14 skills found
rick2785 / JavaCodeI specifically cover the following topics: Java primitive data types, declaration statements, expression statements, importing class libraries, excepting user input, checking for valid input, catching errors in input, math functions, if statement, relational operators, logical operators, ternary operator, switch statement, and looping. How class variables differ from local variables, Java Exception handling, the difference between run time and checked exceptions, Arrays, and UML Diagrams. Monsters gameboard, Java collection classes, Java ArrayLists, Linked Lists, manipulating Strings and StringBuilders, Polymorphism, Inheritance, Protected, Final, Instanceof, interfaces, abstract classes, abstract methods. You need interfaces and abstract classes because Java doesn't allow you to inherit from more than one other class. Java threads, Regular Expressions, Graphical User Interfaces (GUI) using Java Swing and its components, GUI Event Handling, ChangeListener, JOptionPane, combo boxes, list boxes, JLists, DefaultListModel, using JScrollpane with JList, JSpinner, JTree, Flow, Border, and Box Layout Managers. Created a calculator layout with Java Swing's GridLayout, GridBagLayout, GridBagConstraints, Font, and Insets. JLabel, JTextField, JComboBox, JSpinner, JSlider, JRadioButton, ButtonGroup, JCheckBox, JTextArea, JScrollPane, ChangeListener, pack, create and delete files and directories. How to pull lists of files from directories and manipulate them, write to and read character streams from files. PrintWriter, BufferedWriter, FileWriter, BufferedReader, FileReader, common file exceptions Binary Streams - DataOutputStream, FileOutputStream, BufferedOutputStream, all of the reading and writing primitive type methods, setup Java JDBC in Eclipse, connect to a MySQL database, query it and get the results of a query. JTables, JEditorPane Swing component. HyperlinkEvent and HyperlinkListener. Java JApplet, Java Servlets with Tomcat, GET and POST methods, Java Server Pages, parsing XML with Java, Java XPath, JDOM2 library, and 2D graphics. *Created a Java Paint Application using swing, events, mouse events, Graphics2D, ArrayList *Designed a Java Video Game like Asteroids with collision detection and shooting torpedos which also played sound in a JFrame, and removed items from the screen when they were destroyed. Rotating polygons, and Making Java Executable. Model View Controller (MVC) The Model is the class that contains the data and the methods needed to use the data. The View is the interface. The Controller coordinates interactions between the Model and View. DESIGN PATTERNS: Strategy design patternis used if you need to dynamically change an algorithm used by an object at run time. The pattern also allows you to eliminate code duplication. It separates behavior from super and subclasses. The Observer pattern is a software design pattern in which an object, called the subject (Publisher), maintains a list of its dependents, called observers (Subscribers), and notifies them automatically of any state changes, usually by calling one of their methods. The Factory design pattern is used when you want to define the class of an object at runtime. It also allows you to encapsulate object creation so that you can keep all object creation code in one place The Abstract Factory Design Pattern is like a factory, but everything is encapsulated. The Singleton pattern is used when you want to eliminate the option of instantiating more than one object. (Scrabble letters app) The Builder Design Pattern is used when you want to have many classes help in the creation of an object. By having different classes build the object you can then easily create many different types of objects without being forced to rewrite code. The Builder pattern provides a different way to make complex objects like you'd make using the Abstract Factory design pattern. The Prototype design pattern is used for creating new objects (instances) by cloning (copying) other objects. It allows for the adding of any subclass instance of a known super class at run time. It is used when there are numerous potential classes that you want to only use if needed at runtime. The major benefit of using the Prototype pattern is that it reduces the need for creating potentially unneeded subclasses. Java Reflection is an API and it's used to manipulate classes and everything in a class including fields, methods, constructors, private data, etc. (TestingReflection.java) The Decorator allows you to modify an object dynamically. You would use it when you want the capabilities of inheritance with subclasses, but you need to add functionality at run time. It is more flexible than inheritance. The Decorator Design Pattern simplifies code because you add functionality using many simple classes. Also, rather than rewrite old code you can extend it with new code and that is always good. (Pizza app) The Command design pattern allows you to store a list of commands for later use. With it you can store multiple commands in a class to use over and over. (ElectronicDevice app) The Adapter pattern is used when you want to translate one interface of a class into another interface. Allows 2 incompatible interfaces to work together. It allows the use of the available interface and the target interface. Any class can work together as long as the Adapter solves the issue that all classes must implement every method defined by the shared interface. (EnemyAttacker app) The Facade pattern basically says that you should simplify your methods so that much of what is done is in the background. In technical terms you should decouple the client from the sub components needed to perform an operation. (Bank app) The Bridge Pattern is used to decouple an abstraction from its implementation so that the two can vary independently. Progressively adding functionality while separating out major differences using