...Diversity of human species • Human health, past and present • Interpretation of human remains (forensics, osteology). Scientific Method (including steps in the scientific method) The Scientific Method • Basic belief that physical world is empirical and objective • Objective vs. interpretive information • All science is conducted within a cultural and social paradigm Observation Inductive Interpretation Coming up with Hypothesis • Use what we already know about the world to create hypothesis of what bees do: Testing Hypothesis • Create tests or design set of observations to test the hypothesis’ Refine Hypothesis/Create new ones • If hypothesis is rejected, can come up with new ones (alternatives) to test • If hypothesis is supported, can further refine it or come up with alternatives that could also be “correct” – Could tomatoes be naturally pollinated in other ways? • Occam's razor: accept the simplest explanation that works Testing hypothesis is repeatable • Our results will be replicated if others perform the same test, or if we repeat it. Use of the Scientific Method • Use objective methods to catalog the past – Use this to interpret the past • Hypotheses, models, theories • Inductive vs. deductive reasoning • Does the data fit the model? • Use of a research design Pseudosciene • Pseudoscience – Selective use of evidence – Blatantly ignores...
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...Genetics: primarily concerned wit biochemical mechanisms Transmission genetics: patterns of inheritance from one generation to the next Population genetics: why and how the genetic composition of populations changes in time and space. * Brief history of Genetics: 10,000 bc to 1,000 bc domestication of plants animals and artificial selection discovered. The basic principles of heredity were appreciated without any understanding of DNA. Further evidence for knowledge heredity in ancient writings. Greeks: (600 bc – 300 bc) Theory of pangenesis: “seeds” produced by all parts of the body are transmitted to off spring- cause offspring to resemble their parents. Theory of inheritance of acquired characteristics: traits modified during organisms life and changes passed to offspring. Hippocrates: proposed that the body must maintain a balance between four humors: blood, phlegm, black bile, and yellow bile. 1600 to 1800s: -discovery of cells -developments in embryology: is the individual preformed (ovists vs. spermists) -understanding that somatic cells differ from germ cells 1859: Darwin developed his theory of evolution by natural selection, but did not understand the bascis of inheritance. * 1857-1863: * * 1940s: DNA found to be the genetic material * Watson and crick (1953) discover Dna * * Genome: all the dna in the nucleus of a cell. * Chromosome: a single continuous chain of...
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...Creationism Vs. Evolution in Today’s Society The dispute between creationism and evolution is a long debated controversy. It is an argument most people choose to avoid. This debate is extremely controversial due to personal belief, most in part due to secularism and religion. Even with the exponentially increasing knowledge of today, it has remained a long fought controversy throughout the twenty-first century. Science deals with the mind, and is the backbone of modern civilization. Religion deals with emotions, and often teaches people invaluable ethical principles. Both Science and Religion are vital in our culture in order for humanity to progress. In order for society to progress, children should be taught about both evolution and creationism, and taught to question both theories. As a result, our children will be open-minded and have the skills to “think outside of the box”. The theories of today and from the past are ones regarding strong factual evidence that has been extensively tested through the scientific method. The Theory of Evolution states that life has evolved from simple, single-celled organisms that all share a common ancestor. Throughout history these organisms faced the challenges of survival, and because of this, became more complex. Different stimuli, in different parts of the world, cause organisms to evolve differently. When these different simple organisms meet, there is often competition between the two in order to reproduce and/or survive. This occurs...
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...Introduction to artificial Intelligence Intelligence: Definition 1. - Its ability to learn or understand or deal with new or trying situations. - Skilled use of reason. 2. It’s the ability to apply knowledge to manipulate one’s environment or to think abstractly as measured by object criteria (as test) Views of intelligence 1. Autonomous movement – Movement of object; Eg Robot intelligently E.g vaucansor (18th Century), Shaker – 1970, Sony Aibo (1998) 2. Thinking – Ability to use brain. ▪ Eg Deep Blue defeats Garry Kasparov- 1997 ▪ Eg In Playing games 3. Playing Games A computer may be said to be intelligent if it beats the user. Types of Intelligence 1. Linguistic-Verbal intelligence- Ability to communicate effectively, use of in different forms, sensitive means. 2. Logical – Mathematical intelligence – Ability to carry out computations. 3. Musical 1 – Playing piano, singing, recognize non verbal. 4. Spatial – Ability to work with minimal information provided(scarce information) 5. Intrapersonal 1 – Able to understand your self, read others moods, emotions. 6. Interpersonal 1 – Ability to relate well with others. 7. Bodily kinesthetic Intelligence- body flexibility – acrobatics. NB: 1. The above types are referred to as multiple Intelligence Theory by Howard Gardener. 2. People have different strength in each intelligence area. This demonstrates different talents, as people...
