Recopla: An Extendible Graphic Meta-Editor

This chapter describes four interdisciplinary fields originated and defined by Ashu M. G. Solo in 2011 called political engineering, public policy engineering, computational politics, and computational public policy. Political engineering is the application of engineering, computer science, mathematics, or natural science to solving problems in politics. Computational politics is the application of computer science or mathematics to solving problems in politics. Political engineering and computational politics include, but are not limited to, principles and methods for political decision-making, analysis, modeling, optimization, forecasting, simulation, and expression. Public policy engineering is the application of engineering, computer science, mathematics, or natural science to solving problems in public policy. Computational public policy is the application of computer science or mathematics to solving problems in public policy. Public policy engineering and computational public policy include, but are not limited to, principles and methods for public policy formulation, decision-making, analysis, modeling, optimization, forecasting, and simulation. The chapter describes the scope of research and development in these fields, provides examples of research and development in these fields, and provides possible university curricula for academic programs in these fields.

The purpose of the study is to assess the application of Computer science in the fractional distillation of petroleum. The study sought to: Determine the merit of using Computer science in fractional distillation of petroleum. Evaluate the cost effectiveness to the application of Computer science in fractional distillation of petroleum to the welfare of citizens. Determine if Computer science can help in controlling the natural effects of fractional distillation of petroleum on the environment. Computer science has gained wider application in so many areas of human endeavor ranging from the homes to more scientific and industrial applications, hence its application of Computer science in fractional distillation of petroleum cannot be overemphasized. Irrespective of the benefits of the application of Computer science in fractional distillation of petroleum as a whole, it has become pertinent to ask how effective and efficient its application of Computer science in fractional distillation of petroleum has been. This research work tries to investigate how effective the application of Computer science in fractional distillation of petroleum. The project exertion covered statement of the problem, purpose of the study, research questions/hypotheses were formulated to enable the researcher find out facts about the study, highlighted in division one. Subdivision two was based on the literature review of this study. The study covers the research methodology which guided the study, the next stage was based on the presentation of data and its’ analysis as well as the discussion of findings. The final stage of the research was concerned with the conclusion as well as recommendations and suggestions for further studies

Finite state automata are of fundamental importance in theory as well as applications in computer science. This dissertation presents several parallel algorithms for processing of them, including minimizing the number of states for a given finite state automaton, finding homing sequences for a given finite state automaton, converting a regular expression into an $\\varepsilon$-free nondeterministic finite automaton. Implementation of some algorithms, as well as their experimental results, are also given. For minimization of deterministic finite automata, we present a parallel algorithm and discuss its implementation on a practical parallel machine using data parallel and message passing paradigms. Its time complexity on a p processor EREW PRAM $(p\\le n)$ for inputs of size n is $O({n\\log\\sp2n\\over p}+\\log n\\ \\log p)$ uniformly on almost all instances. The work done by our algorithm is thus within a factor of $O(\\log n)$ of the best known sequential algorithm. The space used by our algorithm is linear in the input size. The actual resource requirements of our implementations are consistent with these estimates. For finding homing sequences of deterministic finite automata, we present both deterministic and randomized algorithms. Two randomized algorithms of time complexity $O(log\\sp2n)$ are given. One of the randomized algorithms produces a homing sequence of length $O(n\\ log\\sp2n)$ for almost all DFA's with n states. The deterministic algorithm is based on a well known sequential algorithm. The implementation of the deterministic parallel algorithm shows that it can handle randomly generated instances with millions of states, and all DFA's with thousands of states using currently available parallel machines. Finally, we present an efficient parallel algorithm for converting a regular expression into an $\\varepsilon$-free nondeterministic finite automaton. The resulting automaton is natural representation of a regular expression and has some important and practical uses in the document processing area. The time complexity of the algorithm is $O(\\log n)$ and the number of processors used is $O(n\\sp2).$

