表格数据通常包含私人和重要信息；因此，必须在与他人共享之前采取预防措施。尽管已经提出了几种方法（例如，差异隐私和K-匿名性）以防止信息泄漏，但近年来，表格数据合成模型已变得流行，因为它们可以在数据实用程序和隐私之间进行易于权衡。但是，最近的研究表明，图像数据的生成模型容易受到会员推理攻击的影响，这可以确定是否使用给定记录来训练受害者合成模型。在本文中，我们在表格数据合成的背景下研究了成员推理攻击。我们在两个攻击方案（即一个黑色框和一个白盒攻击）下对4个最先进的表格数据合成模型进行实验，并发现成员推理攻击会严重危害这些模型。下一步，我们进行实验，以评估两种流行的差异深度学习训练算法DP-SGD和DP-GAN如何能够保护模型免受攻击。我们的主要发现是，两种算法都可以通过牺牲生成质量来减轻这种威胁。代码和数据可用：https：//github.com/jayoungkim408/mia

基于分数的生成模型（SGM）是生成假图像的最新突破。已知SGM可以超越其他生成模型，例如生成对抗网络（GAN）和变异自动编码器（VAE）。在这项工作中，我们受到了他们的巨大成功的启发，我们将它们完全自定义以生成伪造的表格数据。特别是，我们对过度采样较小的课程感兴趣，因为不平衡的课程经常导致次优训练成果。据我们所知，我们是第一个提出基于得分的表格数据超采样方法的人。首先，我们必须重新设计自己的分数网络，因为我们必须处理表格数据。其次，我们为我们的生成方法提出了两个选项：前者等同于表格数据的样式传输，后者使用SGMS的标准生成策略。最后，我们定义了一种微调方法，该方法进一步提高了过度采样质量。在我们使用6个数据集和10个基线的实验中，我们的方法在所有情况下都优于其他过采样方法。

受微分方程式启发的深度学习是最近的研究趋势，它标志着许多机器学习任务的最先进的表现。其中，具有神经控制的微分方程（NCDE）的时间序列建模被认为是突破。在许多情况下，基于NCDE的模型不仅比复发性神经网络（RNN）提供了更好的准确性，而且还可以处理不规则的时间序列。在这项工作中，我们通过重新设计其核心部分，即从离散的时间序列输入产生连续路径来增强NCDES。 NCDE通常使用插值算法将离散的时间序列样本转换为连续路径。但是，我们向i）提出建议，使用编码器解码器体系结构生成另一个潜在的连续路径，该架构对应于NCDE的插值过程，即我们的基于神经网络的插值与现有的显式插值相对于现有的显式插值以及II）解码器的外推超出了原始数据的时域的外推。因此，我们的NCDE设计可以同时使用插值和外推信息进行下游机器学习任务。在我们使用5个现实世界数据集和12个基线的实验中，我们的外推和基于插值的NCDES超过了非平凡的边缘的现有基线。

已经提出了高效和自适应计算机视觉系统以使计算机视觉任务，例如图像分类和对象检测，针对嵌入或移动设备进行了优化。这些解决方案最近的起源，专注于通过设计具有近似旋钮的自适应系统来优化模型（深神经网络，DNN）或系统。尽管最近的几项努力，但我们表明现有解决方案遭受了两个主要缺点。首先，系统不考虑模型的能量消耗，同时在制定要运行的模型的决定时。其次，由于其他共同居民工作负载，评估不考虑设备上的争用的实际情况。在这项工作中，我们提出了一种高效和自适应的视频对象检测系统，这是联合优化的精度，能量效率和延迟。底层Virtuoso是一个多分支执行内核，它能够在精度 - 能量 - 延迟轴上的不同运行点处运行，以及轻量级运行时调度程序，以选择最佳的执行分支以满足用户要求。要与Virtuoso相当比较，我们基准于15件最先进的或广泛使用的协议，包括更快的R-CNN（FRCNN），YOLO V3，SSD，培训台，SELSA，MEGA，REPP，FastAdapt和我们的内部FRCNN +，YOLO +，SSD +和高效+（我们的变体具有增强的手机效率）的自适应变体。通过这种全面的基准，Virtuoso对所有上述协议显示出优势，在NVIDIA Jetson Mobile GPU上的每一项效率水平上引领精度边界。具体而言，Virtuoso的准确性为63.9％，比一些流行的物体检测模型高于10％，51.1％，yolo为49.5％。

The 3D-aware image synthesis focuses on conserving spatial consistency besides generating high-resolution images with fine details. Recently, Neural Radiance Field (NeRF) has been introduced for synthesizing novel views with low computational cost and superior performance. While several works investigate a generative NeRF and show remarkable achievement, they cannot handle conditional and continuous feature manipulation in the generation procedure. In this work, we introduce a novel model, called Class-Continuous Conditional Generative NeRF ($\text{C}^{3}$G-NeRF), which can synthesize conditionally manipulated photorealistic 3D-consistent images by projecting conditional features to the generator and the discriminator. The proposed $\text{C}^{3}$G-NeRF is evaluated with three image datasets, AFHQ, CelebA, and Cars. As a result, our model shows strong 3D-consistency with fine details and smooth interpolation in conditional feature manipulation. For instance, $\text{C}^{3}$G-NeRF exhibits a Fr\'echet Inception Distance (FID) of 7.64 in 3D-aware face image synthesis with a $\text{128}^{2}$ resolution. Additionally, we provide FIDs of generated 3D-aware images of each class of the datasets as it is possible to synthesize class-conditional images with $\text{C}^{3}$G-NeRF.

