Ancient India Essay Example | Topics and Well Written Essays - 250 words

Ancient India - Essay Example In this section, the chapter looks at the distinctive features of the class system, which Aryans people introduced. It also examines the effects of the class system on the Indian civilization. In addition, the chapter discusses various religions found in India and their assumption regarding life. Specifically, the chapter looks at Brahmanism and Buddhism by highlighting their differences. The section also discusses how these religions influenced the Indian civilization. Additionally, the chapter discusses India after the Mauryas by looking at the rule of fishes. In this section, it tries to answer and examine why India was unable to maintain a unified empire in the first millennium BCE. Similarly, the section highlights reasons making Mauryan Empire to overcome temporary tendencies towards disunity of India. Lastly, the chapter discusses the exuberant world of Indian culture. It achieves this through outlining ways in which ancient India culture is similar and different from the cultural experience of ancient Egypt and

Memoirs of C K Janu Against the Hegemony of the Ruling Class Essay Example for Free

Memoirs of C K Janu Against the Hegemony of the Ruling Class Essay C K Janu’s unfinished autobiography â€Å"Mother Foster: The Unfinished Story of C K Janu† which is translated by N Ravi Shankar is an eloquent testimony to her courage and her convictions. It is the recounting of her experience in her past. C K Janu is the prominent organic woman leader who managed to gain land for the tribals who were stamped under foot by the elites and the government. She is the leader of the Adivasi tribes who dedicated her youth for the betterment of the tribes. In the beginning she became a member of the communist party. Later she realised that the party cheated the tribal people and she left the party in1982. Later she organised a tribal meeting in South India in 1992 as a part of the reclamation of the land of the tribe. She incited and led the agitation by the tribal people. For this she was physically and mentally assaulted nine times by the Kerala police. In 1994 she was awarded as the best tribal social worker by the Government of Kerala. But she returned the award. She became the only Indian ambassador of India in the UN tribal conference. Even though illiterate she gave speeches in 120 countries. Later in 2001 the Government of Kerala signed an agreement with the tribal people, but did not keep it. The tribals protested against this in the land at Muthanga . This ended with a massive violence. Till now the cases are going on against her. She cooperated with national and international organisations for some time and now she spends time serving the tribes.

Factor Language Model Programming

Factor Language Model Programming Language Model Language model helps a speech recognizer figure out how likely a word sequence is, independent of acoustics. There is a linguistic and statistical approach to calculate the probability. The linguistic technique tries to understand the syntactic and semantic structure of a language and derive the probabilities of word sequences using this knowledge. The challenge here is to have proper co occurrence statistics of the unit of recognition. The approach in use evaluates a huge text corpus in a statistical way and word transitions. Current language models make no use of the syntactic properties of natural language but rather use very simple statistics such as word co-occurrences. Recent results show that incorporating syntactic constraints in a statistical language model reduces the word error rate on a conventional dictation task by 10% [M.S.Salam, 2009]. Proposed Language Model The approach proposed here uses factored language model which incorporates the morphological knowledge. Factored language models have recently been proposed for incorporating morphological knowledge in the modeling lexicon. As suffix and compound words are the cause of the growth of the vocabulary, a logical idea is to split the words into shorter units. The language model proposed in this research is based on morphology. A morphological analyser obtains and verifies the internal structure of a given complete word form [Rosenfield, 2000]. Building a morphological analyser for highly