Critical Thinking and Perception Essay Example for Free

Critical Thinking and Perception EssayWhat is real? Our perception of reality is often a product of the environment in which we live. In general, we formulate ideas of reality base on our own experiences as well as the experiences of the people around us. Our lives are influenced constantly by our surroundings. I truly believe that perception is a learned skill and not instinctive. Outside of our communities, television, radio and movies sculpt our perception of the outside world. I moot most of my view at the ordinary level is based on perception, language, and information. At the most there is unrivalled logical system step If this than that. I think most opinion takes place in the perceptual stage. These are the questions that arise, How much do I take in? and how do I look at things? This perception is based on habits of perceptions and what I hear, what I read and how I express myself. I understand that we do not need to use much explicit logic because we have already built the logic into our language. For example, killing is bad unless justified by war or self-dense. I know that with decisions I followed what was recommended and what my friends were doing and then rationalized it with the followers rationalization Everyone does this and the stock rises for a while and when the market eventually gets a severe correction I rationalize that as well.This rationalization is based on informationnot all-available information just now a selection that fits what I was inclined to do anyway. I think that logic can be used to reinforce perceptions (and prejudices) but logic and argument will not change perceptions. Perception is more than sensing it is processing, reacting, and interpreting. Faith Bryne describes perception as, detecting the nature of both outer and home(a) worlds. In many cases, it also means responding in some way, either consciously or unconsciously. (Perception, 57) Perception is the way we look at things and I think processing is what we do with that perception. In my view if we take processing for granted then perception becomes even more important, because the way we look at a situation will determine what we can do about it. The influences (family, teachers, religion, race, environment, and economic level) that have shaped or conditioned my identity by instilling values, beliefs, viewpoints or attitudes that I have accepted without challenge serves as a perceptual block. The situations in which I am less of an individual because of these influences occur when I wane to understand someone elses opinion or look for other points of view because of something I have been conditioned to believe is true.I am not one who handles others opinions without asking for them. I am not very consistent in ensuring that my opinions are informed. Often times I have not taken minute consideration of the evidence and have treated opinions as facts especially if I have expressed it to the point that I have begun to believe i t as truth. At times, in what matters most I am inclined to assume too much and take too much for granted. I feel the strongest urge to adjust when someone is a positive role model and conforming to this type of behavior I believe adds value. However, a situation in which this conformist goal has interfered with my judgment is following others because it seemed the lesser of two evils. All to often at the workplace this is how some decisions are made just to close an issue that at last will recycle. Additionally, I tend to jump or make hasty conclusions more often than I would like. This occurs more so in the area of own(prenominal) relationships. I have learned that there are some errors and bad habits that can lead to shallow or uncritical decisions instead of careful judgments.I have gained the most insight from the following errors and bad habits, which are Resistance to change (habits), Conformity, Rationalization, Stereotyping, common sense, Oversimplification, Hasty conclu sions, and unwarranted assumptions. I think the real pick up to each the of errors and habits mentioned above is my being conscious of the tendency to do them and to get into the habit of applying and practicing the different ways or approaches to avoiding the blocks to critical thinking. This will be an on-going process if I am to be in control of my own beliefs, and to somehow gain an understanding of the truth, then I must know what heartfelt reasoning is, and be aware of the ways in which my reasoning (and that of others) can go astray. How I tend to perceive certain situations in my life and how others whitethorn see it may not always agree with my thinking. This is a big obstacle that I will have to work on being amend at and learning how to understand others perceptions.ReferencesBrynie, Faith Hickman. (2001). Perception. Blackbirch Press, Inc. (p 57

Electric Vehicle Charging Station Market in Southeast Asia Essay Example for Free

Electric Vehicle Charging Station Market in Southeast Asia EssayAn electric vehicle charging station, also called an electric recharging point, or charging point,supplies electric energy to charge PEVs, including all-electric cars, neighborhood EVs, and plug-inhybrids. Two technologies are used in charging stations, wired AC charging and DC charging.Depending on the type of charging station, these are segment into Level 1 Charging Station, Level2 Charging Station, and Level 3 Charging Station.The performance of the equipment is measured in terms of the charging time, input power supply, voltage, and maximum operating current. cover in this Report.This report covers the present scenario and the growth prospects of the Electric Vehicle ChargingStation market in Southeast Asia for the period 2014-2018. To calculate the market size, the reportconsiders gross generated through the sales of level 1, level 2, and level 3 charging stations.The report also presents the vendor landscape and a corresponding detailed analysis of the cardinal keymarket vendors. In addition, it discusses the major drivers that influence the growth of the market andthe challenges faced by the vendors and the market at large. The report also outlines the key trends emerging in the market that will contribute to the growth of the Electric Vehicle Charging Stationmarket in Southeast Asia during the forecast period.

