Does Descartes Provide A Convincing Argument?

Does Descartes Provide A Convincing Argument? Dualism is the philosophical belief that mind and matter are fundamentally distinct substances. William G. Lycan states that according to Cartesian dualism, minds are purely spiritual and radically non-spatial, having neither size nor location (Lycan, 47) and indeed, Descartes reached his conclusion by arguing that the mind and body are completely different in nature, making it possible for one to exist without the other. Whilst Descartes attempted to argue in favour of substance dualism, it can be said that his argument was ultimately weak, with substance monism being a far stronger viewpoint in the distinction between the mental and the physical. In this essay, I will attempt to argue that Descartes does not provide a convincing argument for the claim that mind and matter are distinct substances and instead, I will argue in favour of materialism through the particular exploration of functionalism. In order to understand dualism, we must first come to terms with Leibnizs Law due to the fact that most dualist arguments rely on this principle. The law states that if A=B, then any property of A is also a property of B (Guttenplan, 431). The first argument for dualism which I will discuss is the doubt argument, which compares the difference between an idea of ones own existence and an idea of ones bodys existence. The argument can be put forward as thus: Because of the nature of my being, my existence cannot be doubted. Because of the nature of my body, its existence can be doubted. Therefore, my mind which is the thinking thing that I am is not identical with my body. (Kim, 36) So in other words, whilst I can be convinced that I exist, it is not possible for me to know that my body exists with the exact same certainty. However, a response to this could be a comparison with another argument of similar structure but with a false conclusion, for example; Mary-Jane believes that Spiderman is a hero but Mary-Jane does not believe that Peter Parker is a hero. Therefore, Spiderman and Peter Parker must be fundamentally distinct. From this argument, we can see that the doubt argument is invalid. A second argument for dualism is the divisibility argument which may be reconstructed as follows: The self or soul lacks any parts into which it is divisible. The body, being spatially extended, is divisible and so composed of parts. Hence, the self and the body are distinct substances and the self is, unlike the body unextended. (Lowe, 8) The basic idea behind this argument is that the body is divisible because it is extended and thus can be separated into any number of parts. But Descartes argues that the mind is not divisible because each part of the mind, despite having different processes, has the same force behind it. Therefore, the mind cannot be seen as an extended entity because unlike extended entities, it cannot be divided into parts. So the same conclusion is reached; mind and body are ultimately distinct. However, the divisibility argument, despite being simple, has the same problem as the argument from doubt. We cant be totally sure that both premises are true. But Descartes was sure you couldnt divide the mind, especially someones thoughts and beliefs. However, it possible to argue that the mind can in fact be divided, if we consider the mind equal to the brain. Descartes may be right in stating that thoughts, beliefs, memories etc. cannot be divided but the substance that they are a state of, the physic al brain, can be separated such as in the case of brain surgery. People with personality disorders or split brains may have a divided mind. The first premise in this argument can only be true if we see the mind as a substance distinct from the brain. Thus, the divisibility argument is ultimately a weak argument in support of dualism. The final argument I will discuss is the argument from disembodied existence. Following the previous arguments, Descartes goes even further yet, stating that the mind is not only separate from the body, but that it can exist without it. The argument can be put as follows: If two things can exist apart from one another i.e. mind and body, they must be distinct. If I can imagine these things existing separately, God must be able to bring it about. If God can bring it about that these things exist separately, they must be distinct. Therefore, it is possible for the mind to exist without the body. In Meditation VI, Descartes expresses that he knows that all the things that he conceives clearly and distinctly can be produced by God precisely as he conceives them (Descartes, 28). Basically, the point that is being made is that if two things can exist separately, then they may be considered distinct. Descartes appeals to God to strengthen his argument, stating that if God can allow two things to exist separately, they must be distinct. But the problem lies in the fact that just because it is possible to perceive the mind and body as distinct, are they really distinct? Kim uses the example of the bronze bust of Beethoven. The bust could exist without being the bust of Beethoven, for example it could easily be the bust of say Brahms. However, if the bust is melted down, could it exist without being a material thing? The answer is no, as being material is a part of its essential nature and it cannot exist without being considered a material thing (Kim, 40). Thus, whilst its conceiva ble that I exist without a body, is it really possible? That is the problematic question that ultimately weakens Descartes argument. As I have shown Descartes argument for dualism to be weak, I will now highlight the ways in which materialism and in particular, functionalism refutes the claim that mind and matter are distinct substances, and