AN ANALYSIS OF AFFECTION ON THE WATER TRANSPARENCY FACTOR OF WEST LAKE

This paper is a special topic study on affecting the water transparency factor and supervising the water transparency of West Lake, 10 years in succession, and expounds the present situation variable, regularity variable, reasons and improvement methods to the water transparency of West Lake systematically and thoroughly. It gains the conclusion that the water transparency of West Lake bears less relationship with the dissolved substance, and the water transparency is mainly affected by the suspended substance. Because the nutrient substance from the base sludge dissolves out and takes place biological transformation very quick, the phosphorus density has a certain drop, however not affecting the growth and propagation of the algae, and not presenting the phenomena of the phosphorus inhibition. We regard the phosphorus isn't the nutrient element inhibiting the algae growth.Article信州大学理学部附属諏訪臨湖実験所報告 11: 3-12(1999)departmental bulletin pape

Towards Robust Model Watermark via Reducing Parametric Vulnerability

Deep neural networks are valuable assets considering their commercial benefits and huge demands for costly annotation and computation resources. To protect the copyright of DNNs, backdoor-based ownership verification becomes popular recently, in which the model owner can watermark the model by embedding a specific backdoor behavior before releasing it. The defenders (usually the model owners) can identify whether a suspicious third-party model is ``stolen'' from them based on the presence of the behavior. Unfortunately, these watermarks are proven to be vulnerable to removal attacks even like fine-tuning. To further explore this vulnerability, we investigate the parameter space and find there exist many watermark-removed models in the vicinity of the watermarked one, which may be easily used by removal attacks. Inspired by this finding, we propose a mini-max formulation to find these watermark-removed models and recover their watermark behavior. Extensive experiments demonstrate that our method improves the robustness of the model watermarking against parametric changes and numerous watermark-removal attacks. The codes for reproducing our main experiments are available at \url{https://github.com/GuanhaoGan/robust-model-watermarking}.Comment: This paper is accepted by ICCV 202

Unsupervised Deep Cross-Language Entity Alignment

Cross-lingual entity alignment is the task of finding the same semantic entities from different language knowledge graphs. In this paper, we propose a simple and novel unsupervised method for cross-language entity alignment. We utilize the deep learning multi-language encoder combined with a machine translator to encode knowledge graph text, which reduces the reliance on label data. Unlike traditional methods that only emphasize global or local alignment, our method simultaneously considers both alignment strategies. We first view the alignment task as a bipartite matching problem and then adopt the re-exchanging idea to accomplish alignment. Compared with the traditional bipartite matching algorithm that only gives one optimal solution, our algorithm generates ranked matching results which enabled many potentials downstream tasks. Additionally, our method can adapt two different types of optimization (minimal and maximal) in the bipartite matching process, which provides more flexibility. Our evaluation shows, we each scored 0.966, 0.990, and 0.996 Hits@1 rates on the DBP15K dataset in Chinese, Japanese, and French to English alignment tasks. We outperformed the state-of-the-art method in unsupervised and semi-supervised categories. Compared with the state-of-the-art supervised method, our method outperforms 2.6% and 0.4% in Ja-En and Fr-En alignment tasks while marginally lower by 0.2% in the Zh-En alignment task.Comment: 17 pages,5 figures, Accepted by ECML PKDD 2023(Research Track