The Alphabet for Absolute Beginners

The Alphabet for Absolute Beginners At this point learners need to be able to use the alphabet in order to assimilate new vocabulary and ask spelling questions about new vocabulary they will be learning in future lessons. You should take in an alphabet chart for this lesson, this chart should have pictures of various objects beginning with the various letters of the alphabet (pre-schoolers alphabet books would work well in this situation). Alphabete List Teacher: (Read the alphabet list slowly, pointing to pictures as you speak. The following list is just an example, make sure to use something with pictures if possible. ) A as in appleB as in boyC as in carD as in dogE as in earF as in flagG as in greatH as in houseI as in insectJ as in joyK as in kindL as in lightM as in magicN as in nightO as in orchestraP as in peopleQ as in questionR as in redS as in sureT as in truckU as in uniqueV as in videoW as in wowX as in xeroxY as in yesZ as in zebra Teacher: Repeat after me (Model the idea of repeating after me, thus giving the students a new class instruction that they will understand in the future.) A as in appleB as in boyC as in carD as in dogE as in earF as in flagG as in greatH as in houseI as in insectJ as in joyK as in kindL as in lightM as in magicN as in nightO as in orchestraP as in peopleQ as in questionR as in redS as in sureT as in truckU as in uniqueV as in videoW as in wowX as in xeroxY as in yesZ as in zebra Student(s): (Repeat the above with the teacher) Spelling Names Teacher: Please write your name. (Model the following new class instruction by writing your name on a piece of paper. ) Teacher: Please write your name. (You may have to gesture to students to take a piece of paper out and write their names.) Student(s): (Students write their names on a piece of paper) Teacher: My name is Ken. K - E - N (Model spelling your name.). What is your name?(Gesture to a student.) Student(s): My name is Gregory. G - R - E - G - O - R - Y Continue this exercise around the room with each of the students. If a student makes a mistake, touch your ear to signal that the student should listen and then repeat his/her answer accenting what the student should have said.

Business Decision Making Essay Example | Topics and Well Written Essays - 3000 words - 1

Business Decision Making - Essay Example The individual, the community and the social and political hierarchy that constitute the system, now face new risks brought forth by the choices they have to make or will make in the future. This is a result of the deluge of information, the flooding of goods in a free market economy and the proliferation of environmental and scientific awareness that conflict with other pieces of information, alternative goods and concepts that are readily available at a flick of a finger. These aforementioned conveniences and awareness are sometimes deemed liabilities in contemporary society as access to specialized knowledge and the profound understanding of risks have deemed it difficult for societies to formulate institutional and collective decisions. However, individuals, with their predilection for personal control, are in some ways encouraged by consumerism and their ability to purchase and thus, decision-making can easily be generated in the personal level. The present transition of societies from industrial to knowledge societies has significantly affected not just individuals but also the economy and our political structure as well. With the societies' and the individual's volume of knowledge at the effortless disposal increasing at a high-speed rate and doubling every five years, the rise of the new social order founded on knowledge is inevitable (Stehr, 2001). The swift metamorphosis that our society will undergo in the near future will affect our politics and our democratic ideals. Nico Stehr asserts that knowledge is not just a 'constitutive' factor of the market economy but a fundamental 'organisational principle' upon which we base our very existence - even our way of life (2001). Living in a knowledge society only means that we systematize our social and political structures on the basis of what we know. This has significant implications in that knowledge and technology have freed us from the clutches of religious, military an d monarchic hegemony - monolithic institutions which are now considered obsolete. However, it is important to note that the political system's regulation of social circumstances, involving mainly careful planning, controlling, managing and forecasting of the aforesaid social conditions, has increasingly become difficult as society has faced fragility. This is not brought about by the emergence of the global culture and economy or the 'economisation' of social relations but the disappearance of political power in the face of increasing knowledge. The rise of a more hierarchal society which sprang from the attainment of knowledge has become more noticeable in more liberal democratic states as equality of knowledge of complex issues plaguing many democracies around the world is necessary for political legitimacy - one which arises from democratic participation (Teune et al, 2001). The key concept that most citizens consider is the legitimacy of the hierarchy in the political realm whic h could not be achieved unless democratic participation is encouraged and effected. For democracy to work, the surfacing of the informed citizenry which is passionately involved in participatory democracy is necessary. Understanding of these complex issues, however, requires the use of knowledge, and with the shaping of the general public to a robust knowledge society, differences and conflicts in opinion and ideas usually hampers the swift promulgation of policies that are necessary for the

