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7 secrets of highly effective social impact communicators : how to grow your influence to solve society's most pressing challenges / / Nate Birt
| 7 secrets of highly effective social impact communicators : how to grow your influence to solve society's most pressing challenges / / Nate Birt |
| Autore | Birt Nate |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress L. P., , 2023 |
| Descrizione fisica | 1 online resource (229 pages) |
| Disciplina | 658.4/5 |
| Soggetto topico |
Business communication - Social aspects
Social responsibility of business |
| ISBN | 1-4842-9798-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: Secret #1: Highly effective social impact communicators ... care both about the words and the mission -- Define the mission and why it matters to you -- A word about technical skill -- Harness the power of purpose -- Curiosity drives you to keep discovering new facets of the mission -- Part 1: Commit nothing to memory save impressions and a handful of key insights -- Part 2: Do your homework about that social impact issue -- Part 3: Ask experts directly to fully understand the context -- Know yourself best to be the best mission-bearer -- Practical ways to put your social impact mission before the words you use -- 1. Understand what's at stake -- 2. Understand what's at risk -- 3. Crave a brighter world with better opportunities -- 4. Feel compelled to bring others alongside you -- 5. Fuse communication and leadership together -- 6. Clarify your vision and talk about it -- The mission reminds you social impact is achievable -- Key Questions -- Chapter 2: Secret #2: Highly effective social impact communicators ... are astute translators inside and outside of their organizations -- Navigate friction with the future in mind -- Get comfortable standing out as a translator -- Why social impact communicators must learn to translate among parties-and how -- … Understand and recognize the worldviews and unique sectoral cultures of different stakeholder types -- Private-sector businesses -- Nonprofits -- Government agencies -- … See both sides of an issue -- … Explain a complex issue in ways many can understand -- … Define your personal views on the social impact issues you help solve -- … Build the most compelling case in partnership with others -- … Stay humble and stay focused on the mission.
… Mold exactly the right team for the work you're doing -- … Achieve diverse social impact outcomes across different industries amid rapid change -- … Find a home within your organization and among your peers at last -- How translation has served my social impact journey so far -- Key Questions -- Chapter 3: Secret #3: Highly effective social impact communicators ... leverage the strength of personal and organizational values to tell compelling stories -- The place where values come from is the workshop inside your head-and inside your organization -- What power do values really have? -- Define the solutions you offer as a social impact communicator -- Writing -- Editing -- Facilitating -- Leading -- Empowering -- Building -- Want to be more effective? Pair the right value(s) to the right social impact solutions -- Why you must keep adding values to your toolbox -- But what if you encounter a situation where your values could be compromised? -- Define your "actions accompanying values"-when X happens, I will do Y -- What to do when values conflict: Understand, align as possible, and influence -- Learn to discover others' values quickly-and work toward them together -- Embrace a bigger worldview than you thought possible -- Key Questions -- Chapter 4: Secret #4: Highly effective social impact communicators ... embrace the renewable fuel of teamwork -- Burnout is real in social impact communication, even though we're so focused on the mission we ignore it -- Strategies for strengthening your social impact team by reversing the status quo -- Status quo: Conduct meetings that crush the soul -- Your choice: Conduct meetings that embolden and launch -- Status quo: Cast a vision only you can see -- Your choice: Co-create your vision with your team -- Status quo: Set deadlines impossible from the word "go" -- Your choice: Set realistic and flexible deadlines. Status quo: Treat people like commodities-all alike -- Your choice: Put people first, work together, and make something great -- Status quo: Celebrate statistics