abstract classes. (EntertainmentDevice app) In a Template Method pattern, you define a method (algorithm) in an abstract class. It contains both abstract methods and non-abstract methods. The subclasses that extend this abstract class then override those methods that don't make sense for them to use in the default way. (Sandwich app) The Iterator pattern provides you with a uniform way to access different collections of Objects. You can also write polymorphic code because you can refer to each collection of objects because they'll implement the same interface. (SongIterator app) The Composite design pattern is used to structure data into its individual parts as well as represent the inner workings of every part of a larger object. The composite pattern also allows you to treat both groups of parts in the same way as you treat the parts polymorphically. You can structure data, or represent the inner working of every part of a whole object individually. (SongComponent app) The flyweight design pattern is used to dramatically increase the speed of your code when you are using many similar objects. To reduce memory usage the flyweight design pattern shares Objects that are the same rather than creating new ones. (FlyWeightTest app) State Pattern allows an object to alter its behavior when its internal state changes. The object will appear to change its class. (ATMState) The Proxy design pattern limits access to just the methods you want made accessible in another class. It can be used for security reasons, because an Object is intensive to create, or is accessed from a remote location. You can think of it as a gate keeper that blocks access to another Object. (TestATMMachine) The Chain of Responsibility pattern has a group of objects that are expected to between them be able to solve a problem. If the first Object can't solve it, it passes the data to the next Object in the chain. (TestCalcChain) The Interpreter pattern is used to convert one representation of data into another. The context cantains the information that will be interpreted. The expression is an abstract class that defines all the methods needed to perform the different conversions. The terminal or concrete expressions provide specific conversions on different types of data. (MeasurementConversion) The Mediator design pattern is used to handle communication between related objects (Colleagues). All communication is handled by a Mediator Object and the Colleagues don't need to know anything about each other to work together. (TestStockMediator) The Memento design pattern provides a way to store previous states of an Object easily. It has 3 main classes: 1) Memento: The basic object that is stored in different states. 2) Originator: Sets and Gets values from the currently targeted Memento. Creates new Mementos and assigns current values to them. 3) Caretaker: Holds an ArrayList that contains all previous versions of the Memento. It can store and retrieve stored Mementos. (TestMemento) The Visitor design pattern allows you to add methods to classes of different types without much altering to those classes. You can make completely different methods depending on the class used with this pattern. (VisitorTest)
adamjgrant / AmadeusKeeping CSS variables logical and well-organized can quickly become unwieldy. Amadeus is a set of conventions to keep syntax organized and intuitive.
mhowerton91 / History<!DOCTYPE html> <!-- Copyright 2016 Google Inc. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 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hamidaucc / Link State Protocol In PythonThis project implenments the OSPF using Dijkstra algorithm (Open Shortest Path First) network protocol in python. Link-State Routing protocol is a main class of routing protocols. It is performed by every switching node/router in the network. The basic concept of link-state routing is that every node constructs a map of the connectivity to the network, in the form of a Graph, showing which nodes are connected to which other nodes. Each node then independently calculates the next best logical path from it to every possible destination in the network. The collection of best paths will then form the node's routing table. OSPF: ----- Open Shortest Path First (OSPF) is a link-state routing protocol for Internet Protocol (IP) networks. It uses a link state routing algorithm and falls into the group of interior routing protocols, operating within a single autonomous system (AS). OSPF is perhaps the most widely used interior gateway protocol (IGP) in large enterprise networks. IS-IS, another link-state dynamic routing protocol, is more common in large service provider networks. The most widely used exterior gateway protocol is the Border Gateway Protocol (BGP), the principal routing protocol between autonomous systems on the Internet. * Advantages of link state routing (as opposed to distance vector routing) include that link state routing converges rather quickly and * is not subject to the count-to-infinity problem; hence, no measures to combat this problem need to be taken. As the full network topology * is known to every node, rather advanced routing techniques can be implemented. * Disadvantages include that the link state information needs to be flooded through the network, causing higher overhead than link state protocols. * The memory and computational requirements are also higher. OSPF is an interior gateway protocol that routes Internet Protocol (IP) packets solely within a single routing domain (autonomous system). It gathers link state information from available routers and constructs a topology map of the network. The topology determines the routing table presented to the Internet Layer which makes routing decisions based solely on the destination IP address found in IP packets. OSPF was designed to support variable-length subnet masking (VLSM) or Classless Inter-Domain Routing (CIDR) addressing models.