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...Interbot: A Resume Based Employment Interview Chatbot Using an Enhanced Example Based Dialog Model Andrea May G. Aquino Department of Computer Science University of Santo Tomas Espana, Manila, 1008, PH andreamayaquino@gmail.com Katherine May Ann R. Bayona Department of Computer Science University of Santo Tomas Espana, Manila, 1008, PH kmarbayona@gmail.com Kimberly Ann D.R. Gonzales Department of Computer Science University of Santo Tomas Espana, Manila, 1008, PH kimberlyanngonzales @yahoo.com Gabrielle Ann D. Reyes Department of Computer Science University of Santo Tomas Espana, Manila, 1008, PH gabrielleannreyes@gmail.com Ria A. Sagum Department of Computer Science University of Santo Tomas Espana, Manila, 1008, PH riasagum31@yahoo.com ABSTRACT Traditional resume based recruitment interviews conducted by Human Resources (HR) specialists are time-consuming and costly. In-person interviews only allow companies to handle only a limited number of job applicants at a time. Also, there is no centralized database for resume storage and retrieval. As a result, a substantial amount of time and money is misdirected on interviewing unqualified job applicants. The proponents developed a resume based employment interview chatbot, using an enhanced example based dialog model, to evaluate job applicants’ consistency in their resume details and interview answers. The chatbot will replace the HR interviewer while maintaining the fundamental quality...
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...THESE IMPLY: * 1. Common Ancestry * 2. Changes through time *Natural selection occurs when 3 conditions are met; results in evolution * 1. There is variation in a trait * (Ex. Beetle color) * 2. The trait is heritable * (Ex. Brown beetles tends to have brown babies) * 3. There is differential reproductive success, and not all individuals reproduce to their full potential * (Ex. Green beetles are selected against by natural and Brown beetles are selected for- so they reproduce more) *Adaption: A trait that increases the ability of an individual to survive and reproduce compared with individuals without the trait Adaption in an evolutionary context: An inherited trait that makes an organism more fit in its abiotic and biotic environment, and that has arisen as a result of the direct action of natural selection for its primary function. Ex. Mimicry of the non-toxic king snake to evade predators Natural selection leads to Adaptions * Adjustments or changes In behavior, physiology, or structure of an individual organism to become more suited to an environment * Vestigial Structure: * Feature that was an adaption for the organism’s ancestor, but that evolved to be non-functional because the organism’s environment changed *Exaptation * Traits that serves one purpose today, but evolved under different selection conditions and served a different function...
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...1) Natural selection works on individuals * 2) Individuals do not evolve, populations do * Insecticide application didn’t result in insecticide resistance: some insects carry trait of resistance in their genes * Processes in Microevolution -Mutation -Non-random mating -Genetic Drift -Natural Selection -Gene Flow * Hardy-Weinburg Theorem: Frequencies of alleles and genotypes are preserved from generation to generation in populations that are not evolving -p2 + 2pq + q2 = 1 * Hardy-Weinburg tells us that we will never get rid of bad genes and it’s used to figure how gene populations change over time * The Hardy-Weinberg theorem describes a pop’n that is not evolving. It has 5 assumptions: 1. Genetic Drift: This represents random changes in small gene pools due to sampling errors in propagation of alleles. The bottleneck effect and founder effect are prime examples of genetic drift. In either case the number of individuals in a population is drastically reduced distorting the original allelic frequencies. (H-W assumes large population) 2. Gene Flow: The movement of alleles into and out of a gene pool. Migration of an organism into different areas can cause the allelic frequencies of that population to increase. Most populations are not isolated, which is contrary to the Hardy-Weinberg Theorem. (H-W assumes the population isolated from others) 3. Mutations: These changes in the genome of an organism are an important source of natural selection...
