Ethical issues Essay Example | Topics and Well Written Essays - 250 words - 1

Ethical issues - Essay Example On the other hand, the victims of the burglary want to see justice by the burglar being convicted. This seems fair because they were subjected to psychological torture and maybe physical when the burglary took place and, hence, deserves justice to be accorded to them, as this is their constitutional right. Convicting the burglar with no clear and conclusive evidence that the individual committed the act is not only unfair but also an abuse of the freedom of movement of that individual because the person may not have committed the burglary. If the person is wrongfully convicted, he can sue the D.A (Pollock2011). The most ethical decision for the D.A to make in this case is to let the said burglar serve probation in the community. This is not only moral and ethically justified but it will be a win-win situation for both parties and especially if they told of the repercussions of taking the case to trial (the victims may lose the case due to lack of conclusive evidence or the burglar may be convicted of a crime he did not

Challenges in the Third Sector Essay Example | Topics and Well Written Essays - 2000 words

Challenges in the Third Sector - Essay Example Social benefit structures are usually diverse in different nations. These systems take care of components of social defence, family unit strategy and individual dependability in a different way, and all over the Europe, a combination of these components can be found. Their individual characteristics still control nationalized social exchange of ideas, even though globalisation, altering financial conditions, progressive EU legislative and increased stress on resources appears to direct towards ‘convergence’ .Although in some European nations, civil society - even if the phrase itself is of current derivation - has been a venerable happening, other nations’ experience with them is fresh. Global commonality either has brought active establishments in Europe to start non-governmental organizations or help their counterparts in rising parliamentary governments do this, and their support is usually ongoing.There is no established description of the expression 'civil so ciety establishment'; however, it is normally implicit to take account of the social associates, NGOs, mutual interest organizations and local establishments (Comaroff, p. 99, 2009). What all establishments have in common is their ‘not for profit’ nature, which does not indicate they cannot produce in excess, but does indicate they have to be reinvested in and utilized for the common use of the establishment. In various nations, the global year of volunteers 2001 has evidently been an incentive to reorganize as well as develop volunteering directive.

The Elements of Argument Essay Essay Example for Free

The Elements of Argument Essay Essay In an argument essay, a writer simply argues a point or shall we say defend a side to a controversial issue. He defends his points by stating evidences and with logical reasoning. Many essays are basically argument essays. Most of the time we write essays to give out our opinion on something so that we may also persuade other people to have the same opinions. There are two basic elements to an Argument: a thesis and statement of evidence. The thesis of an argument is what a writer defends. An example of a thesis may be: Smoking is very hazardous. The strength of your argument will depend greatly on the strength of your thesis statement. The next element of an argument would be evidence. No argument will be complete without evidence supporting it. Most of time, reasoning alone will not suffice to strengthen your argument because a counter argument may easily be made to invalidate your claim. This is why evidence is very crucial to your argument. There are quite a lot of sources to find evidence for your argument, to name a few, experiences, books, surveys, polls, published articles, etc. Without these two elements in your argument your work will not be very persuasive to other people and so your essay will be no good. So it is very essential to find a good thesis statement and gather enough evidence to back you up. Argument used in The Declaration of Independence It is clearly seen how Thomas Jefferson uses argument in The Declaration of Independence and that the elements of an argument essay can easily be found. In the second paragraph we can find the thesis of the argument which is that all men are created equal and are all with unalienable rights. The main argument is that the King of Britain is clearly using his power to deny people of their rights. On most parts of the declaration we can find the second element to argumentative essays, evidence. Thomas Jefferson states a number of cases where the King of Britain is in clear violation of human and civil rights. These undeniable facts are what makes this essay very strong. The thesis is very well supported. Here we can see how the elements of an argumentative essay effects how good the work will be. The better use of a thesis and evidence makes your point very clear thus making your work more persuasive which is the overall goal of an argument, persuading people to share your idea.

