Question-and-answer formats provide a novel experimental platform for investigating cybersecurity questions. Unlike previous chatbots, the latest ChatGPT model from OpenAI supports an advanced understanding of complex coding questions. The research demonstrates thirteen coding tasks that generally qualify as stages in the MITRE ATT&CK framework, ranging from credential access to defense evasion. With varying success, the experimental prompts generate examples of keyloggers, logic bombs, obfuscated worms, and payment-fulfilled ransomware. The empirical results illustrate cases that support the broad gain of functionality, including self-replication and self-modification, evasion, and strategic understanding of complex cybersecurity goals. One surprising feature of ChatGPT as a language-only model centers on its ability to spawn coding approaches that yield images that obfuscate or embed executable programming steps or links.

This research revisits the classic Turing test and compares recent large language models such as ChatGPT for their abilities to reproduce human-level comprehension and compelling text generation. Two task challenges -- summarization, and question answering -- prompt ChatGPT to produce original content (98-99%) from a single text entry and also sequential questions originally posed by Turing in 1950. The question of a machine fooling a human judge recedes in this work relative to the question of "how would one prove it?" The original contribution of the work presents a metric and simple grammatical set for understanding the writing mechanics of chatbots in evaluating their readability and statistical clarity, engagement, delivery, and overall quality. While Turing's original prose scores at least 14% below the machine-generated output, the question of whether an algorithm displays hints of Turing's truly original thoughts (the "Lovelace 2.0" test) remains unanswered and potentially unanswerable for now.

Soft labels in image classification are vector representations of an image's true classification. In this paper, we investigate soft labels in the context of satellite object detection. We propose using detections as the basis for a new dataset of soft labels. Much of the effort in creating a high-quality model is gathering and annotating the training data. If we could use a model to generate a dataset for us, we could not only rapidly create datasets, but also supplement existing open-source datasets. Using a subset of the xView dataset, we train a YOLOv5 model to detect cars, planes, and ships. We then use that model to generate soft labels for the second training set which we then train and compare to the original model. We show that soft labels can be used to train a model that is almost as accurate as a model trained on the original data.

为计算机视觉标记大型示例数据集的挑战继续限制图像存储库的可用性和范围。这项研究为自动数据收集，策展，标签和迭代培训提供了一种新的方法，对螺头卫星图像和对象检测的情况进行最少的人为干预。新的操作量表有效地扫描了整个城市（68平方英里）的网格搜索，并通过太空观测得出了汽车颜色的预测。经过部分训练的Yolov5模型是一种初始推理种子，以进一步输出迭代循环中更精致的模型预测。这里的软标签是指接受标签噪声作为潜在的有价值的增强，以减少过度拟合并增强对以前看不见的测试数据的广义预测。该方法利用了一个现实世界的实例，其中汽车的裁剪图像可以自动从像素值中自动接收白色或彩色信息，从而完成端到端管道，而不会过度依赖人类劳动。

基于物理学的模拟通常结合了复杂的区分方程以及许多科学和几何输入。我们的工作涉及从这些模拟中收集数据，并了解基于树的机器学习方法如何模拟所需的输出而不“知道”模拟中涉及的复杂支持。选定的基于物理学的模拟包括Navier-Stokes，应力分析和电磁场线，以基准性能作为数值和统计算法。我们特别着重于在两个模拟输出和增加空间分辨率之间预测特定的时空数据，以将物理预测推广到更精细的测试网格，而无需重复数值计算的计算成本。

诸如OpenAI的生成预训练的变压器（GPT-2/3）之类的语言模型捕获了在各种域（例如语言翻译器）和最近在游戏玩法（国际象棋，GO和Checkers）中生成文本所需的长期相关性。本研究同时应用较大的（GPT-3）和较小的（GPT-2）语言模型来探索奥赛罗（或逆转）游戏的复杂策略。鉴于《财富快速逆转》的游戏规则，语言模型不仅代表了基于以前的游戏动作的下一步动作的候选预测指标，而且还避免了游戏玩法中的稀疏奖励。语言模型会自动捕获或模拟冠军级策略。微调的GPT-2型号产生的Othello游戏范围从13-71％的完成范围不等，而较大的GPT-3型号则达到完整游戏的41％。像以前的国际象棋和Go一样，这些语言模型提供了一种新颖的方式来生成合理的游戏档案，尤其是用于比较比人类更大的样本的开放动作。这些模型的主要贡献（从两倍）放大（从1977 - 2022年开始的45年以来的45年）上放大了先前的记录，从而为研究界提供了使用其他强化学习技术进行采样的更多样化和原始的策略。

