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Discovering Computer Science: Interdisciplinary Problems, Principles, and Python Programming (Chapman & Hall/CRC Textbooks in Computing #15)
by Jessen HavillDiscovering Computer Science: Interdisciplinary Problems, Principles, and Python Programming introduces computational problem solving as a vehicle of discovery in a wide variety of disciplines. With a principles-oriented introduction to computational thinking, the text provides a broader and deeper introduction to computer science than typical introductory programming books.Organized around interdisciplinary problem domains, rather than programming language features, each chapter guides students through increasingly sophisticated algorithmic and programming techniques. The author uses a spiral approach to introduce Python language features in increasingly complex contexts as the book progresses.The text places programming in the context of fundamental computer science principles, such as abstraction, efficiency, and algorithmic techniques, and offers overviews of fundamental topics that are traditionally put off until later courses.The book includes thirty well-developed independent projects that encourage students to explore questions across disciplinary boundaries. Each is motivated by a problem that students can investigate by developing algorithms and implementing them as Python programs.The book's accompanying website — http://discoverCS.denison.edu — includes sample code and data files, pointers for further exploration, errata, and links to Python language references.Containing over 600 homework exercises and over 300 integrated reflection questions, this textbook is appropriate for a first computer science course for computer science majors, an introductory scientific computing course or, at a slower pace, any introductory computer science course.
Discovering Computer Science: Interdisciplinary Problems, Principles, and Python Programming (Chapman & Hall/CRC Textbooks in Computing)
by Jessen Havill"Havill's problem-driven approach introduces algorithmic concepts in context and motivates students with a wide range of interests and backgrounds." -- Janet Davis, Associate Professor and Microsoft Chair of Computer Science, Whitman College "This book looks really great and takes exactly the approach I think should be used for a CS 1 course. I think it really fills a need in the textbook landscape." -- Marie desJardins, Dean of the College of Organizational, Computational, and Information Sciences, Simmons University "Discovering Computer Science is a refreshing departure from introductory programming texts, offering students a much more sincere introduction to the breadth and complexity of this ever-growing field." -- James Deverick, Senior Lecturer, The College of William and Mary "This unique introduction to the science of computing guides students through broad and universal approaches to problem solving in a variety of contexts and their ultimate implementation as computer programs." -- Daniel Kaplan, DeWitt Wallace Professor, Macalester College Discovering Computer Science: Interdisciplinary Problems, Principles, and Python Programming is a problem-oriented introduction to computational problem solving and programming in Python, appropriate for a first course for computer science majors, a more targeted disciplinary computing course or, at a slower pace, any introductory computer science course for a general audience. Realizing that an organization around language features only resonates with a narrow audience, this textbook instead connects programming to students’ prior interests using a range of authentic problems from the natural and social sciences and the digital humanities. The presentation begins with an introduction to the problem-solving process, contextualizing programming as an essential component. Then, as the book progresses, each chapter guides students through solutions to increasingly complex problems, using a spiral approach to introduce Python language features.The text also places programming in the context of fundamental computer science principles, such as abstraction, efficiency, testing, and algorithmic techniques, offering glimpses of topics that are traditionally put off until later courses.This book contains 30 well-developed independent projects that encourage students to explore questions across disciplinary boundaries, over 750 homework exercises, and 300 integrated reflection questions engage students in problem solving and active reading. The accompanying website — https://www.discoveringcs.net — includes more advanced content, solutions to selected exercises, sample code and data files, and pointers for further exploration.