inflecting, agglutinative languages is a challenging task. It is very difficult to build a high performance analyser for such languages. The main idea here is to divide a given word form into a stem and single suffix. Morphology plays a much greater role in Telugu. An inflected Telugu word starts with a stem and may have suffix(s) added to the right according to complex rules of saMdhi. This research proposes a new data structure based on Inverted Index and an efficient algorithm for accessing its elements. Few researchers have used tries for efficient retrieval from dictionary, earlier. This research work is different from earlier work in two ways: a) variation to the structure of trie b) the method of identifying and combining inflections. Modified Trie Structure A trie is a tree based data structure for storing strings in order to support fast pattern matching. A trie T represents the strings of set S of n strings with paths from root to the external node of T. Fig 5.1: Original Trie Structure The trie considered here is different from standard trie in two ways: 1) A standard trie does not allow a word to be prefix of another, but the proposed trie structure allows a word to be prefix of another word. The node structure and search algorithm also is given according to this new property. 2) Each word in a standard trie ends at an external node, where as in the modified trie a word may end at either an external node, or the internal node. Irrespective of whether the word ends at internal node or external node, the node stores the index of the associated word in the occurrence list. The node structure is changed such that, each node of the trie is represented by a triplet C,R,Ind>. C represents character stored at that node. R represents whether the concatenation of characters from root till that node forms a meaningful stem word. Its value is 1, if characters from root node to that node form a stem, 0 otherwise. Ind represents index of the occurrence list. Its value depends on the value of R. Its value is -1 (negative 1), if R=0, indicating it is not a valid stem. So no index of occurrence list matches with it. If R=1, its value is index of occurrence list of associated stem. Fig 5.2: Modified Trie Structure Advantages relative to binary search tree: The following are the main advantages of tries overbinary search trees(BSTs): Looking up keys is faster. Looking up a key of lengthmtakes worst caseO(m) time. A BST performs O(log(n)) comparisons of keys, wherenis the number of elements in the tree, because lookups depend on the depth of the tree, which is logarithmic in the number of keys if the tree is balanced. Hence in the worst case, a BST takes O(mlogn) time. Moreover, in the worst case log(n) will approachm. Also, the simple operations tries use during lookup, such as array indexing using a character, are fast on real machines. Tries can require less space when they contain a large number of short strings, because the keys are not stored explicitly and nodes are shared between keys with common initial subsequences. Tries facilitatinglongest-prefix matching, helping to find the key sharing the longest possible prefix of characters all unique. Corpus structure of proposed Language Model The corpus consists of the following modules: Stem word dictionary This accommodates all the stems of the language. Stem word dictionary is implemented as an Inverted Index for better efficiency. The Inverted index will have the following two data structures in it: 1) Occurrence list: It is an array of pairs, 2) Stem trie: consisting of stem words Occurrence list is constructed based on the grammar of the language, where each entry of the list contains the pair (ii) Inflection Dictionary This dictionary contains the list of all possible inflections of the Telugu language. Each entry of Stem word dictionary lists the indexes of this dictionary to indicate which all inflections are possible with that stem. The proposed corpus structure helps in reducing the corpus size drastically. Every stem word may have number of inflections possible. If the inflected words are stored as it is, then corpus size would be m*n, where m is number of stem words and n is number of inflections. Instead of storing all the inflected words, the proposed corpus structure stores stem words and inflections separately, and handles