Persuasive paper Essay Example for Free

Persuasive paper Essay Is it right to die? I would state the question in another format, is there a right to die? It is the most difficult question to receive an answer as we focus on people suffering from different conditions, be it psychological, physical or emotional, that beyond doubt, have led to terminal illness. I cannot give a straight yes or a straight no. The debate has been so hot in several nations. Several stakeholders are considering going the Oregon way. The whole debate focuses on suicide. By way of philosophy and other disciplines of ethics, it is very difficult to judge whether suicide is wrong or right. It has hitherto paused hard questions that get diverse responds from different persons. These persons can be philosophers from different eras, different geographical regions, and customs. It is further mesmerizing that those of the same times, similar traditions, and even same places arrive at different answers as pertains this very subject. If euthanasia was to be made legal, there are no criteria that can be used to determine the very genuine cases. Those people who proposes this action, as they define the rights of a person focuses narrowly on the normal cases only; an adult person, who is in his or her right mind, acting in their own volition, putting in consideration his or her own possessions or those entrusted to this person. I would therefore question the basis for determining the abnormal circumstances, and the limits that are sensible in today’s cultural situation. In this, we think of the slippery slope concern, soon many cases will transit to explicit murder. We will not have guarantee for people who instigate murder and claim that the people they killed were more than willing to die. The people that will fall as victims of this murder are the disable, disadvantaged, or those considered to be â€Å"undesirable† in the society – those who are a burden to their caregivers or even the state, which should be obliged to giving indiscriminate care to all groups of people. Goldberg (n. d), states that, â€Å"Thus, many U. S. ommentators fear that, if assisted suicide and euthanasia were legalized, death would be inflicted unwillingly on disabled, disadvantaged, or otherwise undesirable individuals who might be considered a burden by their caregivers or the state† (Goldberg, n. d). He continues to say that â€Å"Biased physicians, family members, or managed care organizations might consciously or subconsciously influence difficult or expensive patients to take advantage of assisted suicide† (Goldberg, n. d). It is also clear that no human endeavour is immune of abuse. This will make the Oregon requirement difficult to trust. Even ‘acting on one’s own volition’ is still not good because many patients may act quickly without enough information of existing medical care, thinking that their fate is just death. So why wont we restrict the ‘person’s autonomy’ till the person is fully informed? Thinking this way will definitely call for not legalising euthanasia. John Stuart Mill gives an example of person who wants to cross a broken bridge, as he concludes he says that this person would not really continue to do that if he is fully informed about the dangers of going that way (Mill, 2005). The other concern that we have is that this practice will be in total contradiction with the present physicians’ role as healer. It is a stipulation that physicians should always do their best to save lives and not destroy them at all. The physicians’ role should be limited to saving lives as it has been over time. Legalizing euthanasia means that the physicians’ role is broadened to the point of the patients’ advocate in the maters concerning their own health and ways they want it to be handled. This will arouse the craving of patients to commit suicide and allow many cases that would otherwise be alleviated, to run to the worst. Still on the issue of rights, every one has a right that is inherent in nature and anyone should not interfere with the individual’s rights. People should therefore exercise their own rights without interfering with others’ and no one should interfere with the autonomy of this individual. As we say that rights are inherent in an individual, we are saying that these person posses this rights because of the life that he has. Without this life, the rights he claims to have are null and void. This takes us to the point that no one should interfere with the life because it is the carrier of this same rights. Mill states that, â€Å"But by selling himself for a slave, he abdicates his liberty; he forgoes any future use of it, beyond that single act† (Mill, 2005, pp 67). He continues to say, â€Å"He therefore defeats, in his own case, the very purpose which is the justification of allowing him to dispose of himself† (Mill, 2005, pp 67). In our case the person who decides to die no longer has the autonomy that we advocate to give in allowing them to die. The person defeats his own reason for wanting to die. Mill continues to say, â€Å"He is no longer free; but is thenceforth in a position which has no longer the presumption in its favor, that would be afforded by his voluntarily remaining in it† (Mill, 2005, pp 67). He concludes on this matter that, â€Å"The principle of freedom cannot require that he should be free not to be free, it is not freedom, to be allowed to alienate his freedom† (Mill, p 67). If we have to protect the autonomy of individuals then we should protect their lives too. We can still work without euthanasia because many of our physicians have worked hard and are still working hard to come up will the best palliative care for the terminally ill people. Under good circumstances of proper palliative care, this practice will be unnecessary. This care can conserve the dignity of terminally ill people till they die. It is therefore our responsibility to give them this care rather than to help them kill themselves, which is not dignified at all (Chochinov, 2002). Though, the numbers of people supporting euthanasia is growing with time, everyone should think about the above-discussed concerns. This will help each one of us know that we are capable of giving good care to terminally ill patients without letting them die suicidal deaths. We can think it right that allowing them to die is actually denying them their autonomy, and hence the inherent rights. We should always strive to give perfect care than to kill.

