Left38354000right38290500Ground water Tyler Duffy Essays - Water

left38354000right38290500Ground water Tyler Duffy center21018500 Water table: The surface below which the ground is saturated with water Zone of aeration: The region between the earth's surface and the water table. The main components of this region are the soil and rocks. The zone of aeration is also known as the unsaturated area, pore spaces contain some water but mostly air. Zone of saturation: The area in an aquifer, below the water table, in which relatively all pores and fractures are saturated with water. All pore spaces completely filled with water Capillary fringe: The subsurface layer in which groundwater seeps up from a water table by capillary action to fill pores. Region above the water table in which water rises against gravity as a result of surface tension. Water budget Recharge: The refilling of soil water supply at times when plants need little moisture Surplus: Condition of having rainfall greater than the need for moisture when the soil is already saturated Usage: The condition where the plants draw water from the soil at times when the need for moisture is greater than the rainfall Deficit: A condition in which stored soil water is gone and the need for moisture is greater than the rainfall 368683511582400Pollution Materials from the land's surface can move through the soil and end up in the groundwater. For example, pesticides and fertilizers can find their way into groundwater supplies over time. Road salt, toxic substances from mining sites, and used motor oil also may seep into groundwater. In addition, it is possible for untreated waste from septic tanks and toxic chemicals from underground storage tanks and leaky landfills to contaminate groundwater. Drinking contaminated groundwater can have serious health effects. Diseases such as hepatitis and dysentery may be caused by contamination from septic tank waste. Poisoning may be caused by toxins that have leached into well water supplies. Wildlife can also be harmed by contaminated groundwater. SOURCES OF GROUNDWATER CONTAMINATION Storage Tanks May contain gasoline, oil, chemicals, or other types of liquids and they can either be above or below ground. Over time the tanks can corrode, crack and develop leaks. Septic Systems Septic systems are designed to slowly drain away human waste underground at a slow, harmless rate. An improperly designed, located, constructed, or maintained septic system can leak bacteria, viruses, household chemicals, and other contaminants into the groundwater. Uncontrolled Hazardous Waste Hazardous waste sites can lead to groundwater contamination if there are barrels or other containers laying around that are full of hazardous materials. If there is a leak, these contaminants can eventually make their way down through the soil and into the groundwater. Landfills Landfills are supposed to have a protective bottom layer to prevent contaminants from getting into the water. However, if there is no layer or it is cracked, contaminants from the landfill make their way down into the groundwater. Chemicals and Road Salts Chemicals include products used on lawns and farm fields to kill weeds and insects and to fertilize plants, and other products used in homes and businesses. When it rains, these chemicals can seep into the ground and eventually into the water. Road salts are used in the wintertime. When the ice melts, the salt gets washed off the roads and eventually ends up in the water. Atmospheric Contaminants Since groundwater is part of the hydrologic cycle, contaminants in other parts of the cycle, such as the atmosphere or bodies of surface water, can eventually be transferred into our groundwater supplies. Minerals in groundwater The most common dissolved mineral substances are sodium, calcium, magnesium, potassium, chloride, bicarbonate, and sulfate. Drainage from salt springs and sewage, oil fields, and other industrial wastes may add large amounts of chloride to streams and groundwater reservoirs. Small quantities of chloride have little effect on the use of water. Minerals dissolve into the groundwater from ground water running along lower soil levels or bed rock, it dissolves minerals. Artesian Formations 285963327510800Sometimes an aquifer dips underground between impermeable rock layers. A sandwich of permeable and impermeable rocks is formed. This arrangement is called anartesianformation. The upper impermeable layer of anartesian formation, usually shale, is called the cap rock. Gravity moves the water downward in the dipping aquifer. The water is pushed along by the weight of all the water above and behind it. Great quantities of water may enter the aquifers