the inflected words through morphology. Hence the corpus size required is for m stem words and n inflections i.e., m+n. Thus there is a great reduction in the corpus size. For a corpus of 1000 stem words and 10 inflections, the required corpus size is 1000+10=1010, which otherwise would have required 1000*10=10000. Fig 5.3 : Corpus structure of proposed Language Model Textual Word Segmentation using Proposed Language Model The proposed language model is used to develop a textual word segmenter. A word segmenter is used to divide the given inflected word into a stem and single inflection. This is required as the corpus stores stems and inflections separately. Input the word segmenter is an Inflected word. Syllabifier takes this word and divides the word into syllables and identifies if the letter is a vowel or a consonant. After applying the rules syllabified form of the input will be obtained. Once the process of syllabification is done, this will be taken up by the analyzer. Analyzer separates the stem and inflection part of the given word. This stem word will be validated by comparing it with the stem words present in stem dictionary. If the stem word is present, then the inflection of the input word will be compared with the inflections present in inflection dictionary of the given stem word. If both the inflections get matched then it will directly displays the output otherwise it takes the appropriate inflection(s) through comparison and then displays. Syllabification is the separation of the words into syllables, where syllables are considered as phonological building blocks of words. It is dividing the word in the way of our pronunciation. The separation is marked by hyphen. In the morphological analyzer, the main objective is to divide the given word into root word and the inflection. For this, we divide the given input word into syllables and we compare the syllables with the root words and inflections to get the root word and appropriate inflection. Fig 5.4: Block diagram of Word Segmentr for text Steps for word segmentation Receiving the inflected word as an input from the user. Syllabify the input Analyze the input and validating the stem word. Identify the appropriate inflection for the given stem word by comparing the inflection of given word with the inflections present in inflection dictionary of the stem word. Displaying the appropriate inflected word. For example, considering the word â€Å"nAnnagariki† (à  Ã‚ °Ã‚ ¨Ãƒ  Ã‚ °Ã‚ ¾Ãƒ  Ã‚ °Ã‚ ¨Ãƒ  Ã‚ ±Ã‚ Ãƒ  Ã‚ °Ã‚ ¨Ãƒ  Ã‚ °-à  Ã‚ °Ã‚ ¾Ãƒ  Ã‚ °Ã‚ °Ãƒ  Ã‚ °Ã‚ ¿Ãƒ  Ã‚ °Ã¢â‚¬ ¢Ãƒ  Ã‚ °Ã‚ ¿) meaning â€Å"to father†, the input is given the user in Roman transliteration format. This input is basically divided into lexemes as: Now, the array is processed which gives the type of lexeme by applying the rules of syllabification one by one. Applying Rule 1: â€Å" No two vowels come together in Telugu literature.† The given user input does not have two vowels together. Hence this rule is satisfied by the given user input. The output after applying this rule is same as above. If the rule is not satisfied, an error message is displayed that the given input is incorrect. Now the array is: c – v – c – c – v – c – v – c – v – c – v Applying Rule 2: â€Å" Initial and final consonants in a word go with the first and last vowel respectively.† Telugu literature rarely has the words which end up with a consonant. Mostly all the Telugu words end with a vowel. So this rule does not mean the consonant that ends up with the string, but it means the last consonant in string. The application of this rule2 changes the array as following: c – v – c – c– v – c – v – c – v – c – v cv – c – c – v – c – v – c – v – cv This generated output is further processed by applying the other rules. Applying Rule 3: â€Å" VCV: The C goes with the right vowel.† The string wherever has the form of VCV, then this rule is applied by dividing it as V – CV. In the above rule the consonant is combined with the vowel, but here in this rule the consonant is combined with the right vowel and separated from the left vowel. To the output generated by the application of rule2, this rule is applied and the output will be as: cv – c – c – v – c – v – c – v – cv cv – c – c – v – cv – cv – cv This output is not yet completely syllabified, one more rule is to be applied which finishes the syllabification of the given user input word. Applying Rule 4: â€Å" Two or more Cs between Vs First C goes to the left and the rest to right.