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Computer Research Paper

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Humans have always looked for “technologies” to help them count—from stick-markings prehistoric foragers made to keep track of cattle to the first programmable, room-filling mainframes employed by post–World War II business. In the twenty-first century computers do far more than calculate; forecasters predict that the computing power of today’s desktop will someday be packaged in a device the size of a shirt button and for the cost of a dime.

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Computers have transformed work, communication, and leisure activity, and they promise future changes of equal magnitude. For many years, computer technology was dominated by groups in the United States, because that nation had the largest single market and its government invested heavily in military applications and fundamental science and engineering. But many nations contributed to the technological basis on which computing arose, and with the development of the World Wide Web computing became a global phenomenon.

Mechanical Predecessors

Programmable digital computers were developed just before the middle of the twentieth century, but the more general history of devices that help people think goes back to prehistoric times, when someone first carved marks on a stick to count the cattle in a herd or mark the days in the phases of the moon. Complex additions and subtractions were done in ancient days by arranging pebbles in piles on the ground, and our word calculate derives from the Latin word calculus (pebble). The most complex known “computer” of classical civilization is the remarkable geared Antikythera device, which apparently was designed to predict the motions of the sun, moon, and planets. Found in a shipwreck on the bottom of the Mediterranean Sea, it is believed to date from about 80 BCE.

Computing has always been closely allied with mathematics, and the invention of logarithms by the Scottish mathematician John Napier around 1614 was a major advance for practical calculating. With a mechanical calculating device, it is much easier to add than to multiply, and subtraction is much easier than division. Logarithms turned multiplication into addition, and division into subtraction, at the cost of looking up numbers in vast books of tables that also had to be calculated by hand. From Napier’s time until the introduction of transistorized electronic calculators around 1970, a book of logarithm tables was a standard tool for engineers and scientists. They were cumbersome to use, so for quick estimates slide rules were employed. A slide rule is an analog calculating device based on logarithmic scales marked along rulers that slide past each other. The term analog refers to the analogy between the abstract numbers and corresponding physical distances along a line.

Digital mechanical calculators that represented numbers as precise digits were also developed—for example, by the French mathematician and philosopher Blaise Pascal in 1642. A common approach was to connect a series of wheels, each of which would turn in ten steps for the digits 0 through 9. A legend has developed that the eccentric English dilettante Charles Babbage was the father of computing because around 1835 he designed a mechanical calculator that could be programmed with punched cards. Science fiction writers William Gibson and Bruce Sterling wrote a novel imagining that Babbage succeeded in building it, launching a golden age of British scientific and technological dominance but magnifying social problems. However, in reality Babbage failed, and historian Doron Swade estimates that his influence on the development of electronic computers was insignificant.

The first comprehensive digital data-processing system using cards was developed by the American engineer Herman Hollerith, who began patenting his ideas in the 1880s. By 1902, when his machines were used to process the vast sea of information collected in the 1900 U.S. census, they already incorporated electric relays that could do conditionals (if-then operations).

The Mainframe Era

There is considerable debate among historians over which programmable, electronic digital computer was first or most influential. By 1941, professor John Atanasoff and graduate student Clifford Berry had created a demonstration machine at Iowa State University, but they did not develop it further. In Britain, a special-purpose electronic computer called Colossus began cracking German codes in 1943, but its design was kept secret for more than three decades. Perhaps the most influential early electronic digital computer was ENIAC (Electronic Numerical Integrator and Computer), completed at the University of Pennsylvania in 1946 by a team headed by physicist John W. Mauchly and engineer J. Presper Eckert.

ENIAC’s primary job was calculating accurate artillery firing tables for the U.S. Army. In the frenzy of World War II, many new models of long-range guns were being produced, and soldiers in the field needed complex tables to tell them how to aim to hit a target at a certain distance under various conditions. It was impossible to fire the guns under all the likely conditions, so data from some judiciously chosen test firings were used to anchor elaborate sets of mathematical calculations. Vannevar Bush, who was the chief science advisor to President Roosevelt, had a huge mechanical analog computer, the differential analyzer, built for this purpose in 1930. In theory, an electronic computer would be much faster and more accurate, but there were serious questions about whether it could be sufficiently reliable, because before the development of transistors they were built with vacuum tubes that tended to burn out. ENIAC weighed 30 tons, covered 1,800 square feet, and contained 18,000 vacuum tubes.