fundamentally proves to be the stronger argument. One of the main questions that come from dualism is how the causal interaction of two distinct substances is possible. Materialism states that the universe contains only physical matter rather than distinctions, as in Descartes case. Whilst there are many strands of the materialist theory, functionalism appears to be the strongest. Functionalism is a theory which concerns itself with the idea that mental states are comprised exclusively by their functional roles. It explains how having a non-human brain can still result in mental states and also manages to explain how mental states can come from matter in the first place, without being completely perplexing. One of the strongest arguments in favour of functionalism is the argum ent involving the idea that mental states (such as pain) can be multiply realised because they involve functions. Fodor and Putnam argued that the importance of the state of pain does not lie on the c-fibres firing but what they are doing and what their contribution is to the operation. The point is that the role of the c-fibres could have been performed by anything suitable, as long as it was indeed performed (Lycan, 52). Clark puts the functionalist claim in an interesting way: the mind is to the body/brain as the program is to the physical machine. (Clark, 169) This idea can be seen as a direct response to the dualist view that mind and matter are distinct as the software is the product of material processes rather than being material in itself and any change in the software will cause changes in the computers physical components. Furthermore, it is impossible for the software to function without the hardware and vice versa, indicating that there is no real distinction between th e mental and physical. Ultimately, as I have argued in this essay, I believe Descartes dualist theory to be wholly unconvincing. This is primarily due to the weakness of the three arguments highlighted but also, the lack of strengths that appear in the theory. Descartes is convinced that mind and body are distinct but substance dualism doesnt seem to give an explanation as to why exactly mind and body are distinct and what the purpose of this distinction in. On the contrary, functionalism, a strand of materialism is a far more convincing theory in the discussion of mind and matter. By stating that there is no distinction between the two and that in fact, a mental state is concerned more with its function and its role in the wider system, an analogy can be made between the mind and a computer programme. This analogy is possible to conceive and essentially makes sense. So overall, substance dualism proves to be a far weaker argument in comparison to functionalism.

Predictive Analytics: the Future of Business Intelligence

The market is witnessing an unprecedented shift in business intelligence (BI), largely because of technological innovation and increasing business needs. The latest shift in the BI market is the move from traditional analytics to predictive analytics. Although predictive analytics belongs to the BI family, it is emerging as a distinct new software sector. Analytical tools enable greater transparency, and can find and analyze past and present trends, as well as the hidden nature of data. However, past and present insight and trend information are not enough to be competitive in business. Business organizations need to know more about the future, and in particular, about future trends, patterns, and customer behavior in order to understand the market better. To meet this demand, many BI vendors developed predictive analytics to forecast future trends in customer behavior, buying patterns, and who is coming into and leaving the market and why. Traditional analytical tools claim to have a real 360 ° view of the enterprise or business, but they analyze only historical data—data about what has already happened. Traditional analytics help gain insight for what was right and what went wrong in decision-making. Today’s tools merely provide rear view analysis. However, one cannot change the past, but one can prepare better for the future and decision makers want to see the predictable future, control it, and take actions today to attain tomorrow’s goals. What is Predictive Analytics? Predictive analytics are used to determine the probable future outcome of an event or the likelihood of a situation occurring. It is the branch of data mining concerned with the prediction of future probabilities and trends. Predictive analytics is used to automatically analyze large amounts of data with different variables; it includes clustering, decision trees, market basket analysis, regression modeling, neural nets, genetic algorithms, text mining, hypothesis testing, decision analytics, and more. The core element of predictive analytics is the predictor, a variable that can be measured for an individual or entity to predict future behavior. For example, a credit card company could consider age, income, credit history, other demographics as predictors when issuing a credit card to determine an applicant’s risk factor. Multiple predictors are combined into a predictive model, which, when subjected to analysis, can be used to forecast future probabilities with an acceptable level of reliability. In predictive modeling, data is collected, a statistical model is formulated, predictions are made, and the model is validated (or revised) as additional data become available. Predictive analytics combine business knowledge and statistical analytical techniques to apply with business data to achieve insights. These insights help organizations understand how people behave as customers, buyers, sellers, distributors, etc. Multiple related predictive models can produce good insights to make