scholarly journals n-Gram-Based Text Compression

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Vu H. Nguyen ◽  
Hien T. Nguyen ◽  
Hieu N. Duong ◽  
Vaclav Snasel
Keyword(s):  
Compression Ratio ◽  
Efficient Method ◽  
State Of The Art ◽  
Sliding Window ◽  
Experimental Results ◽  
Text Compression ◽  
News Agencies ◽  
Art Methods ◽  
N Gram ◽  
Significant Compression

We propose an efficient method for compressing Vietnamese text usingn-gram dictionaries. It has a significant compression ratio in comparison with those of state-of-the-art methods on the same dataset. Given a text, first, the proposed method splits it inton-grams and then encodes them based onn-gram dictionaries. In the encoding phase, we use a sliding window with a size that ranges from bigram to five grams to obtain the best encoding stream. Eachn-gram is encoded by two to four bytes accordingly based on its correspondingn-gram dictionary. We collected 2.5 GB text corpus from some Vietnamese news agencies to buildn-gram dictionaries from unigram to five grams and achieve dictionaries with a size of 12 GB in total. In order to evaluate our method, we collected a testing set of 10 different text files with different sizes. The experimental results indicate that our method achieves compression ratio around 90% and outperforms state-of-the-art methods.

scholarly journals Automatic Detection of Discrimination Actions from Social Images

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 325
Author(s):  
Zhihao Wu ◽  
Baopeng Zhang ◽  
Tianchen Zhou ◽  
Yan Li ◽  
Jianping Fan
Keyword(s):  
Action Recognition ◽  
State Of The Art ◽  
Automatic Detection ◽  
Experimental Results ◽  
Practical Approach ◽  
Detection And Identification ◽  
Art Methods ◽  
Image Set ◽  
Social Images ◽  
Relationship Identification

In this paper, we developed a practical approach for automatic detection of discrimination actions from social images. Firstly, an image set is established, in which various discrimination actions and relations are manually labeled. To the best of our knowledge, this is the first work to create a dataset for discrimination action recognition and relationship identification. Secondly, a practical approach is developed to achieve automatic detection and identification of discrimination actions and relationships from social images. Thirdly, the task of relationship identification is seamlessly integrated with the task of discrimination action recognition into one single network called the Co-operative Visual Translation Embedding++ network (CVTransE++). We also compared our proposed method with numerous state-of-the-art methods, and our experimental results demonstrated that our proposed methods can significantly outperform state-of-the-art approaches.

scholarly journals Random Forest with Adaptive Local Template for Pedestrian Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Tao Xiang ◽  
Tao Li ◽  
Mao Ye ◽  
Zijian Liu
Keyword(s):  
Computer Vision ◽  
Random Forest ◽  
Classification Accuracy ◽  
Template Matching ◽  
Detection Method ◽  
State Of The Art ◽  
Pedestrian Detection ◽  
Sliding Window ◽  
Experimental Results ◽  
Training Samples

Pedestrian detection with large intraclass variations is still a challenging task in computer vision. In this paper, we propose a novel pedestrian detection method based on Random Forest. Firstly, we generate a few local templates with different sizes and different locations in positive exemplars. Then, the Random Forest is built whose splitting functions are optimized by maximizing class purity of matching the local templates to the training samples, respectively. To improve the classification accuracy, we adopt a boosting-like algorithm to update the weights of the training samples in a layer-wise fashion. During detection, the trained Random Forest will vote the category when a sliding window is input. Our contributions are the splitting functions based on local template matching with adaptive size and location and iteratively weight updating method. We evaluate the proposed method on 2 well-known challenging datasets: TUD pedestrians and INRIA pedestrians. The experimental results demonstrate that our method achieves state-of-the-art or competitive performance.

scholarly journals RefNet: A Reference-Aware Network for Background Based Conversation

2020 ◽  
Vol 34 (05) ◽  
pp. 8496-8503 ◽  
Author(s):  
Chuan Meng ◽  
Pengjie Ren ◽  
Zhumin Chen ◽  
Christof Monz ◽  
Jun Ma ◽  
...  
Keyword(s):  
Semantic Information ◽  
State Of The Art ◽  
Experimental Results ◽  
Background Information ◽  
Art Methods ◽  
The Right

Existing conversational systems tend to generate generic responses. Recently, Background Based Conversation (BBCs) have been introduced to address this issue. Here, the generated responses are grounded in some background information. The proposed methods for BBCs are able to generate more informative responses, however, they either cannot generate natural responses or have difficulties in locating the right background information. In this paper, we propose a Reference-aware Network (RefNet) to address both issues. Unlike existing methods that generate responses token by token, RefNet incorporates a novel reference decoder that provides an alternative way to learn to directly select a semantic unit (e.g., a span containing complete semantic information) from the background. Experimental results show that RefNet significantly outperforms state-of-the-art methods in terms of both automatic and human evaluations, indicating that RefNet can generate more appropriate and human-like responses.

