In mechanical manufacturing and assembly, precise alignment of the spindle and impeller is essential for improving the efficiency and reliability of automated production lines. With the development of IoT technologies, high-resolution 3D scanning systems enable real-time acquisition of point cloud data, supporting accurate geometric modeling and intelligent alignment. However, traditional RANSAC-based cylindrical fitting methods often suffer from poor robustness and accuracy in complex, noisy environments. This paper proposes a Dual-Consensus RANSAC (DC-RANSAC) algorithm that enhances fitting reliability by fusing global consensus degree—which evaluates the overall agreement of point samples—and local spatial density consistency—which captures the structural coherence of inlier neighborhoods. This dual mechanism addresses the limitations of conventional inlier-count-based evaluations by suppressing pseudo-inlier effects and improving model integrity. Experimental results demonstrate that the proposed algorithm significantly outperforms conventional RANSAC in both fitting accuracy and noise robustness, effectively reducing alignment errors in practical spindle-impeller assemblies.

This paper introduces a novel framework, M4, designed to estimate per-flow quantiles in data streams accurately. M4 is a versatile framework that can be integrated with a wide array of single-flow quantile estimation algorithms, thereby enabling them to perform per-flow estimation. The framework employs a sketch-based approach to provide a space-efficient method for recording and extracting distribution information. M4 incorporates two techniques:MINIMUMandSUM. TheMINIMUMtechnique minimizes the noise on a flow from other flows caused by hash collisions, while theSUMtechnique efficiently categorizes flows based on their sizes and customizes treatment strategies accordingly. We demonstrate the application of M4 on three single-flow quantile estimation algorithms (DDSketch,$t$-digest, and ReqSketch), detailing the specific implementation of theMINIMUMandSUMtechniques. We provide theoretical proof that M4 delivers high accuracy while utilizing limited memory. Additionally, we conduct extensive experiments to evaluate the performance of M4 regarding accuracy and speed. The experimental results indicate that across all three example algorithms, M4 significantly outperforms two comparison frameworks in terms of accuracy for per-flow quantile estimation while maintaining comparable speed.

The field of quantile estimation has grown in importance due to its myriad practical applications. Recent research trends have evolved from estimating the quantile for a single data stream to developing data structures that can concurrently estimate quantiles for multiple sub-streams, also known as flows. This paper introduces a novel framework, M4, designed to estimate per-flow quantiles in data streams accurately. M4 is a versatile framework that can be integrated with a wide array of single-flow quantile estimation algorithms, thereby enabling them to perform per-flow estimation. The framework employs a sketch-based approach to provide a space-efficient method for recording and extracting distribution information. M4 incorporates two techniques: MINIMUM and SUM. The MINIMUM technique minimizes the noise on a flow from other flows caused by hash collisions, while the SUM technique efficiently categorizes flows based on their sizes and customizes treatment strategies accordingly. We demonstrate the application of M4 on three single-flow quantile estimation algorithms (DDSketch, t-digest, and ReqSketch), detailing the specific implementation of the MINIMUM and SUM techniques. We provide theoretical proof that M4 delivers high accuracy while utilizing limited memory. Additionally, we conduct extensive experiments to evaluate the performance of M4 regarding accuracy and speed. The experimental results indicate that across all three example algorithms, M4 significantly outperforms two comparison frameworks in terms of accuracy for per-flow quantile estimation while maintaining comparable speed.

Summary Privacy leakage in images attracts increasing concerns these days, as photos uploaded to large social platforms are usually not processed by proper privacy protection mechanisms. Moreover, with advanced artificial intelligence (AI) tools such as deep neural network (DNN), an adversary can detect people's identities and collect other sensitive personal information from images at an unprecedented scale. In this paper, we introduce a novel face image de‐identification framework using adversarial perturbations in the feature space. Manipulating the feature space vector ensures the good transferability of our framework. Moreover, the proposed feature space adversarial perturbation generation algorithm can successfully protect the identity‐related information while ensuring the other attributes remain similar. Finally, we conduct extensive experiments on two face image datasets to evaluate the performance of the proposed method. Our results show that the proposed method can generate real‐looking privacy‐preserving images efficiently. Although our framework has only been tested on two real‐life face image datasets, it can be easily extended to other types of images.

Privacy protection attracts increasing concerns these days. People tend to believe that large social platforms will comply with the agreement to protect their privacy. However, photos uploaded by people are usually not treated to achieve privacy protection. For example, Facebook, the world's largest social platform, was found leaking photos of millions of users to commercial organizations for big data analytics. A common analytical tool used by these commercial organizations is the Deep Neural Network (DNN). Today's DNN can accurately identify people's appearance, body shape, hobbies and even more sensitive personal information, such as addresses, phone numbers, emails, bank cards and so on. To enable people to enjoy sharing photos without worrying about their privacy, we propose an algorithm that allows users to selectively protect their privacy while preserving the contextual information contained in images. The results show that the proposed algorithm can select and perturb private objects to be protected among multiple optional objects so that the DNN can only identify non-private objects in images.