Optimization of Benchmark Functions using VTS-ABC Algorithm

Optimization of Benchmark Functions using VTS- alphabet algorithmPerformance Optimization of Benchmark Functions using VTS- alphabet Algorithm ostentate Guptaand Dharmender KumarAbstractA new variant based on tourney selection called VTS-ABC algorithm is provided in this paper. Its effect is comp bed with standard ABC algorithm with disparate size of info on several Benchmark functions and results show that VTS-ABC provides better quality of resultant role than original ABC algorithm in every case.Keywords slushy Bee colonisation Algorithms, Nature-Inspired Meta-heuristics,Optimizations, Swarm Intelligence Algorithms, Tournament selection.NOMENCLATUREABC factitious Bee ColonyACO Ant Colony OptimizationBFS Blocking Flow-Shop computer programingDE Differential organic evolutionEA Evolutionary AlgorithmGA Genetic AlgorithmMCN Maximum Cycle NumberPSO molecule Swarm OptimizationTS Tournament sizeTSP Travelling Salesman riddle1.INTRODUCTIONFor optimisation problems , various algorithms arrive atbeendesigned which are basedonnature-inspiredconcepts 1.Evolutionary algorithms(EA) and bourgeonoptimizationalgorithmsare two divers(prenominal) classes in which nature inspired algorithms are classified.Evolutionary algorithms like Geneticalgorithms (GA)andDifferentialevolution (DE) attempt to carry out the phenomenon ofnaturalevolution 2. However, a swarm like ant dependence, a flock of birds tolerate be described as collection of interacting agents and their intelligence lieintheir way of interactions with other individuals andtheenvironment 3. Swarm optimization includes Particle swarm optimization (PSO) modelon socialbehaviorofbirdflocking 4, Ant closure optimization (ACO) model on swarmofants and Artificial Bee Colony (ABC) model on the intelligent foraging behaviour of honey bees 5. Some outstanding characteristics of ABC algorithm which makesitmoreattractivethanotheroptimizationalgorithms areEmploys only three control parameters (populati on size, maximum motorcycle deem and limit) 6.Fastconvergencespeed.Quite simple, flexible and robust 7 8.Easyintegrationwithotheroptimizationalgorithms.Therefore, ABC algorithm is a very popular nature inspired meta-heuristic algorithm used to solve various kinds of optimization problems. In recent years, ABC has earned so ofttimes popularity and used widely in various application such as Constrained optimization, Image processing, crew, Engineering Design, Blocking flow shop scheduling (BFS), TSP, Bioinformatics, Scheduling and many others 9-18.Similar to other stochastic population-based approaches like GA, Ant Colony etc. ABC algorithm also utilize Roulette Wheel selection implement which chooses best(p) solution always with high selection pressure and leads the algorithm into premature convergence. With ever-growing size of dataset, optimization of algorithm has become a big concern. This calls for a need of better algorithm.The aim of this paper is to create such an alg orithm named VTS-ABC algorithm. This new variant is based on tournament selection mechanism and selects inconsistent tournament size individually time in order to select the utilize bees sharing their information with onlooker bees. Onlooker bees select solution from selected tournament size of solutions with less selection pressure so that high fitness solutions cant dominate and give better quality of solutions with large data set as well. A worst solution is also replaced by better solution generated randomly in each cycle.Rest of the paper is shared in different sections as follows Introduction to standard ABC algorithm is described in section 2. Section 3 describes the proposed VTS-ABC algorithm. Experiments and its simulation results to show performance on several Benchmark functions are described in section 4 and in the last Conclusion of the paper is discussed.2.ARTIFICIAL BEE COLONY algorithmic programIn 2005, Karaboga firstly proposed Artificial Bee Colony algorithm fo r optimizing numerical problems 19 which includes employed bees, onlooker bees and scouts. The bee carrying out search randomly is known as a scout. The bee going to the nutrient writer visited by it before and sharing its information with onlooker bees is known as employed bee and the bee waiting on the dance area called onlooker bee. ABC algorithm as a collective intelligence searching model has three essential components Employed bees, Unemployed bees (onlooker and scout bees) and Food sources. In the consume of optimization problem, a aliment source represents a possible solution. The position of a good aliment source indicates the solution providing better results to the given optimization problem. The quality of nectar of a food source represents the fitness value of the associated solution.Initially, a randomly distributed food source position of SNsize, the size of employed bees or onlooker bees is generated. Each solution xi is a D-dimensional vector that represents th e number of optimized parameters and produced usingthe equation 1where,xmaxandxminare the upper and lower bound of the parameterxi, respectively and j denotes the dimension. The fitness of food sources to find the global optimal is metrical by the following formulawhere, fm(xm)is the objective function value of xm. Then the employed bee phase starts. In this phase, each employed bee xi finds a new food source viin its neighborhood using the equation 3where, t Cycle number Randomly elect employed bee and k is not equal to i ( ) A series of random variable in the range -1, 1. The fitness of new solution produced is equald with that of up-to-the-minute solution and memorizes the better one by means of a greedy selection mechanism.Employed bees share