Image Super-Resolver using Cascaded Linear Regression

Image Super-Resolver using Cascaded Linear Regression Abstract A number of existing super-resolution algorithms fail in modeling the relationship between high and low resolution image patches and time complexity in training the model. To overcome the above-stated problem, simple, effective, robust and fast image super-resolver (SERF) based on cascaded linear regression has been used for learning the model parameters. The image divided into patches are grouped into clusters using k-means clustering algorithm for learning the model parameter based on series of linear least square function, named cascaded linear regression to identify the missing detail information. This approach has been simulated using MATLAB for various images. The simulation results show that SERF gives better PSNR and less computation cost compared to existing methods. Keywords-Cascaded linear regression, example learning based image super-resolution, K-means. Super-Resolution (SR) is the process of producing a high-resolution (HR) image or video from low-resolution images or frames. In this technology, multiple low-resolution (LR) images are applied to generate the single high-resolution image. The image super-resolution is applied in a wide range, including the areas of military, medicine, public safety and computer vision, all of which will be in great need of this technology. The SR process is an ill-posed inverse problem, even though the estimation of HR image from LR input image has many possible solutions. There are many SR algorithms available to resolve this ill-pose problem. Interpolation Based method is the most intuitive method for the image super-resolution. This kind of algorithm has the low-resolution image registered on the grid of the high-resolution image to be calculated. Reconstruction based method is mainly based on iterative back projection method. This algorithm is very convergent, simple and direct, but the resoluti on is not steady and unique. Because of the limitation of the reconstruction algorithm, the learning-based super-resolution technology emerges as an active research area. Learning based approach synthesize HR image from a training set of HR and LR image pairs. This approach commonly works on the image patches (Equal-sized patches which is divided from the original image with overlaps between neighbouring patches). Since, learning based method achieves good performance result for HR image recovery; most of the recent technologies follow this methodology. Freeman et al [1] describe a learning based method for low-level vision problem-estimating scenes from images and modeling the relation between synthetic world of images and its corresponding images with markov network. This technique use Bayesian belief propagation to find out a local maximum of the posterior probability for the scene of given image. This method shows the benefits of applying machine learning network and large datasets to the problem of visual interpretation. Sun et al [2] use the Bayesian approach to image hallucination where HR images are hallucinated from a generic LR images using a set of training images. For practical applications, the robustness of this Bayesian approach produces an inaccurate PSF. To overcome the estimation of PSF, Wang et al [3] propose a framework. It is based on annealed Gibbs sampling method. This framework utilized both SR reconstruction constraint and a patch based image synthesis constraint in a general probabilistic and also has poten tial to reduce the other low-level vision related problems. A new approach introduced by Yang et al [4] to represent single image super-resolution via sparse representation. With the help of low resolution input image sparse model, output high resolution image can be generated. This method is superior to patch-based super-resolution method [3]. Zedye et al [5] proposed a sparse representation model for single image scale-up problem. This method reduces the computational complexity and algorithmic architecture than Zhan [6] model. Gao et al [7] introduce the sparsity based single image super-resolution by proposing a structure prior based sparse representation. But, this model lags in estimation of model parameter and sparse representation. Freedman et al [8] extend the existing example-based learning framework for up-scaling of single image super-resolution. This extended method follows a local similarity assumption on images and extract localized region from input image. This techn ique retains the quality of image while reducing the nearest-neighbour search time. Some recent techniques for single image SR learn a mapping from LR domain to HR domain through regression operation. Inspired by the concept of regression [9], Kim [10] and Ni Nguyen [11] use the regression model for estimating the missing detail information to resolve SR problem. Yang and Wang [12] presented a self-learning approach for SR, which advance support vector regression (SVR) with image sparse co-efficient to make the model relationship between LR and HR domain. This method follows bayes decision theory for selecting the optimal SVR model which produces the minimum SR reconstruction error Kim and Kwon [13] proposed kernel ridge regression (KRR) to train the model parameter for single image SR. He and siu [14] presented a model which estimates the parameter using Gaussian process regression (GPR).Some efforts have been taken to reduce the time complexity. Timofte et al [15] proposed Anchored neighbourhood regression (ANR) with projection matrices for mapping the LR image patches onto the HR image patches. Yang et al [16] combined two fundamental SR approaches-learning from datasets and l earning from self-examples. The effect of noise and visual artifacts are suppressed by combining the regression on multiple in-place examples for better estimation. Dong et al [17] [18] proposed a deep learning convolutional neural network (CNN) to model the relationship between LR and HR images. This model performs end-to-end mapping which formulates the non-linear mapping and jointly optimize the number of