Discovering Computer Science: Interdisciplinary Problems, Principles, and Python Programming (Chapman & Hall/CRC Textbooks in Computing)
by Jessen Havill"Havill's problem-driven approach introduces algorithmic concepts in context and motivates students with a wide range of interests and backgrounds." -- Janet Davis, Associate Professor and Microsoft Chair of Computer Science, Whitman College "This book looks really great and takes exactly the approach I think should be used for a CS 1 course. I think it really fills a need in the textbook landscape." -- Marie desJardins, Dean of the College of Organizational, Computational, and Information Sciences, Simmons University "Discovering Computer Science is a refreshing departure from introductory programming texts, offering students a much more sincere introduction to the breadth and complexity of this ever-growing field." -- James Deverick, Senior Lecturer, The College of William and Mary "This unique introduction to the science of computing guides students through broad and universal approaches to problem solving in a variety of contexts and their ultimate implementation as computer programs." -- Daniel Kaplan, DeWitt Wallace Professor, Macalester College Discovering Computer Science: Interdisciplinary Problems, Principles, and Python Programming is a problem-oriented introduction to computational problem solving and programming in Python, appropriate for a first course for computer science majors, a more targeted disciplinary computing course or, at a slower pace, any introductory computer science course for a general audience. Realizing that an organization around language features only resonates with a narrow audience, this textbook instead connects programming to students’ prior interests using a range of authentic problems from the natural and social sciences and the digital humanities. The presentation begins with an introduction to the problem-solving process, contextualizing programming as an essential component. Then, as the book progresses, each chapter guides students through solutions to increasingly complex problems, using a spiral approach to introduce Python language features.The text also places programming in the context of fundamental computer science principles, such as abstraction, efficiency, testing, and algorithmic techniques, offering glimpses of topics that are traditionally put off until later courses.This book contains 30 well-developed independent projects that encourage students to explore questions across disciplinary boundaries, over 750 homework exercises, and 300 integrated reflection questions engage students in problem solving and active reading. The accompanying website — https://www.discoveringcs.net — includes more advanced content, solutions to selected exercises, sample code and data files, and pointers for further exploration.
Discovering Cybersecurity: A Technical Introduction for the Absolute Beginner
by Seth James NielsonThe contemporary IT landscape is littered with various technologies that vendors claim will “solve” an organization’s cybersecurity challenges. These technologies are powerful and, in the right context, can be very effective. But misunderstood and misused, they either do not provide effective protection or do not protect the right things. This results in unnecessary expenditures, false beliefs of security, and interference with an organization’s mission.This book introduces major technologies that are employed in today’s cybersecurity landscape and the fundamental principles and philosophies behind them. By grasping these core concepts, professionals in every organization are better equipped to know what kind of technology they need, ask the right questions of vendors, and better interface with their CISO and security organization. The book is largely directed at beginners, including non-technical professionals such as policy makers, compliance teams, and business executives. What You Will Learn Authentication technologies, including secure password storage and how hackers “crack” password listsAccess control technology, such as BLP, BIBA, and more recent models such as RBAC and ABACCore cryptography technology, including AES encryption and public key signaturesClassical host security technologies that protect against malware (viruses, trojans, ransomware)Classical network security technologies, such as border security (gateways, firewalls, proxies), network IDS and IPS, and modern deception systemsWeb security technologies, including cookies, state, and session defenses, and threats that try to subvert themEmail and social media security threats such as spam, phishing, social media, and other email threats Who This Book Is ForProfessionals with no technical training in engineering, computers, or other technology; those who want to know things at a technical level but have no previous background; professionals with a background in policy, compliance, and management; technical professionals without a background in computer security who seek an introduction to security topics; those with a security background who are not familiar with this breadth of technology.