One significant tool for addressing the concerns associated with the use of one of the applications in computer science is the law. Numerous laws and rules governing the use of computer science are designed to safeguard users, customers, and society at large. Law uses norms created by governmental and social institutions to control behavior. Programming is used to explore digital information in computer science. Computers got more potent and smaller as they evolved through generations, utilizing various technologies such as integrated circuits. Cyber law regulates online behavior and deals with matters pertaining to intellectual property, privacy, domain names, and other legal concerns. A few of the many concerns that the law must address in order to mitigate the electronic crimes are protecting privacy and security, upholding justice, limiting civil and criminal liability, ensuring safety, and controlling the application of AI in the workplace. Guidelines that people and organizations have to follow when utilizing computer science applications, making sure that these requirements are met in full. Additionally, as companies must commit to recording AI usage procedures and making clear the use of data and algorithms, the regulation fosters accountability and openness. By doing this, the possibility of prejudice and mistakes when utilizing AI is decreased. This survey provides a self-contained introduction to cybercrimes and types of cybercrimes. We also present ways to combat cybercrime and limit its spread.

Additive combinatorics is the branch of combinatorics where the objects of study are subsets of the integers or of other abelian groups, and one is interested in properties and patterns that can be expressed in terms of linear equations. More generally, arithmetic combinatorics deals with properties and patterns that can be expressed via additions and multiplications. In the past ten years, it has been an extremely successful area of mathematics, featuring a convergence of techniques from graph theory, analysis and ergodic theory. It has helped prove long-standing open questions in additive number theory, and it offers much promise of future progress. Arithmetic combinatorics has found several applications in computer science, to property testing, PCP constructions, lower bounds, and extractor constructions. Typically, whenever a technique from arithmetic combinatorics becomes understood by computer scientists, it finds some application. Considering that there is still a lot of additive and arithmetic combinatorics that computer scientists do not understand (and, the field being very active, even more will be developed in the near future), there seems to be much potential for future connections and applications.

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Since the term “software engineering” was coined some 20+ years ago, [4] a number of definitions for both the practice and the practitioner, a “software engineer,” have been proposed. The definition from a recent report on undergraduate software engineering curricula [3] states:Software engineering entails the understanding and application of engineering principles, design skills, good management practice, computer science and mathematical formalism. …The Software Engineer must be able to estimate the cost and duration of the software development process, and to determine correctness and reliability and express them with a degree of precision meaningful to other engineers and informed clients. …In recent years, several advanced degree programs in “software engineering” have been instituted in the US and abroad. One example of this can be found at the SEI itself. Two of the goals of the SEI Education Program are to: (1) develop software engineering curriculum modules that identify, organize, and document the body of knowledge that could be taught in software engineering degree programs, and (2) design, develop, support, and maintain model curricula for undergraduate and graduate software engineering programs.The question remains, however, how much of this can be taught in a classroom setting or even in a design lab, and how much must be learned on the job or via some sort of apprentice program? What does Ada have to do with software engineering ? Can Ada exist without it? Can software engineering be done without Ada?There are those who believe the term “software engineering” is just plain wrong: [1]… I deeply resent the misuse of the word “engineering” in the compound term “software engineering.” Such misuse constitutes, in my opinion, a cheap, deceptive attempt to take advantage of the professional reputation and image developed by real engineers over a century or so … it constitutes theft of professional reputation.In 1986, I coordinated a similar panel for the ACM Computer Science Conference (CSC) [2]. The introduction from that session still holds true for this one, and is paraphrased and tuned below:Most universities prepare students for continued growth and development during a lifetime of employment, whether in an industrial or government setting. Since traditionally, such an education has implied better job opportunities, the university student believes that by studying computer science and/or software engineering, she2 will be assured of a good job at a reasonable salary performing interesting and challenging tasks.On the other hand, some universities prepare students for graduate education and research, placing a heavy emphasis on theory. From this perspective, the university student believes that upon completion of a computer science or software engineering degree, she will be able to choose where to pursue her research, at the university, industry, or government site of her choosing.Three years have passed; and I have found other group of articulate people to join us and present new and exciting viewpoints on the topic of software engineering, university education, and, of course Ada. Richard Fairley, who participated in that CSC panel, continues to represent the academic point of view; Marcia Finfer represents the industrial point of view; Garth Glynn also provides the industrial point of view with the interesting addition of evaluating graduate-level software engineering degree curricula; David Markus brings the perspective of the human resource issues; Lyn Plinta provides the view of the recent graduate dealing with veterans of the hacking era; and David Umphress represents the DoD interests.These panelists, bringing together a wealth of experience as educators, practitioners, and policy makers, provide insights from both the demand-side of software engineering and the supply-side, as they take a multi-faceted, multi-national look at industrial, academic, and government expectations of software engineering as it is taught and practiced today.