their information about food sources with onlooker bees waiting in the hive and onlooker bees probabilistically choose their food sources using fitness based selection technique such as roulette rotate selection shown in equation 4whe re, Pi Probability of selecting the ith employed bee, S Size of employed bees, i Position of the ith employed bee and F seaworthiness value. Afterthatonlookerbeescarried outrandomly searchintheirneighborhood similar to employed bees and memorize the better one.Employed bees whose solutions cant be improved through a predetermined number of cycles, called limit, become scouts and their solutions are abandoned. Then, they find a new random food source position using the following equation 5Where, r A random number between 0 and 1 and these steps are repeated through a predetermined number of cycles called Maximum Cycle Number (MCN).3.PROPOSED WORK VTS-ABC ALGORITHMIn every meta-heuristic algorithm in the main two factors need to be balanced for global optimization outcome i.e. Exploration and Exploitation but ABC is a poor balance of these two factors. Various variants of ABC have been modelled for its improvement in different phases by number of researchers like Sharma and Pant hav e proposed a variant of ABC called RABC for solving the numerical optimization problem 20 and Tsai et al. have presented an interactive ABC optimization algorithm to solve combinational optimization problem 21 in which the concept of universal gravitational force for the movement of onlooker bees is introduced to enhance the exploration ability of the ABC algorithm. D. Kumar and B. Kumar also reviewed various papers on ABC and give a modified RABC algorithm based on topology for optimization of benchmark functions 22 23.Intelligence of ABC algorithm mainly depends upon the communication between individual agents. Employed beesshare their information with onlooker bees waiting in the hive and flow of this information from one individual to another depends on the selection mechanism used. Different selection schemes select different individuals to share the information which affect the communication ability of individuals and primarily the outcome of the algorithm. ABC algorithm uses Roulette cycle per second selection mechanism in which each onlooker bee selects the food source based on certain probability. Each onlooker bee selects the best food source with high selection pressure and lead to premature convergence. To overcome this problem, its new variant is proposed in which Tournament Selection method is utilize based on Cycle number and number of employed bees.In Tournament selection, a tournament size (TS) is chosen to select the number of employed bees sharing the information with onlooker bees. For better exploration, TS=2 i.e. Binary Tournament is apply in early stages and for better exploitation, variable tournament size is applied based on the current cycle number (CYL) and size of employed bee in middle stages. As the stages grow, this method works similar to Roulette wheel method in the end. Hence, the selection pressure is less in early stages and more in final stages which provide us better quality of solution. As variable size of tournament is used at different stages of the algorithm, hence the algorithm named VTS-ABC (Variable Tournament Size Artificial Bee Colony) algorithm. Method used for calculating TS is shown in equation 6 and equation 7If SN = 20If SNWhere Here, two equations are shown for calculating tournament size of tournament selection method. The drive of using these two equations is to increase the speed of algorithm. When the size of employed bee i.e. given population of food source positions is small like 10, a solution can be easily found by changing the tournament size by 1 but as the size grows i.e. when best food source position is to be found in large set of population for example when SN=40 or more than 40, increasing size of tournament by 1 and 2 only is a very tedious task as it will take more time to run the algorithm. Hence, in order to increase speed of algorithm, the tournament size based on current cycle and size of population is increased.One more concept is applied to increase its conv ergence speed. At each iteration or cycle, a new solution is generated randomly similar to scout and its fitness value is calculated. prehensile selection mechanism is applied between new solution and worst one and the better solution is memorized. Hence, it helps in finding good quality of solution as well as improving the convergence speed and provides better balance between exploration and exploitation.4.experiments and simulation results4.1 Benchmark FunctionsThe Benchmark Functions used to equal the performance of VTS-ABC algorithm with original ABC algorithm are illustrated belowSphere FunctionSchwefel FunctionGriewank FunctionWhere Ackley FunctionHere, ObjVal is the function value calculated for each food source position. A food source is represented by X and population size is taken of n*p hyaloplasm where n is the no. of possible food source positions and p represents the dimension of each position.4.2 Performance Measures trick ResultThe experimental results of VTS-AB C and ABC algorithm in MATLAB are taken under the parameter of size of food source positions (n*p) i.e. different size of population with different dimensions are taken to run and compare both algorithms. MCN is set as 2000 and each algorithm is run for 3 iteration i.e. Runtime=3. Limit for scouts is set equals to 300. In order to provide the quantitative sagacity of the performance of an optimization algorithm, Mean of Global Minimum i.e. mean of minimum objective function value at each cycle of all iterations are taken as performance measure whose values are shown in table1and figure 1-4.Table1 Mean of Global minimum on different size of dataFig. 