andrewmogbolu2 / Blockchain TechnologyBlockchain and AI are on just about every chief information officers watchlist of game-changing technologies that stand to reshape industries. Both technologies come with immense benefits, but both also bring their own challenges for adoption. It is also fair to say that the hype surrounding these technologies individually may be unprecedented, so the thought of bringing these two ingredients together may be viewed by some as brewing a modern-day version of IT pixie dust. At the same time, there is a logical way to think about this mash-up that is both sensible and pragmatic. Today, AI is for all intents and purposes a centralized process. An end user must have extreme faith in the central authority to produce a trusted business outcome. By decentralizing the three key elements of AI — that is, data, models, and analytics — blockchain can deliver the trust and confidence often needed for end users to fully adopt and rely on AI-based business processes. Let’s explore how blockchain is poised to enrich AI by bringing trust to data, models and analytics. Your data is your data Many of the world’s most notable AI technology services are centralized — including Amazon, Apple, Facebook, Google, as well as Chinese companies Alibaba, Baidu and Tencent. Yet all have encountered challenges in establishing trust among their eager, but somewhat cautious users. How can a business provide assurance to its users that its AI has not overstepped its bounds? Imagine if these AI services could produce a “forensic report,” verified by a third party, to prove to you, beyond a reasonable doubt, how and when businesses are using your data once those are ingested. Imagine further that your data could be used only if you gave permission to do so. A blockchain ledger can be used as a digital rights management system, allowing your data to be “licensed” to the AI provider under your terms, conditions and duration. The ledger would act as an access management system storing the proofs and permission by which a business can access and use the user’s data. Trusted AI models Consider the example of using blockchain technology as a means of providing trusted data and provenance of training models for machine learning. In this case, we’ve created a fictitious system to answer the question of whether a fruit is an apple or orange. This question-answering system that we build is called a model, and this model is created via a process called training. The goal of training is to create an accurate model that answers our questions correctly most of the time. Of course, to train a model, we need to collect data to train on — for this example, that could be the color of the fruit (as a wavelength of light) and the sugar content (as a percentage). With blockchain, you can track the provenance of the training data as well as see an audit trail of the evidence that led to the prediction of why a particular fruit is considered an apple versus an orange. A business can also prove that it is not “juicing up” its books by tagging fruit more often as apples, if that is the more expensive of the two fruits. Explaining AI decisions The European Union has adopted a law requiring that any decision made by a machine be readily explainable, on penalty of fines that could cost companies billions of dollars. The EU General Data Protection Regulation (GDPR), which came into force in 2018, includes a right to obtain an explanation of decisions made by algorithms and a right to opt out of some algorithmic decisions altogether. Massive amounts of data are being produced every second — more data than humans have the ability to assess and use as the basis for drawing conclusions. However, AI applications are capable of assessing large data sets and many variables, while learning about or connecting those variables relevant to its tasks and objectives. For this very reason, AI continues to be adopted in various industries and applications, and we are relying more and more on their outcomes. It is essential, however, that any decisions made by AI are still verified for accuracy by humans. Blockchain can help clarify the provenance, transparency, understanding, and explanations of those outcomes and decisions. If decisions and associated data points are recorded via transactions on a blockchain, the inherent attributes of blockchain will make auditing them much simpler. Blockchain is a key technology that brings trust to transactions in a network; therefore, infusing blockchain into AI decision-making processes could be the element needed to achieve the transparency necessary to fully trust the decisions and outcomes derived from AI. Blockchain and the Internet of Things More than a billion intelligent, connected devices are already part of today’s IoT. The expected proliferation of hundreds of billions more places us at the threshold of a transformation sweeping across the electronics industry and many other areas. With the advancement in IoT, industries are now enabled to capture data, gain insight from the data, and make decisions based on the data. Therefore, there is a lot of “trust” in the information obtained. But the real truth of the matter is, do we really know where these data came from and should we be making decisions and transacting based on data we cannot validate? For example, did weather data really originate from a censor in the Atlantic Ocean or did the shipping container really not exceed the agreed temperature limit? The IoT use cases are massive, but they all share the same issue with trust. IoT with blockchain can bring real trust to captured data. The underlying idea is to give devices, at the time of their creation, an identity that can be validated and verified throughout their lifecycle with blockchain. There is great potential for IoT systems in blockchain technology capabilities that rely on device identity protocols and reputation systems. With a device identity protocol, each device can have its own blockchain public key and send encrypted challenge and response messages to other devices, thereby ensuring a device remains in control of its identity. In addition, a device with an identity can develop a reputation or history that is tracked by a blockchain. Smart contracts represent the business logic of a blockchain network. When a transaction is proposed, these smart contracts are autonomously executed within the guidelines set by the network. In IoT networks, smart contracts can play a pivotal role by providing automated coordination and authorization for transactions and interactions. The original idea behind IoT was to surface data and gain actionable insight at the right time. For example, smart homes are a thing of the present and most everything can be connected. In fact, with IoT, when something goes wrong, these IoT devices can even take action — for example, ordering a new part. We need a way to govern the actions taken by these devices, and smart contracts are a great way to do so. In an ongoing experiment I have followed in Brooklyn, New York, a community is using a blockchain to record the production of solar energy and enable the purchase of excess renewable energy credits. The device itself has an identity and builds a reputation through its history of records and exchange. Through the blockchain, people can aggregate their purchasing power more easily, share the burden of maintenance, and trust that devices are recording actual solar production. As IoT continues to evolve and its adoption continues to grow, the ability to autonomously manage devices and actions taken by devices will be essential. Blockchain and smart contracts are positioned well to integrate those capabilities into IoT.