ENIAC’s data input and output employed Hollerith’s punch cards, a method that remained one of the standard approaches through the 1970s. However, programming was done manually by setting hundreds of rotary switches and plugging in wires that connected electronic components. Mauchly and Eckert designed a successor that could store a program in its memory. They formed a small company, launched a line of machines called UNIVAC, and then sold out to a private company in 1950. This example typifies mid-twentieth-century computing. The technology for large and expensive mainframe computers was developed with government funding for military purposes and then transferred to the civilian sector where it was used by large corporations for financial record-keeping and similar applications. Much of the research work was done at universities, and the availability of a few mainframe computers on campus gave scientists the chance to adapt them to many research purposes.

The Personal Computer

The birth of the computer industry involved nothing less than development of an entire computer culture, including programming languages and compilers to control the machines, networks and input-output devices to transmit information between users and machines, and new courses in universities leading to the emergence of computer science and engineering as a distinct field. For years, the dominant model was expensive mainframe computers with batch processing of data—computer runs that were carefully prepared and then placed in a queue to await time on the mainframe—although there were some experiments with time sharing in which several individuals could use a computer simultaneously in real-time. Then, in the mid-1970s, both inside information technology companies and outside among electronics hobbyists, the personal computer revolution offered a radically new concept of computing.

In April 1973, Xerox corporation’s Palo Alto Research Center ran its first test of the Alto, the prototype desktop personal computer. Alto innovated many of the technologies that would become standard for home and office computers, including the mouse, windows and icons on the screen, desktop printing with many different fonts, incorporation of images and animations, and local area networks that allowed individuals to send files back and forth between their machines. Xerox was not able to exploit the technology at the time, because of the high cost and low performance of microelectronics. In the 1960s, Gordon Moore, a founder of the Intel computer chip corporation, propounded what has become known as Moore’s Law, the observation that the performance of computer chips was doubling every eighteen or twenty-four months. Alto’s technology finally hit the home market when the first Apple Macintosh was sold in 1984, soon followed by Microsoft’s Windows operating system.

Before any of the big information technology companies offered personal computers to the public, hobbyists were building their own from kits, notably the Altair first announced in the January 1975 issue of Popular Electronics magazine. A technological social movement, drawing on some of the cultural radicalism of the 1960s, quickly spread across America and Western Europe, although in retrospect it is difficult to estimate how much this radicalism contributed to the rapid advance of the computer revolution. It is true that Apple was founded in a garage by two friends, and Bill Gates dropped out of college to help his buddies found Microsoft. For a few years after the Apple II computer appeared in 1977, an individual could write a commercially viable software program and start a small company to market it. But the greatest advances after the mid-1980s again required the combination of massive government funding and large corporations.

Internet and the World Wide Web

Internet was born in 1969 as ARPAnet, a research network funded by the Advanced Research Projects Agency of the U.S. government that connected computers at the University of California at Los Angeles, the Stanford Research Institute, the University of California at Santa Barbara, and the University of Utah. In 1972 it was first demonstrated to the public, and in the same year it began carrying email. More and more educational institutions, government agencies, and corporations began using the Internet—and finding new uses for it—until by the end of the 1980s it was an essential tool for research and had begun to demonstrate its value for business and personal applications. For example, in 1978 Roy Trubshaw and Richard Bartle invented the first online fantasy game or MUD (Multiple-User Dungeon) at Essex University in England, and in 1989 Alan Cox at the University College of Wales released his own version onto the Internet.

In 1990 at the high-energy physics laboratories of the Conseil Europeen pour la Recherche Nucleaire (CERN) near Geneva, Switzerland, Tim Berners-Lee developed the first hypertext browser and coined the term World Wide Web. Early in 1993, University of Illinois student Marc Andreessen at the National Center for Supercomputing Applications, funded by the U.S. National Science Foundation, programmed the first version of Mosaic, the easy-to-use browser that would introduce millions of people to the Web. Both the Netscape and Microsoft Internet Explorer browsers were based on Mosaic, and it is estimated that more than 10 percent of the world’s population used the Internet in 2002.