† It is the string which is in the form of VCCC*V, then according to this rule it is split as VC – CC*V. In the above output VCCV in the string can be syllabified as VC – CV. Then the output becomes: cv – c – c – v – cv – cv – cv cvc– cv – cv – cv – cv Now this output is converted to the respective consonants and vowels. Thus giving the complete syllabified form of the given user input. nAn – na –cA – ri – ku cvc – cv – cv – cv – cv Hence, for the given user input, â€Å"nAnnagAriki†, the generated syllabified form is, â€Å"nAn – na – gA – ri – ki†. Fig 5.5: Word Segmenter showing an inflected word without change in stem form Fig 5.6: Word Segmenter showing an inflected word with a change in stem form SCIL Speech Corrector for Indian Languages In inflectional language every word consists of one or several morphemes into which the word can be segmented. The approach used here aims at reducing the above mentioned problem of having a very huge corpus for good recognition accuracy. It exploits the characteristic of Telugu language that every word consists of one or several morphemes into which the word can be segmented. SCIL is a procedure To deal with complex word forms applied after recognition Using which misrecognized words are corrected Architecture of SCIL The design of Speech Corrector for Indian Languages, consists of the Syllable Identifier, Phone Sequence Generator, Word Segmenter, and Morpho- Syntactic Analyzer modules. Input speech is decoded by a normal ASR system which gives the identified word as a string. The sequence of phones would be the input to the Word Segmenter module which matches the phonetized input with the root words stored in dictionary module, and generates a possible set of root words. Morpho-Syntactic Analyzer compares the inflection part of the signal with the possible inflections list from the database and gives correct inflection. This will be given to Morph Analyzer to apply morpho-syntactic rules of the language and gives the correct inflected word. Fig 5.7: Block diagram of SCIL i) Syllable Identifier Syllable identifier marks the rough boundaries of the syllables and labels them. At this stage , we get list of syllables separated with hyphen. The user input is syllabified and this would be the input to the next module. E.g. dE-vA-la-yA-ku ii) Phone Sequence Generator As the words in the dictionary are stored at phone level transcription, this module generates the phone sequences from the syllables. E.g. d-E-v-A-l-a-y-A-k-u iii) Word Segmentor This module compares the phonetized input from starting with the root words stored in dictionary module and lists the possible set of root words. The possible root word is dEvAlayamu. iv) Dictionary Dictionary contains stems and inflections separately. It does not store inflected words as it is very difficult, if not impossible, to cover all inflected words of the language. The database consists of 2 dictionaries: Stem Dictionary Inflection Dictionary Stem dictionary contains the stem words of the language, signal information for that stem which includes the duration and location of that utterance and list of indices of inflection dictionary which are possible with that stem word. Inflection Dictionary contains the inflections of the language, signal information for that inflection which includes the duration and location of that utterance. Both the dictionaries are implemented using trie structure in order to reduce the search space. v) Morpho Syntactic Analyzer This module compares the inflection part of the signal with the possible inflections list from the database and gives correct inflection. This will be given to Morph Analyzer to apply morpho-syntactic rules of the language and gives the correct inflected word. Post Recognition Procedure Capture the utterance, an isolated inflected word. Get its syllabified form. Generate phone sequence from the syllabified word. Compare the phone sequences with stem words in the dictionary and identify the stem. Segment the word into stem and inflection. Get the list of possible inflections. Compare the inflection signals possible with that stem one by one and apply morpho-syntactic rules of the language to combine stem and inflection. Display