Search Results - (((((((anti OR wiant) OR semantic) OR when) OR cantor) OR anne) OR shape) OR hints) algorithms.

Informatics and technology in clinical care and public health

Table of Contents: “…Intro -- Title Page -- Preface -- ICIMTH 2021 Scientific Programme Committee and Reviewers -- Contents -- Using Artificial Intelligence to Develop a Lexicon-Based African American Tweet Detection Algorithm to Inform Culturally Sensitive Twitter-Based Social Support Interventions for African American Dementia Caregivers -- A Comparison of Word Embeddings to Study Complications in Neurosurgery -- Gulf Cooperation Council Clinical Trials in the Pursuit of Medications for COVID-19…”
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Media and the affective life of slavery by Page, Allison, 1983-

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Integration of demand response into the electricity chain : challenges, opportunities and smart grid solutions

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Measurement and control of granular materials : selected peer reviewed papers from the 9th International Conference on Measurement and Control of Granular Materials, MCGM 2011, (Gl...

Table of Contents: “…Process Tomographic Measurements of Granular Flow in a Pneumatic Conveying SystemImage Reconstruction Based on Compressed Sensing with Split Bregman Algorithm and Fuzzy Bases; Optimum Design of an Internal 8-Electrode Electrical Capacitance Tomography Sensor Array; Research on Human ADL Data Real-Time Transmission Optimization Method Based on Bayesian Network; Optimization Design of Capacitance Sensor with Helical Shaped Surface Plates; An Image Quality Assessment Algorithm for Palm-Dorsa Vein Based on Multi-Feature Fusion; Chapter 4:Powder Explosion and System Protection…”
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Reviews in computational chemistry.

Table of Contents: “…Small Molecule Docking and Scoring; Introduction; Algorithms for Molecular Docking; The Docking Problem; Placing Fragments and Rigid Molecules; Flexible Ligand Docking; Handling Protein Flexibility; Docking of Combinatorial Libraries; Scoring; Shape and Chemical Complementary Scores; Force Field Scoring; Empirical Scoring Functions; Knowledge-Based Scoring Functions; Comparing Scoring Functions in Docking Experiments: Consensus Scoring.…”
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Modern aerodynamic methods for direct and inverse applications by Chin, Wilson C.

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The finite element method for three-dimensional thermomechanical applications by Dhondt, Guido D. C.

Table of Contents: “…. -- General Equations. -- The Shape Functions. -- Numerical Integration. -- Extrapolation of Integration Point Values to the Nodes. -- Problematic Element Behavior. -- Linear Constraints. -- Transformations. -- Loading. -- Modal Analysis. -- Cyclic Symmetry. -- Dynamics: the alpha-method. -- 3. …”
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Image processing and jump regression analysis by Qiu, Peihua, 1965-