Scan of Emerson, American Scholar and Factory Life As It Is Assignment

Scan of Emerson, American Scholar and Factory Life As It Is - Assignment Example an amendable constitution, which can be revised by the people as they please, is a political innovation that guarantees peace and happiness through self-governance. James Winthrop, a public official from Massachusetts, emphasized on the importance of the constitutional Bill of Rights. In reference to theoretical frames of politics, Winthrop asserted that systems of government belonging to the majority have inherent tendencies to disregard fundamental rights of the minority. Majority rulers exert tyrant principles upon minority subjects (Winthrop, 269). In order to safeguard the fundamental freedoms of the minority, Winthrop opposed the federal ratification of the constitution. Winthrop maintained that ratification of the constitution would consolidate constitutional powers to tyrant majorities, thus threatening the self-governance of American states. In order to prevent governmental despotism and usurpation against citizens, Winthrop proposed that the Bill of Rights should remain an essential constitutional component. With respect to the principle of representation, David Ramsay favors proportional representation of citizens in deliberation assemblies. Ramsay stated that proportional representation is a miniature form of communities; hence the representatives have interests and feelings of the majority (Ramsay, 268). On the contrary, James Winthrop opposes the concept of proportional representation. According to Winthrop, proportional representation does not guarantee preservation of minority rights against the wanton use of numerical tyranny by the majority (Winthrop, 269). Instead of proportional representation, Winthrop says that laws should be made by local officials who can establish immediate contact with their

Nursing Future Essay Example | Topics and Well Written Essays - 1000 words

Nursing Future - Essay Example Nursing varies from general nursing to nursing specialties, and after the four year course a nurse gets autonomous registration. There is scope for diversification after the basic graduate degree in the form of postgraduate courses. An interview was conducted with Dr. Llasus & Dr. Dover on topics like Nursing Faculty Teaching Expectations, Nursing Faculty Scholarship Expectations, and Future of Nursing Education. Dr.Llasus is the Assistant Dean of the School of Nursing, Nevada State College. As an Assistant Dean he performs of a multitude of functions ranging from performing regular instructional teaching duties on a half-time basis, and collaborates with the Associate Dean and to oversee the academic program and ensures quality of faculty and student support services. In addition to these, Dr.Llasus coordinates courses and faculty to promote communication and consistency in meeting School of Nursing policies and procedures. He provides leadership in curriculum and course development , orients new faculty to the SON, and connects faculty with Course Coordinator or Mentor for course orientation. He conducts orientation for new students and prepares program-specific materials, leads orientation program for each upcoming semester, schedules and leads make-up orientations. He organizes Semester Faculty Meetings, schedules and attends meetings for assigned curriculum tracks, mediates student and faculty issues and problems, follows â€Å"chain of command† in BSN Student Handbook, Performs peer evaluation of faculty by visiting classes and reviews clinical site evaluations by faculty and students, recommends new clinical contracts, prepares student cohort numbers and availability in each cohort, prepares records of students going forward for Reinstatement. Apart from the above said duties as an Assistant, Dean Dr. Llasus shoulders the following general duties such as ATI Assessments (Regular, Accelerated and Part-time Tracks), sets up ATI semester fees/deadline s ,schedules ATI make-up tests at end of semester, collects end of semester ATI exam, analyses from faculty and post on X drive, CSCLV,CSCLV skills lab & room, schedules for upcoming semester, CSCLV simulation schedules for upcoming semester, arranges for Marlock keys and parking information, end of semester scheduling of simulations for clinical make-up, class schedules for upcoming semester--working with the Dean and Associate Dean. Dr. Cheryl Dover is the Program Chair of Nursing Department, Prince George's Community College, Maryland. He performs the following duties like scheduling all classes, appointment of classes, supervising day to day activities like budgets, grants, Committee activities, Faculty/student issues etc. Nursing Faculty Teaching Expectations Dr.Llasus explains the tenure track of Assistant professor Post and explains the responsibilities of an Assistant professor. According to him, Assistant Professors shall be appointed to the School of Nursing upon recommend ation of the nursing faculty and the Dean of the School of Nursing. They shall hold an advanced degree in nursing and a doctorate. The responsibilities of an Assistant Professor are in accordance with college by laws. The position of Assistant Professor shall be primarily that of nursing faculty who teaches nursing courses under policy determined by the Faculty of the College, consistent with College-wide academic policy. An Assistant Professors shall aid in the planning, development, and teaching of nursing