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...for growing purposes because of its resistance and productivity characteristics. The word “Variety” and its equivalent “Cultivar” have been applied to coffee, as for other plants, referring to variations due to two reasons: Mutations: These are expressed as more or less abrupt changes, such as a marked deviation in height or leave shape, etc. Mutated plants are descendants from a normal one and are considered as such when they permanently keep the new type after being propagated by seed. (i.e.: Maragogipe, Pacas, Caturra, etc.) Hybrids: These are natural or artificial crosses between varieties or species. When these plants are propagated by seeds they don’t maintain the new type uniformly, rather segregation of characters is common. Two types found include: Intraspecific Hybrid: They take place between the same species. (i.e.: Mundo Novo is a natural hybrid between Sumatra and Bourbon; Pacamara is an artificial hybrid between Pacas and Maragogipe) Interspecific Hybrid: They are the result of a cross between different species. (i.e.: between...
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...ARTIFICIAL NEURAL NETWORKS METHODOLOGICAL ADVANCES AND BIOMEDICAL APPLICATIONS Edited by Kenji Suzuki Artificial Neural Networks - Methodological Advances and Biomedical Applications Edited by Kenji Suzuki Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2011 InTech All chapters are Open Access articles distributed under the Creative Commons Non Commercial Share Alike Attribution 3.0 license, which permits to copy, distribute, transmit, and adapt the work in any medium, so long as the original work is properly cited. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Ivana Lorkovic Technical Editor Teodora Smiljanic Cover Designer Martina Sirotic Image Copyright Bruce Rolff, 2010. Used under license from Shutterstock.com First published March, 2011 Printed in...
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...Exam 1 Outline Porters Five Forces Model: Evaluating industry attractiveness 1. Bargaining power of customers (Power of buyers to decrease price) 2. Bargaining power of suppliers (Power of suppliers to increase price) 3. Rivalry of competitors 4. Threat of new entrants 5. Threat of new substitutes (Power of customers to purchase alternatives) Apple Case and Class Discussion Which of Porter’s Five Forces did Apple address through its introduction of the iPhone? * Strong Supplier Power; Customers have low buyer power * Apple would’ve gone bankrupt if not for iPhone. * Their strategic model consists of Narrow Market & High Cost Competitive Advantages Competitive Intelligence: Process of gathering information about the competitive environment to improve the company’s ability to succeed Competitive Intelligence Tools: Porter’s Five Forces Model – Refer Above Porter’s 3 Generic Strategies (Marketing and Sales Strategy, IT Strategy, Supply Chain Strategy) Porter’s Value Chain Analysis – (SUPPORT ACTIVITIES: Firm Infrastructure, Human Resource MGT, R&D, Procurement). (PRIMARY: Raw Materials, Making product, Delivering Product, Market & Sell, Service After Sale). -----------ALL MAKE UP VALUE ADDED------------- Porter’s Generic Strategies Organizations follow one of these strategies when entering a new market: Broad Market and Low Cost Broad Market and High Cost Narrow Market and Low Cost Narrow Market and High Cost ...
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...Applied Mathematics and Computation 214 (2009) 108–132 Contents lists available at ScienceDirect Applied Mathematics and Computation journal homepage: www.elsevier.com/locate/amc A comparative study of Artificial Bee Colony algorithm Dervis Karaboga *, Bahriye Akay Erciyes University, The Department of Computer Engineering, Melikgazi, 38039 Kayseri, Turkey a r t i c l e i n f o a b s t r a c t Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms. ABC simulates the intelligent foraging behaviour of a honeybee swarm. In this work, ABC is used for optimizing a large set of numerical test functions and the results produced by ABC algorithm are compared with the results obtained by genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm and evolution strategies. Results show that the performance of the ABC is better than or similar to those of other population-based algorithms with the advantage of employing fewer control parameters. Ó 2009 Elsevier Inc. All rights reserved. Keywords: Swarm intelligence Evolution strategies Genetic algorithms Differential evolution Particle swarm optimization Artificial Bee Colony algorithm Unconstrained optimization 1. Introduction Population-based optimization algorithms find near-optimal solutions to the difficult optimization problems by motivation from nature. A common feature of all population-based algorithms is that the population consisting...