layers. An important issues of the example learning based image SR technique are how to model the mapping relationship between LR and HR image patches; most existing models either hard to diverse natural images or consume a lot of time to train the model parameters. The existing regression functions cannot model the complicated mapping relationship between LR and HR images. Considering this problem, we have developed a new image super-resolver for single image SR which consisting of cascaded linear regression (series of linear regression) function. In this method, first the images are subdivided into equal-sized image patches and these image patches are grouped into clusters during training phase. Then, each clusters learned with model parameter by a series of linear regression, thereby reducing the gap of missing detail information. Linear regression produces a closed-form solution which makes the proposed method simple and efficient. The paper is organized as follows. Section II describes a series of linear regression, results are discussed in section III and section IV concludes the paper. Inspired by the concept of linear regression method for face detection [19], a series of linear regression framework is used for image super-resolution. Here, the framework of cascaded linear regression in and how to use it for image SR were explained. A. Series of Linear Regression Framework The main idea behind cascaded linear regression is to learn a set of linear regression function for each cluster thereby gradually decreasing the gaps of high frequency details between the estimated HR image patches and the ground truth image patches. In order to produce the original HR image from LR input image, first interpolate LR image to obtain the interpolated LR image with same size as HR image. This method works at the patch level, each linear regressor parameter computes an increment from a previous image patch, and the present image patch is then updated in cascaded manner. (1) (2) denotes the estimated image patch after t-stages. denotes the estimated increment. denotes feature extractor by which the f-dimensional feature vector can be obtained. Linear regressor parameters at t-stage T Total number of regression stages. The next step is learning of the linear regression parameters and for T stages. Relying on these linear regression T stages, parameters for regressors are subsequently learnt to reduce the total number of reconstruction errors and to make presently updated image patch more appropriate to generate the HR patch. Using least squares form to optimize and , it can be written as, (3) The regularization term accomplishes a constraint on the linear regression parameters and to avert over-fitting and ÃŽÂ ² be the data fidelity term and the regularization term. At each regression stage, a new dataset values can be created by recurrently applying the update rule in (1) with learnedand. Next, and can be learned subsequently using (2) in cascade manner. Fig. 1. Flow of cascaded linear regression framework B. Pseudo code For Cascaded Linear Regression Algorithm The Pseudo code for cascaded linear regression algorithm for training phase is given below, Input: , image patch size à ¢Ã‹â€ Ã… ¡d xà ¢Ã‹â€ Ã… ¡d for t=1 to T do { Apply k-means to obtain cluster centres for i = 1 to c do { compute A and b. update the values of A and b in . } end for } end for The output of this training phase is and cluster centroid. C. SERF Image Super-Resolver This section deals with cascaded linear regression based SERF image. The process starts by converting color image from the RGB space into the YCbCr space where the Y channel represents luminance, and the Cb and Cr channels represent the chromaticity. SERF is only applied to the Y channel. The Cb and Cr channels reflect G and B channels of the interpolated LR image. D. SERF Implementation To extract the high frequency details from each patch by subtracting the mean value from each patch as feature patch denoted as . Since the frequency content is missing from the initially estimated image patches, the goal of a series of linear regression is to compensate for high frequency detail (4), (4) To diminish the error between HR feature patch and the estimated feature patch, it is normal that the regression output should be small. Hence, by putting the constraint on regularization term to (4), the output is, (5) Where, ÃŽÂ » is the regularization parameter. t Denotes the number of regression stages. denotes the feature extractor. ÃŽÂ ² and ÃŽÂ » are set to 1 and 0.25. A closed-form solution for equation (5) can be computed by making the partial derivative of equation (5) equal to zero. In testing phase, for a given LR image, bicubic interpolation is applied to up sample it by a factor of r. This interpolated image is divided into M image patches. Feature patches are calculated by subtracting the mean value from each image patch. At the tth stage, each feature patch is assigned to a cluster l according to the Euclidean distance. To obtain the feature subsequently, linear regression parameters are applied to compute the increment. Concurrently, the feature patch is updated using, (6) After passing through T-stages, reconstructed image patches are obtained by adding mean value to the final feature patches. All the reconstructed patches are then combined with the overlapping area and then averaged to generate the original HR image. E. Pseudo code For SERF Image Super-Resolver Algorithm The pseudo code for SERF image super-resolver algorithm is as follows: Inputs: Y, a, r, for t=1 to T do { Adapt each patch clusterto a cluster. Compute. Update the values of A and b in } End for The output will be the High Resolution image (HR). The simulation of the SERF image super-resolver is done by using MATLAB R2013a for various images. The LR image is read from image folder and is processed using the algorithms explained before. The output HR image is taken after regression stages. The implementation is done by considering many reference images. The colour image (RGB) is first converted into YCbCr space, where Y channel represents luminance. Cb and Cr