In both terrestrial and marine ecology, physical tagging is a frequently used method to study population dynamics and behavior. However, such tagging techniques are increasingly being replaced by individual re-identification using image analysis. This paper introduces a contrastive learning-based model for identifying individuals. The model uses the first parts of the Inception v3 network, supported by a projection head, and we use contrastive learning to find similar or dissimilar image pairs from a collection of uniform photographs. We apply this technique for corkwing wrasse, Symphodus melops, an ecologically and commercially important fish species. Photos are taken during repeated catches of the same individuals from a wild population, where the intervals between individual sightings might range from a few days to several years. Our model achieves a one-shot accuracy of 0.35, a 5-shot accuracy of 0.56, and a 100-shot accuracy of 0.88, on our dataset.

Feature selection helps reduce data acquisition costs in ML, but the standard approach is to train models with static feature subsets. Here, we consider the dynamic feature selection (DFS) problem where a model sequentially queries features based on the presently available information. DFS is often addressed with reinforcement learning (RL), but we explore a simpler approach of greedily selecting features based on their conditional mutual information. This method is theoretically appealing but requires oracle access to the data distribution, so we develop a learning approach based on amortized optimization. The proposed method is shown to recover the greedy policy when trained to optimality and outperforms numerous existing feature selection methods in our experiments, thus validating it as a simple but powerful approach for this problem.

The purpose of this work was to tackle practical issues which arise when using a tendon-driven robotic manipulator with a long, passive, flexible proximal section in medical applications. A separable robot which overcomes difficulties in actuation and sterilization is introduced, in which the body containing the electronics is reusable and the remainder is disposable. A control input which resolves the redundancy in the kinematics and a physical interpretation of this redundancy are provided. The effect of a static change in the proximal section angle on bending angle error was explored under four testing conditions for a sinusoidal input. Bending angle error increased for increasing proximal section angle for all testing conditions with an average error reduction of 41.48% for retension, 4.28% for hysteresis, and 52.35% for re-tension + hysteresis compensation relative to the baseline case. Two major sources of error in tracking the bending angle were identified: time delay from hysteresis and DC offset from the proximal section angle. Examination of these error sources revealed that the simple hysteresis compensation was most effective for removing time delay and re-tension compensation for removing DC offset, which was the primary source of increasing error. The re-tension compensation was also tested for dynamic changes in the proximal section and reduced error in the final configuration of the tip by 89.14% relative to the baseline case.

According to the rapid development of drone technologies, drones are widely used in many applications including military domains. In this paper, a novel situation-aware DRL- based autonomous nonlinear drone mobility control algorithm in cyber-physical loitering munition applications. On the battlefield, the design of DRL-based autonomous control algorithm is not straightforward because real-world data gathering is generally not available. Therefore, the approach in this paper is that cyber-physical virtual environment is constructed with Unity environment. Based on the virtual cyber-physical battlefield scenarios, a DRL-based automated nonlinear drone mobility control algorithm can be designed, evaluated, and visualized. Moreover, many obstacles exist which is harmful for linear trajectory control in real-world battlefield scenarios. Thus, our proposed autonomous nonlinear drone mobility control algorithm utilizes situation-aware components those are implemented with a Raycast function in Unity virtual scenarios. Based on the gathered situation-aware information, the drone can autonomously and nonlinearly adjust its trajectory during flight. Therefore, this approach is obviously beneficial for avoiding obstacles in obstacle-deployed battlefields. Our visualization-based performance evaluation shows that the proposed algorithm is superior from the other linear mobility control algorithms.

In robotics and computer vision communities, extensive studies have been widely conducted regarding surveillance tasks, including human detection, tracking, and motion recognition with a camera. Additionally, deep learning algorithms are widely utilized in the aforementioned tasks as in other computer vision tasks. Existing public datasets are insufficient to develop learning-based methods that handle various surveillance for outdoor and extreme situations such as harsh weather and low illuminance conditions. Therefore, we introduce a new large-scale outdoor surveillance dataset named eXtremely large-scale Multi-modAl Sensor dataset (X-MAS) containing more than 500,000 image pairs and the first-person view data annotated by well-trained annotators. Moreover, a single pair contains multi-modal data (e.g. an IR image, an RGB image, a thermal image, a depth image, and a LiDAR scan). This is the first large-scale first-person view outdoor multi-modal dataset focusing on surveillance tasks to the best of our knowledge. We present an overview of the proposed dataset with statistics and present methods of exploiting our dataset with deep learning-based algorithms. The latest information on the dataset and our study are available at https://github.com/lge-robot-navi, and the dataset will be available for download through a server.