Constructing Social Knowledge Graph from Twitter Data

Constructing Social Knowledge Graph from Twitter Data   Yue Han Loke 1.1 Introduction The current era of technology allows its users to post and share their thoughts, images, and content via networks through different forms of applications and websites such as Twitter, Facebook and Instagram. With the emerging of social media in our daily lives and it is becoming a norm for the current generation to share data, researchers are starting to perform studies on the data that could be collected from social media [1] [2].The context of this research will be solely dedicated to Twitter data due to its publicly available wealth of data and its public Stream API. Twitters tweets can be used to discover new knowledge, such as recommendations, and relationships for data analysis. Tweets in general are short microblogs consisting of maximum 140 characters that can consists of normal sentences to hashtags and tags with @, other short abbreviation of words (gtg, 2night), and different form of a word (yup, nope). Observing how tweets are posted shows the noisy and short lexical natu re of these texts. This presents a challenge to the flexibility of Twitter data analysis. On the other hand, the availability of existing research conducted on entity extraction and entity linking has decreased the gap between entities extracted and the relationships that could be discovered. Since 2014, the introduction of the Named Entity rEcognition and Linking (NEEL) Challenge [3] has proved the significance of automated entity extraction, entity linking and classification appearing in different event streams of English tweets in the research and commercial communities to design and develop systems that could solve the challenging nature in tweets and to mine semantics from them. 1.2 Project Aim The focus of this research aims to construct a social knowledge graph (Knowledge Base) from Twitter data. A knowledge graph is a technique to analyse social media networks using the method of mapping and measurement for both relationships and information flows among group, organizations, and other connected entities in social networks [4]. A few tasks are required to successfully create a knowledge graph based on Twitter data A method to aid in the construction of knowledge graph is by extracting named entitiessuch as persons, organizations, locations, or brands from the tweets [5]. In the domain of this research, the named entity to be referenced in the tweet is defined as a proper noun or acronym if it is found in the NEEL Taxonomy in the Appendix A of [3], and is linked to an English DBpedia [6] referent and a NIL referent. The second component in creating a social knowledge graph is to utilize those extracted entities and link them to their respective entities in a knowledge base. For example, Tweet: The ITEE department is organizing a pizza gettogether at UQ. #awesome ITEE refers to an organization and UQ refers to an organization as well. The annotation for this is [ITEE, organization, NIL1], where NIL1 refers to the unique NIL referent describing the real-world entity ITEE that does not have the equivalent entry in DBpedia and [UQ, Organization, dbp:University_of_Queensland] which represents the RDF triple (subject, predicate, object). 1.3 Project Goals Firstly, getting the Twitter tweets. This can be achieved by crawling Twitter data using Public Stream API[1] available in the Twitter developer website. The Public Stream API allows extraction of Twitter data in real time. Next, entity extraction and typing with the aid of a specifically chosen information extraction pipeline called TwitIE[2] open-source and specific to social media and has been tested most extensively on microblog sentences. This pipeline receives the tweets as input and recognises the entities in the same tweet. The third task is to link those entities mined from tweets to the entities in the available knowledge base. The knowledge base that has been selected for the context of this project is DBpedia. If there is a referent in DBpedia, the entity extracted will be linked to that referent. Thus, the entity type is retrieved based on the category received from the knowledge base. In the event of the unavailability of a referent, a NIL identifier is given as shown in section 1.2. The selection of an entity linking system with the appropriate entity disambiguation and candidate entity generation that receives the extracted entities from the same Tweet and produce a list with all the candidate entities in the knowledge base. The task is to accurately link the correct entity extracted to one of the candidates. The social knowledge graph is an entity-entity graph combining two extracted sources of entities. The first is the analysis of the co-occurrence of those entities in same tweet or same sentence. Besides that, the existing relationships or categories extracted from DBpedia. Thus, the project aims to combine the extraction of co-occurrence of extracted entities and the extracted relationships to create a social knowledge graph to unlock new knowledge from the fusion of the two data sources. Named Entity Recognition (NER), Information Extraction (IE) are generally well researched in the domain of longer text such as newswire. However, overall, microblogs are possibly the hardest kind of content to process. For Twitter, some methods have been proposed by the research community such as [7] that uses a pipeline approach to perform the first tokenisation and POS tagging and topic models were used to find named entities. [8] propose a gradient-descent graph-based method for doing joint text normalisation and recognition, reaching 83.6% F1 measure. Besides that, entity linking in knowledge graphs have been studied in [9] using graph-based method by collectively gather the referent entities of all named entities in the same document and by modelling and exploiting the global interdependence between Entity Linking decisions. However, the combination of NER, and Entity Linking in Twitter tweets is still a new area of research since the NEEL challenge was first established in 2013 . Based on the evaluation conducted in [10] on the NEEL challenge, lexical similarity mention detection strategy that exploit the popularity of the entities and apply a distance similarity functions to rank entities efficiently, and n-gram [11] features are used. Besides that, Conditional Random Forest (CRF) [12] is another mentioned entity extraction strategy. In the entity detection context, graph distances and various ranking features were used. 2.1. Twitter crawling [13] defined the public Twitter Streaming API provides the ability of collecting a sample of user tweets. Using the statuses/filter API provides a constant stream of public Tweets. Multiple optional parameters may be specified such as language and locations. Applying the method CreateStreamingConnection,a POST request to the API has the capability of returning the public statuses as a stream. The rate limit of the Streaming API allows each application to submit up to 5,000 Twitter. [13] Based on the documentation, Twitter currently allows the public to retrieve at most a 1% sample of their data posted on Twitter at a specific time. Twitter will begin to return the sample data to the user when the number of tweets reaches 1% of all tweets on Twitter. According to [14] research comparing Twitter Streaming API and Twitter Firehouse, the final results of the Streaming API depends strongly on the coverage and the type of analysis that the researcher wishes to perform. For example, the researchers found that if given a set of parameters and the number of tweets matching them increases, the coverage of the Streaming API is reduced. Thus, if the research is concerning a filtered content, the Twitter Firehose would be a better choice with regards to its drawback of restrictive cost. However, since our project requires random sampling of Twitter data without filters except for English language, Twitter Streaming API would be an appropriate choice since it is freely available. 