传感器节点（SNS）的部署总是在无线传感器网络（WSN）的系统性能中起决定性作用。在这项工作中，我们提出了一种实用异构WSN的最佳部署方法，该方法可以深入了解可靠性和部署成本之间的权衡。具体而言，这项工作旨在提供SNS的最佳部署，以最大程度地提高覆盖率和连接学位，同时最大程度地减少整体部署成本。此外，这项工作充分考虑了SNS的异质性（即差异化的传感范围和部署成本）和三维（3-D）部署方案。这是一个多目标优化问题，非凸，多模态和NP-HARD。为了解决它，我们开发了一种新型的基于群体的多目标优化算法，称为竞争性多目标海洋掠食者算法（CMOMPA），其性能通过与十种其他多个多目标优化的全面比较实验验证算法。计算结果表明，在收敛性和准确性方面，CMOMPA优于他人，并且在多模式多目标优化问题上表现出卓越的性能。还进行了足够的模拟来评估基于CMOMPA的最佳SNS部署方法的有效性。结果表明，优化的部署可以平衡部署成本，感知可靠性和网络可靠性之间的权衡平衡。源代码可在https://github.com/inet-wzu/cmompa上找到。

This paper investigates a phenomenon where query-based object detectors mispredict at the last decoding stage while predicting correctly at an intermediate stage. We review the training process and attribute the overlooked phenomenon to two limitations: lack of training emphasis and cascading errors from decoding sequence. We design and present Selective Query Recollection (SQR), a simple and effective training strategy for query-based object detectors. It cumulatively collects intermediate queries as decoding stages go deeper and selectively forwards the queries to the downstream stages aside from the sequential structure. Such-wise, SQR places training emphasis on later stages and allows later stages to work with intermediate queries from earlier stages directly. SQR can be easily plugged into various query-based object detectors and significantly enhances their performance while leaving the inference pipeline unchanged. As a result, we apply SQR on Adamixer, DAB-DETR, and Deformable-DETR across various settings (backbone, number of queries, schedule) and consistently brings 1.4-2.8 AP improvement.

Large language models that are capable of zero or few-shot prompting approaches have given rise to the new research area of prompt engineering. Recent advances showed that for example Chain-of-Thought (CoT) prompts can improve arithmetic or common sense tasks significantly. We explore how such approaches fair with legal reasoning tasks and take the COLIEE entailment task based on the Japanese Bar exam for testing zero-shot/few-shot and fine-tuning approaches. Our findings show that while CoT prompting and fine-tuning with explanations approaches show improvements, the best results are produced by prompts that are derived from specific legal reasoning techniques such as IRAC (Issue, Rule, Application, Conclusion). Based on our experiments we improve the 2021 best result from 0.7037 accuracy to 0.8148 accuracy and beat the 2022 best system of 0.6789 accuracy with an accuracy of 0.7431.

Fabric manipulation is a long-standing challenge in robotics due to the enormous state space and complex dynamics. Learning approaches stand out as promising for this domain as they allow us to learn behaviours directly from data. Most prior methods however rely heavily on simulation, which is still limited by the large sim-to-real gap of deformable objects or rely on large datasets. A promising alternative is to learn fabric manipulation directly from watching humans perform the task. In this work, we explore how demonstrations for fabric manipulation tasks can be collected directly by human hands, providing an extremely natural and fast data collection pipeline. Then, using only a handful of such demonstrations, we show how a sample-efficient pick-and-place policy can be learned and deployed on a real robot, without any robot data collection at all. We demonstrate our approach on a fabric folding task, showing that our policy can reliably reach folded states from crumpled initial configurations.

Graph structure learning aims to learn connectivity in a graph from data. It is particularly important for many computer vision related tasks since no explicit graph structure is available for images for most cases. A natural way to construct a graph among images is to treat each image as a node and assign pairwise image similarities as weights to corresponding edges. It is well known that pairwise similarities between images are sensitive to the noise in feature representations, leading to unreliable graph structures. We address this problem from the viewpoint of statistical tests. By viewing the feature vector of each node as an independent sample, the decision of whether creating an edge between two nodes based on their similarity in feature representation can be thought as a ${\it single}$ statistical test. To improve the robustness in the decision of creating an edge, multiple samples are drawn and integrated by ${\it multiple}$ statistical tests to generate a more reliable similarity measure, consequentially more reliable graph structure. The corresponding elegant matrix form named $\mathcal{B}\textbf{-Attention}$ is designed for efficiency. The effectiveness of multiple tests for graph structure learning is verified both theoretically and empirically on multiple clustering and ReID benchmark datasets. Source codes are available at https://github.com/Thomas-wyh/B-Attention.