Discovering Hidden Gems in Foreign Languages (Terrorism, Security, and Computation)
by M.D. MillerThis book offers a practical approach to conducting research in foreign languages on topics with a global nexus. It introduces the problem researchers face when getting started with a research problem, such as setting up the research environment and establishing goals for the research. The researcher then needs to prepares and to conduct foreign-language research by generating key terms and searching the right places where the information they seek is most likely to be stored. Using the appropriate advanced search operators, the researcher narrows down the search results to the desired sources, thereby eliminating the irrelevant sources. Specialized knowledge of country-specific domains advances the specificity and relevance of the researcher’s efforts. The methods and tools demonstrated in this book are applicable to a variety of academic and practical fields. A doctor may ask “what are other experts in my field saying about ABC disease?” A sommelier may ask “where else in the world are XYZ grape varietals grown?” A businessman may ask “who are my global competitors in my market?” A doctoral student may ask “have any other students at universities abroad ever written a dissertation about my topic, too?” With the tools and techniques demonstrated in this book, all of these questions are answerable. This book concludes with chapters on translation and citation methods, and includes three case studies that demonstrate the practical use of the methods discussed above. This book targets academic researchers as well as students and faculty. This book will also be a good fit as an assigned reading for a college course on thesis/dissertation research.
Discovering Knowledge in Data: An Introduction to Data Mining (Wiley Series on Methods and Applications in Data Mining #4)
by Daniel T. Larose Chantal D. LaroseThe field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before. This book provides the tools needed to thrive in today’s big data world. The author demonstrates how to leverage a company’s existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will “learn data mining by doing data mining”. By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining. The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis. Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization Offers extensive coverage of the R statistical programming language Contains 280 end-of-chapter exercises Includes a companion website for university instructors who adopt the book
Discovering Knowledge in Data: An Introduction to Data Mining (Wiley Series on Methods and Applications in Data Mining)
by Daniel T. Larose Chantal D. LaroseThe field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before. This book provides the tools needed to thrive in today’s big data world. The author demonstrates how to leverage a company’s existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will “learn data mining by doing data mining”. By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining. The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis. Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization Offers extensive coverage of the R statistical programming language Contains 280 end-of-chapter exercises Includes a companion website for university instructors who adopt the book
Discovering Mathematics with Magma: Reducing the Abstract to the Concrete (Algorithms and Computation in Mathematics #19)
by Wieb Bosma John CannonBased on the ontology and semantics of algebra, the computer algebra system Magma enables users to rapidly formulate and perform calculations in abstract parts of mathematics. Edited by the principal designers of the program, this book explores Magma. Coverage ranges from number theory and algebraic geometry, through representation theory and group theory to discrete mathematics and graph theory. Includes case studies describing computations underpinning new theoretical results.
Discovering Mathematics with Maple: An interactive exploration for mathematicians, engineers and econometricians
by R.J. Stroeker J.F. KaashoekThis unusual introduction to Maple shows readers how Maple or any other computer algebra system fits naturally into a mathematically oriented work environment. Designed for mathematicians, engineers, econometricians, and other scientists, this book shows how computer algebra can enhance their theoretical work. A CD-ROM contains all the Maple worksheets presented in the book.
Discovering Requirements: How to Specify Products and Services
by Ian F. Alexander Ljerka Beus-Dukic"This book is not only of practical value. It's also a lot of fun to read." Michael Jackson, The Open University. Do you need to know how to create good requirements? Discovering Requirements offers a set of simple, robust, and effective cognitive tools for building requirements. Using worked examples throughout the text, it shows you how to develop an understanding of any problem, leading to questions such as: What are you trying to achieve? Who is involved, and how? What do those people want? Do they agree? How do you envisage this working? What could go wrong? Why are you making these decisions? What are you assuming? The established author team of Ian Alexander and Ljerka Beus-Dukic answer these and related questions, using a set of complementary techniques, including stakeholder analysis, goal modelling, context modelling, storytelling and scenario modelling, identifying risks and threats, describing rationales, defining terms in a project dictionary, and prioritizing. This easy to read guide is full of carefully-checked tips and tricks. Illustrated with worked examples, checklists, summaries, keywords and exercises, this book will encourage you to move closer to the real problems you're trying to solve. Guest boxes from other experts give you additional hints for your projects. Invaluable for anyone specifying requirements including IT practitioners, engineers, developers, business analysts, test engineers, configuration managers, quality engineers and project managers. A practical sourcebook for lecturers as well as students studying software engineering who want to learn about requirements work in industry. Once you've read this book you will be ready to create good requirements!
