CTP-Net: Character Texture Perception Network for Document Image Forgery Localization

Due to the progression of information technology in recent years, document images have been widely disseminated in social networks. With the help of powerful image editing tools, document images are easily forged without leaving visible manipulation traces, which leads to severe issues if significant information is falsified for malicious use. Therefore, the research of document image forensics is worth further exploring. In a document image, the character with specific semantic information is most vulnerable to tampering, for which capturing the forgery traces of the character is the key to localizing the forged region in document images. Considering both character and image textures, in this paper, we propose a Character Texture Perception Network (CTP-Net) to localize the forgery of document images. Based on optical character recognition, a Character Texture Stream (CTS) is designed to capture features of text areas that are essential components of a document image. Meanwhile, texture features of the whole document image are exploited by an Image Texture Stream (ITS). Combining the features extracted from the CTS and the ITS, the CTP-Net can reveal more subtle forgery traces from document images. To overcome the challenge caused by the lack of fake document images, we design a data generation strategy that is utilized to construct a Fake Chinese Trademark dataset (FCTM). Through a series of experiments, we show that the proposed CTP-Net is able to capture tampering traces in document images, especially in text regions. Experimental results demonstrate that CTP-Net can localize multi-scale forged areas in document images and outperform the state-of-the-art forgery localization methods

Identificación de las especies: Ommastrephes bartramii, Dosidicus gigas, Sthenoteuthis oualaniensis e Illex argentinus (Ommastrephidae) a través de medidas morfológicas de sus picos

Four oceanic squid species, Ommastrephes bartramii, Dosidicus gigas, Sthenoteuthis oualaniensis and Illex argentinus, not only support important commercial fisheries, but also play a vital role in their marine ecosystems. It is therefore important to identify them in the analyses of their predators’ stomach contents as this can yield critical information on the trophic dynamics of ecosystems. Hard beaks of the four species frequently found in their predators’ stomachs can be used to identify them. In this study, to remove the effect of size differences among individuals, measurements of upper and lower beaks were standardized with an allometric model. A discriminant analysis was carried out to compare morphological differences among the four species and between the sexes for each species. The upper rostral width and upper rostral length showed the greatest interspecific variation in the beak morphological variables of the four Ommastrephidae. The linear discriminant functions of beak morphological variables were developed for the four Ommastraphidae, which resulted in a rate of correct species classification of over 97%. Sexual dimorphism was also found in the beak morphology of O. bartramii and I. argentinus. This study suggests that morphological variables can be used to reliably classify Ommastrephidae at genus level, which can help identify the specie in the stomachs of cephalopod predators. This helps to improve the understanding of the role cephalopods play in their marine ecosystems.Las cuatro especies de calamares: Ommastrephes bartramii, Dosidicus gigas, Sthenoteuthis oualaniensis e Illex argentinus, sometidas a una importante presión pesquera, juegan un papel significativo dentro de los ecosistemas marinos a los que pertenecen. Al ser los picos de estas especies resistentes, las medidas de diversos aspectos de su morfología pueden servir para identificarlas en análisis de contenidos estomacales de sus depredadores. Ello permite obtener una información crucial sobre la dinámica trófica de los ecosistemas. En el presente estudio, las medidas realizadas en los picos superior e inferior de los Ommastrephidae se han normalizado mediante un modelo de crecimiento alométrico, para evitar la influencia del efecto tamaño de los individuos. A continuación, mediante un análisis discriminante, se han estudiado las diferencias morfológicas entre las cuatro especies, así como entre machos y hembras. Las medidas que presentaban mayores variaciones eran la anchura y longitud del rostro superior. Mediante funciones discriminantes lineales de las medidas morfológicas normalizadas de sus picos, se han conseguido clasificar las cuatro especies de Ommastraphidae, con una fiabilidad superior al 97%. Asimismo, a través de sus medidas morfológicas, se ha encontrado un claro dimorfismo sexual en los picos de O. bartramii e I. argentinus. El presente estudio sugiere que las medidas morfológicas pueden ser útiles para clasificar correctamente los Ommastrephidae a nivel de género, y puede permitir identificar la especie en contenidos estomacales de depredadores de cefalópodos, lo cual mejorará el conocimiento del papel de los cefalópodos en los ecosistemas marinos en los que se integran

Modelos lineales generalizados bayesianos para la estandardización de CPUE: aplicación a la pesquería de calamar mediante jigging en el Pacífico noroccidental

