Relative Clause vs. Appositive Essay Example for Free

Relative Clause vs. Appositive Essay An appositive is a word placed after another word to explain or identify it. The appositive always appears after the word it explains or identifies. It is always a noun or a pronoun, and the word it explains is also a noun or pronoun. Example: My uncle, a lawyer, is visiting us. My teacher, Miss Marshall, is very strict. An appositive phrase consists of the appositive and its modifiers which may themselves be phrases. Example: My radio, an old portable, is in the repair shop. The boys climbed the mountain, one of the highest in the West. THE RELATIVE CLAUSE A relative clause—also called an adjective or adjectival clause—will meet three requirements. * First, it will contain a subject and verb. * Next, it will begin with a relative pronoun [who, whom, whose, that, or which] or a relative adverb [when, where, or why]. * Finally, it will function as an adjective, answering the questions What kind? How many? or Which one? The relative clause will follow one of these two patterns: relative pronoun or adverb + subject + verb relative pronoun as subject + verb Here are some examples: Which Francine did not accept Which = relative pronoun; Francine = subject; did accept = verb [not, an adverb, is not officially part of the verb]. Where George found Amazing Spider-Man #96 in fair condition Where = relative adverb; George = subject; found = verb. That dangled from the one clean bathroom towel That = relative pronoun functioning as subject; dangled = verb. Who continued to play video games until his eyes were blurry with fatigue Who = relative pronoun functioning as subject; played = verb. Avoid creating a sentence fragment. A relative clause does not express a complete thought, so it cannot stand alone as a sentence. To avoid writing a fragment, you must connect each relative clause to a main clause. Read the examples below. Notice that the relative clause follows the word that it describes. To calm his angry girlfriend, Joey offered an apology which Francine did not accept. We tried our luck at the same flea market where George found Amazing Spider-Man #96 in fair condition. Michelle screamed when she saw the spider that dangled from the one clean bathroom towel. Brian said goodnight to his roommate Justin, who continued to play video games until his eyes were blurry with fatigue. Examples of Sentences with Relative Clauses 1. The family fulfills functions that are divided among many specialized institutions in modern societies. 2. Yesterday I called our friend Julie, who lives in New York. 3. The photographer called to the Queen, who looked annoyed. 4. Last week I bought a new computer, which I dont like now. 5. I really love the new Chinese restaurant, which we went to last night. 6. The term networking, which has appeared in popular speech, refers to using or even developing social networks. 7. My boss, who is very nice, lives in Manchester. 8. My sister, who I live with, knows a lot about cars. 9. My bicycle, which Ive had for more than ten years, is falling apart. 10. My mothers house, which I grew up in, is very small. Examples of Sentences with an Appositive Phrase 1. Queen Victoria, one of Englands greatest monarchs, ruled for sixty-three years. 2. Jane made the salad, a tossed one with French dressing. 3. Harvey Jensen, the pro at the country club, is giving me golf lessons. 4. James Hiltons book, Lost Horizon, has been filmed twice. 5. Chemistry, Sues favorite subject, is easy for her. 6. Jerry is visiting in Peoria, his old home town. Â  7. Mr. and Mrs. Miller, our neighbors for the past eight years, are moving to Dallas. 8. Have you ever read The Red Pony, a novel by John Steinbeck? 9. Groucho Marx, the star of many film comedies, also had his own television show. 10. The boys repaired our television set, an eighteen-year-old portable. References: http://www.perfect-english-grammar.com http://www.sinclair.edu http://www.chompchomp.com

Essay --

Throughout our lives we all have been in a situation where we are outside at a sporting event, concert, or some type of outside event. While we are at the events all bundled up in our coats and hats and such, what do we do about our hands when the gloves just aren’t cutting it? My mother always suggests I just open a pack of Grabber hand warmers. Grabbers are a pack of 2 individual mini hand warmers that use a mixture of chemicals and different chemical reactions to produce heat us by you rubbing the two together. I must admit these little things have saved me from many cold nights out at the football field. Although when you are trying to warm yourself up you probably aren’t worried about what chemicals are in these warmers or how they work, but I am so I figure I’ll fill you in too. The reason I decided to try and go in debt on the idea of how a hand warmer works and what goes in to it to cause it to exert heat. Also because these are things I use almost every day during the winter and never knew how they worked. For starters all grabbers hand warmers are air activated, they are n... Essay -- Throughout our lives we all have been in a situation where we are outside at a sporting event, concert, or some type of outside event. While we are at the events all bundled up in our coats and hats and such, what do we do about our hands when the gloves just aren’t cutting it? My mother always suggests I just open a pack of Grabber hand warmers. Grabbers are a pack of 2 individual mini hand warmers that use a mixture of chemicals and different chemical reactions to produce heat us by you rubbing the two together. I must admit these little things have saved me from many cold nights out at the football field. Although when you are trying to warm yourself up you probably aren’t worried about what chemicals are in these warmers or how they work, but I am so I figure I’ll fill you in too. The reason I decided to try and go in debt on the idea of how a hand warmer works and what goes in to it to cause it to exert heat. Also because these are things I use almost every day during the winter and never knew how they worked. For starters all grabbers hand warmers are air activated, they are n...