strategic company decisions, like where to explore new markets, acquisitions, and retentions; find up-selling and cross-selling opportunities; and discovering areas that can improve security and fraud detection. Predictive analytics indicates not only what to do, but also how and when to do it, and to explain what-if scenarios. A Microscopic and Telescopic View of Your Data Predictive analytics employs both a microscopic and telescopic view of data allowing organizations to see and analyze the minute details of a business, and to peer into the future. Traditional BI tools cannot accomplish this functionality. Traditional BI tools work with the assumptions one creates, and then will find if the statistical patterns match those assumptions. Predictive analytics go beyond those assumptions to discover previously unknown data; it then looks for patterns and associations anywhere and everywhere between seemingly disparate information. Let’s use the example of a credit card company operating a customer loyalty program to describe the application of predictive analytics. Credit card companies try to retain their existing customers through loyalty programs. The challenge is predicting the loss of customer. In an ideal world, a company can look into the future and take appropriate action before customers switch to competitor companies. In this case, one can build a predictive model employing three predictors: frequency of use, personal financial situations, and lower annual percentage rate (APR) offered by competitors. The combination of these predictors creates a predictive model, which works to find patterns and associations. This predictive model can be applied to customers who are start using their cards less frequently. Predictive analytics would classify these less frequent users differently than the regular users. It would then find the pattern of card usage for this group and predict a probable outcome. The predictive model could identify patterns between card usage; changes in one’s personal financial situation; and the lower APR offered by competitors. In this situation, the predictive analytics model can help the company to identify who are those unsatisfied customers. As a result, company’s can respond in a timely manner to keep those clients loyal by offering them attractive promotional services to sway them away from switching to a competitor. Predictive analytics could also help organizations, such as government agencies, banks, immigration departments, video clubs etc. , achieve their business aims by using internal and external data. On-line books and music stores also take advantage of predictive analytics. Many sites provide additional consumer information based on the type of book one purchased. These additional details are generated by predictive analytics to potentially up-sell customers to other related products and services. Predictive Analytics and Data Mining The future of data mining lies in predictive analytics. However, the terms data mining and data extraction are often confused with each other in the market. Data mining is more than data extraction It is the extraction of hidden predictive information from large databases or data warehouses. Data mining, also known as knowledge-discovery in databases, is the practice of automatically searching large stores of data for patterns. To do this, data mining uses computational techniques from statistics and pattern recognition. On the other hand, data extraction is the process of pulling data from one data source and loading them into a targeted database; for example, it pulls data from source or legacy system and loading data into standard database or data warehouse. Thus the critical difference between the two is data mining looks for patterns in data. A predictive analytical model is built by data mining tools and techniques. Data mining tools extract data by accessing massive databases and then they process the data with advance algorithms to find hidden patterns and predictive information. Though there is an obvious connection between statistics and data mining, because methodologies used in data mining have originated in fields other than statistics. Data mining sits at the common borders of several domains, including data base management, artificial intelligence, machine learning, pattern recognition, and data visualization. Common data mining techniques include artificial neural networks, decision trees, genetic algorithms, nearest neighbor method, and rule induction. Major Predictive Analytics Vendors Some vendors have been in the predictive analytical tools sector for decades; others have recently emerged. This section will briefly discuss the capabilities of key vendors in predictive analytics. SAS SAS is one of the leaders in predictive analytics. Though it is a latecomer to BI, SAS started making tools for statistical analysis at least thirty years prior, which has helped it to move into data mining and create predictive analytic tools. Its application, SAS Enterprise Miner, streamlines the entire data mining process from data access to model deployment by supporting all necessary tasks within a single, integrated solution. Delivered as a distributed client-server system, it is well suited for data mining in large organizations. SAS provides financial, forecasting, and statistical analysis tools critical for problem-solving and competitive agility. SAS is geared towards power users, and is difficult to learn. Additionally, in terms of real-time analytics, building dashboards and scorecards, SAS is a laggard compared to competitors like Cognos, Business Objects, and Hyperion; however, its niche product in data mining and predictive analytics has made it stand out of the crowd. SPSS SPSS Inc. is another leader in providing predictive analytics software and