only, not accompanying success stories -- Your choice: Celebrate stories of impact underpinned by data -- How to foster a mindful and impactful team using the PURSUE framework -- Positive -- Understandable -- Relatable -- Stalwart -- Useful -- Enthusiastic -- When the going gets tough, turn to daily micro habits to get unstuck and get going -- Micro Habit #1: Great teams embrace the work and one another, flaws and all -- Micro Habit #2: Great teams overpower the isolation and misunderstood nature of social impact work -- Micro Habit #3: Great teams know one another personally as well as professionally -- Micro Habit #4: Great teams draw energy from complementary capabilities -- Micro Habit #5: Great teams show gratitude before attitude-there's nothing to prove until you have something to prove -- Micro Habit #6: Great teams navigate complexity with calm, not chaos -- Micro Habit #7: Great teams seek cohesion rather than full clarity-people feel part of something great, even external partners -- Micro Habit #8: Great teams champion honesty, admit when things could be better, and work to make it so -- Teamwork is only renewable to the extent you invest in it -- Key Questions -- Chapter 5: Secret #5: Highly effective social impact communicators ... reframe every setback as the crest of a hill -- New insights yield new direction-and the potential for disorienting doubt -- Crest-climbing insight No. 1: Hills provide unprecedented perspective -- Crest-climbing insight No. 2: Each hill must be tackled differently, though there are patterns -- Crest-climbing insight No. 3: Once you've defined what's hard, you can determine what's possible. Crest-climbing insight No. 4: Your flawed fear needs a free ride home -- Crest-climbing insight No. 5: You can't climb all the hills today -- Great social impact communicators build a resilient mindset to keep going in the face of adversity -- Act with integrity -- Lead with humility -- Be scientific in your approach -- Stay skeptical -- Communicate with transparency and openness -- After your hike past the crest, learn to navigate the cloud forest beyond -- Wait -- Analyze -- Take Gratitude -- Evaluate Options -- Run Again -- The watchout and the opportunity -- Key Questions -- Chapter 6: Secret #6: Highly effective social impact communicators … cede perfection to the messy reality of change-making -- How messy looks for social impact communicators -- Paint the vision, not Van Gogh -- Why you should embrace satisfied persistence rather than perfection -- Stop trying to tie up all the loose ends -- Move ahead instead of around in circles -- When the dust flies, remember it will settle -- Capture attention and direct focus rather than sowing chaos -- Turn panic into peaceful action -- Guard the silent moments -- Engage in regular conversation that sparks ideas and opportunities -- Trade predictability for adaptability -- Key Questions -- Chapter 7: Secret #7: Highly effective social impact communicators … build personal and professional legacies that outlive them and their careers -- First principles of legacy building for social impact communicators -- Principle No. 1: Legacy is the ripple effect of our actions -- Principle No. 2: Legacy is shiftable, not set in stone -- Principle No. 3: Legacy exists beyond our lifetime -- Principle No. 4: Legacy can be a blessing or a byword -- Principle No. 5: Legacy persists in the background -- How you can create a legacy that outlives you -- Legacy Builder 1: Give away your grace and mercy liberally. Legacy Builder 2: Guard your time and share it so it aligns with your values and the mission -- Legacy Builder 3: Be honest with yourself and others about who you really are -- Legacy Builder 4: Focus on actions that hold true 500 years from now -- Legacy Builder 5: Seek forgiveness over apologies -- Legacy Builder 6: Be slow yet deliberate, not fast and reckless -- Legacy Builder 7: Bring trusted partners into your inner circle -- Legacy Builder 8: Help others remember the promise of their own multigenerational dream -- Ask the experts: How can social impact communicators build personal and professional legacies that outlive them and their careers? -- Some final thoughts on legacy and the journey before you -- Key Questions -- Chapter 8: Some helpful resources for social impact communicators -- Recommended Reading -- Index. |
| Altri titoli varianti | Seven secrets of highly effective social impact communicators |