2.2. Entity Extraction [15] suggested an open-source pipeline, called TwitIE which is solely dedicated for social media components in GATE [16]. TwitIE consists for 7 parts: tweet import, language identification, tokenisation, gazetteer, sentence splitter, normalisation, part-of-speech tagging, and named entity recogniser. Twitter data is delivered from the Twitter Streaming API in JSON format. TwitIE included a new Format_Twitter plugin in the most recent GATE codebase which converts the tweets in JSON format automatically into GATE documents. This converter is automatically associated with documents names that end in .json, if not text/x-json-twitter should be specified. The TwitIE system uses TextCat a language processing and identification algorithm for its language identification. It has the capability to provide reliable tweet language identification for tweets written in English using the English POS tagger and named entity recogniser. Tokenisation oversees different characters, class sequence and rules. Since the TwitIE system is dealing with microblogs, it treats abbreviations and URLs as one token each by following the Ritters tokenisation scheme. Hashtags and user mentions are considered as two tokens and is covered by a separate annotation hashtags. Normalisation in TwitIE system is divided into two task: the identification of orthographic errors and correction of the errors found. The TwitIE Normaliser is designed specific to social media. TwitIE reuses the ANNIE gazetteer lists which contain lists such as cities, organisations, days of the week, etc. TwiTie uses the adapted version of the Stanford Part-of speech tagger which is tweets tagged with Penn TreeBank(PTB) tagset trained. The results of using the combination of normalisation, gazetteer name lookup, and POS tagger, the performance was increased to 86.93%. It was further increased to 90.54% token accuracy when the PTB tagset was used. Named entity recognition in TwitIE has a +30% absolute precision and +20% abso lute performance increase as compare to ANNIE, mainly respect to date, Organizations and Person. [7] proposed an innovative approach to distant supervision using topic models that pulls large amount of entities gathered from Freebase, and large amount of unlabelled data. Using those entities gathered, the approach combines information about an entitys context across its mentions. T-NER POS Tagging system called T-POS has added new tags for Twitter specific phenomenal retweets such as usernames, urls and hashtags. The system uses clustering to group together distributionally similar words for lexical variations and OOV words. T-POS utilizes the Brown Clusters and Conditional Random Fields. The combination of both features results in the ability to model strong dependencies between adjacent POS tags and make use of highly correlated features. The results of the T-POS are shown on a 4-fold cross validation over 800 tweets. It is proved that T-POS outperforms the Standford tagger, obtaining a 26% reduction in error. Besides that, when trained on 102K tokens, there is an error reduct ion of 41%. The system includes shallow parsing which can identify non-recursive phrases such as noun, verb and prepositional phrases in text. T-NERs shallow parsing component called T-CHUNK, obtained a better performance at shallow parsing of tweets as compared against the off the shelf OpenNLP chunker. As reported, a 22% reduction in error. Another component of the T-NER is the capitalization classifier, T-CAP, which analyse a tweet to predict capitalization. Named entity recognition in T-NER is divided into two components: Named Entity Segmentation using T-SEG, and classifying named entities by applying LabeledLDA. T-SEG uses IOB encoding on sequence-labelling task to represent segmentations. Furthermore, Conditional Random Fields is used for learning and inference. Contextual, dictionary and orthographic features: a set of type lists is included in the in-house dictionaries gathered from Freebase. Additionally, outputs of T-POS, T-CHUNK and T-CAP, and the Brown clusters are used to generate features. The outcome of the T-SEG as stated in the research paper, Compared with the state-of-the-art news-trained Stanford Named Entity Recognizer. T-SEG obtains a 52% increase in F1 score. To address the issues of lack of context in tweets to identify the types of entities they contain and excessive distinctive named entity types present in tweets, the research paper presented and assessed a distantly supervised approach based on LabeledLD. This approach utilizes modelling of every entity as a combination of types. This allows information about an entitys distribution over types to be shared across mentions, naturally handling ambiguous entity strings whose mentions could refer to different types. Based on the empirical experiments conducted, there is a 25% increase in F1 score over the co-training approach to Named Entity Classification suggested by Collins and Singer (1999) when applie d to Twitter. [17] proposed a Twitter adapted version of Kanopy called Kanopy4Tweets that uses the approach of interlinking text documents with a knowledge base by using the relations between concepts and their neighbouring graph structure. The system consists of four parts: Name Entity Recogniser (NER), Named Entity Linking (NEL), Named Entity Disambiguation(NED) and Nil Resources Clustering(NRC). The NER of Kanopy4Tweets uses a TwitIE a Twitter information extraction pipeline mentioned above. For the Named Entity Linking. For NEL, a DBpedia index is build using a selection of datasets to search for suitable DBpedia resource candidates for each extracted entity. The datasets are store in a single binary file using HDT RDF format. This format has compact structures due to its binary representation of RDF data. It allows for faster search functionality without the need of decompression. The datasets can be quickly browse and scan through for a specific object, subject or predicate at glance. For e ach named entity found by NER component, a list of resource candidates retrieved from DBpedia can be obtain using the top-down strategy. One of the challenges found is the large volume of found resource candidates impacts negatively on the processing time for disambiguation process. However, this problem can be resolved by reducing the number of candidates using a ranking method. The proposed ranking method ranks the candidates according to the document score assigned by the indexing engine and selects the top-x elements. The NED takes an input of a list of named entities which are candidate DBpedia resources after the previous NEL process. The best candidate resource for each named entity is selected as output. A relatedness score is calculated based on the number of paths between the resources weighted by the exclusivity of the edges of these paths which is applied to candidates with respect to the candidate resources of all other entities. The input named entities are jointly dis ambiguated and linked to the candidate resources with the highest combined relatedness. NRC is a stage whereby if there are no resource in the knowledge base that can be linked to a named entity extracted. Using the Monge-Elkan similarity measure, the first NIL element is assign into a new cluster, then the next element is used to differentiate from the previous ones. An element is added to a cluster when the similarity between an element and the present clusters is above a fixed threshold, the element is added to that particular cluster, whereas a new cluster is formed if there are no current cluster with a similarity above the threshold is found. 2.3. Entity Extraction and Entity Linking [18]proposed a lexicon-based joint Entity Extraction and Entity Linking approach, where n-grams from tweets are mapped to DBpedia entities. A pre-processing stage cleans and classifies the part-of-speech tags, and normalises the initial tweets converting alphabetic, numeric, and symbolic Unicode characters to ASCII equivalents. Tokenisation is performed on non-characters except special characters joining compound words. The resulting list of tokens is fed into a shingle filter to construct token n-grams from the token stream. In the candidate mapping component, a gazetteer is used to map each token that is compiled from DBpedia redirect labels, disambiguation labels and entities labels that is linked to their own DBpedia entities. All labels are lowercase indexed and linked by exact matches only to the list of candidate entities in the form of tokens. The researcher used a method of prioritizing longer tokens than shorter ones to remove possible overlaps of tokens. For each entity ca ndidate, it considers both local and context-related features via a pipeline of analysis scorers. Examples of local features included are string distance between the candidate labels and the n-gram, the origin of the label, its DBpedia type, the candidates link graph popularity, the level of uncertainty of the token, and the surface form that matches best. On the other hand, the relation between a candidate entity and other candidates with a given context is accessed by the context-related features. Examples of mentioned context-related features are direct links to other context candidates in the DBpedia link graph, co-occurrence of other tokens surface forms in the corresponding Wikipedia article of the candidate under consideration, co-references in Wikipedia article, and further graph based feature of the link graph induced by all candidates of the context graph which includes graph distance measurements, connected component analysis, or centrality and density observations. Besid es that, the candidates are sorted per their confidence score based on how an entity describes a mention. If the confidence score is lower than the threshold chosen, a NIL referent is annotated. [19] proposed a lexical based and n-grams features to look up resources in DBpedia. The role of the entity type was assigned by a Conditional Random Forest (CRF) classifier, that is specifically trained using DBpedia related feature (local features), word embedding (contextual features), temporal popularity knowledge of an entity extracted from Wikipedia page view data, string similarity measures to measure the similarity between the title of the entity and the