solutions. Founded in 1968, SPSS has a long history of creating programs for statistical analysis in social sciences. SPSS today is known more as a predictive analytics software developer than statistical analysis software. SPSS has played a thought-leadership role in the emergence of predictive analytics, showcasing predictive analytics as an important, distinct segment within the broader business intelligence software sector. SPSS performs almost all general statistical analyses (regression, logistic regression, survival analysis, analysis of variance, factor analysis, and multivariate nalysis) and now has a full set of data mining and predictive analytical tools. Though the program comes in modules, it is necessary to have the SPSS Base System in order to fully benefit from the product. SPSS focuses on ease; thus beginners enjoy it, while power users may quickly outgrow it. SPSS is strong in the area of graphics, and weak in more cutting edge statistical procedures and lacks robust methods a nd survey methods. The latest SPSS 14. 0 release has improved links to third-party data sources and programming languages. Insightful Along similar lines is Insightful Corporation, a supplier of software and services for statistical data analysis, data mining of numeric, and text data. It delivers software and solutions for predictive analytics and provides enterprises with scalable data analysis solutions that drive better decisions by revealing patterns, trends, and relationships. Insightful’s S-PLUS 7, is a standard software platform for statistical data analysis and predictive analytics. Designed with an open architecture and flexible interfaces, S-PLUS 7 is an ideal platform for integrating advanced statistical techniques into existing business processes. Another tool offered by Insightful is Insightful Miner, a data mining tool. Its ability to scale to large data sets in an accessible manner in one of its strengths. Insightful Miner is also a good tool for data import/export, data exploration, and data cleansing tasks, and its reduces dimensionality prior to modeling. While it has powerful reporting and modeling capabilities, it has relatively low levels of automation StatSoft Inc. StatSoft, Inc. is a global provider of analytic software. Its flagship product is Statistica, a suite of analytics software products. Statistica provides comprehensive array of data analysis, data management, data visualization and data mining procedures. Its features include the wide selection of predictive modeling, clustering, classification and exploratory techniques made available in one software platform. Because of its open architecture, it is highly customizable and can be tailored to meet very specific and demanding analysis requirements. Statistica has a relatively easy to use graphical programming user interface, and provides tools for all common data mining tasks; however, its charts are not easily available for the evaluation of neural net models. Statistica Data Miner another solution that offers a collection comprehensive data mining solutions. It is one of two suites that provides a support vector machine (SVM), which provides the framework for modeling learning algorithms. Knowledge Extractions Engines (KXEN) Knowledge Extraction Engines (KXEN) is the other vendor that provides a suite that includes SVM. KXEN is a global provider of business analytics software. Its self-named tool, KXEN provides (SVM) and merges the fields of machine learning and statistics. KXEN Analytic Framework is a suite of predictive and descriptive modeling engines that create analytic models. It places the latest data mining technology within reach of business decision makers and data mining professionals. The key components of KXEN are robust regression, smart segmenter, time series, association rules, support vector machine, consistent coder, sequence coder, model export, and event log. One can embed the KXEN data mining tool into existing enterprise applications and business processes. No advanced technical knowledge is required to create and deploy models and KXEN is highly accurate data mining tool and it is almost fully automatic. However, one record must be submitted for every entity that must be modeled, and this record must contain a clean data set. Unica Affinium Model is Unica’s data mining tool. It is used for response modeling to understand and anticipate customer behavior. Unica is enterprise marketing management (EMM) software vendor and Affinium Model is a core component of the market-leading Affinium EMM software suite. The software empowers marketing professionals to recognize and predict customer behaviors and preferences—and use that information to develop relevant, profitable, and customer-focused marketing strategies and interactions. The automatic operation of the modeling engine shields the user from many data mining operations that must be manually performed by users of other packages, including a choice of algorithms. Affinium is an easy to use response modeling product on the market and is suitable for the non-data miner or statistician, who lacks statistical and graphical knowledge. New variables can be derived in the spreadsheet with a rich set of macro functions; however, the solution lacks data exploration tools and data preparation functions. Angoss Software Corporation Another leading provider of data mining and predictive analytics tools is Angoss Software Corporation. Its products provide information on customer behavior and marketing initiatives to help in the development of business strategies. Main products include KnowledgeSTUDIO and KnowledgeSEEKER, which are data mining and predictive analytics tools. The company also offers customized training to its clients, who are primarily in the financial services industry. Angoss developed industry specific predictive analytics software like Angoss Expands FundGuard, Angoss Telecom Marketing Analytics, and Angoss Claims & Payments Analytics. Apart from financial industry Angoss software is used by telecom, life sciences, and retail organizations. Fair Isaac Corporation Along similar lines, Fair Isaac Corporation is the leading provider of credit scoring systems. The firm offers statistics-based predictive tools for the consumer credit industry. Model Builder 2. 