the inflected word. Using the rules the possible set of root words are combined with possible set of inflections and the obtained results are compared with the given user input and the nearest possible root word and inflection are displayed if the given input is correct. If the given input is not correct then the inflection part of the given input word is compared with the inflections of that particular root word and identifies the nearest possible inflection and combines the root word with those identified inflections, applies sandhi rules and displays the output. When there is more than one root word or more than one inflection has minimum edit distance then the model will display all the possible options. User can choose the correct one from that. For example, when the given word is pustakaMdO (à  Ã‚ °Ã‚ ªÃƒ  Ã‚ ±Ã‚ Ãƒ  Ã‚ °Ã‚ ¸Ãƒ  Ã‚ ±Ã‚ Ãƒ  Ã‚ °Ã‚ ¤Ãƒ  Ã‚ °Ã¢â‚¬ ¢Ãƒ  Ã‚ °Ã¢â‚¬Å¡Ãƒ  Ã‚ °Ã‚ ¦Ãƒ  Ã‚ ±Ã¢â‚¬ ¹), the inflections tO making it pustakaMtO (à  Ã‚ °Ã‚ ªÃƒ  Ã‚ ±Ã‚ Ãƒ  Ã‚ °Ã‚ ¸Ãƒ  Ã‚ ±Ã‚  à  Ã‚ °Ã‚ ¤Ãƒ  Ã‚ °Ã¢â‚¬ ¢Ãƒ  Ã‚ °Ã¢â‚¬Å¡Ãƒ  Ã‚ °Ã‚ ¤Ãƒ  Ã‚ ±Ã¢â‚¬ ¹) meaning ‘with the book’ and lO making it pustakaMlO (à  Ã‚ °Ã‚ ªÃƒ  Ã‚ ±Ã‚ Ãƒ  Ã‚ °Ã‚ ¸Ãƒ  Ã‚ ±Ã‚ Ãƒ  Ã‚ °Ã‚ ¤Ãƒ  Ã‚ °Ã¢â‚¬ ¢Ãƒ  Ã‚ °Ã¢â‚¬Å¡Ãƒ  Ã‚ °Ã‚ ²Ãƒ  Ã‚ ±Ã¢â‚¬ ¹) meaning ‘in the book’) mis are possible. Present work will list both the words and user is given the option. We are working on improving this by selecting the appropriate word based on the context. SCIL Algorithm W=Utterance.wav Syl[]=SyllableIdentifier(W) Phone[]=phonetizer(Syl[]) Stem=getStem(Syl[]) Infl[]=getInflections(Stem) While (not exactMatch) word=MorphAnalyzer(stem,inflMatch) display word Stop Working of SCIL Once possible root words identified the given word is segmented into two parts, first being the root word and second part inflection. Now the inflection part is compared in the reverse direction for a match in the inflection dictionary. It will consider only the inflections that are mentioned against the possible root words, thus reducing the search space and making the algorithm faster. For example consider â€Å"nAnnagariki† (à  Ã‚ °Ã‚ ¨Ãƒ  Ã‚ °Ã‚ ¾Ãƒ  Ã‚ °Ã‚ ¨Ãƒ  Ã‚ ±Ã‚ Ãƒ  Ã‚ °Ã‚ ¨Ãƒ  Ã‚ °-à  Ã‚ °Ã‚ ¾Ãƒ  Ã‚ °Ã‚ °Ãƒ  Ã‚ °Ã‚ ¿Ãƒ  Ã‚ °Ã¢â‚¬ ¢Ãƒ  Ã‚ °Ã‚ ¿) meaning â€Å"to father†, is misrecognized as nAn-na-cA-ri-ku (à  Ã‚ °Ã‚ ¨Ãƒ  Ã‚ °Ã‚ ¾Ãƒ  Ã‚ °Ã‚ ¨Ãƒ  Ã‚ ±Ã‚ Ãƒ  Ã‚ °Ã‚ ¨Ãƒ  Ã‚ °Ã… ¡Ãƒ  Ã‚ °Ã‚ ¾Ãƒ  Ã‚ °Ã‚ °Ãƒ  Ã‚ °Ã‚ ¿Ãƒ  Ã‚ °Ã¢â‚¬ ¢Ãƒ  Ã‚ ±Ã‚ ) then SCIL is applied and will correct the recognition error as follows: The output from ASR is nAn-na-cA-ri-ku. The phone sequence generator will generate the phone sequence as n-A-n-n-a-c-A-r-i-k-u. Now, match it with the set of root words stored in dictionary module. This process will identify the possible set of root words from the Stem dictionary as follows: Once possible root words identified the given word is segmented into two parts, first being the root word and second part inflection. Now the inflection part is compared for a match in the inflection dictionary. It will consider only the inflections that are mentioned against the possible root words, thus reducing the search space and making the algorithm faster. Possible set of inflections in inflections dictionary After getting the possible set of root words and possible set of inflections they are combined with the help of SaMdhi formation rules. Here in this example cA-ri-ku is compared with the inflections of the root word nAnna After comparing it identifies gAriki as the nearest possible inflection and combines the root word with the inflection and displays the output as â€Å"nAnnagAriki†. Conclusions Language model proposed in this work results in reduction in corpus size by using factored approach. The search process is fastened by use of trie based structure. A change to standard trie is proposed. A post recognition procedure SCIL, is designed which uses the proposed language model and corrects the words misrecognized at inflections. The approach is tested using 1500 speech samples. These samples consist of 100 distinct words , each word repeated 3 times and recorded by 5 speakers in the age group 18-50. It is implemented as a speaker dependent system. An average model is built from the three utterances of each word for each speaker. Each speaker is given a unique ID, using which average model of that speaker is used for testing.