With the proliferation of computing technologies and an ongoing trend of introducing digital and blended learning aspects into higher education, innovative approaches to teaching complex topics like artificial intelligence (AI) have emerged. Of particular interest is the use of game-based learning approaches. According to problem-based learning theory, providing students with an interactive problem and encouraging them to independently find solutions promotes deeper understanding and skill acquisition. Thus, game-based approaches offer an engaging way for students to explore challenging concepts. However, despite the growing use of game-based methods in fields like economics, their application in computer science - especially in teaching game AI - remains under-explored.Understanding AI is increasingly critical for game development, as modern games emulate human-like behaviour in areas such as decision-making, character routines, and adaptive strategies. While many mechanisms and approaches of agent-level decision-making and planning are well understood, their application in video games poses unique challenges, such as accommodating unpredictable player interactions and ensuring performance efficiency without degrading the player experience. While strategy games like StarCraft or multiplayer online battle arenas like DotA 2 have been of interest as proving grounds for advanced AI training methods, their use in education has been limited due to their high complexity and associated learning curve.This work proposes the development of a novel interactive application to fill this niche. Taking inspiration from city building and management games, the application simulates the campus of the University of Applied Sciences Mittweida, where students are given control of agents acting as archetypal roles of students. The agents' goal is the acquisition of knowledge, an abstract resource gained by participating in courses, requiring the agent to navigate the campus. Students interact with the system through an API that provides information on the state of the simulation and allows issuing commands to specific agents. For example, agents who continuously acquire knowledge over a prolonged period do so at decreasing efficiency. To remedy this, a student implements a routine checking the learning efficiency of all agents, commanding "tired" agents to take a break. Alternatively, the student could train a machine learning algorithm to do the same task, albeit more adaptive. Additionally, the application enables dynamic changes to the environment at runtime, such as adding or removing courses or buildings, simulating player-driven alterations to the game world. By designing decision-making algorithms for these agents, students gain hands-on experience with fundamental AI concepts, i.e. decision trees, bridging the gap between theoretical knowledge and practical application.To evaluate the effectiveness of the application, a comparative study with undergraduate students is planned. Over the course of two semesters, two groups of students will be taught the basics of game AI - one using traditional teaching methods (primarily lectures), the other using a game-based method incorporating the new application. The learning progress of both groups will be monitored using assignments, with students being given a sample project and tasked to develop a game AI solution, i.e. for a non-player character in a first-person shooter.

This paper reviews applications in computer science that decision theorists have addressed for years, discusses the requirements posed by these applications that place great strain on decision theory/social science methods, and explores applications in the social and decision sciences of newer decision-theoretic methods developed with computer science applications in mind. The paper deals with the relation between computer science and decision-theoretic methods of consensus, with the relation between computer science and game theory and decisions, and with “algorithmic decision theory.”