Internet and Modern Technology Essay Example for Free

Internet and Modern Technology Essay There are many forms of modern technology that have played important roles throughout my life, and the computer has affected my life the most. There are both positive and negatives aspects on how the computer has helped me. There are also advantages and disadvantages to this form of modern technology, called the computer. The first and foremost advantage of the computer is how it has helped me with my school work. With having Google, Dictionary, and Ask, these are sources on the computer that are very helpful with getting information. It is a lot faster and more convenient. Another thing I love about it, is the communication you can have with friends and family. With Facebook, email, and Skype, it’s a lot faster and easier way to communicate with your loved ones. I feel that one of the most important is that these communication avenues are free. Even though I use a lot of these technologies almost daily they can also cause distractions. On the down side, some of the negatives aspects of technology with computers are that it can be an expensive form of access to information. Also not only do you have to pay for the computer but you also have to pay for the internet access to get to some of these information sites. Another down fall is the networks do shut down. So if you are doing homework or talking with friends it can sometimes aimlessly stop working. Another negative to having a computer is it can affect people’s work ethic, by looking at Facebook, or playing online games. So they aren’t getting their job done. Knowing there are still disadvantages to computers there are still advantages in using it correctly. As a conclusion to technology and the ever ways it has changed and will change in the future. Hopefully some of the changes will be headed for advantages for the future, in a form of no charge for internet access, and not having to worry about towers going down. Also for the younger generations to know how to still look information without taking advantages of everything being at their fingertips. Unfortunately we cannot see into the future to see what advantages and disadvantages are in store for us, but hopefully they are for the better and I am very thankful for the modern technology that is available and the advantages it has for me.