A Proposal of Metrics for Botnet Detection based on its Cooperative Behavior

The primary contribution of the paper is the proposal of three metrics that can help identify the presence of botnets in a wide area network (WAN). The proposed metrics, namely relationship, response and synchronization are measured with respect to the traffic over a WAN. It is assumed that the behavior of botnets will recurrently exhibit these metrics. The authors define relationship as the connection that exists between the bots and bot master of a botnet over one protocol. This metric tries to detect the structure of a botnet’s relationship by analyzing the network traffic.It is observed that the response time to commands received by a legitimate host varies significantly while that of botnets is comparatively constant. The response time as a metric can thus help detect botnets. As the bots present in a botnet are programmed to carry out instructions from the bot master on a predetermined basis, it is assumed that their activities will synchronize. An analysis of the networ k traffic can possible help identify synchronized activity between hosts, thus detecting botnets.The metrics are evaluated by analyzing traffic measured in the Asian Internet Interconnection Initiatives (AIII) infrastructure over a period of 24 hours. The analysis validates the metrics proposed as a dense topology relationship, short range of response times and synchronization of activities are detected in the presence of a botnet. The authors propose that a combination of all the metrics be used for detecting a botnet. The design of an algorithm to detect botnets based on a combination of the three metrics has been identified as future work. Summary of â€Å"IRC Traffic Analysis for Botnet Detection†The paper addresses the problem of detecting botnets by modeling the behavior of botnets. The main idea of the paper is to analyze network traffic, model the behavior of botnets based on the analysis and use pattern recognition techniques to identify a particular behavior model a s belonging to a botnet. The proposed model for detecting botnets analyses traffic that uses the IRC protocol. A traffic sniffer is used to analyze packets in the promiscuous mode. The protocol detector detects traffic using the protocol of interest to the analysis, in this case IRC.The packets are decoded using the IRC decoder and the behavior models are built. The detection engine detects a botnet based on the behavior model. The features used to build a behavior model include features related to a linguistic analysis of the data that passes through an IRC channel in addition to the rate of activity in the channel. It is observed that the language used by bots has a limited vocabulary and uses many punctuation marks. The language used by humans is observed to have a wider mean and variance with respect to the words used in a sentence. The features used to model the behavior of botnets hare listed.The experiments have been conducted with clean data collected from chat rooms and bot net data collected at the Georgia Institute of Technology. Pattern recognition is performed using support vector machines (SVMs) and J48 decision trees and the results are reported in terms of confusion matrices. Though the botnets are detected using the above methods, the authors report that a further analysis of the data is necessary. Unsupervised testing of the model and expansion of the model for adaptation to other scenarios is proposed as future work. Summary of â€Å"The Automatic Discovery, Identification and Measurement of Botnets†The paper proposes a technique for identifying and measuring the botnets used to deliver malicious email such as spam. The implementation and performance of the proposed technique has been presented. The authors are of the opinion that the existing methods for detecting botnets used to send spam use significant amount of resources and are often applicable only after a botnet has been operational over a period of time. The authors propose a passive method for identifying botnets by classifying the email content. The headers present in the emails are used to group the mails.The authors assume that a botnet has a central center for control and that the same program is used by a botnet for creating and sending spam emails. Based on these the authors propose to classify emails by a passive analysis of the header content present in them. The Plato algorithm is proposed to identify the sender and the program used to send the email. The performance of the Plato algorithm is analyzed based on the following factors: clustering, durability, isolation and conflicts. The analysis is performed on a sample data containing 2. 