许多现有数据挖掘算法使用直接在其模型中的特征值，使它们对用于测量/表示数据的单位/尺度敏感。已经提出了基于秩转换的数据的预处理作为克服这个问题的潜在解决方案。然而，在使用秩转换预处理后的结果数据均匀分布，这在许多数据挖掘应用中可能不是非常有用的。在本文中，我们基于多个子样本的级别提供了更好且有效的替代方案。我们称之为拟议的预处理技术为ARE |在子样本的集合中的平均排名。我们广泛使用的数据挖掘算法的经验结果，用于在各种数据集中进行分类和异常检测表明，ARE在特定于更加一致的任务方面会导致ares跨各种算法和数据集的结果。除此之外，它会导致大多数时间更好地或竞争的结果与最广泛使用的最大初始化和传统排名转换相比。

Algorithms that involve both forecasting and optimization are at the core of solutions to many difficult real-world problems, such as in supply chains (inventory optimization), traffic, and in the transition towards carbon-free energy generation in battery/load/production scheduling in sustainable energy systems. Typically, in these scenarios we want to solve an optimization problem that depends on unknown future values, which therefore need to be forecast. As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area. This paper presents the findings of the ``IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling," held in 2021. We present a comparison and evaluation of the seven highest-ranked solutions in the competition, to provide researchers with a benchmark problem and to establish the state of the art for this benchmark, with the aim to foster and facilitate research in this area. The competition used data from the Monash Microgrid, as well as weather data and energy market data. It then focused on two main challenges: forecasting renewable energy production and demand, and obtaining an optimal schedule for the activities (lectures) and on-site batteries that lead to the lowest cost of energy. The most accurate forecasts were obtained by gradient-boosted tree and random forest models, and optimization was mostly performed using mixed integer linear and quadratic programming. The winning method predicted different scenarios and optimized over all scenarios jointly using a sample average approximation method.

Cloth in the real world is often crumpled, self-occluded, or folded in on itself such that key regions, such as corners, are not directly graspable, making manipulation difficult. We propose a system that leverages visual and tactile perception to unfold the cloth via grasping and sliding on edges. By doing so, the robot is able to grasp two adjacent corners, enabling subsequent manipulation tasks like folding or hanging. As components of this system, we develop tactile perception networks that classify whether an edge is grasped and estimate the pose of the edge. We use the edge classification network to supervise a visuotactile edge grasp affordance network that can grasp edges with a 90% success rate. Once an edge is grasped, we demonstrate that the robot can slide along the cloth to the adjacent corner using tactile pose estimation/control in real time. See http://nehasunil.com/visuotactile/visuotactile.html for videos.

Unhealthy dietary habits are considered as the primary cause of multiple chronic diseases such as obesity and diabetes. The automatic food intake monitoring system has the potential to improve the quality of life (QoF) of people with dietary related diseases through dietary assessment. In this work, we propose a novel contact-less radar-based food intake monitoring approach. Specifically, a Frequency Modulated Continuous Wave (FMCW) radar sensor is employed to recognize fine-grained eating and drinking gestures. The fine-grained eating/drinking gesture contains a series of movement from raising the hand to the mouth until putting away the hand from the mouth. A 3D temporal convolutional network (3D-TCN) is developed to detect and segment eating and drinking gestures in meal sessions by processing the Range-Doppler Cube (RD Cube). Unlike previous radar-based research, this work collects data in continuous meal sessions. We create a public dataset that contains 48 meal sessions (3121 eating gestures and 608 drinking gestures) from 48 participants with a total duration of 783 minutes. Four eating styles (fork & knife, chopsticks, spoon, hand) are included in this dataset. To validate the performance of the proposed approach, 8-fold cross validation method is applied. Experimental results show that our proposed 3D-TCN outperforms the model that combines a convolutional neural network and a long-short-term-memory network (CNN-LSTM), and also the CNN-Bidirectional LSTM model (CNN-BiLSTM) in eating and drinking gesture detection. The 3D-TCN model achieves a segmental F1-score of 0.887 and 0.844 for eating and drinking gestures, respectively. The results of the proposed approach indicate the feasibility of using radar for fine-grained eating and drinking gesture detection and segmentation in meal sessions.