EFFICIENT METHOD FOR DESIGNING MODULO {2n ± k} MULTIPLIERS

2014 ◽  
Vol 23 (01) ◽  
pp. 1450001 ◽  
Author(s):  
HECTOR PETTENGHI ◽  
SORIN COTOFANA ◽  
LEONEL SOUSA
Keyword(s):  
Efficient Method ◽  
State Of The Art ◽  
Number System ◽  
Residue Number System ◽  
The State ◽  
Experimental Results ◽  
Average Improvement ◽  
Residue Number

In this paper, an efficient method for designing memoryless modulo {2n ± k} multipliers is proposed, which can be used to compose larger residue number system (RNS) moduli sets. This technique includes a novel choice for the weights associated with the partial products of the inputs is used, which improves the performance of the resulting multipliers. Experimental results suggest that the use of this choice of input weights in the structure herein proposed, provides an average improvement of 36.3% in area-delay-product (ADP) in comparison with the related state-of-the-art. Furthermore, the structures presented in the state-of-the-art are also improved by 43.5% in ADP.

scholarly journals Burrows–Wheeler Transform Based Lossless Text Compression Using Keys and Huffman Coding

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1654
Author(s):  
Md. Atiqur Rahman ◽  
Mohamed Hamada
Keyword(s):  
Compression Ratio ◽  
State Of The Art ◽  
Huffman Coding ◽  
Text Compression ◽  
Small Problem ◽  
Research Fields ◽  
Huffman Encoding ◽  
Significant Research ◽  
Burrows Wheeler Transform ◽  
Use Of Internet

Text compression is one of the most significant research fields, and various algorithms for text compression have already been developed. This is a significant issue, as the use of internet bandwidth is considerably increasing. This article proposes a Burrows–Wheeler transform and pattern matching-based lossless text compression algorithm that uses Huffman coding in order to achieve an excellent compression ratio. In this article, we introduce an algorithm with two keys that are used in order to reduce more frequently repeated characters after the Burrows–Wheeler transform. We then find patterns of a certain length from the reduced text and apply Huffman encoding. We compare our proposed technique with state-of-the-art text compression algorithms. Finally, we conclude that the proposed technique demonstrates a gain in compression ratio when compared to other compression techniques. A small problem with our proposed method is that it does not work very well for symmetric communications like Brotli.

scholarly journals Feature Prioritization and Regularization Improve Standard Accuracy and Adversarial Robustness

2019 ◽  
Author(s):  
Chihuang Liu ◽  
Joseph JaJa
Keyword(s):  
Classification Accuracy ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Trade Off ◽  
Experimental Strategy ◽  
Art Methods ◽  
Adversarial Training ◽  
Standard Classification ◽  
Improve Standard

Adversarial training has been successfully applied to build robust models at a certain cost. While the robustness of a model increases, the standard classification accuracy declines. This phenomenon is suggested to be an inherent trade-off. We propose a model that employs feature prioritization by a nonlinear attention module and L2 feature regularization to improve the adversarial robustness and the standard accuracy relative to adversarial training. The attention module encourages the model to rely heavily on robust features by assigning larger weights to them while suppressing non-robust features. The regularizer encourages the model to extract similar features for the natural and adversarial images, effectively ignoring the added perturbation. In addition to evaluating the robustness of our model, we provide justification for the attention module and propose a novel experimental strategy that quantitatively demonstrates that our model is almost ideally aligned with salient data characteristics. Additional experimental results illustrate the power of our model relative to the state of the art methods.

scholarly journals Smoothing methods for the treatment of digital texts

2015 ◽  
Vol 2 (1) ◽  
pp. 42
Author(s):  
Ruben Dorado
Keyword(s):  
Statistical Models ◽  
State Of The Art ◽  
The State ◽  
Smoothing Methods ◽  
Digital Texts ◽  
Language Representation ◽  
Art Methods ◽  
N Gram ◽  
Exploration Task

ONTARE. REVISTA DE INVESTIGACIÓN DE LA FACULTAD DE INGENIERÍAThis article describes the exploration task known as smoothing for statistical language representation. It also reviews some of the state- of-the-art methods that improve the representation of language in a statistical way. Specifically, these reported methods improve statistical models known as N-gram models. This paper also shows a method to measure models in order to compare them. 

scholarly journals Modeling Multi-Purpose Sessions for Next-Item Recommendations via Mixture-Channel Purpose Routing Networks