are simply copied from the interpolated LR image. The number of cluster size is 200. Image patch size 5 x 5 and magnification factor is set to 3. a)LR input b)HR input (c)Zooming result Fig.2. SERF Result under Magnification Factor 3 a)LR input b)HR output c)zooming result Fig.3. SERF Result under Magnification Factor 2 a)LR input b)HR output c)zooming result Fig.4. SERF Result under Magnification Factor 1 (a) (b) (c) (d) (e) (f) (g) (h) Fig.5. Comparisons ResultsButterfly (a) ground truth image (original size is 256 ÃÆ'- 256); (a)super-resolution results of (b) SRCNN, (c) ScSR, (d) Zeydes, (e) ANR, (f) BPJDL,(g) SPM, and (h) SERF. Zeydes [5] method gives noiseless image, but texture details are not well reconstructed as shown Figure (d). The BPJDL [14] methods generate sharper edges when compared to other methods as shown Figure (f). Figure (h) shows the zooming results of SERF method that performs well for both reconstruction and visual artifacts suppression. TABLE I:PSNR AND SSIM VALUES UNDER MAGNIFICATION FACTOR OF 1, 2 AND 3. Magnification Factor PSNR SSIM TIME(s) 3 29.0775 0.839 0.4323 2 30.5 0.812 0.4000 1 38.4 0.798 0.3870 TABLE II:PSNR AND SSIM VALUES UNDER MAGNIFICATION FACTOR OF 3 FOR TESTING IMAGES. S.NO IMAGES PSNR SSIM TIME(s) 1 Baboon 23.63 0.532 0.3115 2 Baby 35.29 0.906 0.4148 3 Butterfly 26.87 0.883 0.2018 4 Comic 24.32 0.755 0.2208 5 Man 28.19 0. 778 0.5468 6 zebra 29.09 0.839 0.4324 For magnification factor of 3, SERF outplays ScSR method by an average PSNR gain of 0.43dB, Zeydes [5] method by 0.37dB, ANR [15] by 0.44dB, BPJDL [14] method by 0.23dB and the SPM [7] method by 0.16dB. SERF gives average SSIM value of 0.8352 and it is fastest method compared to existing methods (TABLE III). TABLE III: PSNR AND SSIM VALUE COMPARISON OF SERF METHOD WITH EXISTING METHODS UNDER MAGNIFICATION FACTOR OF 3. EXISTING METHODS PSNR SSIM TIME(s) ScSR [4] 23.69 0.8835 7.27 Zeydes [5] 23.60 0.8765 0.06 ANR [15] 24.32 0.8687 0.02 BPJDL [14] 24.17 0.8890 17.85 SPM [7] 24.63 0.8982 0.74 SERF 29.0775 0.8352 0.23 SERF has few parameters to control the model, and results in easy adaption for training a new model when the experimental settings, zooming factors and databases were changed. The cascaded linear regression algorithm and SERF image super-resolver has been simulated in MATLAB2013a. SERF Image super-resolver achieves better performance with sharper details for magnification factor up to 3. This model reduces the gaps of high-frequency details between the HR image patch and the LR image patch gradually and thus recovers the HR image in a cascaded manner. This cascading process promises the convergence of SERF image super-resolver. This method can also be applied to other heterogeneous image transformation fields such as face sketch photo synthesis. Further this algorithm will be implemented on FPGA by proposing suitable VLSI architectures. REFERENCES [1] W. Freeman, E. Pasztor, and O. Carmichael, Learning low-level vision, International Journal of Computer Vision, vol. 40, no. 1, pp. 25-47,2000. [2] J. Sun, N. Zheng, H. Tao, and H. Shum, Image hallucination with primal sketch priors, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2003, pp. 729-736. [3] Q. Wang, X. Tang, and H. Shum, Patch based blind image super resolution, in Proceedings of IEEE international Conference on Computer Vision, 2005, pp. 709-716. [4] J. Yang, J. Wright, T. Huang, and Y. Ma, Image super-resolution via sparse representation, IEEE Transactions on Image Processing, vol. 19,no. 11, pp. 2861-2873,2010. [5] R. Zeyde, M. Elad, and M. Protter, On single image scale-up using sparse-representations, in Proceedings of Curves and Surfaces, 2012, pp. 711-730. [6] X. Gao, K. Zhang, D. Tao, and X. Li, Joint learning for single-image super-resolution via a coupled constraint, IEEE Transactions on Image Processing, vol. 21, no. 2, pp. 469-480, 2012. [7] K. Zhang, X. Gao, D. Tao, and X. Li, Single image super-resolution with multiscale similarity learning, IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 10, pp. 1648-1659, 2013. [8] G. Freedman and G. Fattal, Image and video upscaling from local selfexamples, ACM Transactions on Graphics, vol. 28, no. 3, pp. 1-10, 2011. [9] K. Zhang, D. Tao, X. Gao, X. Li, and Z. Xiong, Learning multiple linear mappings for efficient single image super-resolution, IEEE Transactions on Image Processing, vol. 24, no. 3, pp. 846-861, 2015. [10] K. Kim, D. Kim, and J. Kim, Example-based learning for image super resolution, in Proceedings of Tsinghua-KAIST Joint Workshop Pattern Recognition, 2004, pp. 140-148. [11] K. Zhang, D. Tao, X. Gao, X. Li, and Z. Xiong, Learning multiple linear mappings for efficient single image super-resolution, IEEE Transactions on Image Processing, vol. 24, no. 3, pp. 846-861, 2015. [12] M. Yang and Y. Wang, A self-learning approach to single image super resolution, IEEE Transactions on Multimedia, vol. 15, no. 3, pp. 498-508, 2013. [13] K. Kim and K. Younghee, Single-image super-resolution using sparse regression and natural image prior, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 6, pp. 1127-1133, 2010. [14] H. He and W. Siu, Single image super-resolution using gaussian process regression, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2011, pp. 449-456. [15] R. Timofte, V. Smet, and L. Gool, Anchored neighborhood regression for fast example-based super-resolution, in Proceedings of IEEE Conference on Computer Vision, 2013, pp. 1920-1927. [16] J. Yang, Z. Lin, and S. Cohen, Fast image super-resolution based on in-place example regression, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 1059-1066. [17] C. Dong, C. Loy, K. He, and X. Tang, Learning a deep convolutional network for image super-resolution, in Proceedings of European Conference on Computer Vision, 2014, pp. 184-199. [18] C. Dong, C. Loy, K. He, and X. Tang, Image super-resolution using deep convolutional networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, DOI:10.1109/TPAMI.2015.2439281, 2015. [19] P. Viola and M. Jones, Robust real-time face detection, International Journal of Computer Vision, vol. 57, no. 2, pp. 137-154, 2004.