mention (string distance), and linguistic features, with additional pruning stage to increase the precision of Entity Linking. The whole process of the system is split into five stages: pre-processing, mention candidate generation, mention detection and disambiguation (candidate selection), NIL detection and entity mention typing prediction. In the pre-processing stage, tweet tokenisation and part-of-speech tags were used based on ARK Twitter Part-of-Speech Tagger, together with the tweet timestamps extracted from tweet ID. Th e researchers used an in-house mention-entity dictionary of acronyms. This dictionary computes the n-grams (n [20] research paper proposed an entity linking technique to link named entity mentions appearing in Web text with their corresponding entities in a knowledge base. The solution mentioned is by employing a knowledge base. Due to the vast knowledge shared among communities and the development of information extraction techniques, the existence of automated large scale knowledge bases has been ensured. Thus, this rich information about the worlds entities, their relationships, and their semantic classes which are all possibly populated into a knowledge base, the method of relation extraction techniques is vital to obtain those web data that promotes discovery of useful relationships between entities extracted from text and their extracted relation. Once possible way is to map those entities extracted and associated them to a knowledge base before it could be populated into a knowledge base. The goal of entity linking is to map ever textual entity mention m à ¢Ã‹â€ Ã‹â€  M to its corres ponding entry e à ¢Ã‹â€ Ã‹â€  E in the knowledge base. In some cases, when the entity mentioned in text does not have its corresponding entity record in the given knowledge base, a NIL referent is given to indicate a special label of un-linkable. It is mentioned in the paper that named entity recognition and entity linking o be jointly perform for both processes to strengthen one another. A method proposed in this paper is candidate entity generation. The objective of the entity linking system is to filter out irrelevant entities in the knowledge base that for each entity extracted. A list of candidates which might be the possible entities that the extracted entity is referring to is retrieved. The paper suggested three techniques to handle this goal such as name based dictionary techniques entity pages, redirect pages, disambiguation pages, bold phrases from the first paragraphs, and hyperlinks in Wikipedia articles. Another method proposed is the surface form expansion from the local document that consists of heuristics based methods and supervised learning methods, and methods based on search engine. In the context of candidate entity ranking method, five categories of methods are advised. The supervised ranking methods, unsupervised ranking methods, independent ranking methods, collective ranking methods and collaborative ranking methods. Lastly, the research paper mentioned ways to evaluate entity linking systems using precision, recall, F1-measure and accuracy. Despite all these methods used in the three main approaches is proposed to handle entity linking system, the paper clarified that it is still unclear which are the best techniques and systems. This is since different entity linking system react or perform differently according to datasets and domains. [21] proposed a new versatile algorithm based on multiple addictive regression trees called S-MART (Structured Multiple Additive Regression Trees) which emphasized on non-linear tree-based models and structured learning. The framework is a generalized Multiple Addictive Regression Trees (MART) but is adapted for structured learning. This proposed algorithm was tested on entity linking primarily focused on tweet entity linking. The evaluation of the algorithm is based on both IE and IR situations. It is shown that non-linear performs better than linear during IE. However, for the IR setting, the results are similar except for LambdaRank, a neural network based model. The adoption of polynomial kernel further improves the performance of entity linking by non-LINEAR SSVM. The paper proved that entity linking of tweets perform better using tree-based non-linear models rather than the alternative linear and non-linear methods in IE and IR driven evaluations. Based on the experiments condu cted, the S-MART framework outperforms the current up-to-date entity linking systems. 2.4. Entity Linking and Knowledge Base Based on [22], an approach to free text relation extraction was proposed. The system was trained to extract the entities from the text from existing large scale knowledge base in a cooperatively manner. Furthermore, it utilizes the learning of low-dimensional embedding of words, entities and relationships from a knowledge base with regards to score functions. Built upon the norm of employing weakly labelled text mention data but with a modified version which extract triples from the existing knowledge bases. Thus, by generalizing from knowledge base, it can learn the plausibility of new triples (h, r, t); h is the left-hand side entity (or head), the right-hand side entity (or tail) and r the relationship linking them, even though this specific triple does not exist. By using all knowledge base triples rather than training only on (mention, relationship), the precision on relation extraction was proved to be significantly improved. [1] presented a novel system for named entity linking over microblog posts by leveraging the linked nature of DBpedia as knowledge base and using graph centrality scoring as disambiguation methods to overcome polysemy and synonymy problems. The motivation for the authors to create this method is because linked entities tend to appear in the same tweets because tweets are topic specific and together with the assumption since tweets are topic specific, related entities tend to appear in the same tweet. Since the system is tackling noisy tweets acronyms handling and Hashtags in the process of entity linking were integrated. The system was compared with TAGME, a state-of-the-art system for named entity linking designed for short text. The results shown that it outperformed TAGME in Precision, Recall and F1 metrics with 68.3%, 70.8% and 69.5%. [23] presented an automated method to populate a Web-scale probabilistic knowledge base called Knowledge Vault (KV) that uses the combination of extractions from the Web such as text documents (TXT), HTML trees (DOM), Html tables (TBL), and Human Annotated pages (ANO). By using RDF triples (subject, predicate, object) with association to a confidence score that represents the probability that KV believes the triple is correct. In addition, all 4 extractors are merged together to form one system called FUSED-EX by constructing a feature vector for each extracted triple. Next, a binary classifier is applied to compute the formula. The advantages of using this fusion extractor is that it can learn the relative reliabilities of each system as well as creating a model of the reliabilities. The benefits of combining multiple extractors include 7% higher confidence triples and a high AUC score (the higher probability that a classifier will choose a randomly chosen positive instance to be ra nked) of 0.927. To overcome the unreliability of facts extracted from the Web, prior knowledge is used. In the domain of this paper, Freebase is used to fit the existing models. Two ways were proposed in the paper which are Path ranking algorithm with AUC scores of 0.884 and the Neural network model with a AUC score of 0.882. A fusion of both methods stated was conducted to increase performance with an increased AUC score of 0.911. With the evidence of the benefits of fusion quantitatively, the authors of the paper proposed another fusion of the prior methods and the extractors to gain additional performance boost. The result of the fusion is a generation of 271M high confidence facts with 33% new facts that are unavailable in Freebase. [24]proposed TremenRank, a graph based model to tackle the target entity disambiguation challenge, task of identifying target entities of the same domain. The motivation of this system is due to the challenges and unreliability of current methods that relies on knowledge resources, the shortness of the context which a target word occurs, and the large scale of the document collected. To overcome these challenges, first TremenRank was built upon the notion of collectively identity target entities in short texts. This reduces memory storage because the graph is constructed locally and is continuously scale-up linearly as per the number of target entities. This graph was created locally via inverted index technology. There are two types of indexes used: the document-to-word index and the word-to-document index. Next, the collection of documents (the shorts texts) are modelled as a multi-layer directed graph that holds various trust scores via propagation. This trust score provided an in dication of the possibility of a true mention in a short text. A series of experiments was conducted on TremenRank and the model is more superior than the current advanced