In this paper, we propose a novel technique, namely INVALIDATOR, to automatically assess the correctness of APR-generated patches via semantic and syntactic reasoning. INVALIDATOR reasons about program semantic via program invariants while it also captures program syntax via language semantic learned from large code corpus using the pre-trained language model. Given a buggy program and the developer-patched program, INVALIDATOR infers likely invariants on both programs. Then, INVALIDATOR determines that a APR-generated patch overfits if: (1) it violates correct specifications or (2) maintains errors behaviors of the original buggy program. In case our approach fails to determine an overfitting patch based on invariants, INVALIDATOR utilizes a trained model from labeled patches to assess patch correctness based on program syntax. The benefit of INVALIDATOR is three-fold. First, INVALIDATOR is able to leverage both semantic and syntactic reasoning to enhance its discriminant capability. Second, INVALIDATOR does not require new test cases to be generated but instead only relies on the current test suite and uses invariant inference to generalize the behaviors of a program. Third, INVALIDATOR is fully automated. We have conducted our experiments on a dataset of 885 patches generated on real-world programs in Defects4J. Experiment results show that INVALIDATOR correctly classified 79% overfitting patches, accounting for 23% more overfitting patches being detected by the best baseline. INVALIDATOR also substantially outperforms the best baselines by 14% and 19% in terms of Accuracy and F-Measure, respectively.

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.

Variational autoencoders model high-dimensional data by positing low-dimensional latent variables that are mapped through a flexible distribution parametrized by a neural network. Unfortunately, variational autoencoders often suffer from posterior collapse: the posterior of the latent variables is equal to its prior, rendering the variational autoencoder useless as a means to produce meaningful representations. Existing approaches to posterior collapse often attribute it to the use of neural networks or optimization issues due to variational approximation. In this paper, we consider posterior collapse as a problem of latent variable non-identifiability. We prove that the posterior collapses if and only if the latent variables are non-identifiable in the generative model. This fact implies that posterior collapse is not a phenomenon specific to the use of flexible distributions or approximate inference. Rather, it can occur in classical probabilistic models even with exact inference, which we also demonstrate. Based on these results, we propose a class of latent-identifiable variational autoencoders, deep generative models which enforce identifiability without sacrificing flexibility. This model class resolves the problem of latent variable non-identifiability by leveraging bijective Brenier maps and parameterizing them with input convex neural networks, without special variational inference objectives or optimization tricks. Across synthetic and real datasets, latent-identifiable variational autoencoders outperform existing methods in mitigating posterior collapse and providing meaningful representations of the data.

Cashews are grown by over 3 million smallholders in more than 40 countries worldwide as a principal source of income. As the third largest cashew producer in Africa, Benin has nearly 200,000 smallholder cashew growers contributing 15% of the country's national export earnings. However, a lack of information on where and how cashew trees grow across the country hinders decision-making that could support increased cashew production and poverty alleviation. By leveraging 2.4-m Planet Basemaps and 0.5-m aerial imagery, newly developed deep learning algorithms, and large-scale ground truth datasets, we successfully produced the first national map of cashew in Benin and characterized the expansion of cashew plantations between 2015 and 2021. In particular, we developed a SpatioTemporal Classification with Attention (STCA) model to map the distribution of cashew plantations, which can fully capture texture information from discriminative time steps during a growing season. We further developed a Clustering Augmented Self-supervised Temporal Classification (CASTC) model to distinguish high-density versus low-density cashew plantations by automatic feature extraction and optimized clustering. Results show that the STCA model has an overall accuracy of 80% and the CASTC model achieved an overall accuracy of 77.9%. We found that the cashew area in Benin has doubled from 2015 to 2021 with 60% of new plantation development coming from cropland or fallow land, while encroachment of cashew plantations into protected areas has increased by 70%. Only half of cashew plantations were high-density in 2021, suggesting high potential for intensification. Our study illustrates the power of combining high-resolution remote sensing imagery and state-of-the-art deep learning algorithms to better understand tree crops in the heterogeneous smallholder landscape.