1 Mean of Sphere function values on different size of dataFig. 2 Mean of Schwefel function values on different size of dataFig. 3 Mean of Griewank function values on different size of dataFig. 4 Mean of Ackley function values on different size of dataFigure 1 to 4 show simulation results of ABC and VTS-ABC algorithm with different size o f data on Sphere, Schwefel, Griewank, Ackley respectively and reveal that VTS-ABC algorithm provides us better quality of solution than original ABC algorithm by minimizing objective function value or producing higher(prenominal) fitness solutions.5. DISCUSSION AND CONCLUSIONIn this paper, a new algorithm VTS-ABC is presented. In this algorithm, firstly variable tournament size (TS) is applied to select the food source position for onlooker bees which helps to achieve diversity in solution. Then to increase convergence speed, a new solution is generated in each cycle which replaced the worst one. In order to demonstrate the performance of proposed algorithm, it is applied on several Benchmark functions with different size of data set as input. Simulation results show that it provides better quality of solution than original ABC algorithm in every case. Therefore, it can be applied in different palm of optimization with large and higher dimensions data set efficiently.ReferencesYuga l Kumar and Dharmender Kumar, Parametric Analysis of Nature Inspired Optimization TechniquesInternational Journal of Computer Applications, vol. 32, no. 3, pp. 42-49, Oct. 2011.P. J. Angeline, J. B. pollack and G.M. Saunders, An evolutionary algorithm that constructs recurrent neural networks, Neural Networks in IEEE Transactions on, vol. 5, no. 1, 1994, pp. 54-65.J. Kennedy and R. Eberhart, Particle swarm optimization, in Proceedings of IEEE international conference on neural networks, 1995, vol. 4, pp. 19421948.E. Bonabeau, M. Dorgio, and G. Theraulaz, Swarm intelligence from neural network to artificial intelligence, NY oxford university press, New York, 1999.D. Karaboga, An idea based on honey bee swarm for numerical optimization, Techn.Rep. TR06, Erciyes Univ. Press, Erciyes, 2005.D. Karaboga and B. Akay, A comparative study of artificial bee dependance algorithm, Applied Mathematics and Computation, vol. 214, no. 1, pp. 108132, 2009.R. S. Rao, S. V. L. Narasimham, and M. Ra malingaraju, Optimization of distribution network configuration for loss reduction using artificial bee colony algorithm, International Journal of Electrical Power and Energy Systems Engineering, vol. 1, no.2, pp. 116122, 2008.A. Singh, An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem, Applied Soft Computing, vol. 9, no. 2, pp. 625631, Mar. 2009.D. Karaboga and B. Basturk, Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems, in Foundations of Fuzzy Logic and Soft Computing, Springer, 2007, pp. 789798.C. Chidambaram and H. S. Lopes, A new approach for template matching in digital images using an Artificial Bee Colony Algorithm, in World recounting on Nature Biologically Inspired Computing, 2009. NaBIC 2009, IEEE, 2009, pp. 146151.N. K. Kaur Mann, Review Paper on Clustering Techniques, Global Journal of Computer Science and Technology, vol. 13, no. 5, 2013.S. Okdem, D. Karaboga, and C. Ozturk, An ap plication of Wireless Sensor Network routing based on Artificial Bee Colony Algorithm, in 2011 IEEE intercourse on Evolutionary Computation (CEC), 2011, pp. 326330.T. K. Sharma, M. Pant, and J. C. Bansal, Some modifications to enhance the performance of Artificial Bee Colony, in 2012 IEEE Congress on Evolutionary Computation (CEC), 2012, pp. 18.L. Bao and J. Zeng, Comparison and compendium of the selection mechanism in the artificial bee colony algorithm, in Hybrid Intelligent Systems, 2009. HIS09. Ninth International Conference on, 2009, vol. 1, pp. 41141.C. M. V. Bentez and H. S. Lopes, Parallel Artificial Bee Colony Algorithm Approaches for Protein Structure Prediction Using the 3DHP-SC Model, in Intelligent Distributed Computing IV, M. Essaaidi, M. Malgeri, and C. Badica, Eds. Springer Berlin Heidelberg, 2010, pp. 255264.D. L. Gonzlez-lvarez, M. A. Vega-Rodrguez, J. A. Gmez-Pulido, and J. M. Snchez-Prez, Finding Motifs in DNA Sequences Applying a Multiobjective Artificial Bee Colony (MOABC) Algorithm, in Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, C. Pizzuti, M. D. Ritchie, and M. Giacobini, Eds. Springer Berlin Heidelberg, 2011, pp. 89100.L. Wang, G. Zhou, Y. Xu, S. Wang, and M. Liu, An effective artificial bee colony algorithm for the flexible job-shop scheduling problem, Int J Adv Manuf Technol, vol. 60, no. 14, pp. 303315, Apr. 2012.S.-W. Lin and K.-C. Ying, Increasing the total net revenue for single machine order acceptance and scheduling problems using an artificial bee colony algorithm, J Oper Res Soc, vol. 64, no. 2, pp. 293311, Feb. 2013.D. Karaboga, An idea based on honey bee swarm for numerical optimization, Techn.Rep. TR06, Erciyes Univ. Press, Erciyes, 2005.T. K. Sharma, M. Pant, and J. C. Bansal, Some modifications to enhance the performance of Artificial Bee Colony, in 2012 IEEE Congress on Evolutionary Computation (CEC), 2012, pp. 18.TSai, Pei-Wei, et al. , Enhanced artificial bee colony optimization.Int ernational Journal of Innovative Computing, Information and Control,vol. 5, no. 12, 2009, pp.5081-5092.B. K. Verma and D. Kumar, A review on Artificial Bee Colony algorithm, International Journal of Engineering Technology, vol. 2, no. 3, pp. 175186, 2013.D. Kumar and B. Kumar, Optimization of Benchmark Functions Using Artificial Bee Colony (ABC) Algorithm, IOSR Journal of Engineering, vol. 3, no. 10, pp. 09-14, October 2013.