methods with a difference of 24.8% increase in accuracy and 15.2% increase in F1. [25]introduced a probabilistic fusion system called SIGMAKB that integrates strong, high precision knowledge base and weaker, and nosier knowledge bases into a single monolithic knowledge base. The system uses the Consensus Maximization Fusion algorithm to validate, aggregate, and ensemble knowledge extracted from web-scale knowledge bases such as YAGO and NELL and 69 Knowledge Base Population. The algorithm combines multiple supervised classifiers (high-quality and clean KBs), motivated by distant supervision and unsupervised classifiers (noisy KBs) Using this algorithm, a probabilistic interpretation of the results from complementary and conflicting data values can be shown in a singular response to its user. Thus, using a consensus maximization component, the supervised and unsupervised data collected from the method stated above produces a final combined probability for each triple. The standardization of string named entities and alignment of different ontologies is done in the pre-processing stage. Project plan Semester 1 Task Start End Duration(days) Milestone Research: 23/03/2017 Twitter Call 27/02/2017 02/03/2017 4 Entity Recognition 27/02/2017 02/03/2017 4 Entity Extraction 02/03/2017 02/03/2017 7 Entity Linking 09/03/2017 16/03/2017 7 Knowledge Base Fusion 16/03/2017 23/03/2017 7 Proposal 27/02/2017 30/03/2017 30 30/03/2017 Crawling Twitter data using Public Stream API 31/03/2017 15/04/2017 15 15/04/2017 Collect Twitter data for training purp

Three Major Types Of Buying Situation Commerce Essay

Three Major Types Of Buying Situation Commerce Essay Consumer behaviour is the study of individuals, groups, or organizations and the processes they use to choose, expend, and dispose of products, services, experiences, or ideas to satisfy their needs and the impacts that these processes have on the consumer and society (Noel, 2009). Consumer behaviour mixes elements from psychology, sociology, social anthropology and economics and it also intends to understand the buyer decision making process, both in individual and in groups (Noel, 2009). There are various elements which can influence consumer behaviour, recent research implies that it may vary depending on the buying situation. This essay is going to define the main types of buying situations, outline the characteristics of them and explain factors which are likely to impact customer involvement in each situation. In general, there are three major types of buying situations (BE, 2005). †¢ The new task is a business buying situation in which the buyer purchases a product or service for the first time. †¢ The modified rebuy is defined as a business buying situation in which the buyer wants to modify product specifications, prices, terms, or suppliers. †¢ Straight rebuy is a buying situation in which the buyer routinely reorders something without any modifications. The three types of buying situations could be significantly different. Various factors may work in different situations. Every time when the buyer is to take a purchase decision, buying situation can be different, it may or may not be the same as the previous one. The differentiation between the two buying situations may be caused by the absence of any or all of the following factors (LME, 2006). †¢ Awareness about competing brands in a product group. †¢ Customer has a decision criterion. †¢ Customer is able to evaluate and decide on his choice. According to the factors above, the three major types of buying situation could be obviously different. The new task could also be defined as extensive problem solving  situation (LME, 2006). In this situation, the buyer has no past experience for products and he is totally new to buy the products which require some and extensive efforts for a buyer to decide about the product purchase. It may take customers longer time to make a decision because it could have a greater risk or cost and take more time in getting know of the new products. Modified rebuy could also be called as limited problem solving situation in which supplies a change and gives the customer with new experience and new preference (CM, 2005). It gives a chance to the customer to try something new. If the introduction of a new brand or a product shows many advantages to the customer, it could require a change in the customers decision criterion. For example, a housewife decides to buy a soap and she sees a new liquid toilet soap which promises to keep her skin soft and moisturized, the brand also promises to give vitamin E, which the manufacturer claims is required in temperate conditions. The liquid toilet soap brand is available in four fragrances .The pack can be refilled every time the soap gets fully consumed .Now this introduction is likely to change her decision and may be the choice criterion. If she spends some time in evaluating the liquid toilet soap against the normal bar soap and then decides to try it, we conclude that for her it was a li mited problem solving situation (CM, 2005). As can be seen, modified rebuy might often lead to a trial purchase. The customer may even decide to continue with her current product choice. Generally it has been admitted that brand extension strategy helps the customer to reduce the elements of newness in the purchase decision. Straight rebuy is also known as extensive problem solving situation  and it is characterized by the presence of all three criterion for differentiation (CM, 2005). In other words, customers are aware of his or her choices, they know what they are searching for, as his or her choice, what exactly his need is and which is based on personal experience of either self or others might be relatives, friends or the customers have heard about it that is known to be called as good messages. Generally, the customers spend little or no time choosing alternatives of the product and the substitutes of the product .Brand loyalty is relatively higher here. Moreover, this is a buying situation where a customer perceives a low risk in buying the product and/or the brand. For example, a housewife goes to the shop or a supermarket and spends much less time in choosing her toiletries, drinks like tea or coffee and other food products. For each time she goes to buy the things for family requirements and needs, she generally finishes up buying the same brand. As it is shown above, the three factors which make the differentiations between the buying situations appear different in each situation. Thus, there could be different factors which affect customer involvement in each situation. In general, there are four of them (Song, J.H. and Adams, C.R., 1993): †¢Capacity: What it does for a buyer; †¢Quality: How well or poorly it does the specified functions; †¢Price: The amount paid by the buyer; †¢Effort: The time and energy expended by the buyer. These four factors are most likely to affect customers to make the decision when they are doing a purchase, which could participate differently in the three buying situations. In the new task buying situation, because customer has no experience for the products, it may have more factors affecting the customer involvement. The customers could consider all the factors: capacity, quality, price and also brand, it could take the customers more time than other buying situations. It could be the buying situation which is most likely to affect customer involvement. The firms have to set all the factors right if they want to attract new customers or they want to develop a new product. In the modified rebuy situation, customers may contrast the previous products with the new ones. As the customers understand what they need and what the products can do, the factors like quality and price are important to affect the customer involvement. Sometimes, a good introduction of the capacity is important as well. In this situation, the brand loyalty could also act as an important factor of affecting the customer involvement. It also may take customers quite a long time befor e making a decision. In the straight rebuy situation, the customers know exactly what they need, they have already know the information about the products they want. Therefore, the most likely factors which could affect the customer involvement are the capacity and the quality. Once the firms have done well on the quality of their products and also make a good introduction of the capacity of products, it could make customers spending less time on making a decision. It may let customers feel easy to be involved in the consuming. In conclusion, there are three major types of buying situations, which are new task, modified rebuy and straight rebuy. Three factors make the buying situations be different from the others, customers may face different problems in these situations. Thus, there are four main factors which are likely to affect customer involvement. Each situation could also have different types of factors which effect the customer involvement. All of the above suggests that consumer behaviour do vary depending on the buying situation. More research could be done on what firms can do to improve the customer involvement while choosing their products.