The mainframe-timesharing concept of the 1970s has evolved into what is called client-server architecture. A server is a dedicated computer, often large, that houses centralized databases (in companies, universities, or government agencies) or connects directly to the Internet. Originally, clients were dumb terminals with little or no computing power of their own, but today they are powerful personal computers connected to the server and able to access its resources. A very different approach has arisen recently, called peer-to-peer architecture—for example, the music-file-sharing programs like Napster that link personal computers over the Web, in which each computer simultaneously functions as both server and client. The grid computing concept distributes big computation jobs across many widely distributed computers, or distributes data across many archives, eroding the distinction between individual computers and the Internet.

The Era of Ubiquitous Computing

Computers today are found nearly everywhere, embedded in automobiles and grocery store checkout counters, or packaged as pocket-sized personal digital assistants that allow a user to send email or surf the Web from almost anywhere. They have begun to take over the roles of traditional devices such as telephones and televisions, while other devices have become accessories to computers, notably cameras and music players. Old forms of computing do not die, but expand. Children’s toys now have vastly greater computing power than ENIAC, but ENIAC’s direct descendents are supercomputers capable of doing dozens of trillions of calculations per second.

Computer science continues to advance, and nanotechnology promises to sustain Moore’s Law until perhaps about 2025, halting only after the smallest electronic components have shrunk to the size of a single molecule. Two decades of doubling every eighteen months means improvement by a factor of 8,000. That would imply the computing power of today’s desktop computer packaged in a shirt button and costing a dime. What will people do with such power?

In 2003, the Interagency Working Group on Information Technology Research and Development of the U.S. government identified the following “grand challenges” that computing could address in the following decade:

• Knowledge environments for science and engineering
• Clean energy production through improved combustion
• High confidence infrastructure control systems
• Improved patient safety and health quality
• Informed strategic planning for long-term regional climate change
• Nanoscale science and technology: explore and exploit the behavior of ensembles of atoms and molecules
• Predicting pathways and health effects of pollutants
• Real-time detection, assessment, and response to natural or man-made threats
• Safer, more secure, more efficient, higher-capacity, multimodal transportation system
• Anticipate consequences of universal participation in a digital society
• Collaborative intelligence: integrating humans with intelligent technologies
• Generating insights from information at your fingertips;
• Managing knowledge-intensive dynamic systems
• Rapidly acquiring proficiency in natural languages
• SimUniverse [educational computer simulations]: learning by exploring
• Virtual lifetime tutor for all

Bibliography:

• Austrian, G. D. (1982). Herman Hollerith: Forgotten giant of information processing. New York: Columbia University Press.
• Bainbridge, W. S. (Ed.). (2004). Berkshire encyclopedia of human-computer interaction. Great Barrington, MA: Berkshire Publishing Group.
• Berners-Lee, T., & Fischetti, M. (1999). Weaving the Web. New York: HarperCollins.
• Freiberger, P., & Swaine, M. (1999). Fire in the valley: The making of the personal computer (2nd. ed.). New York: McGraw-Hill.
• Gibson, W., & Sterling, B. (1991). The difference engine. New York: Bantam.
• Gillies, J., & Cailliau, R. (2000). How the Web was born. Oxford, U.K.: Oxford University Press.
• Grudin, J. (2004). History of human-computer interaction. In W. S. Bainbridge (Ed.), Berkshire Encyclopedia of human-computer interaction. Great Barrington, MA: Berkshire Publishing Group.
• Interagency Working Group on Information Technology Research and Development. (2003). Grand challenges: Science, engineering, and societal advances requiring networking and information technology research and development. Arlington, Virginia: National Coordination Office for Information Technology Research and Development.
• Lavendel, G. (1980). A decade of research: Xerox Palo Alto Research Center. New York: Bowker.
• Metropolis, N., Howlett, J., & Rota, G.-C. (Eds.). (1980). A history of computing in the twentieth century. New York: Academic Press.
• Mollenhoff, C. R. (1988). Atanasoff: Forgotten father of the computer. Ames: Iowa State University Press.
• National Research Council. (1999). Funding a revolution: Government support for computing research. Washington, DC: National Academy Press.
• Price, D. J. S. de. (1959). An ancient Greek computer. Scientific American 200(6), 60 –67.
• Stern, N. (1981). From ENIAC to UNIVAC: An appraisal of the Eckert-Mauchly computers. Bedford, MA: Digital Press.
• Swade, D. (2000). The difference engine: Charles Babbage and the quest to build the first computer. New York: Viking.
• Waldrop, M. M. (2001). The dream machine: J. C. R. Licklider and the revolution that made computing personal. New York: Viking.