Cement is the most used construction material. The performance of cement hydrate depends on the constituent phases, viz. alite, belite, aluminate, and ferrites present in the cement clinker, both qualitatively and quantitatively. Traditionally, clinker phases are analyzed from optical images relying on a domain expert and simple image processing techniques. However, the non-uniformity of the images, variations in the geometry and size of the phases, and variabilities in the experimental approaches and imaging methods make it challenging to obtain the phases. Here, we present a machine learning (ML) approach to detect clinker microstructure phases automatically. To this extent, we create the first annotated dataset of cement clinker by segmenting alite and belite particles. Further, we use supervised ML methods to train models for identifying alite and belite regions. Specifically, we finetune the image detection and segmentation model Detectron-2 on the cement microstructure to develop a model for detecting the cement phases, namely, Cementron. We demonstrate that Cementron, trained only on literature data, works remarkably well on new images obtained from our experiments, demonstrating its generalizability. We make Cementron available for public use.

The human ear is generally universal, collectible, distinct, and permanent. Ear-based biometric recognition is a niche and recent approach that is being explored. For any ear-based biometric algorithm to perform well, ear detection and segmentation need to be accurately performed. While significant work has been done in existing literature for bounding boxes, a lack of approaches output a segmentation mask for ears. This paper trains and compares three newer models to the state-of-the-art MaskRCNN (ResNet 101 +FPN) model across four different datasets. The Average Precision (AP) scores reported show that the newer models outperform the state-of-the-art but no one model performs the best over multiple datasets.

Signature-based malware detectors have proven to be insufficient as even a small change in malignant executable code can bypass these signature-based detectors. Many machine learning-based models have been proposed to efficiently detect a wide variety of malware. Many of these models are found to be susceptible to adversarial attacks - attacks that work by generating intentionally designed inputs that can force these models to misclassify. Our work aims to explore vulnerabilities in the current state of the art malware detectors to adversarial attacks. We train a Transformers-based malware detector, carry out adversarial attacks resulting in a misclassification rate of 23.9% and propose defenses that reduce this misclassification rate to half. An implementation of our work can be found at https://github.com/yashjakhotiya/Adversarial-Attacks-On-Transformers.

现代社会有兴趣由于复杂的相机的激增而捕获高分辨率和优质图像。但是，如果在计算机视觉任务中使用了此类图像，则图像中的噪声污染不仅较低，而且相反会影响随后的过程，例如遥感，对象跟踪等。高分辨率图像的时间处理受图像捕获仪器的硬件限制的限制。 Geodesic Gramian denoising（GGD）是一种基于多种噪声滤波方法，我们在过去的研究中介绍了该方法，它利用了Geodesics的Gramian Gramian矩阵的一些突出的奇异向量进行噪声滤波过程。 GDD遇到$ \ MATHCAL {O}（n^6）$时，GDD的适用性受到限制^2 $数据矩阵由单数值分解（SVD）实现。在这项研究中，我们通过用四种不同的单数矢量近似技术代替其SVD步骤来提高GGD框架的效率。在这里，我们比较集成到GGD中的四个技术之间的计算时间和噪声过滤性能。

人类在整个生命周期中不断学习，通过积累多样化的知识并为未来的任务进行微调。当出现类似目标时，神经网络会遭受灾难性忘记，在学习过程中跨顺序任务跨好任务的数据分布是否不固定。解决此类持续学习（CL）问题的有效方法是使用超网络为目标网络生成任务依赖权重。但是，现有基于超网的方法的持续学习性能受到整个层之间权重的独立性的假设，以维持参数效率。为了解决这一限制，我们提出了一种新颖的方法，该方法使用依赖关系保留超网络来为目标网络生成权重，同时还保持参数效率。我们建议使用基于复发的神经网络（RNN）的超网络，该网络可以有效地生成层权重，同时允许在它们的依赖关系中。此外，我们为基于RNN的超网络提出了新颖的正则化和网络增长技术，以进一步提高持续的学习绩效。为了证明所提出的方法的有效性，我们对几个图像分类持续学习任务和设置进行了实验。我们发现，基于RNN HyperNetworks的建议方法在所有这些CL设置和任务中都优于基准。

由于医疗技术的进步，预防性医疗保健以及对老年医学健康的越来越重视，全球平均预期寿命正在增加。因此，必须开发检测和跟踪老年人人群认知功能中与衰老相关的疾病的技术。特别是，鉴于该疾病的患病率和当前方法的成本，与自动检测和评估有关阿尔茨海默氏病（AD）的研究至关重要。由于广告影响了语音和词汇的声学，因此自然语言处理和机器学习为可靠地检测AD提供了有希望的技术。我们比较和对比十个线性回归模型的性能，以预测Adress挑战数据集上的迷你门会状态考试得分。我们提取了13000多种手工制作和学习的功能，可捕捉语言和声学现象。使用通过两种方法选择的54个顶部功能的子集：（1）递归消除和（2）相关得分，我们的表现要优于同一任务的最新基线。在评分和评估每个模型选定特征子集的统计显着性时，我们发现，对于给定的任务，手工制作的语言特征比声学和学识渊博的特征更为重要。