The Glass Ceiling Phenomenon :: Business Management Studies

The Glass Ceiling Phenomenon ‘The Glass Ceiling’ is a barrier to prevent women rising to the highest positions in an organization as a result of informal exclusionary practices (cited in Giddens 2002, pg, 552). These practices include sexual harassment, sexual discrimination and pregnancy discrimination. Explanations for the ‘glass ceiling’ phenomena derive from the stereotype of women into traditional roles. Many men still carry the attitude despite living in this modern day and age that women are not capable of higher managerial roles and that their place rightfully belongs at home along with the house-hold chores. There is also the point that many corporate firms think twice before employing women for the top position, in terms of their level of commitment, for it is inevitable that every woman will want to have a child at some point in their life. However maternity leave is viewed upon as an expense in terms of money and the valuable time that is wasted in order to fill the vacant position. The organisational structure is another barrier that women have to contend with for it is evident that most firms are male dominated and huddle together when it comes to after work social activities, thus leaving the woman to feel as an out cast. An article published in The Guardian 25/09/2002 backs ‘The Glass Ceiling’ phenomena as it shows that women are still not making it to the top of their professions, despite thirty years of equal opportunities policies in the public and voluntary sectors. While there may be some women higher up in management it can be argued that these are just ‘token’ positions so that the corporate management cannot be accused of discrimination. Those few who are successful in making it are then dealt the blow of being paid substantially less