Table of Contents: “…Cover -- Contents -- Preface -- 1 Introduction -- 1.1 Images and image representation -- 1.2 Regression curves and sugaces with jumps -- 1.3 Edge detection, image restoration, and jump regression analysis -- 1.4 Statistical process control and some other related topics -- 1.5 Organization of the book -- Problems -- 2 Basic Statistical Concepts and Conventional Smoothing Techniques -- 2.1 Introduction -- 2.2 Some basic statistical concepts and terminologies -- 2.2.1 Populations, samples, and distributions -- 2.2.2 Point estimation of population parameters -- 2.2.3 Confidence intervals and hypothesis testing -- 2.2.4 Maximum likelihood estimation and least squares estimation -- 2.3 Nadaraya- Watson and other kernel smoothing techniques -- 2.3.1 Univariate kernel estimators -- 2.3.2 Some statistical properties of kernel estimators -- 2.3.3 Multivariate kernel estimators -- 2.4 Local polynomial kernel smoothing techniques -- 2.4.1 Univariate local polynomial kernel estimators -- 2.4.2 Some statistical properties -- 2.4.3 Multivariate local polynomial kernel estimators -- 2.4.4 Bandwidth selection -- 2.5 Spline smoothing procedures -- 2.5.1 Univariate smoothing spline estimation -- 2.5.2 Selection of the smoothing parameter -- 2.5.3 Multivariate smoothing spline estimation -- 2.5.4 Regression spline estimation -- 2.6 Wavelet transformation methods -- 2.6.1 Function estimation based on Fourier transformation -- 2.6.2 Univariate wavelet transformations -- 2.6.3 Bivariate wavelet transformations -- Problems -- 3 Estimation of Jump Regression Curves -- 3.1 Introduction -- 3.2 Jump detection when the number of jumps is known -- 3.2.1 Difference kernel estimation procedures -- 3.2.2 Jump detection based on local linear kernel smoothing -- 3.2.3 Estimation of jump regression functions based on semiparametric modeling -- 3.2.4 Estimation of jump regression functions by spline smoothing -- 3.2.5 Jump and cusp detection by wavelet transformations -- 3.3 Jump estimation when the number of jumps is unknown -- 3.3.1 Jump detection by comparing three local estimators -- 3.3.2 Estimation of the number of jumps by a sequence of hypothesis tests -- 3.3.3 Jump detection by DAKE -- 3.3.4 Jump detection by local polynomial regression -- 3.4 Jump-preserving curve estimation -- 3.4.1 Jump curve estimation by split linear smoothing -- 3.4.2 Jump-preserving curve fitting based on local piecewise-linear kernel estimation -- 3.4.3 Jump-preserving smoothers based on robust estimation -- 3.5 Some discussions -- Problems -- 4 Estimation of Jump Location Curves of Regression Surfaces -- 4.1 Introduction -- 4.2 Jump detection when the number of jump location curves is known -- 4.2.1 Jump detection by RDKE -- 4.2.2 Minimax edge detection -- 4.2.3 Jump estimation based on a contrast statistic -- 4.2.4 Algorithms for tracking the JLCs -- 4.2.5 Estimation of JLCs by wavelet transformations -- 4.3 Detection of arbitrary jumps by local smoothing -- 4.3.1 Treat JLCs as a pointset in the design space -- 4.3.2 Jump detection by local linear estimation -- 4.3.3 Two modijication procedures -- 4.4 Jump detection in two or more given directions -- 4.4.1 Jump detection in two given directions -- 4.4.2 Measuring the p.…”
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Bayesian Analysis with Python. by Osvaldo Martin

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Machine learning for protein subcellular localization prediction by Wan, Shibiao, Mak, M. W.

Table of Contents: “…5.2.2 Problem transformation methods -- 5.2.3 Multi-label classification in bioinformatics -- 5.3 mGOASVM: A predictor for both single- and multi-location proteins -- 5.3.1 Feature extraction -- 5.3.2 Multi-label multiclass SVM classification -- 5.4 AD-SVM: An adaptive decision multi-label predictor -- 5.4.1 Multi-label SVM scoring -- 5.4.2 Adaptive decision for SVM (AD-SVM) -- 5.4.3 Analysis of AD-SVM -- 5.5 mPLR-Loc: A multi-label predictor based on penalized logistic regression -- 5.5.1 Single-label penalized logistic regression -- 5.5.2 Multi-label penalized logistic regression -- 5.5.3 Adaptive decision for LR (mPLR-Loc) -- 5.6 Summary -- 6 Mining deeper on GO for protein subcellular localization -- 6.1 Related work -- 6.2 SS-Loc: Using semantic similarity over GO -- 6.2.1 Semantic similarity measures -- 6.2.2 SS vector construction -- 6.3 HybridGO-Loc: Hybridizing GO frequency and semantic similarity features -- 6.3.1 Hybridization of two GO features -- 6.3.2 Multi-label multiclass SVM classification -- 6.4 Summary -- 7 Ensemble random projection for large-scale predictions -- 7.1 Random projection -- 7.2 RP-SVM: A multi-label classifier with ensemble random projection -- 7.2.1 Ensemble multi-label classifier -- 7.2.2 Multi-label classification -- 7.3 R3P-Loc: A compact predictor based on ridge regression and ensemble random projection -- 7.3.1 Limitation of using current databases -- 7.3.2 Creating compact databases -- 7.3.3 Single-label ridge regression -- 7.3.4 Multi-label ridge regression -- 7.4 Summary -- 8 Experimental setup -- 8.1 Prediction of single-label proteins -- 8.1.1 Datasets construction -- 8.1.2 Performance metrics -- 8.2 Prediction of multi-label proteins -- 8.2.1 Dataset construction -- 8.2.2 Datasets analysis -- 8.2.3 Performance metrics -- 8.3 Statistical evaluation methods -- 8.4 Summary.…”
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Handbook of biometrics for forensic science