The Title IX Decision against the Quinnipiac University

The Title IX Decision against the Quinnipiac University The question of gender is actively discussed in relation to sport with references to providing the equal opportunities for female athletes. According to Title IX, any discrimination regarding the sex or gender issues is prohibited (Thornton, 2010). The Title IX Decision against the Quinnipiac University of 2010 became one of the most controversial cases associated with the question.Advertising We will write a custom essay sample on The Title IX Decision against the Quinnipiac University specifically for you for only $16.05 $11/page Learn More It was stated that the Quinnipiac University intended to eliminate the women’s varsity volleyball team because of the lack of funding and to develop a competitive cheerleading team instead. The volleyball team’s players and their coach insisted on trying the case in court because of violating Title IX in relation to providing the equal opportunities for university athletes. According to the injunction prov ided by the Judge Stefan Underhill, the volleyball team was allowed to continue the activities during the next season when the development of the competitive cheerleading team could not be discussed as the alternative to the female sport team to meet the Title IX requirements. To evaluate the effectiveness of Underhill’s decision, it is necessary to pay attention to the details of the case. The volleyball team of the Quinnipiac University and the team’s coach accentuated that the plan to eliminate the team violates Title IX because the proportion of the male and female athletes would be defied. From this point, it is possible to speak about the direct violation of Title IX in relation to providing the equal opportunities for male and female athletes. Underhill stated that the fact of discriminating female athletes’ rights was presented, and the Quinnipiac University was obliged to provide the opportunities for the team to perform during the next season (The Quin nipiac University Case, 2010). Thus, the legal strength of the argument was accentuated, and the team could be discussed as winning the case. However, there are two visions of the decision. On the one hand, the rights and interests of the women’s varsity volleyball team were met, and the fact of discrimination was stated. On the other hand, the team was allowed to perform only during the 2010-2011 season, and the question was discussed again in 2012. Thus, the decision provided by the judge lacked some details.Advertising Looking for essay on health medicine? Let's see if we can help you! Get your first paper with 15% OFF Learn More Furthermore, Underhill concentrated on the fact that it was impossible to refer to the cheerleading team as the competitive team and to discuss that team as the alternative to the volleyball team to meet the Title IX requirements. The decision provided by Underhill was rather effective while discussing the cheerleading tea m as inappropriate alternative to the female volleyball team. Nevertheless, the controversy was associated with the fact that Underhill focused on the standards of the competitive sport teams and judged about the relevance of the standards and cheerleading team’s features to discuss it as the sport team. Underhill supported the decision in relation to the definition of the varsity sport with references to the Title IX standards (The Quinnipiac University Case, 2010). It is important to pay attention to the fact that the position of the judge as the advisor or an expert to determine the standards for the varsity sport is rather controversial, and it could be more effective to focus on violating the Title IX requirements regarding the women’s volleyball team rather than on discussing the features and standards of the varsity sport. In spite of the general win of the women’s volleyball team of the Quinnipiac University in relation to Title IX, the judge’s ar gument cannot be discussed as strong and effective because it was important to concentrate on the issue of discrimination to determine the position of the team not only for one season but for the long period of time. References The Quinnipiac University Case. (2010). Retrieved from http://courtweb.pamd.uscourts.gov/courtwebsearch/ctxc/KX330R32.pdf Thornton, P. K. (2010). Sports law. USA: Jones Bartlett Publishers.

Report assignment Essay Example | Topics and Well Written Essays - 1750 words

Report assignment - Essay Example Finally, some recommendations will be presented towards the end of the report. 2. Can IS Save the Public Services a. Topic Overview The topic of the lecture was quite interesting and catches the attention of listener instantly. Moreover the facts and examples given during the course of presentation were closely connected to the public interest. Generic conclusions were drawn about various aspects of the public Information Systems towards the end of the lecture. The lecture was formatted as a combination of presentation with background audio of the presenter. The topic actually talks about the development of Information systems in public domain and the value they deliver to the public. The lecturer has also pointed out some important methods for decision maker to ensure attainment of perceived value from public Information Systems. b. Level and Amount of Information The topic of the presentation was generic in nature and therefore amount and level of information can be questioned. The re were several aspects of the public information systems that could be discussed but were not covered in the presentation. This gives a feel of incompleteness and deficiency of the information on the given topic. The issue further highlighted the knowledge gape and lack of preparations. Even the lecturer did not clearly mention the scope of his presentation in the beginning. There logical development of the ideas was not of high standard and it seems that topic was beefed up with material from here and there to make a point. The speaker initially talked about the area where information systems are implemented in public sector but the list of these sectors was not comprehensive and only pointed out some major areas. Then speaker straightway jumped to the discussion on how these systems were outsourced and what problems were faced by the commissioning authority and the outsourced company. Here the speaker concluded that the bidding process is not a correct procedure for outsourcing i nformation systems development in public sector. The bidders try to bid at lowest rate to win the bid, later he find it hard to fulfill the Service Level Agreements (SLAs) and the either he quits or public could not get the value out of the system it was conceived with. This was followed by an appropriate example of a private firm EDS who suffered a more than12 billion lose on this account. An interesting comparison was drawn on how much the information system differs depending on the organizational structure of the department commissioning these systems. (Chris, 2010) c. Organization of Information There is enough room for improvement in the organization of information and we can note many inaccuracies in the presentation. On a slide with the title of â€Å"Can we look abroad for help† there was no discussion on external bidders for the development of information systems. Some slide looked a little crowded and packet. It was quite difficult for the audience to read the data at these slides and to focus on what the speaker was talking about. Moreover, reading text was not helpful in getting to the point what the lecturer was trying to deliver. Often reading the text at the slide resulted in mismatch of the point being talked about. d. Presentation Skills Beyond the contents of the lecture, the presentation and delivery of the contents was not professionally elevated. In the opening of the lecture there were few unrecognizable voices and audience could see the first slide of the presentation