| Record Nr. | UNINA-9910754099803321 |
Birt Nate
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| Berkeley, CA : , : Apress L. P., , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Accelerating Unity Through Automation : Power up Your Unity Workflow by Offloading Intensive Tasks
| Accelerating Unity Through Automation : Power up Your Unity Workflow by Offloading Intensive Tasks |
| Autore | Jackson Simon |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress L. P., , 2023 |
| Descrizione fisica | 1 online resource (312 pages) |
| Soggetto topico |
Automation
Workflow |
| ISBN | 1-4842-9508-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Table of Contents -- About the Author -- About the Technical Reviewer -- Chapter 1: What Is Automation? -- What Does Automation Mean for a Unity Developer? -- What to Expect from This Title? -- Looking Ahead -- Chapter 2: What Is Gained Through Automation, the Highlights! -- Example Workflows -- 1. Checkout -- 2. Running Scripts – Bash/PowerShell/Python -- 3. NPM Build and Test -- 4. MSBuild Build and Test -- 5. Chat/Mail Integration (Slack/Email) -- 6. Schedule/Remove Backups -- 7. Upload Artifacts, Build, and Publish -- 8. Create Releases on GitHub -- 9. Update Project Management Solutions and Workflows -- 10. Generate Release Documentation -- 11. Publish Documentation -- 12. Varying Flows Based on the Submitter -- 13. Call External APIs for Analytics, Reporting, or PowerBI -- 14. Generate/Search for Gifs to Add to a Post/Issue When Craziness Is Detected or a “lmgtfy” Tag Is Used by an Admin -- 15. Convert Source Files (yaml/xml/json) to Other Formats or Perform Bespoke Actions -- 16. Integrate with Docker/Kubernetes -- 17. Manage Versioning -- 18. Run Unit Tests and Perform Actions Based on Results -- References -- Summary -- Chapter 3: Services Covered by This Title |
| Record Nr. | UNINA-9910746295003321 |
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Jackson Simon
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| Berkeley, CA : , : Apress L. P., , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advanced Forecasting with Python : With State-Of-the-Art-Models Including LSTMs, Facebook's Prophet, and Amazon's DeepAR
| Advanced Forecasting with Python : With State-Of-the-Art-Models Including LSTMs, Facebook's Prophet, and Amazon's DeepAR |
| Autore | Korstanje Joos |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress L. P., , 2021 |
| Descrizione fisica | 1 online resource (294 pages) |
| Disciplina | 006.31 |
| Soggetto non controllato | Science |
| ISBN | 1-4842-7150-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Introduction -- Part I: Machine Learning for Forecasting -- Chapter 1: Models for Forecasting -- Reading Guide for This Book -- Machine Learning Landscape -- Univariate Time Series Models -- A Quick Example of the Time Series Approach -- Supervised Machine Learning Models -- A Quick Example of the Supervised Machine Learning Approach -- Correlation Coefficient -- Other Distinctions in Machine Learning Models -- Supervised vs. Unsupervised Models -- Classification vs. Regression Models -- Univariate vs. Multivariate Models -- Key Takeaways -- Chapter 2: Model Evaluation for Forecasting -- Evaluation with an Example Forecast -- Model Quality Metrics -- Metric 1: MSE -- Metric 2: RMSE -- Metric 3: MAE -- Metric 4: MAPE -- Metric 5: R2 -- Model Evaluation Strategies -- Overfit and the Out-of-Sample Error -- Strategy 1: Train-Test Split -- Strategy 2: Train-Validation-Test Split -- Strategy 3: Cross-Validation for Forecasting -- K-Fold Cross-Validation -- Time Series Cross-Validation -- Rolling Time Series Cross-Validation -- Backtesting -- Which Strategy to Use for Safe Forecasts? -- Final Considerations on Model Evaluation -- Key Takeaways -- Part II: Univariate Time Series Models -- Chapter 3: The AR Model -- Autocorrelation: The Past Influences the Present -- Compute Autocorrelation in Earthquake Counts -- Positive and Negative Autocorrelation -- Stationarity and the ADF Test -- Differencing a Time Series -- Lags in Autocorrelation -- Partial Autocorrelation -- How Many Lags to Include? -- AR Model Definition -- Estimating the AR Using Yule-Walker Equations -- The Yule-Walker Method -- Train-Test Evaluation and Tuning -- Key Takeaways -- Chapter 4: The MA Model -- The Model Definition -- Fitting the MA Model -- Stationarity -- Choosing Between an AR and an MA Model.
