NaturalFinger: Generating Natural Fingerprint with Generative Adversarial Networks

Deep neural network (DNN) models have become a critical asset of the model owner as training them requires a large amount of resource (i.e. labeled data). Therefore, many fingerprinting schemes have been proposed to safeguard the intellectual property (IP) of the model owner against model extraction and illegal redistribution. However, previous schemes adopt unnatural images as the fingerprint, such as adversarial examples and noisy images, which can be easily perceived and rejected by the adversary. In this paper, we propose NaturalFinger which generates natural fingerprint with generative adversarial networks (GANs). Besides, our proposed NaturalFinger fingerprints the decision difference areas rather than the decision boundary, which is more robust. The application of GAN not only allows us to generate more imperceptible samples, but also enables us to generate unrestricted samples to explore the decision boundary.To demonstrate the effectiveness of our fingerprint approach, we evaluate our approach against four model modification attacks including adversarial training and two model extraction attacks. Experiments show that our approach achieves 0.91 ARUC value on the FingerBench dataset (154 models), exceeding the optimal baseline (MetaV) over 17\%

AI-Generated Images as Data Source: The Dawn of Synthetic Era

The advancement of visual intelligence is intrinsically tethered to the availability of large-scale data. In parallel, generative Artificial Intelligence (AI) has unlocked the potential to create synthetic images that closely resemble real-world photographs. This prompts a compelling inquiry: how much visual intelligence could benefit from the advance of generative AI? This paper explores the innovative concept of harnessing these AI-generated images as new data sources, reshaping traditional modeling paradigms in visual intelligence. In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability, the rapid generation of vast datasets, and the effortless simulation of edge cases. Built on the success of generative AI models, we examine the potential of their generated data in a range of applications, from training machine learning models to simulating scenarios for computational modeling, testing, and validation. We probe the technological foundations that support this groundbreaking use of generative AI, engaging in an in-depth discussion on the ethical, legal, and practical considerations that accompany this transformative paradigm shift. Through an exhaustive survey of current technologies and applications, this paper presents a comprehensive view of the synthetic era in visual intelligence. A project associated with this paper can be found at https://github.com/mwxely/AIGS .Comment: 20 pages, 11 figure

Influence of D-Amino Acids in Beer on Formation of Uric Acid

Prekomjerna konzumacija piva može dovesti do povećanja koncentracije mokraćne kiseline u serumu, čime se povećava rizik nastanka uričnog artritisa (gihta), što se prethodno dovodilo u vezu s velikim udjelom purina u pivu. Međutim, novija istraživanja pokazuju da konzumacija povrća bogatog purinima i grahorica ne povećava koncentraciju mokraćne kiseline, što opovrgava tu tvrdnju. Još uvijek nije objašnjeno zašto povećana konzumacija piva može povećati rizik nastanka gihta, pa su ispitani drugi uzročnici nakupljanja mokraćne kiseline u krvi. Pivo sadržava relativno velike koncentracije D-aminokiselina koje nastaju racemizacijom L-aminokiselina tijekom prerade hrane. Katalizom pomoću D-aminokiselinske oksidaze iz D-aminokiselina nastaje H2O2, čijom oksidacijom u prisutnosti Fe2+ nastaju hidroksilni radikali. Pritom dolazi do oštećenja DNA i nastanka purinskih baza u većoj količini, iz kojih djelovanjem različitih enzima nastaje mokraćna kiselina. Neki dodaci hrani, kao što su vitamini i ioni joda, potiču nastanak mokraćne kiseline iz D-aminokiselina. D-aminokiseline u pivu imaju ključnu ulogu u povećanju koncentracije mokraćne kiseline. Biološka uloga D-aminokiselina može objasniti pojavu gihta kod osoba koje učestalo konzumiraju pivo.Excessive intake of beer could increase serum uric acid levels, leading to high risk of gout, which was previously attributed to high purine content in beer. Recent reports that purine-rich vegetables and bean products do not cause higher uric acid levels do not support this theory. Why excessive intake of beer could increase a high risk of gout has been unclear. Other factors affecting the accumulation of uric acid in the blood have been explored. Beer contains relatively high levels of D-amino acids due to the racemization of l-amino acids induced by food processing. D-amino acid was catalyzed by D-amino acid oxidase to produce H2O2, which is further oxidized in the presence of Fe2+ to produce hydroxyl radicals, resulting in DNA damage and formation of a large amount of purine bases, which are oxidized to uric acid by a series of enzymes. Some food ingredients, such as vitamins and I–, prompt D-amino acids to form uric acid. D-amino acids in beer are one of the key factors responsible for the increase in uric acid levels. The biological response of D-amino acids could explain gout occurrence in beer drinkers

OutlierReviews

Data and code for manuscript "Why outlier opinions in online reviews offer valuable information: the role of social influence

Polarized Collaboration Benefits Knowledge Production

Code and Data for the paper "Polarized Collaboration Benefits Knowledge Production

CogPat

The dataset and the Python code created for the analyses of "Discontinuous relationship between amounts of knowledge and perceptual patterns

The transformation of the Human Rights-based economy in Europe : comments on the proposed corporate sustainability due diligence directive