3 million emails. In the dataset 96% emails are identified as having a probability of being spam.The algorithm is observed to successfully reflect the features associated with spam email. It helps group the emails based on the characteristics of the sender and the sending program. This grouping of emails can hel p identify a botnet and thus enable the membership and size of the botnet. The authors propose that the algorithm can be further used for classifying bulk emails, to understand the relationship between spam and viruses and as a replacement for spam filters using statistical methods. Summary of â€Å"Towards Practical Framework for Collecting and Analyzing Network-Centric Attacks†The paper proposes a network-centric framework based on an awareness of risk to help detect attacks from a botnet and prevent these attacks. The authors state that the bots follow certain network traffic patterns and these patterns can be used to identify a bot. The proposed framework consists of three main components, namely bot detection, bot characteristics and bot risks. The first component, bot detection, is used to detect known and unknown bots that try to penetrate the system. A honeypot based malware collection system component is used to attract bots to the honeypot and thus help detect bots. After the bots have been detected the characteristics of the bots are analyzed. The behavior of bots and their characteristics are identified by analyzing known malware, network traffic patterns and detecting the existence of any correlation between various instances of a malware. Various components are used to perform each of the tasks involved in bot characterization. To determine the risks posed by bots, the vulnerabilities present in the existing system are identified. The risk posed by a host with certain characteristics is calculated based on the vulnerabilities associated with the system. Thus the risk factor can be modified on demand.A combination of the identified characteristics and the associated risks is evaluated when a decision regarding the blocking of traffic is made. The authors present results that demonstrate the ability of the proposed framework to detect different types of bots. The feasibility of the proposed framework has been demonstrated. Enhancing of the co rrelation system and integration of the risk aware system with the architecture are proposed as future work. Summary of â€Å"Wide-Scale Botnet Detection and Characterization† The paper proposes a methodology based on passive analysis of the traffic flow data to detect and characterize botnets.A scalable algorithm that gives information about controllers of botnets is proposed based on analysis of data from the transport layer. Four steps have been identified in the process of detecting botnet controllers. Suspicious behavior of hosts is identified and the conversations pertaining to this host are isolated for further evaluation. These are identified as suspected bots. Based on the records of suspected bots, the records that possible represent connections with a controller are isolated. This is referred to as candidate controller conversations in the paper.These candidate controller conversations are further analyzed to identify suspected controllers of botnets. The analysis is based on calculating the following: the number of unique suspected bots, distance between model traffic and the remote server ports, heuristics that gives a score for candidates that are possible bot controllers. The suspected controllers are validated in three possible ways: correlation with other available data sources, coordination with a customer for validation and validation of domain names associated with services (Karasaridis, Rexroad, & Hoeflin, 2007).The botnets are classified based on their characteristics using a similarity function. An algorithm is proposed for the same. The authors report the discovery of a large number of botnet controllers on using the proposed system. A false positive of less than 2% is reported based on correlation of the detected controllers with other sources. Also the proposed algorithm is reported to successfully identify and malicious bots. The future work is identified as the need to expand the algorithm for other protocols and analysis of the evolution of botnets.References Akiyama, M. , Kawamoto, T. , Shimamura, M. , Yokoyama, T. , Kadobayashi Y. , & Yamaguchi, S. (2007). A proposal of metrics for botnet detection based on its cooperative behavior. Proceedings of the 2007 International Symposium on Applications and the Internet Workshops. 82-85. Castle, I. , & Buckley, E. (2008). The automatic discovery, identification and measurement of botnets. Proceedings of Second International Conference on Emerging Security Information, Systems and Technologies. 127-132. Karasaridis, A. , Rexroad, B., & Hoeflin, D. (2007). Wide-scale botnet detection and characterization. Proceedings of the First Conference on First Workshop on Hot Topics in Understanding Botnets. 7-14. Mazzariello, C. (2008). IRC traffic analysis for botnet detection. Proceedings of Fourth International Conference on Information Assurance and Security. 318-323. Paxton, N. , Ahn, G-J. , Chu, B. (2007). Towards practical framework for collecting and analyzing n etwork-centric attacks. Proceedings of IEEE International Conference on Information Reuse and Integration. 73-78.