Social Class And A Persons Life Chances

Social Class And A Persons Life ChancesBefore the start of industrial capitalism, in a pre-industrial societies meet is referred to as a general way activities directed at satisfying the human need for survival. Which were all non-industrial, work such as hunting, gathering or farming. Starting in the 18th century and continuing into the 19th century work has become regular paid employment. The simple rural lives were exchanged by mass exertion of goods. Industrialisation guide to urbanisation, it changed the medieval customs, beliefs and ideals. The term industrial revolution is used to describe this transformation. Industrial Productive activity involved Factory systems and mechanisation cater by energy sources that is undertaken outside the home in a building or mill, Where workers has to work as industrial labourers under hazardous conditions. Karl Marx argued the capitalist who be the owners of the means of toil, must essentially exploit the workers for maintaining the ex istence of the structure and organisation. The capitalism is a system based on profit, within the capitalism the workers be disposed(p) a paid wage enabling them to survive. It is necessary that capitalism continue to grow, to give the mass population the surplus wealth. It depends on continual growth and, therefore, it makes scent out to give the mass of the population surplus wealth for enable them to buy goods the more goods they buy, the more the system can produce.Marx was one the first social idealogue to examine in to the conditions of work in factories that were emerging during the industrial revolution, looking at how the transition from self-working craftwork to working for a boss in a factory resulted in alienation and deskilling. For Marx capitalism created the world of work then turned it against the workers, not only workers were prevented from realising themselves but they developed a system where work became the source of alienation and exploitation. Max used th e term alienation to describe what it were like to be wage workers under industrial capitalism. He used four reference of alienation in factory workers first alienated from theobjects of their work as job becomes repetitive and automatic. Second workers argon alienated fromthe activity of working they are forced to work for them. Third Workers are alienated from thechance to determine what it is to be human and finally Alienation from opposite workers, not having to spend era with people you enjoy and are forced to work with people even if you like them or not.Braverman similarly mirrored most of Karl Marxs writing and applied it to work in the twentieth century. Braverman claimed that many jobs in the capitalist economy were subjected to a process of deskilling this is where professional knowledge becomes replaced by machines and automation and tendency toward specialisation of task. He describes this as the period of monopoly capitalism. Taylorism exemplified this managerial st rategy.At the beginning of the twentieth century Henry Ford combines the organisational innovation of taylorism, which introduced special machine tools that standardised production in a continuous flow in the form of assembly line. It was used on a large scale and using semi-skilled workers. Workers had one task for each one that they had to repeatedly do which is why they did not need to be particularly trained. However, he has been criticized for his idea Fords mass-production system. The regulation theory explained that as a capitalist production system, Fordism is alienating and involved deskilling therefore, Fordism is unable to overcome workers dissatisfaction. Another argument is that it is unable to overcome consumer dissatisfaction. Both arguments concludes that during 1970s Fordism was in crisis. Many solutions were adopted to solve the crisis of Fordism with a development of wide range of better quality products with neo-Fordism, McDonaldism, and post-Fordism.Also the pr ocess of de-industrialisation began around 1970s with a decline in employment among the manufacturing and industrial jobs and an step-up in employment in service sector jobs. However, de-industrialisation was not just about the end of particular jobs but the dismantling of social and cultural traffic in some societies. For example, manufacturing employments were sites of masculine occupational cultures and part of working class male identity. De-industrialisation was evident to large number of unemployment in industrial areas in the UK which led to a crisis of masculinity for working class men. On the other hand theorist such as Bell (1973) saw this as a demonstrable way in The coming of Post-industrial society. He argued that it will be less alienating than in industrial societies. Class may also lose its cause as knowledge and professional will have power rather than the anachronistic industrial ruling class.When people are unemployed, they get involved in the labour market. L abour marketsworks through the interaction between workers and employers. They try to understand employers demand and workers supplies by looking at pattern of employment, income, honorarium or often pattern of racism and sexism that are existing in society. These patterns of discrimination have led to what is called dual or segmented labour markets. Trade unions are part of fight against such processes in our society. They provide an important function for millions of workers. They protect workers from being exploited and making sure they have fair wages and working conditions.While work occupies a main role in our lives, its social significance extends beyond our personal identities and daily activities. It is closely involved with other social institutions, social structures, and social processes, especially social inequality.