Green Street Hooligans Film Review Essay Example | Topics and Well Written Essays - 750 words

Green Street Hooligans Film Review - Essay Example The first setting is the university, Harvard University. This college offers educational services to students. It is portrayed as a respectful environment that encourages students to be morally upright. This would be the reason that made Matt be expelled from the institution. He was found in possession of a classmate’s drugs. He is 'therefore' expelled wrongly. Harvard University portrays the learning institutions as being a decent place that mold characters of various students. This place is strict, and any breaking of rules would lead to expulsion. This was made Matt be expelled wrongly even without proper investigation to determine the source or the owner of the drugs. This act would be an example of a loophole that exists in institutions that have strict rules and regulations. In an effort to run away or avoid his parents, Matt takes flight to London to visit his sister who is married to Steve. This gives the film its second setting. This setting is based in London. Moreov er, the London setting encompasses a wide range of settings. From home, Matt goes to the pub where he drinks with colleagues. He goes on to watch a football match and later finds himself in the streets of a city in London where he is involved in a serious fight. This setting in London would be classified into three, the pubs, the football pitch and the streets where fighting occurs. In London that Matt joins a ‘Crew’ that are gangs which would be unofficial and are associated with various football teams. Matt joins new friends who drink a lot, smoke and use vulgar language. This would be associated with football fans in England in support of their teams (Rhodes). The pub where Matt goes to drink indicates a social place where people gather to drink or have their time just before going for football matches. The pubs within England are portrayed as being harbors of hooligans. It would also be reflected as an immoral place that encompasses chaos and evil company. This make s Matt a different person from what he was during his college time. He would now sing rousing within the streets of London. The other setting is the football pitch where fan gather to support their teams. This portrays a football culture in England where fans support their teams. This reflects a culture that is acceptable and has been practiced in England for a long time. The event itself is legitimate or moral, but the activities associated with it thereafter may be ugly. This is what happened in the case of Matt after the football match ended. The enemy team fans were up in arms against each other. This led to serious fights that involved Matt within friends against the fans of the enemy team in the streets. The football game is portrayed as an activity that aggravates hatred and hostility among team fans. Fans drink before going to the pitch to get psyched up so that they can cheer their teams. At times, cheering would affect the result of the game, and this would lead to shoutin g at each other. The result would be a serious fight as in the case of Matt and friends. Although he was initially afraid of violence, through drinking he soon becomes desensitized to Elite gang (Rhodes). The streets have been portrayed as places where fighting takes place. They are evil places that involve blood shedding. In this context, they would reflect dangerous area associated with hooligans, shouting, vulgar language, and painful experiences. This would be the reason why Shannon, her sister is worried and her husband