William Faulkner’s short novel, The Bear Essay -- Bear

William Faulkner’s short novel, The Bear "The Bear" is a short novel in an anthology that begins in Yoknapatwpha County sometime after the Civil War. The story deals with loyalty, honor, truth, bravery, courage, fear, nature, history and choices. Cleanth Brooks best described this story by saying, "Faulkner's villains do not respect nature and their fear of it has nothing in common with the fear of the Lord or with awe in the presence of the divine." (Brooks 149) In the story, we find a bear that has learned to outwit and survive hunters for years. It wasn't until they took a beast of the wild and tamed it before they could even come close to the bear. They took a beast of nature to kill a beast of nature for their own personal pleasure, for sport...a conversation piece. When looking into the history aspect of the story, think of human actions and how People make radical decisions that may affect the rest of their lives, or even the lives of others, not even giving a second thought to the consequences. This book deals with a radical decision made by one man that changed the lives of many. The author of this story, William Faulkner, was a white southern male born "September 25, 1897, in New Albany, Mississippi. He died July 6, 1962." (Compton's CD) Faulkner had a way with Christianity, but more with the nature of man. He believed that man was nature. We can see how this affects the story with the statement "It was of the men, not white nor black nor red but men, hunters, with the will and hardihood to endure and the humility and skill to survive..."(Faulkner 327) The story begins with Ike and C who are going on a hunting trip to try to catch Old Ben, t... ...ficient utilization of it, or when he ceases to love it and to carry on his contention with it in terms of some sort of code, then he not only risks destroying nature but risks bestializing his own nature. (270) This novel is a wonderful book to read for those who like nature and the wilderness. It is through nature that one can achieve their highest level of love for the world. Although it may be hard to get through a few of the chapters, once you truly understand the meaning of the book, it will stick in your heart forever. Work Cited Brooks, Cleanth. "On the prejudices, predilections, and firm beliefs of William Faulkner." [Baton Rouge] LSU Press, 1987. "Six great modern short novels." New York Dell, 1982. 328 Brooks, Cleanth. William Faulkner The Yoknapatawpha County. London Yale University Press, 1974

Television’s Impact on its Viewers :: Television Media TV Essays

Television’s Impact on its Viewers   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Problems arise abundantly in almost every situation, and solutions are even more scarce. To find one great solution to a pressing problem of contemporary society is difficult, yet not impossible. Americans are plagued by the influences of television day in and day out. It is one of the main constants in many individuals lives, while grabbing the attention of families in ninety-eight percent of America’s homes and is kept on for an average of six and a half hours every day (Cheney 2). Perhaps there isn’t a problem so much in the fact that so many people have this mysterious box on for periods of time, but rather the problem lies in the way the viewer interprets the program that is being presented to them. How much control does the viewer really have over what programs they watch and when? How can a viewer monitor their viewing as well as their children’s in a manner that will benefit them both? And what is the solution for those wh o watch television for hours on end, giving up employment, schooling and other important duties? These questions will be addressed throughout the course of this paper, along with the best possible solutions that I have conjured up for such a controversial topic.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Television has become nearly unavoidable and American society is more affected by television than they realize. According to Nielsen Media Research, the average TV household in the United States now owns two sets, which are watched just over 30 hours every week by the typical American adult (Mahler 12). American society is more affected by television than is realized. Television offers thin slices of the real word, becoming a version of reality that is created by numerous components from mechanical parts to people, making up the medium of television. Because what is heard and seen on TV is a lot like what is experienced in real life, it’s easily and unconsciously assumed that what is on television- the sex, the violence, the commercials, the cartoons- is real, true, or normal. Viewers must realize the difference inbetween the fiction of TV and the reality of their own lives.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The term â€Å"couch potato† has been coined for those who sit around and watch television all day. Rutgers researcher Robert Kubey is one of the many academics troubled by the trend of excessive television viewing. Television’s Impact on its Viewers :: Television Media TV Essays Television’s Impact on its Viewers   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Problems arise abundantly in almost every situation, and solutions are even more scarce. To find one great solution to a pressing problem of contemporary society is difficult, yet not impossible. Americans are plagued by the influences of television day in and day out. It is one of the main constants in many individuals lives, while grabbing the attention of families in ninety-eight percent of America’s homes and is kept on for an average of six and a half hours every day (Cheney 2). Perhaps there isn’t a problem so much in the fact that so many people have this mysterious box on for periods of time, but rather the problem lies in the way the viewer interprets the program that is being presented to them. How much control does the viewer really have over what programs they watch and when? How can a viewer monitor their viewing as well as their children’s in a manner that will benefit them both? And what is the solution for those wh o watch television for hours on end, giving up employment, schooling and other important duties? These questions will be addressed throughout the course of this paper, along with the best possible solutions that I have conjured up for such a controversial topic.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Television has become nearly unavoidable and American society is more affected by television than they realize. According to Nielsen Media Research, the average TV household in the United States now owns two sets, which are watched just over 30 hours every week by the typical American adult (Mahler 12). American society is more affected by television than is realized. Television offers thin slices of the real word, becoming a version of reality that is created by numerous components from mechanical parts to people, making up the medium of television. Because what is heard and seen on TV is a lot like what is experienced in real life, it’s easily and unconsciously assumed that what is on television- the sex, the violence, the commercials, the cartoons- is real, true, or normal. Viewers must realize the difference inbetween the fiction of TV and the reality of their own lives.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The term â€Å"couch potato† has been coined for those who sit around and watch television all day. Rutgers researcher Robert Kubey is one of the many academics troubled by the trend of excessive television viewing.