Setting Ideas for Improv Acting and Comedy Sketches

One of the essential ingredients to a good improv scene is a setting. But sometimes, the ideas just dont flow. This list of settings for improv acting and comedy sketches may help grease the wheels. Keys to Success If youre not relying on your audience to suggest a setting, youll need to think quickly and choose one yourself. One of the goals of improv is to learn how to think quickly and creatively when confronted by the unexpected. To do that, youll need to bear a few things in mind: Go with it. If youre told to wear a trench coat, then do it. Now youve got one detail to add to the sketch of the character youre building: one whos a private eye in an old detective movie. Accept everything that people do or say as literal truth and dont try to deceive or outwit your fellow actors.Create a backstory. You can add realism to your character by asking questions or making statements that reference a past event. Maybe your detective character just had a run-in with a police officer who doesnt like him. As the two glare at each other, your character asks, You going to arrest me just like last time? And just like that, youve established a backstory for your audience that gives them more information about the scene youre creating.Be specific. Improv actors rarely work with elaborate sets or with many props. Instead, the challenge is to create a sense of place and character with your words and actions. Dont speak in monosyllables. Be descriptive.  Begin mid-action.  Unlik e scripted acting, improv doesnt have the luxury of building up to a dramatic climax through a prologue. You want to keep the activity (and inspiration) moving. Each sketch should start with your characters already engaged in a scenario, like being up to their elbows in a sink full of dirty dishes.Act without words. Speaking is just one way that an actor can convey information. Try choosing an improv setting and then using pantomime or another means of non-verbal communication.  Dont be yourself. Youre not playing yourself in improv; youre someone else. As you perform, push yourself to act and react in ways the real you may never do. Suggested Improv Settings Once the actors are ready, its time to choose a setting. Some performers let the audience make suggestions, with the troupe picking their favorite. Others leave it to the director or host to pick a scenario. Theres no right or wrong way to do it. Thats the beauty of improv. A:Art GalleryAmbulanceAdoption ClinicAmazon RainforestAntique StoreAttic B:BarbershopBalconyBoatBirds NestBlacksmithBakeryButterfly HabitatBeaver DamBootcamp C:CastleCat Ladys HouseChessboardCheese FactoryClassroom Cemetery(Inside a) Comic BookChiropractors OfficeCircus D:Dance StudioDragons LairDesertDeep Sea DivingDepartment of Motor VehiclesDetentionDrunk Tank E:EgyptElephant SanctuaryElfs ForestExecution ChamberEarthquake Preparedness Class F:Ferris WheelFire StationFishing PondFootball StadiumFutureFortune Tellers Shop G:Grocery StoreGolf CourseGhost TownGondolaGarbage DumpGarageGoldmineGypsy CampGrand Canyon H:Hardware StoreHelicopterHenhouseHogwartsHospitalHawaii I:IglooIsland (Tropical)IcebergIce Cream ShopIce Age J:JungleJet Pilots CockpitJudges ChambersJury BoxJewelry StoreJurassic Age K:Karate ClassKaraoke BarKnights Training GroundsKing Kongs CageKnitting CircleKangaroo Farm L:LagoonLighthouseLibraryLOST (The TV Show)LifeboatLumberjack CampLondonLaundromat M:Make-Up CounterMarathon Finish LineMechanics ShopMoonMousetrapMummys Tomb(Inside a) MicrowaveMountain Top N:Nursing HomeNews StationNeverlandNature TrailNightclubNewspaper Office O:Orchestra PitOffice CubicleOrchardOutback (Australia)Open House (Real Estate)Optometrist P:Picnic SpotPanda ExhibitPromPirate ShipPet StorePost OfficePhotography ClassPolice Station Q:Queen Elizabeths CourtQuiz ShowQuicksand R:Radio ProgramRestaurant Grand OpeningRed Carpet (Movie Premiere)Riverboat(Inside a) Romance NovelRobbers Hideout S:SafariSchool LunchroomSchool Nurses OfficeSantas WorkshopSki SlopeSpider WebSummer CampSmurf VillageSoftball GameSpaceshipSecond-Hand StoreSubmarineStable T:TreehouseTravel AgencyTruckstopTheater AuditionTidepoolTribal CeremonyTourist Trap U:Ugly Princess Birthday PartyUndergroundUnderwaterUnemployment OfficeUtopian Society V:Vampires HomeVolleyball CourtVolcanoVoting Booth W:Witchs CavernWarehouseWhite HouseWaterslide ParkWrestling RingWild WestWoodshop ClassWedding Ceremony X:X-Ray LabXylophone Store Y:Yard SaleYoga ClassYearbook Club Z:Zeppelin (Blimp)Zombie Vacation SpotZoo