1 addresses predictive analytics, and is an advanced modeling platform specifically designed to jump-start the predictive modeling process, enabling rapid development, and deployment of predictive models into enterprise-class decision applications. Fair Isaac's analytic and decision-management products and services are used around the world, and include applicant scoring for insurers, and financial risk and database management products for financial concerns. IBM Not to be left out, the world’s largest information and technology company, IBM also offers predictive analytics tools. DB2 Intelligent Miner for Data is a predictive analytical tool and can be used to gain new business insights and to harvest valuable business intelligence from enterprise data. Intelligent Miner for Data mines high-volume transaction data generated by point-of-sale, automatic transfer machine (ATM), credit card, call center, or e-commerce activities. It better equips an organization to make insightful decisions, whether the problem is how to develop more precisely targeted marketing campaigns, reduce customer attrition, or increase revenue generated by Internet shopping. The Intelligent Miner Scoring is built as an extension to the DB2 tool and works directly from the relational database. It accelerates the data mining process, resulting in the ability to make quicker decisions from a host of culled data. Additionally, because D2B Intelligent Miner Scoring is compatible with Oracle databases, companies no longer have to wait for Oracle to incorporate business intelligence capabilities into their database product. User Recommendations Depending on an organization’s needs, some predictive analytics tools will be more relevant than others. Each has its strengths and weakness and can be highly industry-and model-specific—the algorithms and models built for one industry are not applicable to other industries. Financial industries, for example, have different models than what are used in manufacturing and research industries. Selecting the appropriate predictive analytics tools is not a simple task. The following capabilities must be taken into consideration: algorithm richness, degree of automation, scalability, model portability, web enablement, ease of use, and the capability to access large data sets. The more diversified the business, the more functions and unique models are required. Model portability is important even within different business units in the same company. The scalability of the solution and its ability to handle expanded functionality should also be verified and based on a business’ growth. The tools also have to be tested by the right experts. To understand and interpret predictive analytics results, one has to be knowledgeable about statistical modeling. One should look for the main functions and features of the tool and try to match them with their main requirements, as well as measure the trade off between functionality and cost. For example, some functionalities might be more important for some companies and less important for others. Buyers should also beware. Although marketing campaigns for predictive analytics solutions claim †ease of use†, these tools are not for beginners. Users require extensive training and expertise to use the core functionalities of the predictive analytics solutions, such as identifying data, building the predictive model with right predictors, data mining knowledge to align with business strategy etc. Furthermore, predictive analytics automates model building, but does not automate the integration of business processes and knowledge. Thus expertise and training are required to evaluate the best software relevant to an organization’s unique business model. Nonetheless, if a company has or is willing to attain the expertise required to use predictive analytics it can definitely benefit from the tool. Although most large enterprises use some sort of traditional BI tool or platform, their tools do not provide predictive analytics functionality. Incorporating predictive analytics into an existing BI infrastructure can provide organizations’ a competitive advantage in their industry. Consequently, the integration of BI tools is a key consideration when selecting a predictive analytical tool, as is its integration with key applications such as enterprise resource planning, (ERP), customer resource management (CRM), and supply chain management (SCM) etc. Ultimately, since predictive analytics is currently the only way to analyze and monitor the business trends of the past, present, and future, selecting the right tool can be a key success factor in your BI strategy. About the author Mukhles Zaman has more than twenty five years experience in the IT industry specializing in business intelligence (BI), customer relationship management (CRM), project management, database design, and reporting software. He is a leading BI expert and has worked as a senior project manager on IT projects for Fortune 1000 companies in India, the Middle East, US, and Canada. He has also developed call center systems, software architecture, and portfolio management systems. He holds an MA in Economics, and a BA in Economics and Statistics from the University of Dhaka and is an Oracle Certified Professional. He can be reached at [email  protected] com.

