The automation of an increasingly large number of software engineering tasks is becoming possible thanks to Machine Learning (ML). One foundational building block in the application of ML to software artifacts is the representation of these artifacts (e.g., source code or executable code) into a form that is suitable for learning. Many studies have leveraged representation learning, delegating to ML itself the job of automatically devising suitable representations. Yet, in the context of Android problems, existing models are either limited to coarse-grained whole-app level (e.g., apk2vec) or conducted for one specific downstream task (e.g., smali2vec). Our work is part of a new line of research that investigates effective, task-agnostic, and fine-grained universal representations of bytecode to mitigate both of these two limitations. Such representations aim to capture information relevant to various low-level downstream tasks (e.g., at the class-level). We are inspired by the field of Natural Language Processing, where the problem of universal representation was addressed by building Universal Language Models, such as BERT, whose goal is to capture abstract semantic information about sentences, in a way that is reusable for a variety of tasks. We propose DexBERT, a BERT-like Language Model dedicated to representing chunks of DEX bytecode, the main binary format used in Android applications. We empirically assess whether DexBERT is able to model the DEX language and evaluate the suitability of our model in two distinct class-level software engineering tasks: Malicious Code Localization and Defect Prediction. We also experiment with strategies to deal with the problem of catering to apps having vastly different sizes, and we demonstrate one example of using our technique to investigate what information is relevant to a given task.

Recent years have witnessed an astonishing explosion in the evolution of mobile applications powered by AI technologies. The rapid growth of AI frameworks enables the transition of AI technologies to mobile devices, significantly prompting the adoption of AI apps (i.e., apps that integrate AI into their functions) among smartphone devices. In this paper, we conduct the most extensive empirical study on 56,682 published AI apps from three perspectives: dataset characteristics, development issues, and user feedback and privacy. To this end, we build an automated AI app identification tool, AI Discriminator, that detects eligible AI apps from 7,259,232 mobile apps. First, we carry out a dataset analysis, where we explore the AndroZoo large repository to identify AI apps and their core characteristics. Subsequently, we pinpoint key issues in AI app development (e.g., model protection). Finally, we focus on user reviews and user privacy protection. Our paper provides several notable findings. Some essential ones involve revealing the issue of insufficient model protection by presenting the lack of model encryption, and demonstrating the risk of user privacy data being leaked. We published our large-scale AI app datasets to inspire more future research.

在这项工作中，我们提出了一个新颖的观点，以解决贴片正确性评估的问题：正确的贴片实现了“答案”对越野车行为提出的问题的变化。具体而言，我们将贴片正确性评估变成一个问题回答问题。为了解决这个问题，我们的直觉是，自然语言处理可以提供必要的表示和模型来评估错误（问题）和补丁（答案）之间的语义相关性。具体而言，我们认为是输入错误报告以及生成的补丁的自然语言描述。我们的方法，Quatrain，首先考虑了最先进的消息生成模型，以生成与每个生成的补丁相关的相关输入。然后，我们利用神经网络体系结构来学习错误报告和提交消息之间的语义相关性。针对三个错误数据集生成的9135个补丁的大数据集（缺陷4J，Bugs.s.s.jar和Bears）的实验表明，Quatrain可以在预测补丁的正确性时达到0.886的AUC，并在过滤62％的62％错误的补丁时召回93％正确的补丁。我们的实验结果进一步证明了投入质量对预测性能的影响。我们进一步执行实验，以强调该模型确实了解了错误报告与预测的代码更改描述之间的关系。最后，我们与先前的工作进行比较，并讨论我们方法的好处。

源代码的表示学习对于将机器学习应用于软件工程任务至关重要。已经显示，跨不同编程语言的学习代码表示比从单语言数据集中学习更有效，因为来自多语言数据集的更多培训数据可提高该模型从源代码中提取语言 - 不平衡信息的能力。但是，现有的多语言模型忽略了特定于语言的信息，这对于在多语言数据集中培训的下游任务至关重要，同时仅着眼于学习不同语言之间的共享参数。为了解决这个问题，我们提出了MetatPtrans，这是一种用于多语言代码表示学习的元学习方法。 MetAtPtrans根据输入源代码段的特定编程语言为特征提取器生成不同的参数，从而使模型能够同时学习语言 - 语言和特定于语言的信息。实验结果表明，MetAtPtrans可将最新方法的F1得分显着提高到2.40个百分点，以汇总代码摘要，这是一项语言不可或缺的任务；以及TOP-1（TOP-5）的预测准确性高达7.32（13.15）百分点，以完成代码完成，这是一项特定于语言的任务。