Context Inference from Social Networks

Context Inference from Social Networks Context Inference from Social Networks: A Tie Strength Based Approach Sneha Kamal Reshmi.S Abstract— All online sharing systems gather data that reflects user’s behavior and their shared activities. Relationship degree between two users is varying continuously. Static friend list in the social network is unable to express it completely. Tie strength is used to quantitatively describe real social relations. It is based on lots of features derived from activities of a user’s in social network. We introduce a model to measure tie strength between users in a given context. Here a novel definition of tie strength is introduced which exploits the existence of multiple online social links between the individuals. The proposed system infer the context from user’s interaction using the applications of Natural language processing methods and clustering techniques. From this context the tie strength between users are found. Then evaluates and analysis the performance of this model. Keywords- Clustering, Natural Language Processing, Social Context, Social Network, Tie Strength I. Introduction A tie is formed between two persons it they are friends or they have interactive behaviors in social network. Strength of a tie represents the degree of relationship between two entities and it can be various. Tie strength in social network depends upon the exchange and transmission of information, and influence between social network users. The friend list of user is incompetent to reflect real social relation of users directly, because all friends are equally treated. Interactive activities such as comment make more sense in maintaining the relationship. So strong tie may have more interactions. Strong ties are peoples which are we really trusted. Although such trusted friendships can provide emotional and economic support. The majority of social media do not incorporate tie strength in the creation and management of relationships, and treat all users the same either friend or stranger. The first attempt to take into consideration is the social role of a friendship was done by Facebook and Google+ by the introduction of the circles. Users can use circles as a technique to organize their contacts, creating different groups for relatives, work colleagues, close friends and so on. But this group of contacts does not provide quantitative information about the real strength of the ties. The most common technique used to measure the tie strength is the closeness of relationship. Thus close friends have strong tie while others have weak ties. Numerous other methods of strengthens have also been proposed. This includes frequency of contacts and mutual acknowledgement of contacts. Other possible indicators of tie strength include extend of multiplicity with a tie, the duration of the contacts, the overlap of membership in an organization between the parties to a tie and the overlap of social circles. However, in our opinion these approaches have some shortcomings. Firstly, the intensity of conversations strongly depends from user to user, making it difficult to understand which of these conversations are dedicated to intimate relationships. Secondly, do not take into account that strong ties must be powered by a form of social grooming that is mainly based on geographical nearness and face-to-face contacts. Our contribution in this paper is assessing the tie strength between two friends on social network. Although distinguishing between strong and weak ties. For that we infer the thematic fields talked about by users with their contacts, is called the social contexts of a user. For this purpose we use the textual information such as photo descriptions, comments, post of users. With users permission we gather such information. NLP techniques are applied to gather such information and find most relevant information is called tag or word. A set of such a tag of a user is called the user’s personomy. The user’s social contexts come out after applying a clustering algorithm over this personomy and classifying the user’s contacts in these clusters. From this clusters find the users with more tie strength. II. Related Works The community can be extracted from social network based on the actions of users in [2]. This method extracts the users that are similar in actions, interests or tastes as a community. Initially a small community will be formed consists of two or more users. Seeing the actions performed by their friends may make users curious. Therefore, similar small communities can be extended to form a larger community. Compared to other method extracted nodes in the communities may not have the best density. In [3] introduce the Virtual Tie Strength (VTS)-scale and their scoring methods appear to provide a valid and reliable measure of tie strength in virtual communities. They developed a model that measures the tie-strength in virtual communities. The VTS-scale is able to distinguishing between two components of tie-strength associates and friendship. But, the content of each component needs more investigation. In [4] observe the communication patterns of millions of mobile phone users. That helps to concurrently study the local and the global structure of a society-wide communication network. Observe the coupling between interaction strengths and the network’s local structure. Then weak ties are removed. In [5] four factors are proposed which depends upon the strength of the tie. They are time closeness intensity and reciprocal service. They argued that degree of overlap between the two individuals friendship network varies depending on the tie strength between the users. Tie strength is depends on the diffusion of influence and information. Most of the network model deals implicitly with strong ties. III. Proposed System The model takes the advantage of users interactions in social networks. It infers the social contexts in which users are involved and which of their contacts belongs to that context. The proposed method consists of 4 modules. They are context data generation, affinity propagation algorithm, context based clustering, tie strength of users. And the output is a community with set of users. Figure 1. Proposed method A. Context Data Generation In this step obtain the interaction of a given user with all other users. In the case of facebook data of a particular user is obtained from the post of the users and comments given to the photos etc. Then apply Natural Language Processing (NLP) to this collected information. It provides a predefined model for sentence splitting, tokenizing and POS tagging. POS tag method in NLP is applied here. B. Tag Cloud Generation Initially from the interaction of users stop words such as ‘and’,’the’,’at’ etc. are removed. This will pass as an input to NLP. NLP will split the given text, using POS tagging. Which identifies each word as part-of-speech category such as Noun, Verb, etc. After keeping the noun and verb all other are removed. The resulting words will consider as a set of tags of a user whose interaction with others. The set of tag of u and v in a social interaction is denoted by T(lu|v), whose tag cloud is denoted by TC(lu|v). Figure 3: Tag Cloud of each user C. Similarity Measurement The main features