Common Network Vulnerabilities Essay

â€Å"Businesses, governments, and other organizations face a wide array of information security risks. Some threaten the confidentiality of private information, some threaten the integrity of data and operations, and still others threaten to disrupt availability of critical systems† (Sullivan, 2009). Since such security risks are always going to present in the cyber world, businesses and organizations need to fully be aware of any vulnerabilities in their systems. The initial realization of any organization’s vulnerability can only first be understood through the knowledge of what vulnerability means. A vulnerability is a security weakness but not a security threat. It is what needs to be assessed in order to examine an organization’s network. One of the main network vulnerabilities facing IT managers today is the absence of encrypted data being transferred and received between uninformed users and the lack of knowledge and understanding within an organization’s internal structure. Network vulnerabilities are present in every system and with the constant advancement in knowledge, programs, and technology; it can be extremely difficult to rid all vulnerabilities in any infrastructure. Whether it is implementing hardware or beefing up software security, no one method of protecting a network can be greatly increased unless the users and IT professionals behind the update are up to speed on what is happening. To begin, all users in an organization or business need to be aware. Be aware of your surroundings. Be aware of the software that you use on a daily basis, and the information that is being passed between everyone. Security awareness in any infrastructure needs to be the center of any cyber security business program. In many respects, the challenges of implementing and managing effective technical controls pale in comparison with the difficulties in addressing organizational weaknesses, such as insufficient or ineffective security awareness training† (Sullivan, 2009). Companies that don’t provide security awareness and training are leaving open pathways into their network (McLaughlin, 2006). From an IT manager’s standpoint, companies are fully aware of the threats that their organization is faced with everyday. From a survey conducted from nearly â€Å"550 small and midsize businesses, it was found that human error was the primary cause of nearly 60 percent of security breaches during the past year† (McLaughlin, 2006). This 60 percent clearly states that the primary holes in any organization’s security remain user problems and insufficient training throughout the company. â€Å"The alarming part is that little is being done to change cultural behavior† (McLaughlin, 2006). Even knowing that the lack of education and training cause companywide vulnerabilities, changes and training continue to lie on the wayside and be less of a priority rather than a major one. The Internet is rapidly growing and evolving and people need to evolve with it. The Internet is ultimately becoming the staple for all businesses today. â€Å"Businesses from all over the world have found the Internet to be a cost effective and reliable business tool. Indeed, in the last few years, in addition to conventional business transactions, many of the controls systems (SCADA) that support national and public utilities are adopting the Internet as a core data transport method. This has resulted in businesses and societies becoming critically dependent on the continuous operation of the Internet† (John, n. ). These dependencies need to then be addressed to provide critical support for end user vulnerabilities. End user vulnerabilities need to first be recognized within a business and proper steps need to be taken to adequately train employees. â€Å"Most of the flaws that emerge in the security and vulnerability assessment realm are due to misconfigurations and poor application of corporate security practices, which points to a need for training† (McLaughlin, 2006). Businesses need to include security training and awareness; this being the first step in the correction of network holes. In my opinion, security awareness is the basis of all network flaws. Because network security is extremely important, businesses need to make it a top priority to have a network infrastructure assessment. Networks are becoming increasingly complex and by executing a network assessment it will help IT managers ensure the company’s network is operating at peak efficiency. â€Å"The vulnerability of the system depends on the state of the system itself, on the capacity of a hazard to affect this state and on the undesired consequences the combination of the hazard and the vulnerability will eventually lead to† (Petit & Robert, 2010). Known vulnerabilities of a security infrastructure require a situational awareness. â€Å"This includes knowledge of security software versions for integrity management and anti-malware processing, signature deployments for security devices such as intrusion detection systems, and monitoring status for any types of security collection and processing systems† (Amoroso, 2011). In addition to an entire infrastructure assessment, there must be companywide training classes. These trainings need to help employees understand not only the importance of network security, but also how their actions can impact everyone and everything around them. According to a Booz Allen Hamilton survey, the nation’s cyber defense is seriously challenged by shortages of highly skilled cyber-security experts† (Vanderwerken & Ubell, 2011). This poses one major issue; the people being hired to run elaborate business networks are unqualified and inadequately trained. These businesses must provide high-level in-house training programs to the experts as well as the entire workforce to ensure the integrity of internal and client systems and to avoid the cyber threats surrounding the business. Training must be provided to end users to provide overall awareness and give them the general knowledge needed to maintain the businesses integrity and a sufficiently working network. This simple, yet effective training will provide any business with a sufficient return on