wdimmy / Var2VecThe code is for our AAAI2023 paper: Efficient Embeddings of Logical Variables for Query Answering over Incomplete Knowledge Graphs (Dingmin Wang, Yeyuan Chen, Bernardo Cuenca Grau)
Aryia-Behroziuan / Robot LearningIn developmental robotics, robot learning algorithms generate their own sequences of learning experiences, also known as a curriculum, to cumulatively acquire new skills through self-guided exploration and social interaction with humans. These robots use guidance mechanisms such as active learning, maturation, motor synergies and imitation. Association rules Main article: Association rule learning See also: Inductive logic programming Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".[60] Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.[61] Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems. Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets.[62] For example, the rule {\displaystyle \{\mathrm {onions,potatoes} \}\Rightarrow \{\mathrm {burger} \}}\{{\mathrm {onions,potatoes}}\}\Rightarrow \{{\mathrm {burger}}\} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions. Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.[63] Inductive logic programming (ILP) is an approach to rule-learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming language for representing hypotheses (and not only logic programming), such as functional programs. Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting.[64][65][66] Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples.[67] The term inductive here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set. Models Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems. Artificial neural networks Main article: Artificial neural network See also: Deep learning An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another. Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68]
uhop / Deep6Zero-dependency ESM library for deep equality, deep clone, unification, and pattern matching with logical variables and wildcards. Handles circular references, Map, Set, URL, typed arrays, Date, RegExp, symbols, and property descriptors. Works in Node.js, Deno, Bun, and browsers.
Smat26 / PyLogicFlowChartThe tools helps create a flowchart of logical conditions in python source code, which is limited to particular "variables of interest"".
Hexstream / Positional Lambdapositional-lambda is a concise, intuitive and flexible syntax (macro) for trivial lambdas that eschews explicit (and often contextually-redundant) naming of parameter variables in favor of positional references, with support for a used or ignored &rest parameter and automatic declaration of ignored parameters when logical "gaps" are left in the positional references. Further convenience features are provided.
janeminmin / Bluebikes Project1> Background information Bluebikes is Metro Boston’s public bike share program, with more than 1800 bikes at over 200 stations across Boston and nearby areas. The bikes sharing program launched in 2011. The program aimed for individuals to use it for short-term basis for a price. It allows individuals to borrow a bike from a dock station after using it, which makes it ideal for one-way trips. The City of Boston is committed to providing bike share as a part of the public transportation system. However, to build a transport system that encourages bicycling, it is important to build knowledge about the current bicycle flows, and what factors are involved in the decision-making of potential bicyclists when choosing whether to use the bicycle. It is logical to make hypotheses that age and gender, bicycle infrastructure, safety perception are possible determinants of bicycling. On the short-term perspective, it has been shown that weather plays an important role whether to choose the bicycle. 2> Data collection The Bluebikes collects and provides system data to the public. The datasets used in the project can be download through this link (https://www.bluebikes.com/system-data). Based on this time series dataset (start from 2017-01-01 00:00:00 to 2019-03-31 23:00:00), we could have the information includes: Trip duration, start time and data, stop time and data, start station name and id, end station name and id, bike id, user type (casual or subscribed), birth year, gender. Besides, any trips that were below 60 seconds in length is considered as potentially false starts, which is already removed in the datasets. The number of bicycles used during a particular time period, varies over time based on several factors, including the current weather conditions, time of the day, time of the year and the current interest of the biker to use the bicycle as a transport mode. The current interest is different between subscribed users and casual users, so we should analyze them separately. Factors such as season, day of a week, month, hour, and if a holiday can be extracted from the date and time column in the datasets. Since we would analyze the hourly bicycle rental flow, we need hourly weather conditions data from 2017-01-01 00:00:00 to 2019-03-31 23:00:00 to complete our regression model of prediction. The weather data used in the project is scrapped using python selenium from Logan airport station (42.38 °N, 71.04 °W) webpage (https://www.wunderground.com/history/daily/us/ma/boston/KBOS/date/2019-7-15) maintained by weather underground website. The hourly weather observations include time, temperature, dew point, humidity, wind, wind speed, wind gust, pressure, precipitation, precipitation accumulated, condition. 3> The problem The aims of the project are to gain insight of the factors that could give short-term perspective of bicycle flows in Boston. It also aimed to investigate the how busy each station is, the division of bicycle trip direction and duration of the usage of a busy station and the mean flows variation within a day or during that period. The addition to the factors included in the regression model, there also exist other factors than influence how the bicycle flows vary over longer periods time. For example, general tendency to use the bicycle. Therefore, there is potential to improve the regression model accuracy by incorporating a long-term trend estimate taken over the time series of bicycle usage. Then the result from the machine learning algorithm-based regression model should be compared with the time series forecasting-based models. 4> Possible solutions Data preprocessing/Exploration and variable selection: date approximation manipulation, correlation analysis among variables, merging data, scrubbing for duplicate data, verifying errors, interpolation for missing values, handling outliers and skewness, binning low frequent levels, encoding categorical variables. Data visualization: split number of bike usage by subscribed/casual to build time series; build heatmap to present how busy is each station and locate the busiest station in the busiest period of a busy day; using boxplot and histogram to check outliers and determine appropriate data transformation, using weather condition text to build word cloud. Time series trend curve estimates: two possible way we considered are fitting polynomials of various degrees to the data points in the time series or by using time series decomposition functions and forecast functions to extract and forecast. We would emphasize on the importance to generate trend curve estimates that do not follow the seasonal variations: the seasonal variations should be captured explicitly by the input weather related variables in the regression model. Prediction/regression/time series forecasting: It is possible to build up multilayer perceptron neural network regressor to build up models and give prediction based on all variables of data, time and weather. However, considering the interpretability of model, we prefer to build regression models based on machine learning algorithms (like random forest or SVM) respectively for subscribed/casual users. Then the regressor would be combined with trend curve extracted and forecasted by ARIMA, and then comparing with the result of time series forecasting by STL (Seasonal and Trend decomposition using Loess) with multiple seasonal periods and the result of TBATS (Trigonometric Seasonal, Box-Cox Transformation, ARMA residuals, Trend and Seasonality).