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...Data Mining: Concepts and Techniques (3rd ed.) Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign & Simon Fraser University ©2011 Han, Kamber & Pei. All rights reserved. Adapted for CSE 347-447, Lecture 1b, Spring 2015 1 1 Introduction n n n n n n n n n n Why Data Mining? What Is Data Mining? A Multi-Dimensional View of Data Mining What Kind of Data Can Be Mined? What Kinds of Patterns Can Be Mined? What Technologies Are Used? What Kind of Applications Are Targeted? Major Issues in Data Mining A Brief History of Data Mining and Data Mining Society Summary 2 Why Data Mining? n The Explosive Growth of Data: from terabytes to petabytes n Data collection and data availability n Automated data collection tools, database systems, Web, computerized society n Major sources of abundant data n n n Business: Web, e-commerce, transactions, stocks, … Science: Remote sensing, bioinformatics, scientific simulation, … Society and everyone: news, digital cameras, YouTube n n We are drowning in data, but starving for knowledge! “Necessity is the mother of invention”—Data mining—Automated analysis of massive data sets 3 Evolution of Sciences: New Data Science Era n n Before 1600: Empirical science 1600-1950s: Theoretical science n Each discipline has grown a theoretical component. Theoretical models often motivate experiments and generalize our understanding...
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...Section 3: Predation (part B), Herbivory, parasitism, popn. Regulation, biocontrol, fisheries, conservation biology. PREDATION… Continued (Part B) C. Studying predator effects on prey populations in the field. 1. Manipulative experiments 2. Accidental Experiments 3. Comparative Studies- woodland caribou -Food limited -Predator limited D. Optimal Foraging theory: how do predators choose their diets? (see chapter 3) *What is the optimum strategy? = Energy maximization or time minimization Max En/T Assumptions about the predator (forager): -Eating and searching for the next food (prey) item are mutually exclusive activities. -Items are found sequentially, items are found one at a time. Energetic Value of prey: EACH PREY ITEM HAS AN ENERGENTIC VALUE (ei) Handling time: A HANDLING TIME (hi) Profitability: : e/h= profitability of each prey item (such that prey can be ranked) Rule: optimal foraging rule (prediction) – always eat the most profitable prey and eat the next most profitable prey if the gain is greater than the gain from rejecting it and searching for a more profitable prey. Predictions of Optimal Foraging Theory: The highest ranked prey type should always be eaten when encountered. 1) Predators with small handling times and long search times should be GENERALISTS. 2) Diets should be broad in pre-poor (unproductivr) environments. 3) The abundance of low ranking prey...
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...Decision Support Systems The material has been prepared by considering the prescribed textbook, internet and assignments given by the students (BBM 2011-2014 Batch). The material can be further improved by adding more insightful examples and explanation. The material may not be exhaustive and should be taken as a guide to help in better learning of the subject. ALL THE BEST Unit – I: 1. What is DSS? Explain the Characteristics, Benefits and Limitations of DSS. Definition: A decision support systems is a system under the control of one or more decision makers that assist in the activity of decision making by providing set of tools intended to impose structure to the decision making situation and improve the effectiveness of the decision outcome. Characteristics of DSS: * Employed in semistructured or unstructured decision contexts * Intended to support decision makers rather than replace them * Supports all phases of the decision-making process * Focuses on effectiveness of the process rather than efficiency * Is under control of the DSS user * Uses underlying data and models * Facilitates learning on the part of the decision maker * Is interactive and user-friendly * Is generally developed using an evolutionary, iterative process * Can support multiple independent or interdependent decisions * Supports individual, group or team-based decision-making Situation of Certainty Structured Unstructured Situation...
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...will talk about different designs for successful agents—filling in the question mark in Figure 2.1. We discuss some of the general principles used in the design of agents throughout the book, chief among which is the principle that agents should know things. Finally, we show how to couple an agent to an environment and describe several kinds of environments. 2.2 HOW AGENTS SHOULD ACT RATIONAL AGENT A rational agent is one that does the right thing. Obviously, this is better than doing the wrong thing, but what does it mean? As a first approximation, we will say that the right action is the one that will cause the agent to be most successful. That leaves us with the problem of deciding how and when to evaluate the agent’s success. Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig, c 1995 Prentice-Hall, Inc. 31 32 Chapter 2. Intelligent Agents sensors percepts environment actions ? agent effectors Figure 2.1 Agents interact with environments through sensors and effectors....
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