Machine Learning in Malware Detection

Machine Learning in Malware Detection 1.0 Background Research Malware was first created in 1949 by John von Neumann. Ever since then, more and more malwares are created. Antivirus company are constantly looking for a method that is the most effective in detecting malware. One of the most famous method used by antivirus company in detecting malware is the signature based detection. But over the years, the growth of malware is increasing uncontrollably. Until recent year, the signature based detection have been proven ineffective against the growth of malware. In this research, I have chosen another method for malware detection which is implementing machine learning method on to malware detection. Using the dataset that I get from Microsoft Malware Classification Challenge (BIG 2015), I will find an algorithm that will be able to detect malware effectively with low false positive error. 1.1 Problem Statement With the growth of technology, the number of malware are also increasing day by day. Malware now are designed with mutation characteristic which causes an enormous growth in number of the variation of malware (Ahmadi, M. et al., 2016). Not only that, with the help of automated malware generated tools, novice malware author is now able to easily generate a new variation of malware (Lanzi, A. et al., 2010). With these growths in new malware, traditional signature based malware detection are proven to be ineffective against the vast variation of malware (Feng, Z. et al., 2015). On the other hand, machine learning methods for malware detection are proved effective against new malwares. At the same time, machine learning methods for malware detection have a high false positive rate for detecting malware (Feng, Z. et al., 2015). 1.2 Objective To investigate on how to implement machine learning to malware detection in order to detection unknown malware. To develop a malware detection software that implement machine learning to detect unknown malware. To validate that malware detection that implement machine learning will be able to achieve a high accuracy rate with low false positive rate. 1.3 Theoretical / Conceptual Framework 1.4 Significance With Machine Learning in Malware detection that have a high accuracy and low false positive rate, it will help end user to be free from fear malware damaging their computer. As for organization, they will have their system and file to be more secure. 2.0 Literature Review 2.1 Overview Traditional security product uses virus scanner to detect malicious code, these scanner uses signature which created by reverse engineering a malware. But with malware that became polymorphic or metamorphic the traditional signature based detection method used by anti-virus is no long effective against the current issue of malware (Willems, G., Holz, T. Freiling, F., 2007). In current anti-malware products, there are two main task to be carried out from the malware analysis process, which are malware detection and malware classification. In this paper, I am focusing on malware detection. The main objective of malware detection is to be able to detect malware in the system. There are two type of analysis for malware detection which are dynamic analysis and static analysis. For effective and efficient detection, the uses of feature extraction are recommended for malware detection (Ahmadi, M. et al., 2016). There are various type of detection method, the method that we are using will b e detecting through hex and assembly file of the malware. Feature will be extracted from both hex view and assembly view of malware files. After extracting feature to its category, all category is to be combine into one feature vector for the classifier to run on them (Ahmadi, M. et al., 2016). For feature selection, separating binary file into blocks to be compare the similarities of malware binaries. This will reduce the analysis overhead which cause the process to be faster (Kim, T.G., Kang, B. Im, E.G., 2013). To build a learning algorithm, feature that are extracted with the label will be undergo classification with using any classification method for example Random Forest, Neural Network, N-gram, KNN and many others, but Support Vector Machine (VCM) is recommended for the presence of noise in the extracted feature and the label (Stewin, P. Bystrov, I., 2016). As to generate result, the learning model is to test with dataset with label to generate a graph which indicate detec tion rate and false positive rate. To find the best result, repeat the process using many other classification and create learning model to test on the same dataset. The best result will the one graph that has the highest detection rate and lowest false positive rates (Lanzi, A. et al., 2010). 