My Pitiful Father Essay examples -- Personal Narrative

My Pitiful Father I always thought that family was supposed to stick by family through all of the tough circumstances. So why did my father just walk out on my family when we needed him the most? How could he make us suffer and blame it all on me? Every family has its problems and arguments once in a while. My father was our family’s problem. His presence made us all feel uneasy. I do not know what it was, but when I was a little girl, I feared my father. I feared being alone with him; I feared going out with him; I feared him. Around him I felt like I was imperfect because I thought he was perfection. He seemed to have gotten along with my older and younger brother better than me. I was the one to take his orders and follow them. The more I grew up, the more distant I became from my father. When my father was mad at any one of us, it seemed like the end of the world. Nothing was worse than my father completely ignoring me. When I was little, I went with my father and brother to the toy store. My dad bought my brother a toy, and for the first time ever, I got mad and jealous at him. My dad saw how I reacted towards my brother and when we all got to the car, he told me not to get inside. This happened more than ten years ago, and I still can feel that tightening feeling in my stomach. I still feel the tears that came down as I watched my dad cold-heartedly drive off and leave me in the parking lot. My father had no remorse when he saw me crying. It seemed to me that he was more satisfied with himself when he saw anyone of us crying. My dad was far from perfect. But it was fear that held my brothers and me from telling anyone anything he did, especially my mom. My mom worked during the evening, ... ...om seeing us (particularly me) unhappy. Why? It is hard to say. Events like this never made any sense to me. While I was in my brother’s room, I called my mother and told her what was going on. I did not realize that I had left the door open, and my father was standing there and overheard me. He started screaming and yelling at me and told me I was the reason why he and my mother did not get along anymore. He told me I was the reason why the family was breaking apart—it was all my fault. Was it really my fault? Or was it just a â€Å"cover story,† because he knew it was his fault. I did not understand how a person who brought me into this world, who was supposed to love me unconditionally, could take all his love away. My father helps me to realize that hate is a â€Å"cover story† for love. I know my father loves me regardless of what has happened in the past.

The Politics of Humor :: essays research papers fc

  Ã‚  Ã‚  Ã‚  Ã‚   THE POLITICS OF COMEDY   Ã‚  Ã‚  Ã‚  Ã‚  Is this thing on?   Ã‚  Ã‚  Ã‚  Ã‚  In efforts to promote voter education, laughter is perhaps the best strategery.   Ã‚  Ã‚  Ã‚  Ã‚  Comedy and tragedy prove one in the same, as the saying goes. The variability of the twosome, however, substantiates rather inconsistently in politics, an arena overflowing with participants who take themselves too seriously. And where a potential candidate attempts to utilize comic relief, one often discovers the result to be rather tragic.   Ã‚  Ã‚  Ã‚  Ã‚  In lieu of the consequences politicians may endure with the mere utterance of a poor joke, most tend to stray from the possibility of Meet the Press turned comedy hour. Especially when their reputations are at stake; politicians are well aware, according to Mark Katz, humorist and speechwriter for Bill Clinton, that â€Å"a good joke will last about a week†, whereas a â€Å"bad joke will be reprinted in you obituary.†   Ã‚  Ã‚  Ã‚  Ã‚  Perhaps responsibility lies within the confines of a narrow-minded media, exhibited as a threat to be avoided rather than a tool to be implemented. But certainly with just cause; â€Å"the news media are poorly suited to their role as the principal intermediary between candidates and voters,† rooted in the conception of politics as â€Å"game† and a â€Å"business† rather than a â€Å"struggle† over national policy.   Ã‚  Ã‚  Ã‚  Ã‚  The aforementioned incidents occur frequently, but not absolutely, should candidates appropriately utilize humor. The war on terror provides unavoidable roadblocks on an already-tumultuous campaign trail, and even primitive technology such as television suffices in establishing the general 9/11 ‘fear climate’. The presumption of a candidate’s entrepreneurial priorities over his public relationship undermines the use of humor as the candidate’s ultimate weapon.   Ã‚  Ã‚  Ã‚  Ã‚  The primarily noticeable aspect of political campaign humor pertains to its absence. Undoubtedly humor â€Å"is used more sparingly than songs in political commercials† ; the commodity appears even more so in the early campaign efforts of the mid-nineteenth century than recent endeavors. Humor’s effectiveness originates from its multilevel success in the campaigning process. Involuntarily proliferated by an ever-expanding press, the ideal candidate would employ comedy because of its effervescent facility of concurrent humanization and advertisement. Moreover humorous politicians formulate an equally comical and less aggressive response within the entertainment realm, successfully intertwining the sphere of pop culture with the relatively unscathed territory of political science. The general consensus regarding the benefits of humor relates to its malleability; it can â€Å"be employed to attack the opposition, or it can serve to enhance a campaign.à ¢â‚¬    Ã‚  Ã‚  Ã‚  Ã‚  While it can be argued that humor refers to a conscious campaign maneuver on the part of its source, as a tool it only succeeds with regard to the respective candidate.

