A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging

In this paper, we propose a new approach to construct a system of transformation rules for the Part-of-Speech (POS) tagging task. Our approach is based on an incremental knowledge acquisition method where rules are stored in an exception structure and new rules are only added to correct the errors of existing rules; thus allowing systematic control of the interaction between the rules. Experimental results on 13 languages show that our approach is fast in terms of training time and tagging speed. Furthermore, our approach obtains very competitive accuracy in comparison to state-of-the-art POS and morphological taggers.Comment: Version 1: 13 pages. Version 2: Submitted to AI Communications - the European Journal on Artificial Intelligence. Version 3: Resubmitted after major revisions. Version 4: Resubmitted after minor revisions. Version 5: to appear in AI Communications (accepted for publication on 3/12/2015

Zooplankton from Can Giuoc River in Southern Vietnam

In this study, the variables of zooplankton and water quality were investigated in the Can Giuoc River, Southern Vietnam. Zooplankton was monitored in April and September 2015 at 5 sampling sites in the river. Some basic water quality parameters were also tested, including pH, total suspended solid (TSS), dissolved oxygen (DO), biological oxygen demand (BOD5), inorganic nitrogen (NH4+), dissolved phosphorus (PO43-), and coliform. The zooplankton biodiversity indices were applied for the water quality assessment. The results showed that pH ranged from 6.7 to 7.6 during the monitoring. The TSSs were between 34–117 mg/L. The DO and BOD5 were from 0.6 to 3.8 mg/L and from 6.3 to 13.2 mg/L, respectively. The NH4+ and PO43- concentrations ranged from 0.44 to 3.23 and from 0.08 to 1.85 mg/L, respectively. The coliform number was between 9.3x103–9.3x104 MPN/100 mL. The zooplankton analyses showed that there were 31 species of coelenterates, rotatoria, oligochaetes, cladocerans, copepods, ostracods, mysidacea, and 8 larval types. Thereof, the species of copepods were dominant in the species number. The zooplankton density ranged from 9 500 to 23 600 individuals/m3 with the main dominant species of Moina dubia (Cladocera), Thermocyclops hyalinus, Acartia clausi, Oithona similis (Copepoda), and nauplius copepods. The biodiversity index values during the monitoring were from 1.47 to 1.79 characteristic of mesotrophic conditions of the aquatic environment. Besides, the species richness positively correlated with pH, TSS, DO, BOD5, NH4+, PO43-, and coliform, while the zooplankton densities got a positive correlation with DO, BOD5, NH4+, PO43-, and coliform. The results confirmed the advantage of using zooplankton and its indices for water quality assessment

Approximation of mild solutions of the linear and nonlinear elliptic equations

In this paper, we investigate the Cauchy problem for both linear and semi-linear elliptic equations. In general, the equations have the form $$\frac{\partial^{2}}{\partial t^{2}}u\left(t\right)=\mathcal{A}u\left(t\right)+f\left(t,u\left(t\right)\right),\quad t\in\left[0,T\right],$$ where $\mathcal{A}$ is a positive-definite, self-adjoint operator with compact inverse. As we know, these problems are well-known to be ill-posed. On account of the orthonormal eigenbasis and the corresponding eigenvalues related to the operator, the method of separation of variables is used to show the solution in series representation. Thereby, we propose a modified method and show error estimations in many accepted cases. For illustration, two numerical examples, a modified Helmholtz equation and an elliptic sine-Gordon equation, are constructed to demonstrate the feasibility and efficiency of the proposed method.Comment: 29 pages, 16 figures, July 201

The First Record of Metaphire Birmanica (Rosa, 1888) in Vietnam, with Notes on Several Earthworm Species

The Amynthas and Metaphire species recorded in Vietnam have been rechecked based on original descriptions and preserved specimens. As a result, Metaphire birmanica (Rosa, 1888) is recorded in Vietnam for the first time. The species is recognized by having three pairs of spermathecal pores in 5/6/7/8, male pores in xviii, presence of copulatory pouches, no genial markings, and manicate intestinal caeca. In addition, three species have been rechecked and re-assigned to different genera, namely Amynthas tripidoporophoratus (Thai & Nguyen, 1993) comb. nov., Metaphire dranfocana (Do & Huynh, 1993) comb. nov., Metaphire anhumalatana (Thai & Huynh, 1993) comb. nov

CLIPping the Deception: Adapting Vision-Language Models for Universal Deepfake Detection

The recent advancements in Generative Adversarial Networks (GANs) and the emergence of Diffusion models have significantly streamlined the production of highly realistic and widely accessible synthetic content. As a result, there is a pressing need for effective general purpose detection mechanisms to mitigate the potential risks posed by deepfakes. In this paper, we explore the effectiveness of pre-trained vision-language models (VLMs) when paired with recent adaptation methods for universal deepfake detection. Following previous studies in this domain, we employ only a single dataset (ProGAN) in order to adapt CLIP for deepfake detection. However, in contrast to prior research, which rely solely on the visual part of CLIP while ignoring its textual component, our analysis reveals that retaining the text part is crucial. Consequently, the simple and lightweight Prompt Tuning based adaptation strategy that we employ outperforms the previous SOTA approach by 5.01% mAP and 6.61% accuracy while utilizing less than one third of the training data (200k images as compared to 720k). To assess the real-world applicability of our proposed models, we conduct a comprehensive evaluation across various scenarios. This involves rigorous testing on images sourced from 21 distinct datasets, including those generated by GANs-based, Diffusion-based and Commercial tools

Hybrid Transformer Network for Deepfake Detection

Deepfake media is becoming widespread nowadays because of the easily available tools and mobile apps which can generate realistic looking deepfake videos/images without requiring any technical knowledge. With further advances in this field of technology in the near future, the quantity and quality of deepfake media is also expected to flourish, while making deepfake media a likely new practical tool to spread mis/disinformation. Because of these concerns, the deepfake media detection tools are becoming a necessity. In this study, we propose a novel hybrid transformer network utilizing early feature fusion strategy for deepfake video detection. Our model employs two different CNN networks, i.e., (1) XceptionNet and (2) EfficientNet-B4 as feature extractors. We train both feature extractors along with the transformer in an end-to-end manner on FaceForensics++, DFDC benchmarks. Our model, while having relatively straightforward architecture, achieves comparable results to other more advanced state-of-the-art approaches when evaluated on FaceForensics++ and DFDC benchmarks. Besides this, we also propose novel face cut-out augmentations, as well as random cut-out augmentations. We show that the proposed augmentations improve the detection performance of our model and reduce overfitting. In addition to that, we show that our model is capable of learning from considerably small amount of data.publishedVersio