bryce-bowles / Office Workspace OptimizationWon Optimization model class competition issued by Dr. Brooks (M.D.A. department chair and professor). Proposed Python, Pyomo and GLPK network optimization model approach with binary variables and logical constraints to simulate reorganization of 1700 workspaces across 17 floors, while allocating for changing project teams and requirements. Provided report to IT Vice President, Christine Holzem at the Federal Reserve Bank of Richmond.
ktczakutnyi / Software I C482WGU-Software-I-C482 COMPETENCIES 430.02.05 : Classes and Interfaces The graduate designs software solutions with appropriate classes, objects, methods, and interfaces to achieve specific goals. 430.02.06 : Object-Oriented Principles The graduate implements object-oriented design principles (e.g., inheritance, encapsulation, and abstraction) in developing applications for ensuring the application’s scalability. 430.02.07 : Application Development The graduate produces applications using Java programming language constructs to meet business requirements. 430.02.08 : Exception Handling The graduate incorporates simple exception handling in application development for improving user experience and application stability. 430.02.09 : User Interface Development The graduate develops user interfaces to meet project requirements. INTRODUCTION Throughout your career in software design and development, you will be asked to create applications with various features and functionality based on business requirements. For this assessment, you will create a Java desktop application using the solution statements provided in the requirements section of this assessment. The skills you showcase in your completed application will be useful in responding to technical interview questions for future employment. This application may also be added to your portfolio to show to future employers. The preferred integrated development environment (IDE) for this assignment is NetBeans version 11.1 or later or IntelliJ IDEA (Community Edition). Use the links in the web links section of this assessment to install one of these IDEs. If you choose to use another IDE, you must export your project into NetBeans 11.1 or later or IntelliJ IDEA format or your submission will be returned. This assessment also requires the following software: JDK 11 and JavaFX SDK or Module (for NetBeans or IntelliJ IDEA), and Scene Builder, which are also available for download in the web links section of this assessment, as well as a video demonstration of the completed application. Your submission should include a zip file with all the necessary code files to compile, support, and run your application. Your submission should also include a folder with descriptive Javadoc comments in the .java files. The zip file submission must keep the project file and folder structure intact for the IDE. In NetBeans, zip your file by going to File > Export Project > To ZIP and click Export. In IntelliJ IDEA, go to File > Export to Zip File and click OK. If you try to zip your project files with an external program, it will include the build files and make the zip files too large for submission. Note: You may receive an error message upon submitting your files because the automated plagiarism detectors will not be able to access the zipped file, but the evaluation team members will run their checks manually when evaluating your submission. SCENARIO You are working for a small manufacturing organization that has outgrown its current inventory system. Members of the organization have been using a spreadsheet program to manually enter inventory additions, deletions, and other data from a paper-based system but would now like you to develop a more sophisticated inventory program. You have been provided with a mock-up of the user interface to use in the design and development of the system (see the attached “Software 1 GUI Mock-Up”) and a class diagram to assist you in your work (see the attached “UML Class Diagram”). The organization also has specific business requirements that must be considered for the application. A systems analyst created the solution statements outlined in the requirements section of this task based on the business requirements. You will use these solution statements to develop your application. REQUIREMENTS Your submission must be your original work. No more than a combined total of 30% of the submission and no more than a 10% match to any one individual source can be directly quoted or closely paraphrased from sources, even if cited correctly. The originality report that is provided when you submit your task can be used as a guide. You must use the rubric to direct the creation of your submission because it provides detailed criteria that will be used to evaluate your work. Each requirement below may be evaluated by more than one rubric aspect. The rubric aspect titles may contain hyperlinks to relevant portions of the course. Tasks may not be submitted as cloud links, such as links to Google Docs, Google Slides, OneDrive, etc., unless specified in the task requirements. All other submissions must be file types that are uploaded and submitted as attachments (e.g., .docx, .pdf, .ppt). I. User Interface A. Create a JavaFX application with a graphical user interface (GUI) based on the attached “Software 1 GUI Mock-Up.” You may use JavaFX with or without FXML to create your GUI, or you may use Scene Builder to create your FXML file; use of Swing is not permitted. The user interface (UI) should closely match the organization of the GUI layout and contain all UI components (buttons, text fields, etc.) in each of the following GUI mock-up forms: 1. Main form 2. Add Part form 3. Modify Part form 4. Add Product form 5. Modify Product form Note: You may use one FXML file for forms with an identical UI component structure. You may also use a single window that can be switched to a different menu, or a new window can be launched for each form. As of JDK 11, JavaFX is no longer included in the JDK API but is available as an SDK or module. B. Provide Javadoc comments for each class member throughout the code, and include a detailed description of the following in your comments: • a logical or runtime error that you corrected in the code and how it was corrected • a future enhancement that would extend the functionality of the application if it were to be updated Note: For these comments to accurately export to the Javadoc comments, please add the logical and runtime error comments in the method header declaration comments where the error that was corrected occurred, and include the future enhancement comments in the comments of the main class. Please start these comments with “RUNTIME ERROR” or “FUTURE ENHANCEMENT” as applicable. II. Application C. Create classes with data and logic that map to the UML class diagram and include the supplied Part class provided in the attached “Part.java.” Do not alter the provided class. Include all the classes and members as shown in the UML diagram. Your code should demonstrate the following: • inheritance • abstract and concrete classes • instance and static variables • instance and static methods D. Add the following functionalities to the Main form: 1. The Parts pane • The Add button under the Parts TableView opens the Add Part form. • The Modify button under the Parts TableView opens the Modify Part form. • The Delete button under the Parts TableView deletes the selected part from the Parts TableView or displays a descriptive error message in the UI or in a dialog box if a part is not deleted. • When the user searches for parts by ID or name (partial or full name) using the text field, the application displays matching results in the Parts TableView. (Including a search button is optional.) If the part or parts are found, the application highlights a single part or filters multiple parts. If the part is not found, the application displays an error message in the UI or in a dialog box. • If the search field is set to empty, the table should be repopulated with all available parts. 2. The Products pane • The Add button under the Products TableView opens the Add Product form. • The Modify button under the Products TableView opens the Modify Product form. • The Delete button under the Products TableView deletes the selected product (if appropriate) from the Products TableView or displays a descriptive error message in the UI or in a dialog box if a product is not deleted. • When the user searches for products by ID or name (partial or full name) using the text field, the application displays matching results in the Products TableView. (Including a search button is optional.) If a product or products are found, the application highlights a single product or products or filters multiple products. If a product or products are not found, the application displays an error message in the UI or in a dialog box. • If the search field is set to empty, the table should be repopulated with all available products. Note: A product’s associated parts can exist independent of current inventory of parts. You are not required to display sample data upon launching your application. You do not need to save your data to a database or a file; data for this application is nonpersistent and will reside in computer memory while in use. 3. Exit button • The Exit button closes the application. E. Add the listed functionalities to the following parts forms: 1. The Add Part form • The In-House and Outsourced radio buttons switch the bottom label to the correct value (Machine ID or Company Name). • The application auto-generates a unique part ID. The part IDs can be, but do not need to be, contiguous. - The part ID text field must be disabled. • The user should be able to enter a part name, inventory level or stock, a price, maximum and minimum values, and company name or machine ID values into active text fields. • After saving the data, users are automatically redirected to the Main form. • Canceling or exiting this form redirects users to the Main form. 2. The Modify Part form • The text fields populate with the data from the chosen part. • The In-House and Outsourced radio buttons switch the bottom label to the correct value (Machine ID or Company Name) and swap In-House parts and Outsourced parts. When new objects need to be created after the Save button is clicked, the part ID should be retained. • The user can modify data values in the text fields sent from the Main form except the part ID. • After saving modifications to the part, the user is automatically redirected to the Main form. • Canceling or exiting this form redirects users to the Main form. F. Add the following functionalities to the following product forms: 1. The Add Product form • The application auto-generates a unique product ID. The product IDs can be, but do not need to be, contiguous. - The product ID text field must be disabled and cannot be edited or changed. • The user should be able to enter a product name, inventory level or stock, a price, and maximum and minimum values. • The user can search for parts (top table) by ID or name (partial or full name). If the part or parts are found, the application highlights a single part or filters multiple parts. If the part or parts are not found, the application displays an error message in the UI or in a dialog box. • If the search field is set to empty, the table should be repopulated with all available parts. • The top table should be identical to the Parts TableView in the Main form. • The user can select a part from the top table. The user then clicks the Add button, and the part is copied to the bottom table. (This associates one or more parts with a product.) • The Remove Associated Part button removes a selected part from the bottom table. (This dissociates or removes a part from a product.) • After saving the data, the user is automatically redirected to the Main form. • Canceling or exiting this form redirects users to the Main form. Note: When a product is deleted, so can its associated parts without affecting the part inventory. The Remove Associated Part button removes a selected part from the bottom table. (This dissociates or removes a part from a product.) 