Citizenship and diversity Essay

A citizen is a native or naturalized member of a city, country, or nation who owes allegiance to its government and is entitled to its protection also citizenship is the state of being vested with the rights and duties of a citizen and is also the character of an individual viewed as a member of society. Citizens who help the local community like running clubs for youths and getting them involved in productive stuff helps keep them off the street and also help to decrease the crime rate. The Public service view citizenship as very helpful as the citizens who help tackle crime by being witnesses, reporting crime, and actively doing something helps the Police convict and get justice for crimes done. A legal view on citizenship is a person with rights of residence; views of citizenship by different organisations in society, e.g. public services, religious groups, pressure groups including â€Å"Amnesty International† and â€Å"Greenpeace†. Qualities of a good Citizen -Responsible -Dedicated -A positive attitude towards other people -Participation in community activities -Awareness of the needs of others Responsible -A good citizen takes personal responsibility to improve the community in which they live. They do not complain about picking up litter they will not complain that it is the council’s job, they will get up and pick up the litter themselves. A good citizen see themselves as responsible for improving the neighbourhood and do not wait for others to do it. Dedicated -Good citizens do not give up on tasks; they persevere until change is achieved. They are dedicated to see through until the end a task which they have set themselves. Positive Attitude -Good citizens are positive to all people. They are non-judgemental. They are not racist or homophobic; they welcome diversity as enriching a society and do not judge people on the colour of their skin, age religion or sexual orientation. Participation in the community -Getting involved in neighbourhood watch, community fund raising and environmental campaigns Awareness of the needs of fellow people in your community -Good citizens have awareness that we are not all the same and that some people need more support due to poverty, disability or language issues. A good citizen is aware and sensitive to these issues and help to provide a supportive inclusive community which values all of its members. Diversity The definition of diversity is to allow others to be who they are, i.e. black white Muslim Hindu etc. without expecting them to conform to your ideas of either working or morally, for example, if your working partner is a strict catholic, then not making lewd comments about women would respect his diversity, if you are going to be arresting a Muslim woman, then getting a female police officer to do it would be respecting her diversity, the same would be if you were to respect someone’s diversity in your community, you could not single someone out or treat someone different to other because of their race, religion or social status and so on.