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• cs.AI - Artificial Intelligence ( new , recent , current month ) Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
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• cs.CC - Computational Complexity ( new , recent , current month ) Covers models of computation, complexity classes, structural complexity, complexity tradeoffs, upper and lower bounds. Roughly includes material in ACM Subject Classes F.1 (computation by abstract devices), F.2.3 (tradeoffs among complexity measures), and F.4.3 (formal languages), although some material in formal languages may be more appropriate for Logic in Computer Science. Some material in F.2.1 and F.2.2, may also be appropriate here, but is more likely to have Data Structures and Algorithms as the primary subject area.
• cs.CE - Computational Engineering, Finance, and Science ( new , recent , current month ) Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. Papers here are interdisciplinary and applications-oriented, focusing on techniques and tools that enable challenging computational simulations to be performed, for which the use of supercomputers or distributed computing platforms is often required. Includes material in ACM Subject Classes J.2, J.3, and J.4 (economics).
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• cs.IR - Information Retrieval ( new , recent , current month ) Covers indexing, dictionaries, retrieval, content and analysis. Roughly includes material in ACM Subject Classes H.3.0, H.3.1, H.3.2, H.3.3, and H.3.4.
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• cs.LG - Machine Learning ( new , recent , current month ) Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
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• cs.OH - Other Computer Science ( new , recent , current month ) This is the classification to use for documents that do not fit anywhere else.
• cs.PF - Performance ( new , recent , current month ) Covers performance measurement and evaluation, queueing, and simulation. Roughly includes material in ACM Subject Classes D.4.8 and K.6.2.
• cs.PL - Programming Languages ( new , recent , current month ) Covers programming language semantics, language features, programming approaches (such as object-oriented programming, functional programming, logic programming). Also includes material on compilers oriented towards programming languages; other material on compilers may be more appropriate in Architecture (AR). Roughly includes material in ACM Subject Classes D.1 and D.3.
• cs.RO - Robotics ( new , recent , current month ) Roughly includes material in ACM Subject Class I.2.9.
• cs.SI - Social and Information Networks ( new , recent , current month ) Covers the design, analysis, and modeling of social and information networks, including their applications for on-line information access, communication, and interaction, and their roles as datasets in the exploration of questions in these and other domains, including connections to the social and biological sciences. Analysis and modeling of such networks includes topics in ACM Subject classes F.2, G.2, G.3, H.2, and I.2; applications in computing include topics in H.3, H.4, and H.5; and applications at the interface of computing and other disciplines include topics in J.1--J.7. Papers on computer communication systems and network protocols (e.g. TCP/IP) are generally a closer fit to the Networking and Internet Architecture (cs.NI) category.
• cs.SE - Software Engineering ( new , recent , current month ) Covers design tools, software metrics, testing and debugging, programming environments, etc. Roughly includes material in all of ACM Subject Classes D.2, except that D.2.4 (program verification) should probably have Logics in Computer Science as the primary subject area.
• cs.SD - Sound ( new , recent , current month ) Covers all aspects of computing with sound, and sound as an information channel. Includes models of sound, analysis and synthesis, audio user interfaces, sonification of data, computer music, and sound signal processing. Includes ACM Subject Class H.5.5, and intersects with H.1.2, H.5.1, H.5.2, I.2.7, I.5.4, I.6.3, J.5, K.4.2.
• cs.SC - Symbolic Computation ( new , recent , current month ) Roughly includes material in ACM Subject Class I.1.
• cs.SY - Systems and Control ( new , recent , current month ) cs.SY is an alias for eess.SY. This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.