2.2 Dynamic and Static Analysis Dynamic Analysis runs the malware in a simulated environment which usually will be a sandbox, then within the sandbox the malware is executed and being observe its behavior. Two approaches for dynamic analysis that is comparing image of the system before and after the malware execution, and monitors the malware action during the execution with the help of a debugger. The first approach usually give a report which will be able to obtain similar report via binary observation while the other approach is more difficult to implement but it gives a more detailed report about the behavior of the malware (Willems, G., Holz, T. Freiling, F., 2007). Static Analysis will be studying the malware without executing it which causing this method to be more safe comparing to dynamic analysis. With this method, we will dissemble the malware executable into binary file and hex file. Then study the opcode within both file to compare with a pre-generated opcode profile in order to search for malicious code that exist within the malware executable (Santos, I. et al., 2013). All malware detection will be needed either Static Analysis or Dynamic Analysis. In this paper, we will be focusing on Static Analysis (Ahmadi, M. et al., 2016). This is because, Dynamic analysis has a drawback, it can only run analysis on 1 malware at a time, making the whole analysis process to take a long time, as we have many malware that needed to be analysis (Willems, G., Holz, T. Freiling, F., 2007). As for Static Analysis, it mainly uses to analyze hex code file and assembly code file, and compare to Dynamic Analysis, Static Analysis take much short time and it is more convenient to analyze malware file as it can schedule to scan all the file at once even in offline (Tabish, S.M., Shafiq, M.Z. Farooq, M., 2009). 2.3 Features Extraction For an effective and efficient classification, it will be wise to extract feature from both hex view file and assembly view file in order to retrieve a complementary date from both hex and assembly view file (Ahmadi, M. et al., 2016). Few types of feature that are extracted from the hex view file and assembly view file, which is N-gram, Entropy, Image Representative, String Length, Symbol, Operation Code, Register, Application Programming Interface, Section, Data Define, Miscellaneous (Ahmadi, M. et al., 2016). For N-gram feature, it usually used to classify a sequence of action in different areas. The sequence of malware execution could be capture by N-gram during feature extraction (Ahmadi, M. et al., 2016).   For Entropy feature, it extracts the probability of uncertainty in a series of byte in the malware executable file, these probability of uncertainty is depending on the amount of information on the executable file (Lyda, R.,Hamrock, J,. 2007). For Image Representative feature, the malware binary file is being read into 8-bit vector file, then organize into a 2D array file. The 2D array file can be visualize as a black and gray image whereas grey are the bit and byte of the file, this feature look fo r common in bit arrangement in the malware binary file (Nataraj, L. et al., 2011). For String Length feature, we open each malware executable file and view it in hex view file and extract out all ASCII string from the malware executable, but because it is difficult to only extract the actual string without extract other non-useful element, it is required to choose important string among the extracted (Ahmadi, M. et al., 2016). For Operation Code features, Operation code also known as Opcode are a type of instruction syllable in the machine language. In malware detection, different Opcode and their frequency is extracted and to compare with non-malicious software, different set of Opcodes are identifiable for either malware or non-malware (Bilar, D., n.d.). For Register feature, the number of register usage are able to assist in malware classification as register renaming are used to make malware analysis more difficult to detect it (Christodorescu, M., Song, D. Bryant, R.E., 2005). For Application Programming Interface feature, API calling are code that call the function of other software in our case it will be Windows API. There are large number of type of API calls in malicious and non-malicious software, is hard to differentiate them, because of this we will be focusing on top frequent used API calls in malware binaries in order to bring the result closer (Top maliciously used apis, 2017). For Data Define feature, because not all of malware contains API calls, and these malware that does not have any API calls they are mainly contain of operation code which usually are db, dw, dd, there are sets of features (DP) that are able to define malware (Ahmadi, M. et al., 2016). For Miscellaneous feature, we choose a few word that most malware have in common from the malware dissemble file (Ahmadi, M. et al., 2016). Among so many feature, the most appropriate feature for our research will be N-gram, and Opcode. This is because it is proven that there two feature have the highest accuracy with low logloss. This two feature appears frequently in malware file and it already have sets of well-known features for malware. But the drawback using N-gram and Opcode are they require a lot of resource to process and take a lot of time (Ahmadi, M. et al., 2016). We will also try other feature to compare with N-gram and Opcode to verified the result. 