K-degree-l-diversity Anonymity Model

K-ground level-l-diversity Anonymity ModelAbstractPrivacy is one of the major concerns when produce or sharing mixer network data for social science research and business synopsis. Recently, researchers entertain developed covert models similar to k-anonymity to pr up to nowt node reidentification through structure tuition. However, even when these privacy models be enforced, an attacker may still be able to infer ones private information if a group of nodes gener exclusivelyy shargon the resembling sensitive labels (i.e., attributes). In other words, the label-node relationship is not well protected by comminuted structure anonymization methods. Furthermore, existing approaches, which rely on edge editing or node clustering, may significantly alter pigment graph properties. In this paper, k- grad-l-diversity anonymity model that considers the protection of structural information as well as sensitive labels of individuals. A novel anonymization methodology based on adding hurly burly nodes has proposed. New algorithm by adding noise nodes into the certain graph with the consideration of introducing the least distortion to graph properties. Most importantly, completed the rigorous analysis of the theoretical bounds on the number of noise nodes added and their impacts on an important graph property. Extensive experiments used to evaluate the effectiveness of the proposed technique.IntroductionThe complexity of current softw atomic number 18 program dusts and uncertainty in their environments has led the software engineering science community to look for inspiration in diverse related fields (e.g., robotics, simulated intelligence, control theory, and biology) for new ways to design and manage systems and services. This endeavor, the capability of the system to adjust its behavior in response to the environment in the form of self- reconcileation has become one of the most promising research directions. The self prefix indicates that the systems resolve autonomously (i.e., without or with minimal interference) how to adapt or organize to accommodate changes in their contexts and environments. While some self-adaptive system may be able to function without some(prenominal) human intervention, guidance in the form of higher-level objectives (e.g., through policies) is useful and realized in many systems. The landscapes of software engineering domains and computing environments are constantly evolving. In particular, software has become the bricks and mortar of many complex systems (i.e., a system composed of interconnected parts that as a whole exhibits one or more properties (behaviors among the possible properties) not obvious from the properties of the individual parts). The hallmarks of such complex or ultra-large-scale (ULS) systems are self-adaptation, selforganization, and emergence. Engineers in general, and software engineers in particular, design systems according to requirements and specifications and are not acc ustomed to regulating requirements and orchestrating emergent properties. Ottino argues that the landscape is bubbling with activity and engineers should be at the center of these developments and contribute new theories and tools. In order for the developing of software engineering techniques to keep up with these ever-changing landscapes, software engineers must innovate in the realm of building, running, and managing software systems. Software-intensive systems must be able to adapt more easily to their ever-changing surroundings and be flexible, fault-tolerant, robust, resilient, available, configurable, secure, and selfhealing. Ideally, and necessarily for sufficiently large systems, these adaptations must happen autonomously. The research community that has formed most self-adaptive systems has already generated many encouraging results, helping to establish self-adaptive systems as a significant, interdisciplinary, and active research field. Self-adaptive systems have been studied within the various research areas of software engineering, including requirements engineering, software architecture, middleware, and component-based development however,most of these initiatives have been isolated. Other research communities that have also investigated self-adaptation and feedback from their own perspectives are even more diverse control theory, control engineering, artificial intelligence, mobile and autonomous robots, multi-agent systems, fault-tolerant computing, dependable computing, distributed systems, autonomic computing, self-managing systems, autonomic communications, adaptable user interfaces, biology, distributed artificial intelligence, machine learning, economic and financial systems, business and military strategic planning, detector networks, or pervasive and ubiquitous computing. Over the past decade several self-adaptation-related application areas and technologies have grown in importance. It is important to emphasize that in all these i nitiatives software has become the common element. That enables the provision of self-adaptability. Thus, it is imperative to investigatesystematic software engineering approaches for developing self-adaptive systems, which areideallyapplicable across double domains. Self-adaptive systems can be characterized by how they operate or how they are analyzed, and by multiple dimensions of properties including centralized and decentralized, top-down and bottom-up, feedback latency (slow vs. fast), or environment uncertainty (low vs. high). A top-down self-adaptive system is often centralized and operates with the guidance of a central controller or policy, assesses its own behavior in the current surroundings, and adapts itself if the monitoring and analysis warrants it. Such a system often operates with an explicit internal representation of itself and its global goals. By analyzing the components of a top-down self-adaptive system, one can compose and deduce the behavior of the whole system. In contrast, a cooperative self-adaptive system or self-organizing system is often decentralized, operates without a central authority, and is typically composed bottom-up of a large number of components that interact locally according to simple rules. The global behavior of the system emerges from these local interactions. It is difficult to deduce properties of the global system by analyzing single the local properties of its parts. Such systems do not necessarily use internal representations of global properties or goals they are often inspired by biological or sociological phenomena. Most engineered and nature-inspired self-adaptive systems fall somewhere between these two extreme poles of self-adaptive