2019 ◽  
Author(s):  
Shoujin Wang ◽  
Liang Hu ◽  
Yan Wang ◽  
Quan Z. Sheng ◽  
Mehmet Orgun ◽  
...  
Keyword(s):  
Recommender System ◽  
Channel Model ◽  
State Of The Art ◽  
Recurrent Network ◽  
The State ◽  
Experimental Results ◽  
Art Methods ◽  
Recommendation Accuracy ◽  
The Difference

A session-based recommender system (SBRS) suggests the next item by modeling the dependencies between items in a session. Most of existing SBRSs assume the items inside a session are associated with one (implicit) purpose. However, this may not always be true in reality, and a session may often consist of multiple subsets of items for different purposes (e.g., breakfast and decoration). Specifically, items (e.g., bread and milk) in a subsethave strong purpose-specific dependencies whereas items (e.g., bread and vase) from different subsets have much weaker or even no dependencies due to the difference of purposes. Therefore, we propose a mixture-channel model to accommodate the multi-purpose item subsets for more precisely representing a session. Filling gaps in existing SBRSs, this model recommends more diverse items to satisfy different purposes. Accordingly, we design effective mixture-channel purpose routing networks (MCPRN) with a purpose routing network to detect the purposes of each item and assign it into the corresponding channels. Moreover, a purpose specific recurrent network is devised to model the dependencies between items within each channel for a specific purpose. The experimental results show the superiority of MCPRN over the state-of-the-art methods in terms of both recommendation accuracy and diversity.  

scholarly journals Cross-Modality Paired-Images Generation for RGB-Infrared Person Re-Identification

2020 ◽  
Vol 34 (07) ◽  
pp. 12144-12151
Author(s):  
Guan-An Wang ◽  
Tianzhu Zhang ◽  
Yang Yang ◽  
Jian Cheng ◽  
Jianlong Chang ◽  
...  
Keyword(s):  
State Of The Art ◽  
Experimental Results ◽  
Fine Grained ◽  
Invariant Features ◽  
Proposed Model ◽  
Art Methods ◽  
Conventional Methods

RGB-Infrared (IR) person re-identification is very challenging due to the large cross-modality variations between RGB and IR images. The key solution is to learn aligned features to the bridge RGB and IR modalities. However, due to the lack of correspondence labels between every pair of RGB and IR images, most methods try to alleviate the variations with set-level alignment by reducing the distance between the entire RGB and IR sets. However, this set-level alignment may lead to misalignment of some instances, which limits the performance for RGB-IR Re-ID. Different from existing methods, in this paper, we propose to generate cross-modality paired-images and perform both global set-level and fine-grained instance-level alignments. Our proposed method enjoys several merits. First, our method can perform set-level alignment by disentangling modality-specific and modality-invariant features. Compared with conventional methods, ours can explicitly remove the modality-specific features and the modality variation can be better reduced. Second, given cross-modality unpaired-images of a person, our method can generate cross-modality paired images from exchanged images. With them, we can directly perform instance-level alignment by minimizing distances of every pair of images. Extensive experimental results on two standard benchmarks demonstrate that the proposed model favourably against state-of-the-art methods. Especially, on SYSU-MM01 dataset, our model can achieve a gain of 9.2% and 7.7% in terms of Rank-1 and mAP. Code is available at https://github.com/wangguanan/JSIA-ReID.

scholarly journals Panoptic Segmentation-Based Attention for Image Captioning

2020 ◽  
Vol 10 (1) ◽  
pp. 391
Author(s):  
Wenjie Cai ◽  
Zheng Xiong ◽  
Xianfang Sun ◽  
Paul L. Rosin ◽  
Longcun Jin ◽  
...  
Keyword(s):  
Main Part ◽  
State Of The Art ◽  
Image Representation ◽  
Experimental Results ◽  
Competitive Performance ◽  
Image Captioning ◽  
Feature Vectors ◽  
Fine Grained ◽  
Art Methods

Image captioning is the task of generating textual descriptions of images. In order to obtain a better image representation, attention mechanisms have been widely adopted in image captioning. However, in existing models with detection-based attention, the rectangular attention regions are not fine-grained, as they contain irrelevant regions (e.g., background or overlapped regions) around the object, making the model generate inaccurate captions. To address this issue, we propose panoptic segmentation-based attention that performs attention at a mask-level (i.e., the shape of the main part of an instance). Our approach extracts feature vectors from the corresponding segmentation regions, which is more fine-grained than current attention mechanisms. Moreover, in order to process features of different classes independently, we propose a dual-attention module which is generic and can be applied to other frameworks. Experimental results showed that our model could recognize the overlapped objects and understand the scene better. Our approach achieved competitive performance against state-of-the-art methods. We made our code available.

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