Application of the MA Model -- Multistep Forecasting with Model Retraining -- Grid Search to Find the Best MA Order -- Key Takeaways -- Chapter 5: The ARMA Model -- The Idea Behind the ARMA Model -- The Mathematical Definition of the ARMA Model -- An Example: Predicting Sunspots Using ARMA -- Fitting an ARMA(1,1) Model -- More Model Evaluation KPIs -- Automated Hyperparameter Tuning -- Grid Search: Tuning for Predictive Performance -- Key Takeaways -- Chapter 6: The ARIMA Model -- ARIMA Model Definition -- Model Definition -- ARIMA on the CO2 Example -- Key Takeaways -- Chapter 7: The SARIMA Model -- Univariate Time Series Model Breakdown -- The SARIMA Model Definition -- Example: SARIMA on Walmart Sales -- Key Takeaways -- Part III: Multivariate Time Series Models -- Chapter 8: The SARIMAX Model -- Time Series Building Blocks -- Model Definition -- Supervised Models vs. SARIMAX -- Example of SARIMAX on the Walmart Dataset -- Key Takeaways -- Chapter 9: The VAR Model -- The Model Definition -- Order: Only One Hyperparameter -- Stationarity -- Estimation of the VAR Coefficients -- One Multivariate Model vs. Multiple Univariate Models -- An Example: VAR for Forecasting Walmart Sales -- Key Takeaways -- Chapter 10: The VARMAX Model -- Model Definition -- Multiple Time Series with Exogenous Variables -- Key Takeaways -- Part IV: Supervised Machine Learning Models -- Chapter 11: The Linear Regression -- The Idea Behind Linear Regression -- Model Definition -- Example: Linear Model to Forecast CO2 Levels -- Key Takeaways -- Chapter 12: The Decision Tree Model -- Mathematics -- Splitting -- Pruning and Reducing Complexity -- Example -- Key Takeaways -- Chapter 13: The kNN Model -- Intuitive Explanation -- Mathematical Definition of Nearest Neighbors -- Combining k Neighbors into One Forecast -- Deciding on the Number of Neighbors k. Predicting Traffic Using kNN -- Grid Search on kNN -- Random Search: An Alternative to Grid Search -- Key Takeaways -- Chapter 14: The Random Forest -- Intuitive Idea Behind Random Forests -- Random Forest Concept 1: Ensemble Learning -- Bagging Concept 1: Bootstrap -- Bagging Concept 2: Aggregation -- Random Forest Concept 2: Variable Subsets -- Predicting Sunspots Using a Random Forest -- Grid Search on the Two Main Hyperparameters of the Random Forest -- Random Search CV Using Distributions -- Distribution for max_features -- Distribution for n_estimators -- Fitting the RandomizedSearchCV -- Interpretation of Random Forests: Feature Importance -- Key Takeaways -- Chapter 15: Gradient Boosting with XGBoost and LightGBM -- Boosting: A Different Way of Ensemble Learning -- The Gradient in Gradient Boosting -- Gradient Boosting Algorithms -- The Difference Between XGBoost and LightGBM -- Forecasting Traffic Volume with XGBoost -- Forecasting Traffic Volume with LightGBM -- Hyperparameter Tuning Using Bayesian Optimization -- The Theory of Bayesian Optimization -- Bayesian Optimization Using scikit-optimize -- Conclusion -- Key Takeaways -- Part V: Advanced Machine and Deep Learning Models -- Chapter 16: Neural Networks -- Fully Connected Neural Networks -- Activation Functions -- The Weights: Backpropagation -- Optimizers -- Learning Rate of the Optimizer -- Hyperparameters at Play in Developing a NN -- Introducing the Example Data -- Specific Data Prep Needs for a NN -- Scaling and Standardization -- Principal Component Analysis (PCA) -- The Neural Network Using Keras -- Conclusion -- Key Takeaways -- Chapter 17: RNNs Using SimpleRNN and GRU -- What Are RNNs: Architecture -- Inside the SimpleRNN Unit -- The Example -- Predicting a Sequence Rather Than a Value -- Univariate Model Rather Than Multivariable -- Preparing the Data -- A Simple SimpleRNN. SimpleRNN with Hidden Layers -- Simple GRU -- GRU with Hidden Layers -- Key Takeaways -- Chapter 18: LSTM RNNs -- What Is LSTM -- The LSTM Cell -- Example -- LSTM with One Layer of 8 -- LSTM with Three Layers of 64 -- Conclusion -- Key Takeaways -- Chapter 19: The Prophet Model -- The Example -- The Prophet Data Format -- The Basic Prophet Model -- Adding Monthly Seasonality to Prophet -- Adding Holiday Data to Basic Prophet -- Adding an Extra Regressor to Prophet -- Tuning Hyperparameters Using Grid Search -- Key Takeaways -- Chapter 20: The DeepAR Model -- About DeepAR -- Model Training with DeepAR -- Predictions with DeepAR -- Probability Predictions with DeepAR -- Adding Extra Regressors to DeepAR -- Hyperparameters of the DeepAR -- Benchmark and Conclusion -- Key Takeaways -- Chapter 21: Model Selection -- Model Selection Based on Metrics -- Model Structure and Inputs -- One-Step Forecasts vs. Multistep Forecasts -- Model Complexity vs. Gain -- Model Complexity vs. Interpretability -- Model Stability and Variation -- Conclusion -- Key Takeaways -- Index. |