Discovering Statistics Using IBM SPSS Statistics (PDF)
by Andy FieldUnrivaled in the way it makes the teaching of statistics compelling and accessible to even the most anxious of students, the only statistics textbook you and your students will ever need just got better! Andy Field's comprehensive and bestselling Discovering Statistics Using SPSS 4th Edition takes students from introductory statistical concepts through very advanced concepts, incorporating SPSS throughout. The Fourth Edition focuses on providing essential content updates, better accessibility to key features, more instructor resources, and more content specific to select disciplines. It also incorporates powerful new digital developments on the textbook's companion website.
Discovering Statistics Using SPSS (PDF)
by Andy FieldAndy Field draws on his experience of teaching advanced statistics to extend existing SPSS Windows texts to a higher level. He covers ANOVA, MANOVA, logistic regression, comparing means tests and factor analysis.
Discovery and Selection of Semantic Web Services (Studies in Computational Intelligence #453)
by Xia Wang Wolfgang A. HalangFor advanced web search engines to be able not only to search for semantically related information dispersed over different web pages, but also for semantic services providing certain functionalities, discovering semantic services is the key issue. Addressing four problems of current solution, this book presents the following contributions. A novel service model independent of semantic service description models is proposed, which clearly defines all elements necessary for service discovery and selection. It takes service selection as its gist and improves efficiency. Corresponding selection algorithms and their implementation as components of the extended Semantically Enabled Service-oriented Architecture in the Web Service Modeling Environment are detailed. Many applications of semantic web services, e.g. discovery, composition and mediation, can benefit from a general approach for building application ontologies. With application ontologies thus built, services are discovered in the same way as with single domain ontologies, and the mediation problem between service ontologies is solved. Further, an ontology-based approach to improve service discovery is proposed and validated. Within the service model, a service selection approach oriented at quality criteria is proposed. It normalises diverse qualities of a service in their respective metrics and employs a service selection algorithm based on soundness.
Discovery of Ill–Known Motifs in Time Series Data (Technologien für die intelligente Automation #15)
by Sahar DeppeThis book includes a novel motif discovery for time series, KITE (ill-Known motIf discovery in Time sEries data), to identify ill-known motifs transformed by affine mappings such as translation, uniform scaling, reflection, stretch, and squeeze mappings. Additionally, such motifs may be covered with noise or have variable lengths. Besides KITE’s contribution to motif discovery, new avenues for the signal and image processing domains are explored and created. The core of KITE is an invariant representation method called Analytic Complex Quad Tree Wavelet Packet transform (ACQTWP). This wavelet transform applies to motif discovery as well as to several signal and image processing tasks. The efficiency of KITE is demonstrated with data sets from various domains and compared with state-of-the-art algorithms, where KITE yields the best outcomes.
The Discovery of the Artificial: Behavior, Mind and Machines Before and Beyond Cybernetics (Studies in Cognitive Systems #28)
by R. CordeschiThis series will include monographs and collections of studies devoted to the investigation and exploration of knowledge, information, and data processing systems of all kinds, no matter whether human, (other) animal, or machine. Its scope is intended to span the full range of interests from classical problems in the philosophy of mind and philosophical psychology through issues in cognitive psychology and sociobiology (concerning the mental capabilities of other species) to ideas related to artificial intelligence and to computer science. While primary emphasis will be placed upon theoretical, conceptual, and epistemological aspects of these problems and domains, empirical, experimental, and methodological studies will also appear from time to time. The present volume offers a broad and imaginative approach to the study of the mind, which emphasizes several themes, namely: the importance of functional organization apart from the specific material by means of which it may be implemented; the use of modeling to simulate these functional processes and subject them to certain kinds of tests; the use of mentalistic language to describe and predict the behavior of artifacts; and the subsumption of processes of adaptation, learning, and intelligence by means of explanatory principles. The author has produced a rich and complex, lucid and readable discussion that clarifies and illuminates many of the most difficult problems arising within this difficult domain.