A Comparison of the Merits of using Software or Hardware Transactional Memory, against Traditional ‘Semaphore’ Locking

1. IntroductionTransactional memory is poised to take parallel programming a step higher by making it more efficient and much easier to achieve, compared to traditional ‘semaphore’ locking. This is because transactional memory is easier to handle when tasks are divided into several free threads, especially when these threads do not have common access to data. This implies that each section can operate on a processor core and that there is no connection between cores. It can be challenging when different task sections are not totally free – that is, several threads are forced to upgrade a singly shared value. The traditional approach to this utilizes locks and every time that a thread changes a shared value, it requires a lock. In this case, it is not possible for any other thread to receive the lock if another thread possesses it. Instead, the thread must wait until the thread that has the lock can change the shared value. This is likely to require a complex comp utation, and to take an extended amount of time before eventually releasing the lock (Bright, 2011). The release of the lock allows the waiting thread to continue. While this is an effective system, it faces several major challenges. A key issues concerns updates to the shared value that occurs occasionally; therefore, making it rare for a thread to wait at ‘no time’ – the state in which the lock based system can be efficient (Alexandrescu, 2004). Nonetheless, this efficiency commonly disappears every time updates to the shared value are made. Threads take too much time waiting for a lock to appear and are unable to provide any use when in this state.2.Lock vs. Lock Free Data StructuresWhile it may seem easy to handle a singly shared value, locks are difficult to use correctly and this is a challenge faced in real programs. For instance, a program with dual locks 1 and 2 is likely to encounter a problem called a ‘deadlock’. A deadlock is a case wher eby two threads require two locks and only have the option of acquiring lock 1 then 2, or 2 followed by 1. As long as each thread needs the lock in the same order this will not present any issues; however, if one thread needs lock 1 and the other requires lock 2 at the same time, this can cause a deadlock. In this situation, the first thread waits for lock 2 to become free and the second waits for 1 to be free. This exchange makes it difficult for both to succeed and results in a deadlock. This issue might appear to be preventable and only likely to occur when a program has dual locks; however, it can become a challenge to ensure each section performs the right function when this becomes more complex.3. Transactional MemoryIt can argued that transactional memory can solve the problem of lock conflicts. In this case of a deadlock, the programmers could mark the sections of their programs which change the shared data, so that each of the marked blocks is implemented within a transact ion. This means that either the whole block executes, or none of it does. The program can therefore identify the shared value without locking it. This allows for the program to conduct all the necessary operations and write back the value, eventually committing the transaction (Bright, 2011). The key transaction occurs with the commit operation in which transactional memory system ascertains that shared data has been changed after the commencement of an operation. If this is not identified then the commit updates, allowing the thread to go ahead with its function. In case the shared value has not been modified, the transaction stops and the function of the thread is rolled back (Detlefs et al., 2001). In this instance, the program retries the operation. It can be seen, therefore, transactional memory has several merits over traditional semaphore locking. For example, transactional memory is optimistic; this infers that the threads are positioned to succeed and do not look forward to acquiring a lock. This is in case the other thread makes an attempt to conduct a concurrent operation (Detlefs et al., 2001). In an instance of concurrent modifications occurs when a single thread is forced to retry its function. In addition to this, there are no deadlocks in transactional memory. Transactional memory is a programming approach that programmers are familiar with; the transaction and rollback process is not new to those who have handled relational databases because they offer a similar set of features. Nonetheless, blocks facilitate the ease of developing large programs that are correct (Alexandrescu, 2004). Blocks with nested atomic blocks will perform the correct function, although this is not true in the case of lock-based data structur es.4.Merits of the HardwareThere has been little attention given towards hardware compared to software-based implementations. It has also been noted that most real processors seldom support transactional memory and, therefore, modifications are necessary (Maged, 2004). However, there are systems that use virtual machines to undertake their primary function and in this regard there are changes for the .NET and Java virtual machines (Bright, 2011). In other cases, systems use native codes that require certain special operations to allow access to the shared data. This enables the transactional memory software to ascertain that the right operations have occurred in the background. Such implementations have the advantages of ensuring that the programs that are produced are bug-free (Detlefs et al., 2001). The data in cache contains a version tag whereas the cache itself can maintain many versions of the same data. The software sends a signal to the processor to commence a transaction and performs the necessary action. This then signals the processor to commit the work. If other threads have changed the data, resulting in many versions, the cache will refuse the transaction and the software will be forced to try again. Should other versions not be created, then the data is committed (Bright, 2011). This facility is also applicable for speculative execution. A thread can commence execution with data available, whereas speculatively conducting important work – instead of waiting for upgraded versions of all data needed – might mean waiting for additional cores to complete computation (Alexandrescu, 2004). If the data was upgraded, then the work that is committed provides a performance boost; the work had been completed before the delivery of the final value. Should the data turn out to be stale, then the speculative work is rejected and re-deployed with the correct value (Bright, 2011).5.Logical FunctionsA significant advantage that transactional memory has over traditional lock-based programs is that it support is an extension of a load-link or store conditional. Load-link is an undeveloped operation that can be implemented to build many types of thread-safe constructs (Maged, 2004). This comprises both mechanisms that are known, such as locks, and unconventional data structures, such as lists that can be changed by many threads at the same time without locking at all (Alexandrescu, 2004). The creation of software transactional memory is possible through the use of load-link or store conditional. Load-link or store conditional contains two sections: firstly, it utilizes load link to recover the value from memory where it can then conduct the functions it needs to perform on that value. When there is a need to write a new value to the memory, this utilizes store conditional (Detlefs et al., 2001). Store conditional can only succeed if the memory value has not been changed after the load link. In case the value has been changed, the program has to return to the beginning and start again. These systems are restrictive because they do not follow writes to each memory bytes, but to the whole cache lines. This highlights the fact that store conditional has the potential to fail without modification of monitored value (Bright, 2011). Bright (2011) explains that store conditional is also most likely to fail if a context switch happens between the load link and store conditional. Transactional memory is a version of an enforced link load and store conditional; each thread can perform load link on several different memory locations (Maged, 2004). In addition to this, the commit operation does store conditional. This impacts on multiple locations at the same time, with each store either succeeding or failing (Bright, 2011).6.ConclusionIn conclusion, a lock-free procedure is sure to sustain the progress of a thread executing a procedure. While some threads can be put on hold arbitrarily, one thread is certain to progress each move. The whole system can then make progress despite the fact that some threads might take longer than others. It can be seen, therefore, that the use of software or hardware transactional memory presents better ways of ensuring consistency of stored data when accessed and manipulated by several concurrent threads than traditional ‘semaphore’ locking. Consequently, lock-based programs fail to provide any of the above mentioned guarantees7.ReferencesAlexandrescu, A. (2004) Lock-Free Data Structures. Available at: http://www.drdob bs.com/lock-free-data-structures/184401865 [Accessed 12th March 2014]. Bright, P. (2011) IBM’s new transactional memory: make-or-break time for multithreaded revolution. Available at: http://arstechnica.com/gadgets/2011/08/ibms-new-transactional-memory-make-or-break-time-for-multithreaded-revolution/ [Accessed 12th March 2014]. Detlefs, D., Martin, P.A., Moir, M. & Steele, G.L., (2001) ‘The Twentieth Annual ACM Symposium on Principles of Distributed Computing’, in Lock-free Reference Counting, ACM Press: New York. Maged, M.M. (2004) ‘Proceedings of the ACM SIGPLAN 2004 Conference on Programming Language Design and Implementation’, in Scalable Lock-free Dynamic Memory Allocation, ACM Press: New York.