| Altri titoli varianti | Advanced Forecasting with Python |
| Record Nr. | UNINA-9910488729003321 |
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Korstanje Joos
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| Berkeley, CA : , : Apress L. P., , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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The Agile codex : re-inventing Agile through the science of invention and assembly / / Michael McCormick
| The Agile codex : re-inventing Agile through the science of invention and assembly / / Michael McCormick |
| Autore | McCormick Michael |
| Pubbl/distr/stampa | New York, NY : , : Apress L. P., , [2021] |
| Descrizione fisica | 1 online resource (149 pages) |
| Disciplina | 005.1 |
| Soggetto topico |
Agile software development
Agile software development - Management |
| ISBN | 1-4842-7280-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Contents -- About the Author -- Part I: The Accident -- Chapter 1: Clear Ownership -- Daily Standup Day 1: Who's on first? -- Shared Lists -- Ownership -- Untangling -- Chapter 2: Small, Independent Units of Work -- Daily Standup Day 2: Merge conflicts! -- Daily Standup Day 3: Need a reviewer! -- Daily Standup Day 4: I broke some stuff. I think. -- Daily Standup Day 5: Turns out I need this other thing. -- Chapter 3: Sized -- Daily Standup Day 6: Five hours or five weeks… -- Chapter 4: Sequenced -- Daily Standup Day 7: …or five hours over five weeks? -- Chapter 5: Inputs, Transition Criteria, Outputs -- Daily Standup Day 8: Did you say something? -- Chapter 6: Stakeholder Approval -- Daily Standup Day 9: Oops. I forgot to tell you. Or ask you. -- Part II: The Agile Codex Theory -- Chapter 7: The Problem -- Plan for the Imperfect Plan -- Optimize for Adaptability -- Don't Surrender to Dependencies -- Chapter 8: The Codex -- The Principles of the Agile Codex -- Small Units of Work -- Sized -- Sequenceable -- Acyclic Dependency Tree -- Single Owner -- Application -- Chapter 9: The Agile -- Clear Ownership of Work at All Times in Each Stage -- Clear Inputs -- Clear Transition Criteria -- Clear Outputs -- Stakeholder Approval -- Chapter 10: Benefits -- Low Overhead -- Detailed Auditing -- Quick and Safe Deliveries -- Many Quality Gates -- Chapter 11: From Invention to Assembly Line -- The Importance of Dependencies -- Building the Assembly Line -- In Review -- Chapter 12: Team Functions -- User Experience (UX) -- Product Management (PM) -- Engineering Management (EM) -- Development (DEV) -- Quality Engineering (QE) -- Documentation (DOC) -- Operations (OPS) -- Customer Support Group (CSG) -- Chapter 13: Software Development Life Cycle -- Phases -- Planning -- Execution -- Releasing -- Choosing a Cadence -- How SDLC Length Affects Practices.
Constructing the Codex -- Chapter 14: Risk Management -- Categories of Risk -- Product Risk: How Clearly and Comprehensively the Product Can Be Defined -- Technical Risk: How Clearly and Comprehensively It Is Understood How to Build It -- Market Risk: Any Demand-Side Shift Which Creates an Arbitrage Opportunity for a Quick Feature Pivot -- Business Risk: Any Supply-Side Shift Which Creates an Arbitrage Opportunity for a Quick Feature Pivot -- Today and Tomorrow Risk -- Positive Interactions with Risk -- Risk Quadrants and Risk over Time -- Planning for Resilience -- Conclusion -- Part III: The Agile Codex Practice -- Chapter 15: Building Blocks -- Planned Release -- Epic -- User Story -- Acceptance Criteria -- Tasks -- Dependencies -- Adjacent Teams -- Story Points -- Bug -- All Together -- Chapter 16: Workflow -- Planning -- Release Planning -- Epic Grooming -- User Story Grooming -- Epic Commitment -- Execution -- Setting Up the Tree -- The Board -- Needs Sign-Off -- Signed Off -- In Progress -- Fix Needed -- QE -- PM / UX -- Closed -- External Dependencies -- The Sprint or the Kanban -- Adjusting -- Releasing -- Feature Complete -- QE Complete -- The Terminal Sprint -- Chapter 17: Metrics -- Predicting -- Analyzing -- Adjusting -- Opportunistic and Non-Epic Work -- Multi-release Epics -- Chapter 18: Teaching the Teams -- From Agile to Agile Codex -- Agiling Well with Others When They Don't Agile As Well -- Chapter 19: What Next? -- Tooling -- Synchronization Gap -- Heuristics -- Who Can Do What -- Risk Ranking -- How Perfect Is Perfect Enough? -- Who Is Available, How Much, and When? -- Dials and Knobs for Scenario Planning -- Make It Easy to Visualize -- Reporting -- Conclusion -- Index. |
| Record Nr. | UNINA-9910502636503321 |