of the algorithm are Tri-set calculation, similarity computation, seed construction and clustering.Tri set will consist of Cofeature Set (CFS), Unilateral Feature Set (UFS), and Significant Cofeature Set (SCS). Cofeature Set: Consider di and dj, be the two objects in a data set. Suppose that some features of di is also belong to dj. Therefore, construct a new subset consisting of these features and their values in dj. Unilateral Feature Set: Suppose that some features of di, does not belong to dj. Therefore, construct a new subset consisting of these features and their values in di. Significant Cofeature Set: Suppose that some features of di, also belong to the most significant features of dj. So, Therefore, construct a new subset consisting of these features and their values as the most significant features in dj. From this we can calculate the similarity between two points as (1) Where nm ,nq and np are the values of the features with in the set CFS SCS and UFS respectively. And |CFS|,|UFS|, and |SCS|, indicate the number of tuples in CFS(i,j), UFS(i,j), and SCS(i,j)respectively. The set CFS is result of the intersection of the objects. UFS consists of unshared Features. SCS takes into account the most significant features. D. Inferring the Social Context Seed Affinity Propagation Algorithm: Seed Affinity Propagation (SAP) is a semi supervised clustering. The aim is from the initial labeled object we cluster the large number of unlabeled object efficiently. To guarantee accuracy and avoid a blind search for seeds we use Mean Features Selection method. The similarities between tags are passed as input to the algorithm in the form of matrix. The different steps involved in algorithm are: Let D be a data set which consists of set of features of each object and the value associated with these object. Construct the seed from a few labeled object using Mean feature selection Method. Calculate the tri set between the object i and j(CFS(i,j), UFS(i,j), and SCS(i,j)). Similarity calculation between the object using the equation (1). Self similarity computation: Calculate s (l,l) is the mean value of the similarities. These values are referred to as preferences. Initialize the matrixes of messages Message Matrix Computation: Compute availability matrix a (i,j) and responsibility matrix r(i,j). Exemplar selection: By adding the availability and responsibility matrixes we find the exemplar for each object i is the maximum of r(i,j) + a (i,j). Update the matrix using Rt+1 = (1-)Rt+ Rt-1 (2) At+1 = (1-)At+ At-1 (3) Where â‚ ¬ [0,1] is a damping factor. R and A represent the responsibility matrix and availability matrix respectively, and t indicate the iteration times. Iterating steps 6,7and 8 until the exemplar selection result stays constant for a number of iterations. Here the input to the algorithm is is the similarity between the tags mentioned above. It find a number of clusters, but we does not consider all the clusters. We choose K number of clusters base on a threshold value. 2) Other users to social context: After finding the context of a user, we find the other users with the same context. For finding the similarity compare the tag cloud formed during the interaction of u and v (TC(Iu|v)), with the tags in the context of user u TC(cti). ctu|v= (4) Similarity is calculated by using cosine similarity method. Two inputs will consider as vectors. And calculate the angle between these vectors. So the output will be a cluster consists of several users. From this cluster also we find users with strong tie strength. Then the ads will post to this the wall of users who have strong tie strength. Figure 2. Inferring Social context E. Tie Strength Measurements Cluster consists of set of users. They are represented as a graph where nodes are users and edges represent the link between the users. From the given graph we find the tie strength between the users by using similarity and node interaction methods. Output is a weighted graph, where weight in the edge represents the value of tie strength between the two users. Where w represents the intensity of interaction between the nodes, so here we pass the tag weight between the users as w. Td is the set of neighbors of the nodes. If the value of Str(u,v) is greater, indicate that the tie strength between u and v is strong. Figure.5: Graphical Representation of Tie Strength between Users IV. Applications Find the community based on tie strength, so the users in the community may have similar interest. For providing publicity when the ad is posted in the user’s wall, its visibility will be limited to those of his contacts that share a strong tie with the user. So the companies can target the potential customers. V. Conclusion The model takes the advantage of tie strength between the users in the social network to provide publicity to users. Here we applied NLP and data mining techniques. Interaction of users in social network is analyzed using NLP and find Tag cloud of each user. Then calculated the semantic relationship between each tag in the tag cloud. Then apply affinity propagation and obtain the social context of each user. Then associate users in his contacts to the clusters. From this cluster we find the users with strong tie strength. The method will help the companies to find their potential users. Based on the interest of users we can post the corresponding ads to their walls. References Luca Pappalardo, Giulio Rossetti and Dino Pedreschi. â€Å" How well do we know each other? detecting tie strength in multidimensional social networks,† 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Seyed Ahmad Moosavi and MehrdadJalali. â€Å"Community Detection in Online Social Networks Using Actions of Users,†978-1-4799-3351-8/14/$31.00  ©2014 IEEE. Andrea Petrà ³czi and Tamà ¡s Nepusz. â€Å"Measuring tie-strength in virtual social networks† http://www.insna.org/Connections-Web/Volume27-2/5.Petrà ³czi.pdf. J.P. Onnela and J. Sarama ¨ ki. â€Å"Structure and tie srength in mobile communication network, † PNAS published online April 24, 2007. Mark S. Granovetter â€Å" The Strength of Weak Ties,† American Journal of Sociology Volume 78 Issue6 (May 1973) 1360-1380. Yaxi He, Chunhong Zhang and Yang Ji. â€Å" Principle Features for Tie Strength Estimation in Micro-blog Social Network † 2012 IEEE 12th International Conference on Computer and Information Technology. Renchu Guan and Xiaohu Shi,† Text Clustering with Seeds Affinity Propagation,† IEEE Transactions on Knowledge and Data Engineering, Vol. 23, NO. 4, April 2011. T. Pedersen, S. Patwardhan, and J. Michelizzi â€Å"Wordnet:: Similarity: Measuring the relatedness of concepts,† .Demonstration Papers atHLT-NAACL 2004, 2004, pp. 38–41, Association for Computational Linguistics. FakhriHasanzadeh and MehrdadJalali, â€Å"Detecting Communities in Social Networks by Techniques of Clustering and Analysis of Communications,†978-1-4799-3351-8/14/$31.00  ©2014 IEEE. B. Frey and D. Dueck,†Clustering by passing messages between datapoints,† Science, vol. 315, no. 5814, pp. 972–976, 2007. Sandra Servia-Rodrà ­guez, Ana Fernà ¡ndez-Vilas, Rebeca P. Dà ­az-Redondo, and Josà © J. Pazos-Arias. â€Å"Inferring Contexts From Facebook Interactions: A Social Publicity Scenario,† IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 15, NO. 6, OCTOBER 2013.