system types. In practice, the line between these types is rather blurred and compromises leave often lead to an engineering approach incorporating techniques from both of these two extreme poles. For example, ULS systems embody both top-down and bottom-up self-adaptive characteristics (e.g., the Web is basically decentralized as a global system, but local sub-webs are highly centralized or server farms are both centralized and decentralized). Building self-adaptive software systems cost-effectively and in a predictable manner is a major engineering challenge. New theories are needed to accommodate, in a systematic engineering manner, traditional top-down approaches and bottom-up approaches. A promising starting point to meet these challenges is to mine suitable theories and techniques from control engineering and nature and to apply those when designing and reasoning close self-adaptive software systems. Control engineering emphasizes feedback loops, elevating them to firstclass entities. In this paper we argue that feedback loops are also essential for understanding all types of self-adaptive systems. Over the years, the discipline of software engineering strongly emphasized the static architecture of a system and, to a certain extent, neglecte d the driving aspects. In contrast, control engineering emphasized the self-propelled feedback loops embedded in a system and its environment and neglected the static architecture. A notable exception is the seminal paper by Magee and Kramer on dynamic structure in software architecture, which formed the foundation for many subsequent research projects. However, while these research projects realized feedback systems, the actual feedback loops were hidden or abstracted. technology Self-Adaptive Systems through Feedback Loops 51 Feedback loops have been recognized as important factors in software process management and improvement or software evolution. For example, the feedback loops at every(prenominal) stage in Royces waterfall model or the risk feedback loop in Boehms spiral model are well known. Lehmans work on software evolution showed that the software process constitutes a multilevel, multiloop feedback system and must be treated as such if major fare in its planning, co ntrol, and improvement is to be achieved. Therefore, any attempt to make parts of this multiloop feedback system self-adaptive necessarily also has to consider feedback loops. With the proliferation of self-adaptive software systems, it is imperative to develop theories, methods and tools around feedback loops. Mining the rich experiences and theories from control engineering as well as taking inspiration from nature and biology where we can find systems that adapt in rather complex ways, and then adapting and applying the findings to software-intensive selfadaptive systems is a most worthwhile and promising avenue of research. In the remainder of this paper, we therefore investigate feedback loops as a happen upon aspect of engineering self-adaptive systems. Outlines basic principles of feedback loops and demonstrates their importance and potential benefits for understanding self-adaptive systems. Control engineering and biologically inspired approaches for self-adaptation. We pre sent selected challenges for the software engineering community in general and the SEAMScommunity in particular for engineering self-adaptive computing systems.Existing systemIn Existing system forced by the recognition of the need for a finer grain and more personalized privacy in data publication of social networks. In this paper we implement privacy protection scheme that not only prevents the disclosure of the disclosure of selected features in users profiles and also for identity of users. The features of her profile she wishes to conceal by an individual user can select. The users are nodes and features are labels in social networks are modeled as graphs. The Labels are denoted either as non-sensitive or sensitive. In Existing system the background knowledge an rival may possess, as sensitive information that has to be protected in both node and labels To allow for graph data to be published in a form such that an adversary who possesses information about a nodes neighborhoo d cannot safely infer its identity and its sensitive labels in this we present privacy protection algorithms that. The goals of these algorithms transform the original graph into a graph in which nodes are sufficiently indistinguishable in these algorithms are designed. While losing as detailed information and while preserving as much utility as possible. The algorithms preserve the original graphs structure and properties thats why we evaluate empirically the extent to which. In Existing system that our solution is, efficient, scalable and effective and while offering stronger privacy guarantees than those in previous research.Proposed systemk-degree anonymity with l-diversity to prevent not only the reidentification of individual nodes but also the revelation of a sensitive attribute associated with each node. If the k-degree-l-diversity constraint satisfies create KDLD graph. A KDLD graph protects two aspects of each user when an attacker uses degree information to attack A nove l graph construction technique which makes use of noise nodes to preserve utilities of the original graph. Two key properties are considered Add as few noise edges as possible. Change the distance between nodes as less as possible. The noise edges/nodes added should connect nodes that are close with respect to the social distance. There exist a large number of low degree vertices in the graph which could be used to confuse added noise nodes from being re-identified. By carefully inserting noise nodes, some graph properties could be better preserved than a pure edge-editing method.MODULESData Collection.Reduce lymph node Degree.Add Node Degree.Add Noise Node.1. DATA COLLECTIONIn this module the employee data is collected. from each one employee has unique Id, Name and Sensitive Label Salary. Each employee links with number of other employee. Based on the employee data construct the Social Network Graph a social network graph is a four tuple G(V, E, , ), where V is a set of vertic es, and each vertex represents a node in the social network. is the set of edges between vertices, is a set of labels that vertices have maps vertices to their labels.2. REDUCE NODE DEGREEFor any node whose degree is larger than its target degree in Pnew, decreasing its degree to the target degree by making using of noise nodes.3. ADD NODE DEGREEFor any node whose degree is smaller than its target degree in Pnew, increasing its degree to the target degree by making