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McCormick Michael
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| New York, NY : , : Apress L. P., , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Agile Visualization with Pharo : Crafting Interactive Visual Support Using Roassal
| Agile Visualization with Pharo : Crafting Interactive Visual Support Using Roassal |
| Autore | Bergel Alexandre |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress L. P., , 2021 |
| Descrizione fisica | 1 online resource (268 pages) |
| Soggetto genere / forma | Electronic books. |
| ISBN |
9781484271612
9781484271605 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Chapter 1: Introduction -- Agile Visualization -- The Pharo Programming Language -- The Roassal Visualization Engine -- Roassal License -- Contributing to the Development of Roassal -- Accompanying Source Code -- Want to Have a Chat? -- Book Overview -- Who Should Read This Book? -- Acknowledgments -- Chapter 2: Quick Start -- Installation -- First Visualization -- Visualizing the Filesystem -- Charting Data -- Sunburst -- Graph Rendering -- What Have You Learned in This Chapter? -- Chapter 3: Pharo in a Nutshell -- Hello World -- Visualizing Some Numbers -- From Scripts to Object-Oriented Programming -- Pillars of Object-Oriented Programming -- Sending Messages -- Creating Objects -- Creating Classes -- Creating Methods -- Block Closures -- Control Structures -- Collections -- Cascades -- A Bit of Metaprogramming -- What Have You Learned in This Chapter? -- Chapter 4: Agile Visualization -- Visualizing Classes as a Running Example -- Example in the Pharo Environment -- Closing Words -- What Have You Learned in This Chapter? -- Chapter 5: Overview of Roassal -- Architecture of Roassal -- Shapes -- Canvas -- Events -- Interaction -- Normalizer -- Layouts -- Inspector Integration -- Animation -- What Have You Learned in This Chapter? -- Chapter 6: The Roassal Canvas -- Opening, Resizing, and Closing a Canvas -- Camera and Shapes -- Virtual Space -- Shape Order -- Canvas Controller -- Converting a Canvas to a Shape -- Events -- What Have You Learned in This Chapter? -- Chapter 7: Shapes -- Box -- Circle and Ellipse -- Label -- Polygon -- SVG Path -- Common Features -- Model -- Line -- Line Attach Point -- Line Marker -- Line with Control Points -- What Have You Learned in This Chapter? -- Chapter 8: Line Builder -- Difficulties with Build Lines -- Using a Line Builder.
Using Associations -- Graph Visualization -- What Have You Learned in This Chapter? -- Chapter 9: Shape Composition -- Composite Shapes -- Model Object in Composite -- Labels Part of a Composition -- Labeled Circles -- What Have You Learned in This Chapter? -- Chapter 10: Normalizing and Scaling Values -- Normalizing Shape Size -- The RSNormalizer Class -- Combining Normalization -- Normalizing Shape Position -- Line Width -- Scaling -- What Have You Learned in This Chapter? -- Chapter 11: Interactions -- Useful Interactions -- Using Any Shape in a Popup -- RSLabeled -- RSHighlightable -- What Have You Learned in This Chapter? -- Chapter 12: Layouts -- Circle Layout -- Grid Layout -- Flow Layout -- Rectangle Pack Layout -- Line Layout -- Tree Layout -- Force-Based Layout -- Conditional Layout -- Graphviz Layouts -- Installing Graphviz -- Bridging Roassal and Graphviz -- Graphviz Layout -- What Have You Learned in This Chapter? -- Chapter 13: Integration in the Inspector -- Pharo Inspector -- Visualizing a Collection of Numbers -- Chaining Visualizations -- What Have You Learned in This Chapter? -- Chapter 14: Reinforcement Learning -- Implementation Overview -- Defining the Map -- Modeling State -- The Reinforcement Learning Algorithm -- Running the Algorithm -- What Have You Learned in This Chapter? -- Chapter 15: Generating Visualizations From GitHub -- Requirements -- Creating a Workflow -- Trying the Workflow -- Running Unit Tests -- Running Tests -- Visualizing the UML Class Diagram -- Visualizing the Test Coverage -- What Have You Learned in This Chapter? -- Index. |
| Record Nr. | UNINA-9910510544303321 |
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Bergel Alexandre
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| Berkeley, CA : , : Apress L. P., , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Analytics optimization with columnstore indexes in Microsoft SQL Server : optimizing OLAP workloads / / Edward Pollack
| Analytics optimization with columnstore indexes in Microsoft SQL Server : optimizing OLAP workloads / / Edward Pollack |
| Autore | Pollack Edward (Database administrator) |
| Edizione | [[First edition].] |
| Pubbl/distr/stampa | New York, New York : , : Apress L. P., , [2022] |