Discovery Science: 9th International Conference, DS 2006, Barcelona, Spain, October 7-10, 2006, Proceedings (Lecture Notes in Computer Science #4265)
by Nada Lavra 269 Ljupco Todorovski Klaus P. JantkeThis book constitutes the refereed proceedings of the 9th International Conference on Discovery Science, DS 2006, held in Barcelona, Spain in October 2006, co-located with the 17th International Conference on Algorithmic Learning Theory, ALT 2006. The 23 revised long papers and the 18 revised regular papers presented together with five invited papers were carefully reviewed and selected from 87 submissions.
Discovery Science: 23rd International Conference, DS 2020, Thessaloniki, Greece, October 19–21, 2020, Proceedings (Lecture Notes in Computer Science #12323)
by Annalisa Appice Grigorios Tsoumakas Yannis Manolopoulos Stan MatwinThis book constitutes the proceedings of the 23rd International Conference on Discovery Science, DS 2020, which took place during October 19-21, 2020. The conference was planned to take place in Thessaloniki, Greece, but had to change to an online format due to the COVID-19 pandemic. The 26 full and 19 short papers presented in this volume were carefully reviewed and selected from 76 submissions. The contributions were organized in topical sections named: classification; clustering; data and knowledge representation; data streams; distributed processing; ensembles; explainable and interpretable machine learning; graph and network mining; multi-target models; neural networks and deep learning; and spatial, temporal and spatiotemporal data.
Discovery Science: Second International Conference, DS'99, Tokyo, Japan, December 6-8, 1999 Proceedings (Lecture Notes in Computer Science #1721)
by Setsuo Arikawa Koichi Furukawa
Discovery Science: Third International Conference, DS 2000 Kyoto, Japan, December 4-6, 2000 Proceedings (Lecture Notes in Computer Science #1967)
by Setsuo Arikawa Shinichi MorishitaThis volume contains 3 invited papers, 15 regular papers, and 22 poster papers that were selected for presentation at the Third International Conference on Discovery Science (DS 2000), which was held 4-6 December 2000 in Kyoto. The Program Committee selected the contributed papers from 48 submissions. Three distinguished researchers accepted our invitation to present talks: J- frey D. Ullman (Stanford University), Joseph Y. Halpern (Cornell University), and Masami Hagiya (University of Tokyo). The Program Committee would like to thank all those who submitted papers for consideration and the invited speakers. I would like to thank the Program Committee members, the Local Arrangements Committee members, and the Steering Committee members for their splendid and hard work. Finally, special thanks go to the PC Assistant Shoko Suzuki for her assistance in the development of web pages and the preparation of these proceedings. September 2000 Shinichi Morishita Organization Discovery Science 2000 is organized as part of the activities of the Discovery Science Project sponsored by Grant-in-Aid for Scienti?c Research in the Priority Area from the Ministry of Education, Science, Sports and Culture (MESSC) of Japan, in cooperation with the Japanese Society for Arti?cial Intelligence, and with SIG of Data Mining, Japan Society for Software Science and Technology.
Discovery Science: First International Conference, DS'98, Fukuoka, Japan, December 14-16, 1998, Proceedings (Lecture Notes in Computer Science #1532)
by Setsuo Arikawa Hiroshi MotodaThis book constitutes the refereed proceedings of the First International Conference on Discovery Science, DS'98, held in Fukuoka, Japan, in December 1998.The volume presents 28 revised full papers selected from a total of 76 submissions. Also included are five invited contributions and 34 selected poster presentations. The ultimate goal of DS'98 and this volume is to establish discovery science as a new field of research and development. The papers presented relate discovery science to areas as formal logic, knowledge processing, machine learning, automated deduction, searching, neural networks, database management, information retrieval, intelligent network agents, visualization, knowledge discovery, data mining, information extraction, etc.