Sexual Harassment in the Workplace Essay -- Sex Discrimination

TABLE OF CONTENTS TYPES OF DISCRIMINATION . . . . . . . . . . . . . . . . . . 1 SEXUAL HARASSMENT IS DISCRIMINATION . . . . . . . . .. . . . . . .1 Laws That Govern Sexual Harassment . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 How It Affects The Workplace . . . . . . . . . . . . . . . . . . 4 TYPES OF SEX DISCRIMINATION . . . . . . . . . . . . . . . . . .4 Basic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Quid Pro Quo . . . . . . . . . . . . . . . . . . . . . . . . .5 Hostile Work Environment . . . . . . . . . . . . . . . . . . . . . 6 THE HIGH COST OF LITIGATION FOR EMPLOYERS . . . . . . . . . . . . . 7 TYPES OF SEXUAL HARASSMENT . . . . . . . . . . . . . . . . . . . . ..8 EMPLOYER?S RESPONSIBILITY . . . . . . . . . . . . . . . . . . . . . 9 Training . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . 10 Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11 VII. CONCLUSION . . . . . . . . . . . . . . . . . . . . . . . . . . 12 BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 CASES CITED . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14 I. TYPES OF DISCRIMINATION There are many forms of discrimination, especially in the workplace. Before we get into the different types of discrimination, we need to define the word discriminate which is, "to make a distinction in favor of or against a person or thing on the basis of the group, class, or category to which the person or thing belongs, rather than according to actual merit." Taken from the Unabridged Edition of the Random House Dictionary of the English Language. Like many people I was under the belief that to discriminate simply meant that y... ...ts Acts of 1964 (Internet) http://www.eeoc.gov/laws/vii.html Whitehead, Roy Jr.; Spikes, Pam; Yelvington, Brenda. "Sexual Harassment In The Office." CPA Journal. Vol. 66 No. 2: pp.42-45, February 1996. Note: All periodicals were found through the Nexis/Lexis system in the Library. CASES CITED Ellison v. Brady, (1991) 924 F.2d 842 Equal Employment Opportunity Commission v. Domino?s Pizza, Inc., 909 F.Supp. 1529 (M.D.Fla. 1995) Harris v. Forklift Systems, Inc., 114 S.Ct. 367 (1993) Matthews v. Superior Court (Regents of University of California), (1995) 34 Cal.App.4th 598. Mogilefski v. Superior Court (Silver Pictures), (1993) 20 Cal.App.4th 1409. Neal v. Director, District of Columbia Department of Corrections, U.S.Dist. LEXIS, 11461, 11469, 11515 (D.D.C. 1995) Note: Cases cited were researched through the law library.