using of noise nodes. For each vertex u in G which needs to add its degree, to make its degree reach the target degree. First check whether there exists a node v within two hops of u, and v also needs to increase its degree. Connect n with v. Since v is within two hops of u, connecting v with n will not change the distance between u and v. after(prenominal) this step, if ns degree is bigger than the minimum degree in Pnew but does not appear in Pnew, recursively deleting the last created link until the degree of n equa ls to a degree in Pnew. Otherwise, leave n for processing and continue adding noise to u if ud 4. ADD NOISE NODEIn this module the noise node will added to the original data set. After that adding noise node add new degree for that noise node. For any noise node, if its degree does not appear in Pnew, some adjustment can happen to make it has a degree in Pnew. Then, the noise nodes are added into the same degree groups in Pnew.ConclusionsIn this paper, k-degree-l-diversity model has implemented for privacy preserving social network data publishing. Implementation of both distinct l-diversity and recursive (c, l)-diversity also happened. In order to achieve the requirement of k-degree-l-diversity, a noise node adding algorithm to construct a new graph from the original graph with the constraint of introducing fewer distortions to the original graph. Rigorous analysis of the theoretical bounds on the number of noise nodes added and their impacts on an important graph property. Extensi ve experimental results demonstrate that the noise node adding algorithms can achieve a better result than the previous work using edge editing only. It is an interesting direction to study smart algorithms which can reduce the number of noise nodes if the noise nodes contribute to both anonymization and diversity. Another interesting direction is to consider how to implement this protection model in a distributed environment, where different publishers publish their data independently and their data are overlapping. In a distributed environment, although the data published by each publisher run across certain privacy requirements, an attacker can still break users privacy by combining the data published by different publishers together. Protocols should be designed to help these publishers publish a unified data together to guarantee the privacy.Future EnhancementPrivacy is one of the major concerns when publishing or sharing social network data for social science research and bu siness analysis. The label-node relationship is not well protected by pure structure anonymization methods. k-degree-l-diversity anonymity model that considers the protection of structural information as well as sensitive labels of individuals. Adding noise nodes into the original graph with the consideration of introducing the least distortion to graph properties.

Seven Essay -- Film Movie Movies

For this report I choose the movie Seven. This movie was released back in 1995 and stars Morgan Freeman, Brad Pitt, Gweneth Paltrow, R. Lee Ermey, John McGinley, and Kevin Spacy. Seven was order by David Fincher and written by Andrew K. Walker.The movie begins with the usual old cop, who is about to retire, and teams up with a young, ready to take on the world cop. The first travel begins promisingly, with two cops creation assigned to their first case together. One is white and the other is black and they have vastly different investigative styles. Each murder, being investigated by Lieutenant William Somerset (Morgan Freeman) and Detective David Mills (Brad Pitt), is based on one of the Seven Deadly Sins, which are Gluttony, Greed, Sloth, Envy, Wrath, Pride and Lust. The detectives have an enormously copious man who is forced to eat himself to death-Gluttony. The detectives discover a high profile lawyer who is made to cut off a pound of flesh for Greed. They find hooker who has been killed by having sex with a man that we will just say?s wearing an apparatus on his body for Lust. A rails model is forced to choose death or disfigurement for Pride. Sloth was a man that had been tortured for a whole year. He had been barely kept alive and his hand had been cut off for his fingerprints. He is the only victim that does not die but is a complete vegetable in such(prenominal) a fragile state that he would be better off dead. For envy and wrath we will come back to in a bit.The kil...

The Shining :: essays research papers

The ShiningThe story take moorage in Colorado and begins with squat Torrence, going to a placeby the name of "The Overlook Hotel" to be the caretaker over the winter months,because of coast of keeping a twenty-five mile road, in which it take to get tothe hotel open, because of either the snow. To get the job as the caretaker of thehotel, he would be alone for five months, and have free food, and also free stayat the hotel, all tinkers dam has to do is mantiance and handyman work around the hotel.He arrives for the interview and meets Lloyd (the manager of the OverlookHotel) they discuss certain duties and jobs that will be needed around the hotel,and so Lloyd brings up a certain account that happened a couple of years backat the hotel, about a man that murdered his family from what you call " confinefever" caused by seclusion and away from everything. Jack is stuned by what hehears and then quickly says something to the effect of not having to worryabout someth ing like that happening, and that he needed the months off to getstarted on a book he was writing. Lloyd decides to let him have the job, andasked him to come back the next day. The next day Danny (his son) and married woman Wendyand Jack left for the hotel.They arrive and get there bags dropped off, it is the last day the hotel is openfor the season, and people are checking out, and workers cleaning up, so theycan leave for the spring. Jack and Wendy goto meet up with Lloyd, and Dannyleaves for the game room. Lloyd takes them around the hotel to look around andget a feel of were they will be staying, and shows them their rooms. They stopand meet up with the passing play cook Dick Hallorann, Lloyd ask Dick to show Wendy andDanny around the Kitchen as he takes Jack to see the rest of the hotel. DickHallorann goes threw the kitchen and shows Wendy what to do, and what to use,etc... all threw this time of the crack Dick keeps on using a mind signal, avoice to communicate, at first Da nny does not realize it, but then as the passwraps up, the family meets back up, and Dick offers Danny some ice cream asLloyd takes Wendy and Jack to another part of the hotel. As Danny is eattinghis ice cream, Dick is talking to him, and then talks about, what he was doing