The increasing trend of using Large Language Models (LLMs) for code generation raises the question of their capability to generate trustworthy code. While many researchers are exploring the utility of code generation for uncovering software vulnerabilities, one crucial but often overlooked aspect is the security Application Programming Interfaces (APIs). APIs play an integral role in upholding software security, yet effectively integrating security APIs presents substantial challenges. This leads to inadvertent misuse by developers, thereby exposing software to vulnerabilities. To overcome these challenges, developers may seek assistance from LLMs. In this paper, we systematically assess ChatGPT's trustworthiness in code generation for security API use cases in Java. To conduct a thorough evaluation, we compile an extensive collection of 48 programming tasks for 5 widely used security APIs. We employ both automated and manual approaches to effectively detect security API misuse in the code generated by ChatGPT for these tasks. Our findings are concerning: around 70% of the code instances across 30 attempts per task contain security API misuse, with 20 distinct misuse types identified. Moreover, for roughly half of the tasks, this rate reaches 100%, indicating that there is a long way to go before developers can rely on ChatGPT to securely implement security API code.

This article will cover the health care concern of diabetes that is driving the lifestyle of many people worldwide. This article will cover the usage of machine learning models to create a predictive system. This model will use random-forest to predict if patients have diabetes or not. The article will outline the requirements needed to set up our database GridDB. Following that, we will briefly describe our dataset and model.

Creating a Neural Network from Scratch in JAVA, we will also build our very own Matrix Library. Learn how to create and use neural networks in your Java programs. This course teaches you not only how to implement machine learning AI with your own artificial neural networks (ANNs), but also the principles of how artificial neural networks work -- to the point that you can implement your own. You'll need only a knowledge of Java programming and basic algebra; in this course you'll learn the relevant linear algebra, information theory and calculus, and together we'll build a fast and efficient neural network from scratch, able to recognise handwritten digits and easily adapted to other tasks. After taking the course, artificial neural networks won't be a mystery to you any more.

Learn how to create and use neural networks in your Java programs. This course teaches you not only how to implement machine learning AI with your own artificial neural networks (ANNs), but also the principles of how artificial neural networks work -- to the point that you can implement your own. You'll need only a knowledge of Java programming and basic algebra; in this course you'll learn the relevant linear algebra, information theory and calculus, and together we'll build a fast and efficient neural network from scratch, able to recognise handwritten digits After taking the course, artificial neural networks won't be a mystery to you any more. You'll be able to write your own neural networks and integrate them seamlessly into your Java programs, and understand in detail how they work.

As artificial intelligence (AI) technologies become increasingly powerful and prominent in society, their misuse is a growing concern. In educational settings, AI technologies could be used by students to cheat on assignments and exams. In this paper we explore whether transformers can be used to solve introductory level programming assignments while bypassing commonly used AI tools to detect plagiarism. We find that a student using GPT-J [Wang and Komatsuzaki, 2021] can complete introductory level programming assignments without triggering suspicion from MOSS [Aiken, 2000], a widely used plagiarism detection tool. This holds despite the fact that GPT-J was not trained on the problems in question and is not provided with any examples to work from. We further find that the code written by GPT-J is diverse in structure, lacking any particular tells that future plagiarism detection techniques may use to try to identify algorithmically generated code. We conclude with a discussion of the ethical and educational implications of large language models and directions for future research.

Author Kishori Sharan walks you through writing your first Java program step-by-step. Armed with that practical experience, you'll be ready to learn the core of the Java language. Beginning Java 9 Fundamentals provides over 90 diagrams and 240 complete programs to help you learn the topics faster. The book continues with a series of foundation topics, including using data types, working with operators, and writing statements in Java. These basics lead onto the heart of the Java language: object-oriented programming.

Update on April 18th, 2018: (a) New coding exercise has been added to Collections Framework chapter to test Lists & Queues!, (b) New assignment has been added in Section 3 "This is by far the best advanced as well as beginner course I have ever read/seen since Andre LaMothe quit writing." This one should be the best seller of all the other ... " Brady Adams "This is THE best course on Java on Udemy - Period! Dheeru is not only passionate about what he is coaching but also OBSESSIVE and covers every minute detail of the subject ... Most lessons have demos which Dheeru makes sure that they do work without any glitches. He is a genius coder ... Plus, he bases the course on the best practices from the book "Effective Java" which is great. You get to cover most of this book if you study this course! If you want to learn Java right from installing, configuring and all the way to mastering its advanced topics - look no further - you are at the right place THIS - IS - IT!!!" Richard Reddy "This is a wonderful course.

TensorFlow provides APIs for use in Java programs. These APIs are particularly well-suited to loading models created in Python and executing them within a Java application. This guide explains how to install TensorFlow for Java and use it in a Java application. This guide explains how to install TensorFlow for Java. The installation instructions for Android are in a separate Android TensorFlow Support page.