Discovery Science: 26th International Conference, DS 2023, Porto, Portugal, October 9–11, 2023, Proceedings (Lecture Notes in Computer Science #14276)
by Albert Bifet Ana Carolina Lorena Rita P. Ribeiro João Gama Pedro H. AbreuThis book constitutes the proceedings of the 26th International Conference on Discovery Science, DS 2023, which took place in Porto, Portugal, in October 2023. The 37 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 133 submissions. They were organized in topical sections as follows: Machine learning methods and applications; natural language processing and social media analysis; interpretability and explainability in AI; data analysis and optimization; fairness, privacy and security in AI; control and spatio-temporal modeling; graph theory and network analysis; time series and forecasting; healthcare and biological data analysis; anomaly, outlier and novelty detection.
Discovery Science: 10th International Conference, DS 2007 Sendai, Japan, October 1-4, 2007. Proceedings (Lecture Notes in Computer Science #4755)
by Vincent Corruble Masayuki Takeda Einoshin SuzukiThis book constitutes the refereed proceedings of the 10th International Conference on Discovery Science, DS 2007, held in Sendai, Japan, in October 2007, co-located with the 18th International Conference on Algorithmic Learning Theory, ALT 2007. The papers cover all issues in the area of development and analysis of methods for intelligent data analysis, knowledge discovery and machine learning, as well as their application to scientific knowledge discovery.
Discovery Science: 17th International Conference, DS 2014, Bled, Slovenia, October 8-10, 2014, Proceedings (Lecture Notes in Computer Science #8777)
by Saso Deroski Pane Panov Dragi Kocev Ljupo TodorovskiThis book constitutes the proceedings of the 17th International Conference on Discovery Science, DS 2014, held in Bled, Slovenia, in October 2014. The 30 full papers included in this volume were carefully reviewed and selected from 62 submissions. The papers cover topics such as: computational scientific discovery; data mining and knowledge discovery; machine learning and statistical methods; computational creativity; mining scientific data; data and knowledge visualization; knowledge discovery from scientific literature; mining text, unstructured and multimedia data; mining structured and relational data; mining temporal and spatial data; mining data streams; network analysis; discovery informatics; discovery and experimental workflows; knowledge capture and scientific ontologies; data and knowledge integration; logic and philosophy of scientific discovery; and applications of computational methods in various scientific domains.
Discovery Science: 14th International Conference, DS 2011, Espoo, Finland, October 5-7, Proceedings (Lecture Notes in Computer Science #6926)
by Tapio Elomaa Jaakko Hollmen Heikki MannilaThis book constitutes the refereed proceedings of the 14th International Conference on Discovery Science, DS 2011, held in Espoo, Finland, in October 2011 - co-located with ALT 2011, the 22nd International Conference on Algorithmic Learning Theory. The 24 revised full papers presented together with 5 invited lectures were carefully revised and selected from 56 submissions. The papers cover a wide range including the development and analysis of methods for automatic scientific knowledge discovery, machine learning, intelligent data analysis, theory of learning, as well as their application to knowledge discovery.
Discovery Science: 16th International Conference, DS 2013, Singapore, October 6-9, 2013, Proceedings (Lecture Notes in Computer Science #8140)
by Johannes Fürnkranz Eyke Hüllermeier Tomoyuki HiguchiThis book constitutes the proceedings of the 16th International Conference on Discovery Science, DS 2013, held in Singapore in October 2013, and co-located with the International Conference on Algorithmic Learning Theory, ALT 2013. The 23 papers presented in this volume were carefully reviewed and selected from 52 submissions. They cover recent advances in the development and analysis of methods of automatic scientific knowledge discovery, machine learning, intelligent data analysis, and their application to knowledge discovery.