Table of Contents: “…3.5.2 Application Using Fingermarks3.5.2.1 Forensic Intelligence; 3.5.2.2 Forensic Investigation; 3.5.2.3 Forensic Evaluation; 3.5.3 Current Challenges; 3.5.3.1 Automation and Transparency; 3.5.3.2 Scalability and Interoperability; 3.5.3.3 Forensic Fingermark Processes; 3.6 Conclusion; References; 4 Challenges for Fingerprint Recognition-Spoofing, Skin Diseases, and Environmental Effects; Abstract; 4.1 Spoofing and Anti-spoofing; 4.1.1 Perspiration; 4.1.2 Spectroscopic Characteristics; 4.1.3 Ultrasonic Technology; 4.1.4 Physical Characteristics: Temperature.…”
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Eat, cook, grow : mixing human-computer interactions with human-food interactions

Table of Contents: “…"You don't have to be a gardener to do urban agriculture": understanding opportunities for designing interactive technologies to support urban food production / William Odom -- Augmented agriculture, algorithms, aerospace, and alimentary architectures / Jordan Geiger -- The allure of provenance: tracing food through user-generated production information / Ann Light -- Beyond gardening: a new approach to HCI and urban agriculture / Tad Hirsch -- Hungry for data: metabolic interaction from farm to fork to phenotype / Marc Tuters and Denisa Kera -- Food futures: three provocations to challenge HCI interventions / Greg Hearn and David Lindsay Wright -- Bringing technology to the dining table / Charles Spence -- List of recipes.…”
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Medical and Care Compunetics 2 : Medical and Care Compunetics 2. by Bos, L.

Table of Contents: “…Standardized Semantic Markup for Reference Terminologies, Thesauri and Coding Systems: Benefits for Distributed E-Health ApplicationsDevelopment of an Expert System for Classification of Medical Errors; Model of Good Practice Tools for Risk Reduction and Clinical Governance; Optimisation Issues of High Throughput Medical Data and Video Streaming Traffic in 3G Wireless Environments; A New Algorithm for Content-Based Region Query in Databases with Medical Images; Economic Impact of Telemedicine: A Survey.…”
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Uveitis : a practical guide to the diagnosis and treatment of intraocular unflammation

Table of Contents: “…IndicationsLaser Therapy; Surgery; Steroids; Anti-VEGF Agents; Prognosis; Prevention; Conclusion; References; 11 Rubella; Introduction; Epidemiology; Clinical Manifestations; Diagnosis; Treatment; Conclusion; References; 12 Syphilis; Introduction/Clinical Features; Epidemiology; Diagnostic Evaluation; Treatment and Monitoring; Prognosis; References; 13 Ocular Toxocariasis; Introduction; Epidemiology; Clinical Manifestations; Diagnosis; Treatment; Conclusion; References; 14 Ocular Toxoplasmosis; Introduction; Etiology; Epidemiology; Clinical Presentation.…”
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FRBR, before and after : a look at our bibliographic models by Coyle, Karen

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Automatic indexing and abstracting of document texts by Moens, Marie-Francine, 1957-

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The Johns Hopkins guide to digital media

Table of Contents: “…Berry -- Cognitive implications of new media / Anne Mangen and Jean-Luc Velay -- Collaborative narrative / Scott Rettberg -- Collective intelligence / John Duda -- Combinatory and automatic text generation / Philippe Bootz and Christopher Funkhouser -- Computational linguistics / Inderjeet Mani -- Conceptual writing / Darren Wershler -- Copyright / Benjamin J. …”