Business information system Essay

1. What might have happened to Apple if its top executives had not supported investment in iPads? If the top executives had not supported investment in pads, the new product will be known by less people. Without investment in iPad, people will not receive the information of new technology. 2. Why would it be unethical for Apple to sell its iTunes customer information to other businesses? The customer’s information is about personal privacy, any company can’t sell customer’s information without their permit. 3. Evaluate the effects on Apple’s business if it failed to secure its customer information and all of it was accidentally posted to an anonymous website. The customers will not trust apple again, and they will not leave personal information on iTunes. This will influence Apple that people will not use apple store for purchasing. If the security problem let customer’s credit card information been stolen, Apple will response for that. 1 Do you agree or disagree that Apple’s iTunes, iPhone applications and iPad applications give the company a competitive advantage? Be sure to justify your answer. Yes, I agree with that. One of the main factors that brought Apple back from near oblivion was its ability to produce, market, and sell IT products such as the iPod, iPhone, and customer developed applications. If Apple’s top executives did not have the foresight to view the MP3 players as a competitive advantage, then chances are the company would not have made a strong comeback in the highly competitive electronics market. People who got iPhone, iPad, they will go to Apple’s store to download and buy the application. Other company can only develop application to Apple; they can’t sell or provide their application to customer directly. This is a big advantage for Apple. 2 Why are data, information, business intelligence and knowledge important to Apple? Give an example of each type in relation to the iPad. Data are raw facts that describe the characteristics of an event or object. Before the information age, managers manually collected and analysed data, a time-consuming and complicated task without which they would have little insight into how to run their business. Lacking data, managers often found themselves making business decisions about how many products to make, how much material to order, or how many employees to hire based on intuition or gut feelings. In the information age, successful managers compile, analyse, and comprehend massive amounts of data daily, which helps them make more successful business decisions. Examples include: Sales date, Quantity sold, Cost, Sales price, Total profit, Shipping address, Customer address, Wireless type, Memory amount, Colour. Information is data converted into a meaningful and useful context. Having the right information at the right moment in time can be worth a fortune. Having the wrong information at the right moment; or the right information at the wrong moment can be disastrous. The truth about information is that its value is only as good as the people who use it. People using the same information can make different decisions depending on how they interpret or analyse the information. Thus information has value only insofar as the people using it do as well. Business intelligence (BI) is information collected from multiple sources such as suppliers, customers, competitors, partners, and industries that analyses patterns, trends, and relationships for strategic decision making. BI manipulates multiple variables and in some cases even hundreds of variables including suc h items as interest rates, weather conditions, and even gas prices. For instance, BI can predict inventory requirements for a business for the week before the Super Bowl if, say, the home team is playing, average temperature is above 80 degrees, and the stock market is performing well. This is BI at its finest, incorporating all types of internal and external variables to anticipate business performance. Knowledge includes the skills, experience, and expertise, coupled with information and intelligence that creates a person’s intellectual resources. Knowledge workers are individuals valued for their ability to interpret and  analyse information. Today’s workers are commonly referred to as knowledge workers and they use BI along with personal experience to make decisions based on both information and intuition, a valuable resource for any company. 3 Analyze Apple using Porter’s Five Force model. Apple’s buyer power was low when it first introduced the iPod since it was first to market with the product. Now, there are many competitors to Apple’s iPod and its buyer power is increasing since customers can choose from many different manufacturers of MP3 players. Apple’s supplier power was high and now it is decreasing since buyers have many choices of whom to buy from. Apple can use environmental scanning, or the acquisition and analysis of events and trends in the environment external to an organization, to analyse rivalry. Apple can use environmental scanning to analyse everything from competitor strategies to understanding new and shifting market trends to determining the strategic placement of Apple stores. Without watching its environment and understanding what its competitors are doing and where the market is headed, Apple will have a difficult time setting its strategic direction, as Steve Jobs determined when he thought he had missed the MP3 bandwago n. 4 Which of the three generic strategies is Apple following? Apple follows a focused strategy. 5 Which of Porter’s Five Forces did Apple address through its introduction of the iPhone? Apple decreased the power of its buyers and increased its own supplier power by introducing the iPhone. Since the iPhone was the first to market with an internet access, data storage, MP3 player, etc., its buyers had no power and no choice but to purchase the product from Apple. Unfortunately, Apple could not create an entry barrier and soon many other companies began offering integrated cell phones, which increased buyer power and reduced supplier power. 6 Which of Porter’s Five Forces did Apple address through its customer-developed applications? Apple decreased the power of its buyers and increased its own supplier power by introducing customer developed applications. Since the iPhone was the first to market with an internet access, data storage, MP3 player, which could all accept customer developed applications, its buyers had no power and no choice but to purchase the product and the applications from Apple. Unfortunately, Apple could not create an entry barrier and there are more and more companies offering customer developed applications such as Google and its Android operating system.