2.4 Classification In this section, we will not review about the algorithm or mathematical formula of a classifier but rather their nature to able to have advantage over certain condition in classifying malware feature. The type of classifier that we will review will be Nearest Neighbor, NaÃÆ' ¯ve Bayes, Decision tree, Support Vector Machine and XGBOOST [21] (Kotsiantis, S.B., 2007) (Ahmadi, M. et al., 2016). As we need a classifier to train our data with the malware feature, we will need to review the classifier to choose the most appropriate classifier that are able to have the best result. The Nearest Neighbor classifier are one of the simplest method for classifying and it is normally implement in case-based reasoning [21]. As for NaÃÆ' ¯ve Bayes, it usually generates simply and constraint model and not suitable for irregular data input, which make it not suitable for malware classification because that the data in malware classification are not regular (Kotsiantis, S.B., 2007). For Decision Tree, it classify feature by sorting them into tree node base on their feature values and each branch represent the node value. Decision Tree will determine either try or false based on node value, which make it difficult to dealt with unknown feature that are not stored in tree node (Kotsiantis, S.B., 2007). For Support Vector Machine, it has a complexity model which enable it to deal with lar ge amount of feature and still be able to obtain good result from it, which make it suitable for malware classification as malware contains large number of feature (Kotsiantis, S.B., 2007). For XGBOOST, it is a scalable tree boosting system which win many machine learning competition by achieving state of art result. The advantage for XGBOOST, it is suitable for most of any scenario and it run faster than most of other classification technique (Chen, T., n.d.). To choose a Classification for our malware analysis, we will be choosing XGBOOST, as it is suitable for malware classification, it also recommended by winner from Microsoft Malware Classification Challenge (Ahmadi, M. et al., 2016). But we will also use Support Vector Machine, as it too is suitable for malware classification and we will use it to compare the result with XGBOOST to get a more accurate result. References   Ahmadi, M. et al., 2016. Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification. ACM Conference on Data and Application Security and Privacy, pp.183-194. Available at: http://doi.acm.org/10.1145/2857705.2857713. Amin, M. Maitri, 2016. A Survey of Financial Losses Due to Malware. Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies ICTCS 16, pp.1-4. Available at: http://dl.acm.org/citation.cfm?doid=2905055.2905362. Berlin, K., Slater, D. Saxe, J., 2015. Malicious Behavior Detection Using Windows Audit Logs. Proceedings of the 8th ACM Workshop on Artificial Intelligence and Security, pp.35-44. Available at: http://doi.acm.org/10.1145/2808769.2808773. Feng, Z. et al., 2015. HRSà ¢Ã¢â€š ¬Ã‚ ¯: A Hybrid Framework for Malware Detection. , (10), pp.19-26. Han, K., Lim, J.H. Im, E.G., 2013. Malware analysis method using visualization of binary files. Proceedings of the 2013 Research in Adaptive and Convergent Systems, pp.317-321. Kim, T.G., Kang, B. Im, E.G., 2013. Malware classification method via binary content comparison. Information (Japan), 16(8 A), pp.5773-5788. Kà ¼Ãƒ §Ãƒ ¼ksille, E.U., Yalà §Ãƒâ€žÃ‚ ±nkaya, M.A. Uà §ar, O., 2014. Physical Dangers in the Cyber Security and Precautions to be Taken. Proceedings of the 7th International Conference on Security of Information and Networks SIN 14, pp.310-317. Available at: http://dl.acm.org.proxy1.athensams.net/citation.cfm?id=2659651.2659731. Lanzi, A. et al., 2010. AccessMiner: Using System-Centric Models for Malware Protection. Proceedings of the 17th ACM Conference on Computer and Communications Security CCS10, pp.399-412. Available at: http://dl.acm.org/citation.cfm?id=1866353%5Cnhttp://portal.acm.org/citation.cfm?doid=1866307.1866353. Nicholas, C. Brandon, R., 2015. Document Engineering Issues in Document Analysis. Proceedings of the 2015 ACM Symposium on Document Engineering, pp.229-230. Available at: http://doi.acm.org/10.1145/2682571.2801033. Patanaik, C.K., Barbhuiya, F.A. Nandi, S., 2012. Obfuscated malware detection using API call dependency. Proceedings of the First International Conference on Security of Internet of Things SecurIT 12, pp.185-193. Available at: http://www.scopus.com/inward/record.url?eid=2-s2.0-84879830981partnerID=tZOtx3y1. Pluskal, O., 2015. Behavioural Malware Detection Using Efficient SVM Implementation. RACS Proceedings of the 2015 Conference on research in adaptive and convergent systems, pp.296-301. Santos, I. et al., 2013. Opcode sequences as representation of executables for data-mining-based unknown malware detection. Information Sciences, 231, pp.64-82. Stewin, P. Bystrov, I., 2016. Detection of Intrusions and Malware, and Vulnerability Assessment, Available at: http://dblp.uni-trier.de/db/conf/dimva/dimva2012.html#StewinB12. Willems, G., Holz, T. Freiling, F., 2007. Toward automated dynamic malware analysis using CWSandbox. IEEE Security and Privacy, 5(2), pp.32-39. Tabish, S.M., Shafiq, M.Z. Farooq, M., 2009. Malware detection using statistical analysis of byte-level file content. Proceedings of the ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics CSI-KDD 09, pp.23-31. Available at: http://portal.acm.org/citation.cfm?doid=1599272.1599278. Lyda, R.,Hamrock, J,. 2007.Using Entropy Analysis to Find Encrypted and Packed Malware. Nataraj, L. et al., 2011. Malware Imagesà ¢Ã¢â€š ¬Ã‚ ¯: Visualization and Automatic Classification. Bilar, D., Statistical Structuresà ¢Ã¢â€š ¬Ã‚ ¯: Fingerprinting Malware for Classification and Analysis Why Structural Fingerprintingà ¢Ã¢â€š ¬Ã‚ ¯? Christodorescu, M., Song, D. Bryant, R.E., 2005. Semantics-Aware Malware Detection. Top maliciously used apis. https: //www.bnxnet.com/top-maliciously-used-apis/, 2017. Weiss, S.M. Kapouleas, I., 1989. An Empirical Comparison of Pattern Recognition , Neural Nets , and Machine Learning Classification Methods. , pp.781-787. Kotsiantis, S.B., 2007. Supervised Machine Learningà ¢Ã¢â€š ¬Ã‚ ¯: A Review of Classification Techniques. , 31, pp.249-268. Chen, T., XGBoostà ¢Ã¢â€š ¬Ã‚ ¯: A Scalable Tree Boosting System.

