197 research outputs found
経験的手法に基づく地盤の線形および非線形挙動を考慮した地震時地盤増幅特性に関する総合的研究
京都大学新制・課程博士博士(工学)甲第24603号工博第5109号新制||工||1977(附属図書館)京都大学大学院工学研究科建築学専攻(主査)教授 松島 信一, 教授 竹脇 出, 教授 池田 芳樹学位規則第4条第1項該当Doctor of Philosophy (Engineering)Kyoto UniversityDFA
Learning with Silver Standard Data for Zero-shot Relation Extraction
The superior performance of supervised relation extraction (RE) methods
heavily relies on a large amount of gold standard data. Recent zero-shot
relation extraction methods converted the RE task to other NLP tasks and used
off-the-shelf models of these NLP tasks to directly perform inference on the
test data without using a large amount of RE annotation data. A potentially
valuable by-product of these methods is the large-scale silver standard data.
However, there is no further investigation on the use of potentially valuable
silver standard data. In this paper, we propose to first detect a small amount
of clean data from silver standard data and then use the selected clean data to
finetune the pretrained model. We then use the finetuned model to infer
relation types. We also propose a class-aware clean data detection module to
consider class information when selecting clean data. The experimental results
show that our method can outperform the baseline by 12% and 11% on TACRED and
Wiki80 dataset in the zero-shot RE task. By using extra silver standard data of
different distributions, the performance can be further improved.Comment: 21 pages, 6 figure
Firm Actions Toward Data Breach Incidents and Firm Equity Value: An Empirical Study
Managing information resources including protecting the privacy of customer data plays a critical role in most firms. Data breach incidents may be extremely costly for firms. In the face of a data breach event, some firms are reluctant to disclose information to the public. Firm may be concerned with the potential drop in the market value following the revelation of a data breach. This paper examines the impact of data breach incidents to the firm’s market value/equity value, and explores the possibility that certain firm behaviors may reduce the cost of the incidents. We use regression analysis to identify the factors that affect cumulative abnormal stock return (CAR). Our results indicate that when data breach happens, firms not only should notify customers or the public timely, but also try to control the amount of information disclosed. These findings should provide corporate executives with guidance on managing public disclosure of data breach incidents
Blood-brain barrier disruption and perivascular beta-amyloid accumulation in the brain of aged rats with spontaneous hypertension: Evaluation with dynamic contrast-enhanced magnetic resonance imaging
Self-gated late gadolinium enhancement at 7T to image rats with reperfused acute myocardial infarction
Pseudo-Siamese Network based Timbre-reserved Black-box Adversarial Attack in Speaker Identification
In this study, we propose a timbre-reserved adversarial attack approach for
speaker identification (SID) to not only exploit the weakness of the SID model
but also preserve the timbre of the target speaker in a black-box attack
setting. Particularly, we generate timbre-reserved fake audio by adding an
adversarial constraint during the training of the voice conversion model. Then,
we leverage a pseudo-Siamese network architecture to learn from the black-box
SID model constraining both intrinsic similarity and structural similarity
simultaneously. The intrinsic similarity loss is to learn an intrinsic
invariance, while the structural similarity loss is to ensure that the
substitute SID model shares a similar decision boundary to the fixed black-box
SID model. The substitute model can be used as a proxy to generate
timbre-reserved fake audio for attacking. Experimental results on the Audio
Deepfake Detection (ADD) challenge dataset indicate that the attack success
rate of our proposed approach yields up to 60.58% and 55.38% in the white-box
and black-box scenarios, respectively, and can deceive both human beings and
machines.Comment: 5 page
- …