Published online: 15 December 2022The authors consider that the proposed EU Corporate Sustainability Due Diligence Directive will facilitate the transformation of the Human Rights-based Economy in Europe from voluntary Corporate Social Responsibility to a mandatory Due Diligence Obligation. The sustainable development of human rights and environment will become an essential element in the operation of market economy. Nonetheless, given that the proposed Directive will unilaterally set a high threshold for market access to trade and supply chains, the authors contend that it will be confronted with the legal challenges of incompatibility with EU law and some of its fundamental principles. Additionally, a regulatory gap may occur in implementing the proposed Directive among the member states. In terms of its external dimension, the proposed Directive embodies the technique of extraterritorial extension of EU law to third countries and foreign entities, which will extend its application to non-EU entities through the spill over effect of the supply chain. Moreover, the proposed Directive expands the jurisdictional basis of the member states’ courts to corporate accountability, which will further trigger jurisdictional conflicts between EU member states and third countries as it intervenes in the judicial sovereignty of the latter on human rights and environment. In conclusion, the authors suggest that the Chinese government take constructive measures to address the extraterritorial extension of the proposed Directive on China and Chinese companies

Cooperation patterns of members in networks during co-creation

Abstract Cooperation (i.e., co-creation) has become the principal way of carrying out creative activities in modern society. In co-creation, different participants can play two completely different roles based on two different behaviours: some participants are the originators who generate initial contents, while others are the revisors who provide revisions or coordination. In this study, we investigated different participants’ roles (i.e., the originator vs. the revisor) in co-creation and how these roles affected the final cooperation-group outcome. By using cooperation networks to represent cooperative relationships among participants, we found that peripheral members (i.e., those in the periphery of the cooperation networks) and core members (i.e., those in the centre of the cooperation networks) played the roles of originators and revisors, respectively, mainly affecting the quantity versus the quality of their creative outcomes. These results were robust across the three different datasets and the three different indicators defining core and peripheral members. Previous studies have considered cooperation behaviours to be homogeneous, ignoring that different participants may play different roles in co-creation. This study discusses patterns of cooperation among participants based on a model in which different roles in co-creation are considered. Thus, this research advances the understanding of how co-creation occurs in networks

台灣認購權證市場交易活動變數對標的股票報酬條件波動度影響之研究

衍生性金融商品近幾年來在國際資本市場上可說是扮演著越來越舉足輕重的腳色，由於其槓桿操作的特性，吸引了許多投機、套利、與避險交易者的加入。台灣證券市場衍生性商品直到1997年9月4日才有第一支認購權證上市交易，起步相對於國外許多先進國家可說晚了許多，到研究截稿為止（99年5月底），共計發行過26檔的認購權證。究竟這近二年來台灣證券市場由於認購權證的加入交易，對其標的股票的市場效能（股價波動性、市場深度）是有增益幫助穩定的效果？又或反而是使其更不穩定，不確定性更高？本研究的主要探討內容即是欲研究比較權證的上市交易對其標的股票的市場波動性影響，以及影響的因子為何？本論文的研究主要針對台灣認購權證上市此一事件，以及權證和現貨交易量對其標的股票報酬條件波動性影響作研究，研究結果如下：一、 個股型認購權證的上市對標的股票市場報酬條件波動性有減輕的正面效益，一籃子型權證則無此顯著效果。二、 現貨交易量不論來自於預期、非預期、或長期移動平均其對股票報酬條件波動性都有正相關影響，亦即現貨交易量越大，股票報酬條件波動度越高。三、 未預期權證交易量和長期移動平均權證交易量對權證標的股票報酬條件波動性具有正相關影響，預期權證交易量對其標的股票報酬條件波動度效果則不顯著。四、 標的股票報酬條件波動度不因權證距到期期間變動而有所不同。第一章 緒論 2第一節 研究背景與動機 2第二節 研究目的 3第三節 研究範圍及對象 4第四節 研究架構 5第五節 研究流程 5第六節 研究限制 6第二章 文獻回顧與探討 7第一節 相關理論整理 8第二節 國外相關實證文獻 11第三節 國內相關文獻 15第三章 研究方法 20第一節 資料介紹與整理 21第二節 實證流程 22第三節 實證方法簡介 23第四節 實證方法假設限制 34第五節 實證探討議題 35第四章 實證結果與分析 36第一節 交易量資料的最適ARIMA(p,d,q)模式 36第二節 標的股票交易量對標的報酬條件波動性的影響 40第三節 權證上市對標的股票報酬條件波動性的影響 46第四節 權證與標的股票交易量對標的報酬條件波動性的影響 52第五節 權證距到期期間對標的報酬條件波動性的影響差異研究 58第五章 結論與建議 61第一節 研究結論 61第二節 對後續研究的建議 62第三節 貢獻與影響 63參考文獻 64中文部份： 64英文部份： 6

An Improved Clustering Algorithm of Tunnel Monitoring Data for Cloud Computing

With the rapid development of urban construction, the number of urban tunnels is increasing and the data they produce become more and more complex. It results in the fact that the traditional clustering algorithm cannot handle the mass data of the tunnel. To solve this problem, an improved parallel clustering algorithm based on k-means has been proposed. It is a clustering algorithm using the MapReduce within cloud computing that deals with data. It not only has the advantage of being used to deal with mass data but also is more efficient. Moreover, it is able to compute the average dissimilarity degree of each cluster in order to clean the abnormal data