Charles Dickens, The Old Curiosity Shop :: Free Essays Online

Charles Dickens, The Old Curiosity Shop Charles Dickens 1841 novel The Old Curiosity Shop, entering its third century, mesmerizes readers with either heartfelt sentimentality to the plight of a homeless thirteen year-old girl, Nell Trent, and her aged Grandfather, as they wander the countryside of England, keeping one step ahead of their horrible dwarf nemesis, Daniel Quilp; or as a "crude sentimental" (Harris 137) journey down the path of individual weakness that lead to the death of them both. In Dickens day, a curiosity shop was an establishment where individuals would go to purchase precious or antique gifts, and it is in one of these shops that thirteen year-old Nell lived with her Grandfather. A short summary of the story is that the Grandfather has an addictive gambling problem, and gambles the money needed to run the shop away, all the while borrowing money from Daniel Quilp, a nasty goblin type figure of a man. The losses amount to the shop being taken over by Quilp, leaving Nell and the Grandfather fleeing to avoid him. They wander the English countryside amongst the throng of carnivals, sideshows, philanthropic souls who try to help them, and downtrodden people who try to exploit them. Their deaths, Nell’s especially, whose Dickens wrote of in a lingering, sentimental tone, are where the discussion of the book has been centered on for over a century-and-a-half. The Old Curiosity Shop began as a series of short stories in a publication Dickens created in 1840 called Master Humphrey’s Clock. With a weekly circulation of over 70,000 readers, Dickens was able to finance the work of The Old Curiosity Shop with the income made from Master Humphrey’s Clock. Emotionally, working under a strenuous monthly deadline proved to be straining on Dickens. In July of 1840, Dickens was telling his friend Lord Jeffrey, editor of The Edinbergh Review, that The Old Curiosity Shop "demands my constant attention" (Page 22,23), and by December of that year Dickens seemed to be on the edge of a mental collapse, telling Lord Jeffrey that the "anguish" of writing under the pressure was "unspeakable, the difficulty tremendous" (Page 30). The story was completed in early 1841 and Dickens began the painstaking steps in putting the short stories into a complete novel. One of the immediate obstacles Dickens encountered (actually his printers, Dickens was busy completing his next novel, Barnaby) was marrying the chapters together in proper sequence.

Coconut Tree Essay

The coconut tree as a â€Å"tree of life† is characteristically a food supplier as this tree provides fruit and is well-known to be devoid of any anti-nutrient factors and is known as a whole food with 5,000 years of recorded use in food preparation with health benefits.The fruit is edible at any stage of maturity. It provides not only a solid food but a large volume of very safe and healthy drinking water-based juice. The fruit of the coconut palm is the main source of many food products such as coconut milk/cream, desiccated coconut, coconut chip, coconut water, nata de coco, coconut oil, copra, etc. Apart from these, the unopened inflorescence can produce coconut sap or toddy (tuba) which can be processed into high value and nutritious food products. Coconut sap sugar, considered to be one of the best natural sweeteners, is truly a perfect and healthier substitute for artificial sweeteners because it is not a product of chemical laboratories, not an artificial sweetener and not a by-product of sugar cane, not brown sugar nor muscuvado sugar. Coco sugar is good for both diabetic and non-diabetic consumers because it does not induce high blood sugar because it has a Low Glycemic index. Glycemic index (GI) is a numerical system of measuring how much of a rise in circulating blood sugar a carbohydrate triggers-the higher the number, the greater blood sugar response. So a low GI food will cause a small rise, while a high GI food will trigger the opposite. GI is about the quality of the carbohydrates, not the quantity. Coco sugar can be good for weigh maintenance. (Dr. Trinidad P. Trinidad, Scientist II of the Food and Nutrition Research Institute – Department of Science and Technology.) It is also rich in various amino acids, vitamins and minerals that are essential for the human body to benefit.