistaken for something nasty.
[edit] Junkmail Filta... sorta
Email headers in the Junkmail Filta list are sorted in the "old on top" order, which means that it didn't filter any emails before "Re: Besing Friends" - strange, given the rate at which Homestar sends them.
I don't see any relevance at all...
We don't know if we are seeing a full list. This might be a screenshot after SB has scrolled in a bit.
Plus, perhaps the previous emails have already been deleted.
Sikon
csours
Trey56
[edit] I betcha I will by the end of this voting
Strong Bad says "I betcha I will by the end of this email" because usually in the past when he has received an email like this he has made something up based on the email.
We often list obvious things in Inside References.
Obvious to anyone who has watched a lot of emails.
Even if it's not obvious, it's the joke.
Inside References was designed for comments to help newcomers to the HR site, and this seems obvious to most people who even watch a single email.
Explanation or Inside Reference?
Some rewording probably wouldn't hurt either.
Nicko9y
Point7Q
dylnuge
[edit] Daily dose of sibilance
"Sibilance," a word describing the "s" sound, was used by Tom Hanks in a "Wayne's World" sketch on Saturday Night Live in a soundcheck for Aerosmith. It was also mentioned by The Daily Show's Jon Stewart the night before this email appeared.
Email was a day late, and a mainstream comedy show aired Monday night uses the same reference. Entirely too likely to be a reference to exclude.
How often is it used in pop culture?
Both parts of the FF are saying the same thing: they're showing other times it's been used in pop culture. Niether one says it's a direct reference. If we can accept one, why can't we accept the other? Also, I think the part in question is more worthy than the other part if we're only going to use one, because whether it was a reference or not, it still was the night before (whereas the SNL thing was a long time ago).
Very likely coincidence.
Because the email was a day late, it was probably already most of the way through production the time that show aired.
"Siblants" is a common word to use in testing microphones; there's no reason it had to refer to anything else.
Let's say that TBC were watching The Daily Show, that still doesn't make it a reference. If they heard it decided, "hey let's use that word" It still isn't any more noteworthy than the fact that they talked about colloids on the discovery channel last week.
This is just a case of the Baader-Meinhof Phenomenon.
ONLY the full-sized section is being voted on, NOT the small part.
even tho the intro comes at the beginning of the email it may have been made or redone after the rest of the email.
To the comment about how often it's used in pop culture: obviously at least one other time. The small part of the fact is not in question, just the big part.
And to the comment about how often it's being used in pop culture: We tend to decline facts about things that don't look like references, such as shows getting cancelled after just one episode.
In that case, I think we should be questioning the entire FF, not just the part about the Daily Show, because there's really no solid reason to believe either one is a reference.
What's strange is, one reference is losing, while another is winning. What makes them so different?
See above item for an explanation.
I suggest that because niether the part for which we're voting nor the part that's already on the page is a clear reference, we move the whole thing to a different section of the page.
Note that there have been quite a few of these "near misses" in recent months, where a toon or SBEmail is released within a very short period (either direction) of a real-life event that has something to do with the toon. At first, it looked to me like mere coincidence, but I'm beginning to see a pattern.
Unknownwarrior33
Agnamaracs
Raiderade
Gnome o' Fury
Theologin
Retrieved from "http://hrwiki.org/wiki/HRWiki:STUFF/Archive/candy_product"


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