2. The Modify Product form • The text fields populate with the data from the chosen product, and the bottom TableView populates with the associated parts. • The user can search for parts (top table) by ID or name (partial or full name). If the part or parts are found, the application highlights a single part or filters multiple parts. If the part is not found, the application displays an error message in the UI or a dialog box. • If the search text field is set to empty, the table should be repopulated with all available parts. • The top table should be identical to the Parts TableView in the Main form. • The user may modify or change data values. - The product ID text field must be disabled and cannot be edited or changed. • The user can select a part from the top table. The user then clicks the Add button, and the part is copied to the bottom table. (This associates one or more parts with a product.) • The user may associate zero, one, or more parts with a product. • The user may remove or disassociate a part from a product. • After saving modifications to the product, the user is automatically redirected to the Main form. • Canceling or exiting this form redirects users to the Main form. Note: The Remove Associated Part button removes a selected part from the bottom table. (This dissociates or removes a part from a product.) G. Write code to implement input validation and logical error checks using a dialog box or message in the UI displaying a descriptive error message for each of the following circumstances: • Min should be less than Max; and Inv should be between those two values. • The user should not delete a product that has a part associated with it. • The application confirms the “Delete” and “Remove” actions. • The application will not crash when inappropriate user data is entered in the forms; instead, error messages should be generated. H. Provide a folder containing Javadoc files that were generated from the IDE or via the command prompt from part B. In a comment above the main method header declaration, please specify where this folder is located. I. Demonstrate professional communication in the content and presentation of your submission. File Restrictions File name may contain only letters, numbers, spaces, and these symbols: ! - _ . * ' ( ) File size limit: 200 MB File types allowed: doc, docx, rtf, xls, xlsx, ppt, pptx, odt, pdf, txt, qt, mov, mpg, avi, mp3, wav, mp4, wma, flv, asf, mpeg, wmv, m4v, svg, tif, tiff, jpeg, jpg, gif, png, zip, rar, tar, 7z
divyasaxenaa / Artificial Intelligence WumpusWorldThe task in this programming assignment is to implement, a knowledge base and an inference engine for the wumpus world. First of all, you have to create a knowledge base (stored as a text file) storing the rules of the wumpus world, i.e., what we know about pits, monsters, breeze, and stench. Second, you have to create an inference engine, that given a knowledge base and a statement determines if, based on the knowledge base, the statement is definitely true, definitely false, or of unknown truth value. Command-line Arguments The program should be invoked from the commandline as follows: check_true_false wumpus_rules.txt [additional_knowledge_file] [statement_file] For example: check_true_false wumpus_rules.txt kb1.txt statement1.txt Argument wumpus_rules.txt specifies the location of a text file containing the wumpus rules, i.e., the rules that are true in any possible wumpus world, as specified above (once again, note that the specifications above are not identical to the ones in the book). Argument [additional_knowledge_file] specifies an input file that contains additional information, presumably collected by the agent as it moves from square to square. For example, see kb3.txt. Argument [statement_file] specifies an input file that contains a single logical statement. The program should check if, given the information in wumpus_rules.txt and [additional_knowledge_file], the statement in [statement_file] is definitely true, definitely false, or none of the above. Output Your program should create a text file called "result.txt". Depending on what your inference algorithm determined about the statement being true or false, the output file should contain one of the following four outputs: definitely true. This should be the output if the knowledge base entails the statement, and the knowledge base does not entail the negation of the statement. definitely false. This should be the output if the knowledge base entails the negation of the statement, and the knowledge base does not entail the statement. possibly true, possibly false. This should be the output if the knowledge base entails neither the statement nor the negation of the statement. both true and false. This should be the output if the knowledge base entails both the statement and the the negation of the statement. This happens when the knowledge base is always false (i.e., when the knowledge base is false for every single row of the truth table). Note that by "knowledge base" we are referring to the conjunction of all statements contained in wumpus_rules.txt AND in the additional knowledge file. Also note that the sample code provided below stores the words "result unknown" to the result.txt file. Also, the "both true and false" output should be given when the knowledge base (i.e., the info stored in wumpus_rules.txt AND in the additional knowledge file) entails both the statement from statement_file AND the negation of that statement. Syntax The wumpus rules file and the additional knowledge file contain multiple lines. Each line contains a logical statement. The knowledge base constructed by the program should be a conjunction of all the statements contained in the two files. The sample code (as described later) already does that. The statement file contains a single line, with a single logical statement. Statements are given in prefix notation. Some examples of prefix notation are: (or M_1_1 B_1_2) (and M_1_2 S_1_1 (not (or M_1_3 M_1_4))) (if M_1_1 (and S_1_2 S_1_3)) (iff M_1_2 (and S_1_1 S_1_3 S_2_2)) (xor B_2_2 P_1_2) P_1_1 B_3_4 (not P_1_1) Statements can be nested, as shown in the above examples. Note that: Any open parenthesis that is not the first character of a text line must be preceded by white space. Any open parenthesis must be immediately followed by a connective (without any white space in between). Any close parenthesis that is not the last character of a text line must be followed by white space. If the logical expression contains just a symbol (and no connectives), the symbol should NOT be enclosed in parentheses. For example, (P_1_1) is not legal, whereas (not P_1_1) is legal. See also the example statements given above. Each logical expression should be contained in a single line. The wumpus rules file and the additional knowledge file contain a set of logical expressions. The statement file should contain a single logical expression. If it contains more than one logical expression, only the first one is read. Lines starting with # are treated as comment lines, and ignored. You can have empty lines, but they must be totally empty. If a line has a single space on it (and nothing more) the program will complain and not read the file successfully. There are six connectives: and, or, xor, not, if, iff. No other connectives are allowed to be used in the input files. Here is some additional information: A statement can consist of either a single symbol, or a connective connecting multiple (sub)statements. Notice that this is a recursive definition. In other words, statements are symbols or more complicated statements that we can make by connecting simpler statements with one of the six connectives. Connectives "and", "or", and "xor" can connect any number of statements, including 0 statements. It is legal for a statement consisting of an "and", "or", or "xor" connective to have no substatements, e.g., (and). An "and" statement with zero substatements is true. An "or" or "xor" statement with zero substatements is false. An "xor" statement is true if exactly 1 substatement is true (no more, no fewer). Connectives "if" and "iff" require exactly two substatements. Connective "not" requires exactly one substatement. The only symbols that are allowed to be used are: M_i_j (standing for "there is a monster at square (i, j)). S_i_j (standing for "there is a stench at square (i, j)). P_i_j (standing for "there is a pit at square (i, j)). B_i_j (standing for "there is a breeze at square (i, j)). NO OTHER SYMBOLS ARE ALLOWED. Also, note that i and j can take values 1, 2, 3, and 4. In other words, there will be 16 unique symbols of the form M_i_j, 16 unique symbols of the form S_i_j, 16 unique symbols of the form P_i_j, and 16 unique symbols of the form B_i_j, for a total of 64 unique symbols. The Wumpus Rules Here is what we know to be true in any wumpus world, for the purposes of this assignment (NOTE THAT THESE RULES ARE NOT IDENTICAL TO THE ONES IN THE TEXTBOOK): If there is a monster at square (i,j), there is stench at all adjacent squares. If there is stench at square (i,j), there is a monster at one of the adjacent squares. If there is a pit at square (i,j), there is breeze at all adjacent squares. If there is breeze at square (i,j), there is a pit at one or more of the adjacent squares. There is one and only one monster (no more, no fewer). Squares (1,1), (1,2), (2,1), (2,2) have no monsters and no pits. The number of pits can be between 1 and 11. We don't care about gold, glitter, and arrows, there will be no information about them in the knowledge base, and no reference to them in the statement. Sample code The following code implements, in Java and C++, a system that reads text files containing information for the knowledge base and the statement whose truth we want to check. Feel free to use that code and build on top of it. Also feel free to ignore that code and start from scratch. Java: files CheckTrueFalse.java and LogicalExpression.java C++: files check_true_false.cpp and check_true_false.h Python (ver 2.4): check_true_false.py and logical_expression.py. (Zipped version of files here). You can test this code, by compiling on omega, and running on input files a.txt, b.txt, and c.txt. For example, for the Java code you can run it as: javac *.java java CheckTrueFalse a.txt b.txt c.txt and for C++, you can do: g++ -o check_true_false check_true_false.cpp ./check_true_false a.txt b.txt c.txt Efficiency Brute-force enumeration of the 264 possible assignments to the 64 Boolean variables will be too inefficient to produce answers in a reasonable amount of time. Because of that, we will only be testing your solutions with cases where the additional knowledge file contains specific information about at least 48 of the symbols. For example, suppose that the agent has already been at square (2,3). Then, the agent knows for that square that: There is no monster (otherwise the agent would have died). There is no pit (otherwise the agent would have died). Furthermore, the agent knows whether or not there is stench and/or breeze at that square. Suppose that, in our example, there is breeze and no stench. Then, the additional knowledge file would contain these lines for square 2,3: (not M_2_3) (not P_2_3) B_2_3 (not S_2_3) You can assume that, in all our test cases, there will be at least 48 lines like these four lines shown above, specifying for at least 48 symbols whether they are true or false. Assuming that you implement the TT-Entails algorithm, your program should identify those symbols and their values right at the beginning. You can identify those symbols using these guidelines: Note that the sample code stores the knowledge base as a LogicalExpression object, whose connective at the root is an AND. Let's call this LogicalExpression object knowledge_base. Suppose that you have a line such as "B_2_3" in the additional knowledge file. Such a line generates a child of knowledge_base that is a leaf, and has its "symbol" variable set to "B_2_3". You can write code that explicitly looks for such children of knowledge_base. Suppose that you have a line such as "(not M_2_3") in the additional knowledge file. Such a line generates a child of knowledge_base whose connective is NOT, and whose only child is a leaf with its "symbol" variable set to "M_2_3". You can write code that explicitly looks for such children of knowledge_base. This way, your program will be able to initialize the model that TT-Entails passes to TT-Check-All with boolean assignments for at least 48 symbols, as opposed to passing an empty model. The list of symbols passed from TT-Entails to TT-Check-All should obviously NOT include the symbols that have been assigned values in the initial model. This way, at most 16 symbols will have unspecified values, and TT-Check-All will need to check at most 216 rows in the truth table, which is quite doable in a reasonable amount of time (a few seconds).