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• A Research Guide
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30 Interesting Computer Science Research Paper Topics

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• Biotechnology, medicine, and computer science
• Neuron networks and machine learning
• Big data analysis
• Virtual reality and its connection to human perception
• The success of computer-assisted education
• Computer assistance in support services
• Database architecture and management
• Human-computer interactions. The importance of usability
• The limits of computation and communication
• Computers and media. Where is the line between art and math modeling?
• Why there are so much programming languages?
• Digital security versus private information
• Encrypting and decrypting
• Quantum computers. Are they the future?
• Is the evolution of search algorithms finished?
• The importance of open source software
• Portable gadgets and the peculiarities of software development for them
• Cloud storages: advantages and disadvantages
• Computer viruses: the main principles of work and the hazards
• DDOS attacks, their danger on the global scale and their prevention
• Is SCRUM methodology the best-invented one for computer science?
• The online medicine apps: can they sometimes substitute the treatment of real doctors?
• 5G Wireless System: is it the future?
• Windows, macOS, UNIX – what OS is the most perspective now?
• Biometric systems and recognizing
• Ethical hacking. Who are the “white hat hackers”?
• Cyborgs: is it sci-fi or nearest future?
• The ATM and bank security
• The evolution of torrents
• What is blockchain?

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Scientific methods in computer science free download ABSTRACT This paper analyzes scientific aspects of Computer Science . First it defines science and scientific method in

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Computer science articles from across Nature Portfolio

Computer science is the study and development of the protocols required for automated processing and manipulation of data. This includes, for example, creating algorithms for efficiently searching large volumes of information or encrypting data so that it can be stored and transmitted securely.

Latest Research and Reviews

Novel method of building train and test sets for evaluation of machine learning models related to software bugs assignment

• Lukasz Chmielowski
• Michal Kucharzak
• Robert Burduk

Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning

Unsorted retired batteries pose recycling challenges due to diverse cathodes. Here, the authors propose a privacy-preserving machine learning system that enables accurate sorting with minimal data, important for a sustainable battery recycling industry.

• Shengyu Tao
• Haizhou Liu
• Hongbin Sun

Improved ergonomic layout design of metro control center based on virtual simulation technology and genetic algorithm

• Hanzhao Qiu
• Weining Fang
• Yueyuan Chen

A graph-based approach can improve keypoint detection of complex poses: a proof-of-concept on injury occurrences in alpine ski racing

• Michael Zwölfer
• Dieter Heinrich
• Werner Nachbauer

Data-driven, two-stage machine learning algorithm-based prediction scheme for assessing 1-year and 3-year mortality risk in chronic hemodialysis patients

• Wen-Teng Lee
• Yu-Wei Fang
• Ming-Hsien Tsai

Physics-enhanced deep surrogates for partial differential equations

Data-driven surrogate models are used in computational physics and engineering to greatly speed up evaluations of the properties of partial differential equations, but they come with a heavy computational cost associated with training. Pestourie et al. combine a low-fidelity physics model with a generative deep neural network and demonstrate improved accuracy–cost trade-offs compared with standard deep neural networks and high-fidelity numerical solvers.

• Raphaël Pestourie
• Youssef Mroueh
• Steven G. Johnson

News and Comment

ChatGPT one year on: who is using it, how and why?

In just a year, ChatGPT has permeated scientific research. Seven scientists reveal what they have learnt about how the chatbot should — and shouldn’t — be used.

• Marzyeh Ghassemi
• Abeba Birhane
• Francisco Tustumi

Google AI and robots join forces to build new materials

Tool from Google DeepMind predicts nearly 400,000 stable substances, and an autonomous system learns to make them in the lab.