错误报告是软件开发中的常见文物。它们作为用户与开发人员通信有关使用发布版本的软件程序时遇到的问题的主频道。然而，在对问题的描述中，用户可以故意或不揭示漏洞。在典型的维护方案中，在准备纠正补丁时，开发团队优先考虑此类安全相关错误报告。然而，当安全相关性没有立即表达（例如，通过标签）或通过TRIAJIG团队迅速识别时，开放的安全相关错误报告可能成为攻击者可以利用以执行零日攻击的敏感信息的关键泄漏。为了支持Trizing Bug报告中的从业者，研究社区提出了检测安全相关错误报告的许多方法。近年来，报告了基于机器学习的这方面的方法，具有很有希望的表现。我们的工作侧重于这些方法，并重新审视其积木，为目前的成就提供全面的观点。为此，我们建立了一个大型实验数据集，并在特征集和学习算法中进行了广泛的实验。最终，我们的研究突出了不同的方法配置，从而产生最好的执行分类器。

The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.

Autonomous underwater vehicles (AUVs) are regularly used for deep ocean applications. Commonly, the autonomous navigation task is carried out by a fusion between two sensors: the inertial navigation system and the Doppler velocity log (DVL). The DVL operates by transmitting four acoustic beams to the sea floor, and once reflected back, the AUV velocity vector can be estimated. However, in real-life scenarios, such as an uneven seabed, sea creatures blocking the DVL's view and, roll/pitch maneuvers, the acoustic beams' reflection is resulting in a scenario known as DVL outage. Consequently, a velocity update is not available to bind the inertial solution drift. To cope with such situations, in this paper, we leverage our BeamsNet framework and propose a Set-Transformer-based BeamsNet (ST-BeamsNet) that utilizes inertial data readings and previous DVL velocity measurements to regress the current AUV velocity in case of a complete DVL outage. The proposed approach was evaluated using data from experiments held in the Mediterranean Sea with the Snapir AUV and was compared to a moving average (MA) estimator. Our ST-BeamsNet estimated the AUV velocity vector with an 8.547% speed error, which is 26% better than the MA approach.

Are extralinguistic signals such as image pixels crucial for inducing constituency grammars? While past work has shown substantial gains from multimodal cues, we investigate whether such gains persist in the presence of rich information from large language models (LLMs). We find that our approach, LLM-based C-PCFG (LC-PCFG), outperforms previous multi-modal methods on the task of unsupervised constituency parsing, achieving state-of-the-art performance on a variety of datasets. Moreover, LC-PCFG results in an over 50% reduction in parameter count, and speedups in training time of 1.7x for image-aided models and more than 5x for video-aided models, respectively. These results challenge the notion that extralinguistic signals such as image pixels are needed for unsupervised grammar induction, and point to the need for better text-only baselines in evaluating the need of multi-modality for the task.

Machine learning methods like neural networks are extremely successful and popular in a variety of applications, however, they come at substantial computational costs, accompanied by high energy demands. In contrast, hardware capabilities are limited and there is evidence that technology scaling is stuttering, therefore, new approaches to meet the performance demands of increasingly complex model architectures are required. As an unsafe optimization, noisy computations are more energy efficient, and given a fixed power budget also more time efficient. However, any kind of unsafe optimization requires counter measures to ensure functionally correct results. This work considers noisy computations in an abstract form, and gears to understand the implications of such noise on the accuracy of neural-network-based classifiers as an exemplary workload. We propose a methodology called "Walking Noise" that allows to assess the robustness of different layers of deep architectures by means of a so-called "midpoint noise level" metric. We then investigate the implications of additive and multiplicative noise for different classification tasks and model architectures, with and without batch normalization. While noisy training significantly increases robustness for both noise types, we observe a clear trend to increase weights and thus increase the signal-to-noise ratio for additive noise injection. For the multiplicative case, we find that some networks, with suitably simple tasks, automatically learn an internal binary representation, hence becoming extremely robust. Overall this work proposes a method to measure the layer-specific robustness and shares first insights on how networks learn to compensate injected noise, and thus, contributes to understand robustness against noisy computations.

We propose the Detailed Outline Control (DOC) framework for improving long-range plot coherence when automatically generating several-thousand-word-long stories. DOC consists of two complementary components: a detailed outliner and a detailed controller. The detailed outliner creates a more detailed, hierarchically structured outline, shifting creative burden from the main drafting procedure to the planning stage. The detailed controller ensures the more detailed outline is still respected during generation by controlling story passages to align with outline details. In human evaluations of automatically generated stories, DOC substantially outperforms a strong Re3 baseline (Yang et al., 2022) on plot coherence (22.5% absolute gain), outline relevance (28.2%), and interestingness (20.7%). Humans also judged DOC to be much more controllable in an interactive generation setting.