| Descrizione fisica | 1 online resource (285 pages) : illustrations |
| Disciplina | 005.7585 |
| Soggetto topico |
Client/server computing
Database management |
| ISBN | 1-4842-8048-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Introduction to Analytic Data in a Transactional Database 2. Transactional vs. Analytic Workloads 3. What are Columnstore Indexes? 4. Columnstore Index Architecture 5. Columnstore Compression 6. Columnstore Metadata 7. Batch Execution 8. Bulk Loading Data 9. Delete and Update Operations 10. Segment and Rowgroup Elimination 11. Partitioning 12. Non-Clustered Columnstore Indexes on Rowstore Tables 13. Non-Clustered Rowstore Indexes on Columnstore Tables 14. Columnstore Index Maintenance 15. Columnstore Index Performance |
| Record Nr. | UNINA-9910548185003321 |
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Pollack Edward (Database administrator)
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| New York, New York : , : Apress L. P., , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Anti-Money Laundering in a Nutshell : Awareness and Compliance for Financial Personnel and Business Managers
| Anti-Money Laundering in a Nutshell : Awareness and Compliance for Financial Personnel and Business Managers |
| Autore | Sullivan Kevin |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress L. P., , 2024 |
| Descrizione fisica | 1 online resource (303 pages) |
| Disciplina | 364.2 |
| Soggetto topico |
Money laundering - United States - Prevention
Money laundering - Law and legislation - United States |
| ISBN | 979-88-6880-066-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. What is Money Laundering? -- 2. Methods of Money Laundering -- 3. Federal Regulations -- 4. Building a Quality AML Program for Financial Institutions -- 5. Know Your Customer and Customer Identification Program -- 6. A SAR Is Born -- 7. Money Laundering for Law Enforcement -- 8. International Standards Laundering -- 9. FRAML -- 10. What Could Possibly Go Wrong? -- Appendix A: Money-laundering Red Flags -- Appendix B: Code of Federal Regulations Title 31 Section 103.18. . |
| Record Nr. | UNINA-9910770256603321 |
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Sullivan Kevin
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| Berkeley, CA : , : Apress L. P., , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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API Marketplace Engineering : Design, Build, and Run a Platform for External Developers
| API Marketplace Engineering : Design, Build, and Run a Platform for External Developers |
| Autore | Dorasamy Rennay |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress L. P., , 2021 |
| Descrizione fisica | 1 online resource (281 pages) |
| Soggetto genere / forma | Electronic books. |
| ISBN |
9781484273135
9781484273128 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910512166403321 |
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Dorasamy Rennay
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| Berkeley, CA : , : Apress L. P., , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Arduino measurements in science : advanced techniques and data projects / / Richard J. Smythe
| Arduino measurements in science : advanced techniques and data projects / / Richard J. Smythe |
| Autore | Smythe Richard <1986-> |
| Pubbl/distr/stampa | New York, New York : , : Apress L. P., , [2022] |
| Descrizione fisica | 1 online resource (727 pages) |
| Disciplina | 502.85622 |
| Soggetto topico | Arduino (Programmable controller) |
| ISBN | 1-4842-6781-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910523731803321 |
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Smythe Richard <1986->
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| New York, New York : , : Apress L. P., , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Arduino Measurements in Science : Advanced Techniques and Data Projects
| Arduino Measurements in Science : Advanced Techniques and Data Projects |
| Autore | Smythe Richard J |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress L. P., , 2021 |
| Descrizione fisica | 1 online resource (727 pages) |
| Disciplina | 502.85622 |
| Soggetto genere / forma | Electronic books. |
| ISBN | 1-4842-6781-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Arduino Measurements in Science |
| Record Nr. | UNINA-9910502670803321 |
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Smythe Richard J
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| Berkeley, CA : , : Apress L. P., , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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