This paper describes a programming system used in the teaching of two Computer Science courses at The Pennsylvania State University (7). The courses are; an upper level undergraduate-beginning graduate level course in graph theory, and a graduate level course in the theory of graphs and networks. As can be ascertained from the description of these courses they are not primarily what would normally be called programming courses. However, since they are computer science courses they do stress the computational aspects of graph and network theory and analysis. Past experiences have indicated that if a computational problem of any reasonable complexity is assigned in class the majority of the students focus an inordinate amount of their energies to the programming problems, and consequently fail to grasp the significant features of the theoretical problems involved. In addition, since graph and network theory encompasses not only computer science, but engineering and physical, and to a lesser extent, the social sciences, many of these students would be unduly burdened by writing many rather complex programs. On the other hand, teaching the computational aspects of graph theory without doing some programming is equally ludicrous. Thus, a default type solution of assigning two or three problems from a narrow set of problems was rapidly becoming an undesirable solution to this dilema. It was at this point that the concept of a dynamically growing programming system, as described in this paper, began to formulate itself.

It is generally accepted that a course in discrete structures such as Course B3 in Curriculum 68 recommendations [1] is an important part of a Computer Science program. In many institutions a Discrete Structures course is offered in the Computer Science Department although it overlaps courses in the Mathematics Department [2,3,4]. In smaller schools the size of the student body and staff limitations make such a solution impractical. At Augusta College we have met the objective of early exposure to discrete mathematics with applications in computer science by offering a two-hour computer science course as a co-requisite to a three-hour mathematics course. While this approach is a compromise it does appear to have some merit.

Games provide mathematical models for interaction. Numerous tasks in computer science can be formulated in game-theoretic terms. This fresh and intuitive way of thinking through complex issues reveals underlying algorithmic questions and clarifies the relationships between different domains. This collection of lectures, by specialists in the field, provides an excellent introduction to various aspects of game theory relevant for applications in computer science that concern program design, synthesis, verification, testing and design of multi-agent or distributed systems. Originally devised for a Spring School organised by the GAMES Networking Programme in 2009, these lectures have since been revised and expanded, and range from tutorials concerning fundamental notions and methods to more advanced presentations of current research topics. This volume is a valuable guide to current research on game-based methods in computer science for undergraduate and graduate students. It will also interest researchers working in mathematical logic, computer science and game theory.

Artificial intelligence (AI) is a rapidly evolving field that has seen tremendous growth in recent years. In this chapter, the authors provide an overview of current trends in AI and their applications in computer science. They also discuss the future directions of AI research and their potential impact on the field of computer science. They start by introducing the basic concepts of AI and its various subfields. Then, they present an overview of current trends in AI research, including machine learning, natural language processing, computer vision, and robotics. The authors discuss how these trends are applied in computer science, such as autonomous vehicles, fraud detection, and personalized medicine. Finally, they discuss the future directions of AI research, including the development of more explainable AI systems, the integration of AI with other emerging technologies, and the ethical considerations of AI.

It is generally accepted that a course in discrete structures such as Course B3 in Curriculum 68 recommendations [1] is an important part of a Computer Science program. In many institutions a Discrete Structures course is offered in the Computer Science Department although it overlaps courses in the Mathematics Department [2,3,4]. In smaller schools the size of the student body and staff limitations make such a solution impractical. At Augusta College we have met the objective of early exposure to discrete mathematics with applications in computer science by offering a two-hour computer science course as a co-requisite to a three-hour mathematics course. While this approach is a compromise it does appear to have some merit.

Hidden Markov processes (HMPs) are important objects of study in many areas of pure and applied mathematics, including information theory, probability theory, dynamical systems and statistical physics, with applications in electrical engineering, computer science and molecular biology. This collection of research and survey papers presents important new results and open problems, serving as a unifying gateway for researchers in these areas. Based on talks given at the Banff International Research Station Workshop, 2007, this volume addresses a central problem of the subject: computation of the Shannon entropy rate of an HMP. This is a key quantity in statistical physics and information theory, characterising the fundamental limit on compression and closely related to channel capacity, the limit on reliable communication. Also discussed, from a symbolic dynamics and thermodynamical viewpoint, is the problem of characterizing the mappings between dynamical systems which map Markov measures to Markov (or Gibbs) measures, and which allow for Markov lifts of Markov chains.