A Review of Modeling and Diagnostic Techniques for Eccentricity Fault in Electric Machines

Research on the modeling and fault diagnosis of rotor eccentricities has been conducted during the past two decades. A variety of diagnostic theories and methods have been proposed based on different mechanisms, and there are reviews following either one type of electric machines or one type of eccentricity. Nonetheless, the research routes of modeling and diagnosis are common, regardless of machine or eccentricity types. This article tends to review all the possible modeling and diagnostic approaches for all common types of electric machines with eccentricities and provide suggestions on future research roadmap. The paper indicates that a reliable low-cost non-intrusive real-time online visualized diagnostic method is the trend. Observer-based diagnostic strategies are thought promising for the continued research

Towards Hard-Positive Query Mining for DETR-based Human-Object Interaction Detection

Human-Object Interaction (HOI) detection is a core task for high-level image understanding. Recently, Detection Transformer (DETR)-based HOI detectors have become popular due to their superior performance and efficient structure. However, these approaches typically adopt fixed HOI queries for all testing images, which is vulnerable to the location change of objects in one specific image. Accordingly, in this paper, we propose to enhance DETR's robustness by mining hard-positive queries, which are forced to make correct predictions using partial visual cues. First, we explicitly compose hard-positive queries according to the ground-truth (GT) position of labeled human-object pairs for each training image. Specifically, we shift the GT bounding boxes of each labeled human-object pair so that the shifted boxes cover only a certain portion of the GT ones. We encode the coordinates of the shifted boxes for each labeled human-object pair into an HOI query. Second, we implicitly construct another set of hard-positive queries by masking the top scores in cross-attention maps of the decoder layers. The masked attention maps then only cover partial important cues for HOI predictions. Finally, an alternate strategy is proposed that efficiently combines both types of hard queries. In each iteration, both DETR's learnable queries and one selected type of hard-positive queries are adopted for loss computation. Experimental results show that our proposed approach can be widely applied to existing DETR-based HOI detectors. Moreover, we consistently achieve state-of-the-art performance on three benchmarks: HICO-DET, V-COCO, and HOI-A. Code is available at https://github.com/MuchHair/HQM.Comment: Accepted by ECCV202

Targeted online password guessing:an underestimated threat

While trawling online/offline password guessing has been intensively studied, only a few studies have examined targeted online guessing, where an attacker guesses a specific victim's password for a service, by exploiting the victim's personal information such as one sister password leaked from her another account and some personally identifiable information (PII). A key challenge for targeted online guessing is to choose the most effective password candidates, while the number of guess attempts allowed by a server's lockout or throttling mechanisms is typically very small. We propose TarGuess, a framework that systematically characterizes typical targeted guessing scenarios with seven sound mathematical models, each of which is based on varied kinds of data available to an attacker. These models allow us to design novel and efficient guessing algorithms. Extensive experiments on 10 large real-world password datasets show the effectiveness of TarGuess. Particularly, TarGuess I~IV capture the four most representative scenarios and within 100 guesses: (1) TarGuess-I outperforms its foremost counterpart by 142% against security-savvy users and by 46% against normal users; (2) TarGuess-II outperforms its foremost counterpart by 169% on security-savvy users and by 72% against normal users; and (3) Both TarGuess-III and IV gain success rates over 73% against normal users and over 32% against security-savvy users. TarGuess-III and IV, for the first time, address the issue of cross-site online guessing when given the victim's one sister password and some PII

Enhancing Model Performance in Multilingual Information Retrieval with Comprehensive Data Engineering Techniques

In this paper, we present our solution to the Multilingual Information Retrieval Across a Continuum of Languages (MIRACL) challenge of WSDM CUP 2023\footnote{https://project-miracl.github.io/}. Our solution focuses on enhancing the ranking stage, where we fine-tune pre-trained multilingual transformer-based models with MIRACL dataset. Our model improvement is mainly achieved through diverse data engineering techniques, including the collection of additional relevant training data, data augmentation, and negative sampling. Our fine-tuned model effectively determines the semantic relevance between queries and documents, resulting in a significant improvement in the efficiency of the multilingual information retrieval process. Finally, Our team is pleased to achieve remarkable results in this challenging competition, securing 2nd place in the Surprise-Languages track with a score of 0.835 and 3rd place in the Known-Languages track with an average nDCG@10 score of 0.716 across the 16 known languages on the final leaderboard