Comparing Irony of War in Dulce et Decorum, Regeneration, and Quiet on

Irony of War Exposed in Dulce et Decorum, Regeneration, and Quiet on the Western Front    Many of the young officers who fought in the Great War enlisted in the army with glowing enthusiasm, believing that war was played in fancy uniforms with shiny swords. They considered war as a noble task, an exuberant journey filled with honor and glory. Yet, after a short period on the front, they discovered that they had been disillusioned by the war: fighting earned them nothing but hopelessness, death and terror. They had lost their lives to the lost cause of war, which also killed their innocence and youth. They were no longer boys but callous men. Wilfred Owen's poem "Dulce et Decorum Est", Pat Barker's novel Regeneration, and Erich Maria Remarque's All Quiet on the Western Front, all portray the irony between the delusive glory of war and the gruesome reality of it, but whereas Owen and Sassoon treat the theme from a British point of view, Remarque allows us to look at it from the enemy's. The poem "Dulce et Decorum Est", an anti-war poem by Wilfred Owen who was an English footsoldier, states that it is not sweet and fitting to die a hero's death for a country. Right off in the first line, Owen describes the troops as being "like old beggars under sacks" (1). This metaphor indicates that the men are battle weary and suggests reluctance. They also have been on their feet for days and appear to be drained of youth as they "marched asleep" (5) and "limped on, blood-shod" (6). Overall, in the first stanza, Oundjian 2 there seems to be a tension between old and young because it shows how the impact of an endless war has reduced these once energetic young men to the point where they could be referred to as "old" (1), "lame" (6) and... ...s, demonstrated through the author's talent, are denouncing the authority figures who were supposed to guide his generation into adulthood but instead turned the youth against each other in the pursuit of superficial ideals. The soldiers were simply the victims of a meaningless war. In conclusion, Remarque's firsthand encounters with trench warfare, Owen's vivid descriptions of the soldiers' experiences and Baker's touching accounts of the lives of historical figures, all state that there were no victors in war, only losers in a hopeless battle for territorial supremacy. Works Cited Barker, Pat. Regeneration. Toronto: Plume, 1993. Owen, Wilfred. "Dulce et Decorum Est." The Faber Book of War Poetry. Ed. Kenneth Baker. London: Faber, 1997. 3-4. Remarque, Erich Maria. All Quiet on the Western Front. Trans. A. W. Wheen. New York: Ballantine, 1982.