investment. â€Å"As long as there are cyber criminals ready to strike, your company remains vulnerable. Vigilant cyber-security training and education must be your company’s top priority† (Vanderwerken & Ubell, 2011). Even though a business can provide the necessary training through company ide programs, the biggest vulnerability in an organization are the negligent employees who don’t care or don’t want to participate in the proper security procedures. Most companies are oblivious to the fact that the most pervasive attacks on a network are caused by gullible and negligent employees clicking and opening invasive files embedded in emails and data from beyond the company’s network firewall. â€Å"Despite strenuous efforts by most companies to alert personnel to email and Internet behavior that opens up firms to invasion, employees continue to do foolish things. As more access is given to the end user by means of mobile computing, cyber-crime prevention has to be a top priority. The corporate landscape requiring protection is multiplying at very quick pace† (Vanderwerken & Ubell, 2011). Another major aspect in training is to be familiar with the upgrading of a network with new hardware. Such an update is a suitable idea but the installation and a working knowledge of how to use and implement this new technological hardware is essential. Many companies just don’t understand how vulnerable they are in areas they never would expect there to be flaws, such as hardware purchasing. Inadvertent mistakes are better avoided when consistent and specific training is given to non-IT staff regarding the dangers their everyday activity can incur† (Vanderwerken & Ubell, 2011). Taking it one step further, company wide training can only provide so much assurance but IT management also needs to be aware of the internal threats that may come from dishonest employees. Internal threats from dishonest employees are a major risk. Organizations need to keep a watchful eye on those who misbehave on internal networks, intentional or not (Beidel, 2011). Problems from the inside are often overlooked. â€Å"Hackers have been successful against firms with solid security frameworks by analyzing their employees and going after them with cleverly worded emails, also known as ‘phishing. ’ Companies have begun training all employees on cybersecurity fundamentals. No amount of technology can prevent attacks if employees are not educated† (Beidel, 2011). Phishing incidents are one of the main threats to uneducated employees. Uneducated employees are susceptible to the ‘wolves’ and become prey to the malicious viruses disguised as harmless data or programs. Phishing is one of the easiest ways for enemies to feed off of these uneducated users in an organization. It takes the user’s lack of knowledge and gullible nature and tempts them in to opening or transferring data that has potentially been tampered with. This type of attack plays into the gullibility of the users and tries to get them to open malicious documents and pass them on to create a chain effect within a company and thus cause all sorts of problems. This ultimately could lead to loss of clients and even worse the downfall of the company itself. In conclusion, every network user must be educated and trained on Internet security. It is this training that is going to lesson a business’s network vulnerabilities and provide the education needed to strengthen security gaps on a companywide scale. â€Å"Organizations must provide sophisticated training to in-house experts to ensure the integrity of internal and client systems. They must also offer instruction to their entire workforce to avoid cyber minefields surrounding us all. Simple, yet effective, training must be provided to personnel for general awareness, while graduate education is now globally available to specialists to gain the high level of expertise your company requires. As long as there are cyber criminals ready to strike, your company remains vulnerable. Vigilant cyber-security training and education must be your company’s top priority† (Vanderwerken & Ubell, 2011).

Sir Gawain And The Green Knight - 1078 Words

Throughout one’s life, a person will go through numerous changes, both physically and mentally. These continuous changes in life are a few of the steps to maturing, which also helps build a person’s identity. In the romantic poem Sir Gawain and the Green Knight, by Pearl Poet, the hero, Sir Gawain goes through a passage which develops his perspective on adulthood leading to his maturity. Gawain’s knight errant mentality is what drives him to mature during the adventures he takes on. While on his journey to adulthood, he passes three major tests. First, he shows courage and initiative when he volunteers to take the place of Arthur and accepts the challenge the Green Knight had demanded. Second, he shows discipline, self-control and honor†¦show more content†¦He also displays both courage and initiative when he says, â€Å"I am the weakest, I know, and of wit feeblest† (Kline stanza 16 line 12). When Gawain says this, he tells the Green Knight t hat he is weak because he wants him to think that he would be able to win, but in all reality Gawain would be able to fight back because he is actually very robust. He shows that he is courageous by protecting and remaining loyal towards King Arthur. Another example of Gawain’s courageousness is when he says, â€Å"and roughly he reached out, where the ranks stood,/ latched onto his lovely head, and lifted it so† (Kline stanza 19 line 16-17). During this scene, Gawain takes on the Green Knights request by taking a blow to his head, which shows his courage to take on this game the Green Knight had demanded. It also displays that he is courageous because in this game, he then in return had to take the same blow by the Green Knight a year and one day later. Overall, Gawain displays his great heroism and courageousness by protecting his King just as a true knight should. Gawain demonstrates numerous characteristics such as discipline, self-control, and honor when he refus es the temptations of Lady Bertilak. Lady Bertilak was said to be â€Å"the fairest in feature, in flesh and complexion,/ and in compass and colour and