Ohm’s Law Investigation Essay

Hypothesis: Because I am using copper wire as a resistor, when I increase the length of the wire the resistance should work how resistors in series work. This means that when I increase the length of wire (or add a piece in a series circuit) the resistance should increase too. Due to the increase in the number of atoms and ions between the two terminals it takes the electrons longer to get from one side to the other. For example if I double the length of the wire the resistance will double and the current will halve. My graph at the end should look something like this: The length of wire and resistance should be directly proportional to each other. Equipment: -2 1. 5V cells -Metre ruler -Copper wire -Voltmeter -Ammeter -Wiring -Crocodile clips Method: First I decided upon the type of wire I was going to use (copper), I made sure it was the same thickness each time I took readings because as aforementioned, if the cross sectional area is not kept constant, it will definitely affect the resistance and thus make my results inaccurate. Then I measured it to 100cm by laying it across a metre ruler, because this way I can be accurate to the millimetre. I then hooked the 2 cells up to the copper wire stretched across the metre ruler using my wires and crocodile clips. I connected the two terminals on the metre ruler 40cm apart. For this experiment I will take readings for p. d (potential difference) and the resistance using an ammeter and a voltmeter at distances ranging from 40cm to 100cm – I will take measurements in 5cm intervals (i. e. take measurements from 40cm, 45 cm and so on so forth). I made sure that the voltmeter and ammeter were set up in PARALLEL not series as this would damage them. Below is a diagram of my apparatus and how I set it up: And the (simple) schematic of the circuit: In this experiment I will keep all things constant (apart from the length of wire). I will keep do all my measurements in one day in a short space of time in the same room away from the windows (out of the sun) so the temperature does not change noticeably while I am carrying out the experiment, as this would affect my results and make them inaccurate. Also, when charge flows through the wire and there is resistance, it generates heat in the wire. I will keep the power on the wire for the least amount of time and take my readings quickly so the temperature does not affect my results. In addition to this I will also wait a minute after each reading so that the wire cools to room temperature again and my results are accurate. In theory the graph-line should be straight – if it isn’t then it indicates that there is another variable. The other constant is the cross sectional area of my wire – this is fairly easy to keep constant – just use the same piece of wire. I have to keep this the same because cross sectional area of wire is proportional to the resistance – if I do not keep it the same it will also make my results inaccurate. I will take readings from each distance 3 times and take the average of those, so I can greatly decrease the chances of getting an anomalous result. Results: Here is the results table followed by a graph representing each of the 3 – p. d, Current and then resistance. Length of Wire (cm) Potential Difference (V) Current (A) Resistance (? ). And finally, to calculate the resistance I used the ohm’s law formula of R=V/I. Analysis: My experiment was very successful and the results I got proved to be quite accurate and precise. Therefore my graphs and result tables provide me with a base to understand just why length affects the resistance. My prediction was that â€Å"when I increase the length of wire the resistance should increase too†; my prediction is supported by my results – and appears to be correct. The graphs and tables prove that the longer the copper wire, the higher the resistance. Resistance is also linked to charge flow, if I change the charge flow it will have an affect on the equation I = Q/t. If the current is changed then this will have an affect on the resistance. So with the help of the formulae I=Q/t and R=V/I, I now know that if I increase the charge flow, the current increases and the resistance decreases. Consequently if I double the length of wire the equation I = Q / t will be halved (due to the time increasing) causing the current to be halved and the resistance to be doubled. I can see one anomalous result in the ‘Current’ graph, which is at a length of 80cm – it appears to be at a slightly lower current than it should be in relation to the others. Evaluation: Using my results and my graphs I can clearly tell that my experiment was successful, I can tell this because, generally, none of my results have any inconsistent results and my graphs show straight lines. Even after repeating my experiment many times my graphs still remained just as precise and the graph showing the average results of the experiment is a perfect straight line. The fact that I got the similar results each time I did the experiment suggests that is was successful and also reliable, thus I must have carried out the experiment well. The way in which I conducted the experiment was good because I made sure that the voltage supplied to the wire was equal each time, the cross sectional area of the wire remained the same, and also that the wire cooled down between each result. The use of mm instead of cm made sure that the length was exact and not longer or shorter. Therefore my results were successful and reliable for us to work from. However this did not mean that the way in which I did the experiment couldn’t have been improved. Having to secure the wire so as to measure the length meant that it was difficult to attach the crocodile clips to exactly the end of the wire. I could not be sure that as I left the wire to cool it was not at a different temperature each time I begun again; this could have affected my results if it had been vastly different. In my experiment, I could also have investigated a number of other things, such as the effect of cross sectional area or temperature on the resistance. If I had looked at the effect that the cross sectional area had on resistance I would probably discover that as the wire doubled in cross sectional area the resistance would halve. This would be due to there being twice as many electrons – the current would travel a lot quicker and thus decrease the resistance. If I looked at how temperature affected resistance I would probably find that as the temperature of the wire increases, the particles within begin to vibrate much more because they have some extra energy, therefore it is much harder for the electrons to move through and thus the resistance will rise. So instead of just investigating how length affected the resistance of a piece of wire I could also have investigated the affect of temperature or cross sectional area on the piece of wire Adam Burclaff Page 1 of 10 Show preview only The above preview is unformatted text This student written piece of work is one of many that can be found in our GCSE Electricity and Magnetism section.