How Technology Advancement has influenced Computer Ethics Essay

The 21st century has been dubbed †the digital age† because of the monumental achievements witnessed in the Information, Communication and Technology (ICT) fields, which have impacted every aspect of human life. The internet has become one of the most lucrative Information Technology (IT) tool which has made communication and exchange of information faster and efficient. Consequently, it has transformed how people consume information. In spite of its benefits, the internet has led to a severe decline in ethics among internet users (Salman, Saad & Ali, 2013). The widespread use of internet has led to increased and sophisticated crimes, terrorism and wars (May, 2004). This essay will be based on a case involving hackers who hold Personal Computers (PC) hostage. It will outline the potential computer ethics issues associated with holding computers hostage. It will also highlight methods that computer users could use to prevent this type of attack. Moreover, it will highlight governmental legislations that could be enacted against this type of attack. Ethics is defined as the principles governing both right and wrong among free humans (free moral agents), whose actions are influenced by their freedom of choice (Soskolne, n.d). Advancement of technology presents both positive and negative impacts to a society. Technological advancements have led to a rapid decline in social and ethical issues regarding to security and privacy of personal information (Shieh & Tanthuwanit, 2004). One of the major forms of social ethic violation includes hackers holding personal computers hostage, and hence infringing on both privacy and security of targeted computer users. This in turn compromises information security concerned with the protection of information confidentiality, availability  and integrity (Gordon & Loeb, 2002) According to Perlroth (2014), cyber criminals have devised a new mechanism of freezing computers through malwares, and then demanding a steep ransom pay before unlocking them. Perlroth (2014) adds that computer owners throughout the world have fallen victims of this malicious attack, which denies them access to their personal computers and files stored on them. The hackers instead serve them with a â€Å"pop up† message demanding some ransom payment before their computers could be unlocked. Hackers holding computers hostage infringe on personal privacy, and gain access to sensitive information regarding online bank accounts, security passwords and other highly private information (Perlroth, 2014). Security systems and other general-purpose computers are also vulnerable to this kind of attack because, if these hackers can break into users’ accounts even from [GoDaddy] domains to create malicious subdomains through which they send mails to target clients (Perlroth, 2014), then every computer system is vulnerable to such attacks. This means that not only is the privacy of the computer users compromised, but also the security of their data and the general security of their computer systems (The Canadian Press, 2014). In order to prevent hackers from holding computers hostage, computers users should double check their domain names, user accounts or web addresses before responding to any email clicks. In line to this, Perlroth (2014) observes that: Hackers were breaking into GoDaddy users’ accounts with stolen passwords and setting up what is known as a subdomain †¦ hackers would set up the Web address†¦ then send emails to customers †¦ because it appeared to come from a trusted source – was more likely to lure clicks. Likewise also, users should either contact an IT professional to help them remove the ransomware incase infected, or they can wipe their computers clean through formatting them and reinstalling new Operating system as well (Perlroth, 2014). In addition, professional network security personnel should be consulted in order to ensure that passwords, firewalls, authentications and proxy servers among other security utilities are properly set and enhanced to fortify system security (French, 2012) Although the hunting, catching and eventual convicting of these hackers has not yet achieved substantial success (Perlroth, 2014), the government should enact privacy laws that pronounce higher penalty for perpetrators of personal privacy and security infringement so that their benefit is less in value when compared to pending punishment. Some of the active laws that have been enacted to curb this tragedy include the Computer Fraud and Abuse Acts which cover the use of malicious codes including worms, viruses, and other malicious programs intended to alter or damage data in a user’s computer (Title 18 U.S.C Section 1030). Another legislation that could counter this attack is the National Information Infrastructure Protection Act (NIIPA) which criminalizes the transmission of a command, program, or code with malicious intentions [like holding a compute hostage] (Colombell, 2002). In conclusion, when hackers hold computers hostage, they infringe on the personal privacy and security of personal information. During the period when the computers are held hostage, computer users not only risk having their sensitive information accessed and sometimes damaged, but they are also financially exploited in order to have their computers unlocked. Some of the feasible strategies of avoiding this attack include exercising a lot of care regarding the types of sites visited and clicked, and avoiding sending payments to hackers whenever a computer is hacked. Instead seek immediate assistance from a professional who will help you to manually get rid of the ransomware without paying the hackers off. References Colombell, M., R. (2002). The Legislative Response to the Evolution of Computer Viruses. 8 RICH. J.L. & TECH. 18. Retrieved from < http://jolt.richmond.edu/v8i3/article18.html> French, A., M. (2012). A Case Study on E-Banking Security – When Security Becomes Too Sophisticated for the User to Access Their Information. Journal of Internet Banking and Commerce, 17(2). Gordon, L., & Loeb, M. (2002). The Economics of Information Security Investment. ACM Transactions on Information and System Security, 5 (4), 438–457. May, M. (2004). Federal Computer Crime Laws. SANS Institute. Retrieved from < http://www.sans.org/reading-room/whitepapers/legal/federal-computer-crime-laws-1446? Show=federal-computer-crime-laws-1446&cat=legal > Perlroth, N. (2014, April 22). Hackers holding your PC to ransom. Deccan Herald. Retrieved from < http://www.deccanherald.com/content/297460/hackers-holding-your-pc-ransom.html.> Salman, A., Saad, S., & Ali, M., N. (2013). Dealing with Ethical Issues among Internet Users: Do We Need Legal Enforcement? Asian Social Science, 9(8): 3-8. Shieh, M., & Tanthuwanit, K. (2004). Privacy and Security: Internet Hacking and Surveillance. Retrieved from < http://www.ethicapublishing.com/CH16.pdf> Title 18 U.S.C Section 1030, internet, http://www4.law.cornell.edu/uscode/18/1030.html Soskolne, C., L. (n.d). Workplace Health and Safety Information: Ethical Issues. Retrieved from < http://ilocis.org/documents/chpt19e.htm> The Canadian Press (2014, April 14). Computer hacking expert says more bad news to come from Heartbleed. Edmonton Journal. Retrieved from