• Mark Peplow

Beyond the clinic: the rise of wearables and smartphones in decentralising healthcare

Navigating contemporary healthcare, wearable technology and smartphones are marking the dawn of a transformative era in patient observation and personalised care. Wearables, equipped with various sensing technologies (e.g., accelerometer for movement, optics for heart rate), are increasingly being recognised for their expansive potential in (remote) patient monitoring, diagnostics, and therapeutic applications which suggests a plausible move towards a more decentralised healthcare system. This shift is evident as healthcare providers and patients alike are becoming increasingly accepting of wearable-driven tools, as they enable continuous health monitoring outside of traditional clinical settings. Equally, the ubiquitous nature of smartphones, now more than mere communication tools, is being harnessed to serve as pivotal health monitoring instruments. Their added sensing capabilities with Internet of Things (IoT) driven connectivity enable a (relatively) seamless transition from conventional health practices to a more interconnected, digital age. However, this evolving landscape is not without its challenges, with concerns surrounding data privacy, security, and ensuring equitable access to digital advances. As we delve deeper into digital healthcare, we must harness the full potential of those technologies and ensure their ethical and equitable implementation, envisioning a future where healthcare is not just hospital-centric but is part of our daily lives.

• Victoria Hetherington
• Alan Godfrey

What the OpenAI drama means for AI progress — and safety

A debacle at the company that built ChatGPT highlights concern that commercial forces are acting against the responsible development of artificial-intelligence systems.

• Nicola Jones

How AI is expanding art history

From identifying disputed artworks to reconstructing lost masterpieces, artificial intelligence is enriching how we interpret our cultural heritage.

• David G. Stork

Presentation matters for AI-generated clinical advice

If mistakes are made in clinical settings, patients suffer. Artificial intelligence (AI) generally — and large language models specifically — are increasingly used in health settings, but the way that physicians use AI tools in this high-stakes environment depends on how information is delivered. AI toolmakers have a responsibility to present information in a way that minimizes harm.

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Computer Science Research Papers Samples For Students

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Do you feel the need to examine some previously written Research Papers on Computer Science before you get down to writing an own piece? In this open-access collection of Computer Science Research Paper examples, you are given a thrilling opportunity to discover meaningful topics, content structuring techniques, text flow, formatting styles, and other academically acclaimed writing practices. Adopting them while crafting your own Computer Science Research Paper will surely allow you to finish the piece faster.

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Conceptualization And Applications Of Discrete Mathematics In Modern Computer Science Research Paper Examples

Example of the impact of the internet on my major research paper, introduction.

When compared to the general population, it can be said that college students heavily make use of the Internet (Jones 2). Internet usage is usually a part of their everyday routine, partially because computers have been a part of their lives while they were growing up. It has become a part of their everyday communication habits and has become as ordinary a technology as televisions or telephones.

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E-services can be best described as services offered by governments and organizations via the Internet. This paper intends to measure the usability of E-services for illiterate users in Saudi Arabia. This is done by using a general form specifically designed for data collection. The utilization of a special soft keyboard has been suggested for bringing improvements in the E-services usability.

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{Author Name [first-name middle-name-initials last-name]} {Institution Affiliation [name of Author’s institute]}

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Introduction - Game development is the process of creating a game for consoles, PC, arcades, tablets, mobile phones and other platforms. This has been one of the best and highest paying job in the world. A single game would take years before it is launched and millions of dollars are spent along the process. This job entails everyone “to create your own engine” (Whithall 1).

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E-commerce and social media sites like Facebook and Twitter have become more and more popular over the years. This popularity has produced significant user privacy issues. Oftentimes people are asked to provide personal information like their home addresses, date of birth and even social security numbers by these sites. Amazon, E-bay and even tax filing sites can cause great concern for the privacy and protection of personal information that can be hacked into or distributed by anyone in this information age. (Bose, 2013)

Why the need to protect consumer privacy

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Test Development, Part One Test Plan for Measuring Knowledge of validity and Reliability Test format: multiple choice test Test Length: 20 items (30 Minutes)

Test Universe: This test is designed to measure the knowledge the participant possesses in reliability and validity of testing. KNOWLEDGE OF TERMS AND CONCEPTS TYPES OF RELIABILITY Test-retest method

Internal consistency method

Scorer reliability method

Subtotal: 15% RELIABILITY COEFFICIENTS

Cohen’s kappa

Correlations in two test administrations

Standard error of measurement

Confidence interval

Subtotal: 25% EVIDENCE OF VALIDITY Test content

Response process

Internal structure

Holistic Assessment Of Students Learning Outcome Research Paper

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