Hack proofing your network

Table of Contents: “…</br><br> Looking to the Source Code</br><br> Exploring Diff Tools</br><br> Using File-Comparison Tools</br><br> Working with Hex Editors</br><br> Utilizing File System Monitoring Tools</br><br> Finding Other Tools</br><br> Troubleshooting</br><br> Problems with Checksums and Hashes</br><br> Problems with Compression and Encryption</br><br> Summary</br><br> Solutions Fast Track</br><br> Frequently Asked Questions</br><br>Chapter 6 Cryptography</br><br> Introduction</br><br> Understanding Cryptography Concepts</br><br> History</br><br> Encryption Key Types</br><br> Learning about Standard Cryptographic Algorithms</br><br> Understanding Symmetric Algorithms</br><br> Understanding Asymmetric Algorithms</br><br> Understanding Brute Force</br><br> Brute Force Basics</br><br> Using Brute Force to Obtain Passwords</br><br> Knowing When Real Algorithms Are Being Used Improperly</br><br> Bad Key Exchanges</br><br> Hashing Pieces Separately</br><br> Using a Short Password to Generate a Long Key</br><br> Improperly Stored Private or Secret Keys</br><br> Understanding Amateur Cryptography Attempts</br><br> Classifying the Ciphertext</br><br> Monoalphabetic Ciphers</br><br> Other Ways to Hide Information</br><br> Summary</br><br> Solutions Fast Track</br><br> Frequently Asked Questions</br><br>Chapter 7 Unexpected Input</br><br> Introduction</br><br> Understanding Why Unexpected Data Is Dangerous</br><br> Finding Situations Involving Unexpected Data</br><br> Local Applications and Utilities</br><br> HTTP/HTML</br><br> Unexpected Data in SQL Queries</br><br> Application Authentication</br><br> Disguising the Obvious</br><br> Using Techniques to Find and Eliminate Vulnerabilities</br><br> Black-Box Testing</br><br> Use the Source</br><br> Untaint Data by Filtering It</br><br> Escaping Characters Is Not Always Enough</br><br> Perl</br><br> Cold Fusion/Cold Fusion Markup Language (CFML)</br><br> ASP</br><br> PHP</br><br> Protecting Your SQL Queries</br><br> Silently Removing versus Alerting on Bad Data</br><br> Invalid Input Function</br><br> Token Substitution</br><br> Utilizing the Available Safety Features in Your Programming Language</br><br> Perl</br><br> PHP</br><br> ColdFusion/ColdFusion Markup Language</br><br> ASP</br><br> MySQL</br><br> Using Tools to Handle Unexpected Data</br><br> Web Sleuth</br><br> CGIAudit</br><br> RATS</br><br> Flawfinder</br><br> Retina</br><br> Hailstorm</br><br> Pudding</br><br> Summary</br><br> Solutions Fast Track</br><br> Frequently Asked Questions</br><br>Chapter 8 Buffer Overflow</br><br> Introduction</br><br> Understanding the Stack</br><br> The Stack Dump</br><br> Oddities and the Stack</br><br> Understanding the Stack Frame</br><br> Introduction to the Stack Frame</br><br> Passing Arguments to a Function: A Sample Program</br><br> Stack Frames and Calling Syntaxes</br><br> Learning about Buffer Overflows</br><br> A Simple Uncontrolled Overflow: A Sample Program</br><br> Creating Your First Overflow</br><br> Creating a Program with an Exploitable Overflow</br><br> Performing the Exploit</br><br> Learning Advanced Overflow Techniques </br><br> Stack Based Function Pointer Overwrite</br><br> Heap Overflows</br><br> Advanced Payload Design</br><br> Using What You Already Have</br><br> Summary</br><br> Solutions Fast Track</br><br> Frequently Asked Questions</br><br>Chapter 9 Format Strings</br><br> Introduction</br><br> Understanding Format String Vulnerabilities</br><br> Why and Where Do Format String Vulnerabilities Exist?…”
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Fast finance : does the financial world allow social loafing? by Williams, Gervais

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Google's PageRank and Beyond - the Science of Search Engine Rankings. by Langville, Amy N., Meyer, Carl D.

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