Generalized linear Bayesian (GLBM) non-hierarchical and hierarchical models were developed for standardization of catch per unit effort (CPUE). The GLBM containing the covariates of month, latitude, sea surface temperature (SST), sea surface salinity (SSS) and sea level height (SLH) had the best fit for the Chinese squid-jigging fishery of Ommastrephes bartramii in the northwest Pacific Ocean based on deviance information criteria. This best-fitting model tends to be more ecologically sound than other CPUE standardization models, such as generalized linear models and generalized additive models. GLBM was also used to deal with the problems of estimating stock abundance index (i.e. standardized CPUE) resulting from increased spatial heterogeneity of spatial dynamics of fishing efforts in the squid fishery by predicting the standardized CPUE for unfished areas. The standardized CPUE based on data including predicted CPUE of unfished areas was lower than the derived CPUE based on data with observed CPUE alone, in particular during the fishing peak of August to October. This study indicates that it is more appropriate to use the standardized CPUE derived from data including both predicted CPUE of unfished areas and observed CPUE of fished area as a stock abundance index. We suggest that the proposed method be used in CPUE standardization to account for impacts of large spatial heterogeneity of fishing efforts in fisheries.Se desarrollan modelos lineales generalizados bayesianos (GLBM) jerárquicos y no-jerárquicos para la estandardización de captura por unidad de esfuerzo (CPUE). El modelo GLBM seleccionado para la pesquería del calamar Ommastrephes bartramii mediante jigging en el Pacífico noroccidental incorporó las variables explicativas mes, latitud, temperatura superficial del mar (SST), salinidad superficial del mar (SSS) y altura del nivel del mar (SLH). La selección del modelo se basó en el Criterio de Información de la Desviación (DIC). El modelo que mejor se ajustó a los datos tiene más sentido ecológico comparado con modelos de estandardización de CPUE basado en modelos lineales generalizados y modelos aditivos generalizados. Se utilizó también el GLBM para tratar el problema de la estimación de un índice de abundancia del stock (es decir, CPUE estandardizada) frente a la elevada heterogeneidad espacial en la dinámica del esfuerzo en la pesquería del calamar mediante la predicción de la CPUE estandardizada en áreas no pescadas. La CPUE estandardizada en base a los datos que incluyen la CPUE predicha en áreas no pescadas fue inferior a la CPUE derivada en base solamente a la CPUE observada, especialmente durante el pico de pesca de Agosto a Octubre. Este estudio muestra que es más apropiado usar la CPUE estandardizada derivada de datos que incluyen al mismo tiempo la CPUE predicha de las áreas no pescadas y la CPUE observada en el área pescada como índice de abundancia del stock. Se sugiere que se use el método propuesto para la estandardización de CPUE teniendo en cuenta la gran heterogeneidad espacial del esfuerzo pesquero

Empower Distantly Supervised Relation Extraction with Collaborative Adversarial Training

With recent advances in distantly supervised (DS) relation extraction (RE), considerable attention is attracted to leverage multi-instance learning (MIL) to distill high-quality supervision from the noisy DS. Here, we go beyond label noise and identify the key bottleneck of DS-MIL to be its low data utilization: as high-quality supervision being refined by MIL, MIL abandons a large amount of training instances, which leads to a low data utilization and hinders model training from having abundant supervision. In this paper, we propose collaborative adversarial training to improve the data utilization, which coordinates virtual adversarial training (VAT) and adversarial training (AT) at different levels. Specifically, since VAT is label-free, we employ the instance-level VAT to recycle instances abandoned by MIL. Besides, we deploy AT at the bag-level to unleash the full potential of the high-quality supervision got by MIL. Our proposed method brings consistent improvements (~ 5 absolute AUC score) to the previous state of the art, which verifies the importance of the data utilization issue and the effectiveness of our method.Comment: Accepted by AAAI 202

An improved stochastic EM algorithm for large-scale full-information item factor analysis

In this paper, we explore the use of the stochastic EM algorithm (Celeux & Diebolt, 1985) for large-scale full-information item factor analysis. Innovations have been made on its implementation, including (1) an adaptive-rejection-based Gibbs sampler for the stochastic E step, (2) a proximal gradient descent algorithm for the optimization in the M step, and (3) diagnostic procedures for determining the burn-in size and the stopping of the algorithm. These developments are based on the theoretical results of Nielsen (2000), as well as advanced sampling and optimization techniques. The proposed algorithm is computationally efficient and virtually tuning-free, making it scalable to large-scale data with many latent traits (e.g. more than five latent traits) and easy to use for practitioners. Standard errors of parameter estimation are also obtained based on the missing information identity (Louis, 1982). The performance of the algorithm is evaluated through simulation studies and an application to the analysis of the IPIP-NEO personality inventory. Extensions of the proposed algorithm to other latent variable models are discussed