密码的安全性取决于对攻击者使用的策略的透彻理解。不幸的是，现实世界中的对手使用务实的猜测策略，例如字典攻击，在密码安全研究中很难模拟。字典攻击必须仔细配置和修改，以代表实际威胁。但是，这种方法需要难以复制的特定领域知识和专业知识。本文比较了不需要域知识或对用户密码结构和组合的假设的各种基于深度学习的密码猜测方法。所涉及的模型类别是复发性神经网络，生成对抗网络，自动编码器和注意机制。此外，我们提出了一种有前途的研究实验设计，以使用IWGAN的变体在非针对性的离线攻击下进行密码猜测。使用这些高级策略，我们可以增强密码安全性并创建更准确，更有效的密码强度计。

honeywords是插入数据库中的虚拟密码，以识别密码漏洞。主要困难是如何生产难以区分实际密码的蜂蜜字。尽管过去已经广泛研究了蜂蜜词的产生，但大多数现有研究假设攻击者对用户不了解。如果攻击者利用了用户的个人身份信息（PII），并且实际密码包括用户的PII，则这些蜂蜜词生成技术（HGT）可能会完全失败。在本文中，我们建议建立一个更安全和可信赖的身份验证系统，该系统采用现成的预训练的语言模型，不需要对真实密码进行进一步的培训以产生蜂蜜字，同时保留了相关的真实密码的PII，因此很明显提高攻击者的标准。我们进行了一个试点实验，要求个人在为GPT-3提供用户名和调整技术时区分真实的密码和蜂蜜字。结果表明，对于这两种技术，很难将真实密码与人工密码区分开。我们推测，较大的样本量可以揭示两种HGT技术之间的显着差异，这有利于我们提出的方法。

密码的安全性取决于对攻击者使用的策略的彻底理解。不幸的是，现实世界中的对手使用务实的猜测策略，例如字典攻击，在密码安全研究中很难模拟。字典攻击必须仔细配置和修改以表示实际威胁。但是，这种方法需要难以复制的特定领域知识和专业知识。本文回顾了各种基于深度学习的密码猜测方法，这些方法不需要域知识或有关用户密码结构和组合的假设。它还引入了GNPASSGAN，这是一种基于生成对抗网络的密码猜测工具，用于拖动离线攻击。与最先进的盘子型号相比，Gnpassgan能够猜测88.03 \％的密码更多，并生成31.69 \％的重复。

为了在商店中充分利用计算机视觉技术，需要考虑适合零售场景特征的实际需求。为了实现这一目标，我们介绍了联合零售数据集（Unitail），这是针对检测，阅读和匹配算法的产品的基本视觉任务的大规模基准。凭借注释的180万个四边形实例，该Unitail提供了一个检测数据集，以更好地对齐产品外观。此外，它提供了一个包含1454个产品类别，30k文本区域和21k转录的画廊风格的OCR数据集，以实现对产品的强大阅读并激励增强的产品匹配。除了使用各种最新技术对数据集进行基准测试外，我们还定制了一个新的检测器以进行产品检测，并提供了一个简单的基于OCR的匹配解决方案，以验证其有效性。

In recent years, benefiting from the expressive power of Graph Convolutional Networks (GCNs), significant breakthroughs have been made in face clustering area. However, rare attention has been paid to GCN-based clustering on imbalanced data. Although imbalance problem has been extensively studied, the impact of imbalanced data on GCN- based linkage prediction task is quite different, which would cause problems in two aspects: imbalanced linkage labels and biased graph representations. The former is similar to that in classic image classification task, but the latter is a particular problem in GCN-based clustering via linkage prediction. Significantly biased graph representations in training can cause catastrophic over-fitting of a GCN model. To tackle these challenges, we propose a linkage-based doubly imbalanced graph learning framework for face clustering. In this framework, we evaluate the feasibility of those existing methods for imbalanced image classification problem on GCNs, and present a new method to alleviate the imbalanced labels and also augment graph representations using a Reverse-Imbalance Weighted Sampling (RIWS) strategy. With the RIWS strategy, probability-based class balancing weights could ensure the overall distribution of positive and negative samples; in addition, weighted random sampling provides diverse subgraph structures, which effectively alleviates the over-fitting problem and improves the representation ability of GCNs. Extensive experiments on series of imbalanced benchmark datasets synthesized from MS-Celeb-1M and DeepFashion demonstrate the effectiveness and generality of our proposed method. Our implementation and the synthesized datasets will be openly available on https://github.com/espectre/GCNs_on_imbalanced_datasets.