scholarly journals SynSig2Vec: Learning Representations from Synthetic Dynamic Signatures for Real-World Verification

2020 ◽  
Vol 34 (01) ◽  
pp. 735-742 ◽  
Author(s):  
Songxuan Lai ◽  
Lianwen Jin ◽  
Luojun Lin ◽  
Yecheng Zhu ◽  
Huiyun Mao
Keyword(s):  
State Of The Art ◽  
Synthesis Method ◽  
Representation Learning ◽  
Research Problem ◽  
Signature Verification ◽  
Convolutional Network ◽  
One Dimensional ◽  
Fine Grained ◽  
Open Research ◽  
Dynamic Signature

An open research problem in automatic signature verification is the skilled forgery attacks. However, the skilled forgeries are very difficult to acquire for representation learning. To tackle this issue, this paper proposes to learn dynamic signature representations through ranking synthesized signatures. First, a neuromotor inspired signature synthesis method is proposed to synthesize signatures with different distortion levels for any template signature. Then, given the templates, we construct a lightweight one-dimensional convolutional network to learn to rank the synthesized samples, and directly optimize the average precision of the ranking to exploit relative and fine-grained signature similarities. Finally, after training, fixed-length representations can be extracted from dynamic signatures of variable lengths for verification. One highlight of our method is that it requires neither skilled nor random forgeries for training, yet it surpasses the state-of-the-art by a large margin on two public benchmarks.

scholarly journals Representation Learning for Fine-Grained Change Detection

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4486
Author(s):  
Niall O’Mahony ◽  
Sean Campbell ◽  
Lenka Krpalkova ◽  
Anderson Carvalho ◽  
Joseph Walsh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Model Calibration ◽  
State Of The Art ◽  
Representation Learning ◽  
Machine Intelligence ◽  
The State ◽  
Sensor Data ◽  
Fine Grained ◽  
Learning Techniques

Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.

scholarly journals DMRAN:A Hierarchical Fine-Grained Attention-Based Network for Recommendation

2019 ◽  
Author(s):  
Huizhao Wang ◽  
Guanfeng Liu ◽  
An Liu ◽  
Zhixu Li ◽  
Kai Zheng
Keyword(s):  
Real World ◽  
State Of The Art ◽  
User Preferences ◽  
One Dimensional ◽  
Fine Grained ◽  
Item Level ◽  
Efficiency And Effectiveness ◽  
Time Encoding ◽  
Real World Datasets

The conventional methods for the next-item recommendation are generally based on RNN or one- dimensional attention with time encoding. They are either hard to preserve the long-term dependencies between different interactions, or hard to capture fine-grained user preferences. In this paper, we propose a Double Most Relevant Attention Network (DMRAN) that contains two layers, i.e., Item level Attention and Feature Level Self- attention, which are to pick out the most relevant items from the sequence of user’s historical behaviors, and extract the most relevant aspects of relevant items, respectively. Then, we can capture the fine-grained user preferences to better support the next-item recommendation. Extensive experiments on two real-world datasets illustrate that DMRAN can improve the efficiency and effectiveness of the recommendation compared with the state-of-the-art methods.

scholarly journals Multi-Class Imbalanced Graph Convolutional Network Learning

2020 ◽  
Author(s):  
Min Shi ◽  
Yufei Tang ◽  
Xingquan Zhu ◽  
David Wilson ◽  
Jianxun Liu
Keyword(s):  
State Of The Art ◽  
Representation Learning ◽  
Convolutional Network ◽  
Network Learning ◽  
Convolutional Networks ◽  
Space Experiments ◽  
Latent Distribution ◽  
Adversarial Training ◽  
Quality Of Fit

Networked data often demonstrate the Pareto principle (i.e., 80/20 rule) with skewed class distributions, where most vertices belong to a few majority classes and minority classes only contain a handful of instances. When presented with imbalanced class distributions, existing graph embedding learning tends to bias to nodes from majority classes, leaving nodes from minority classes under-trained. In this paper, we propose Dual-Regularized Graph Convolutional Networks (DR-GCN) to handle multi-class imbalanced graphs, where two types of regularization are imposed to tackle class imbalanced representation learning. To ensure that all classes are equally represented, we propose a class-conditioned adversarial training process to facilitate the separation of labeled nodes. Meanwhile, to maintain training equilibrium (i.e., retaining quality of fit across all classes), we force unlabeled nodes to follow a similar latent distribution to the labeled nodes by minimizing their difference in the embedding space. Experiments on real-world imbalanced graphs demonstrate that DR-GCN outperforms the state-of-the-art methods in node classification, graph clustering, and visualization.

scholarly journals MRLR: Multi-level Representation Learning for Personalized Ranking in Recommendation

2017 ◽  
Author(s):  
Zhu Sun ◽  
Jie Yang ◽  
Jie Zhang ◽  
Alessandro Bozzon ◽  
Yu Chen ◽  
...  
Keyword(s):  
State Of The Art ◽  
Representation Learning ◽  
Full Potential ◽  
Prediction Problem ◽  
Fine Grained ◽  
Multi Level ◽  
Rating Prediction ◽  
Real World Datasets ◽  
Interaction Record ◽  
Personalized Ranking

Representation learning (RL) has recently proven to be effective in capturing local item relationships by modeling item co-occurrence in individual user's interaction record. However, the value of RL for recommendation has not reached the full potential due to two major drawbacks: 1) recommendation is modeled as a rating prediction problem but should essentially be a personalized ranking one; 2) multi-level organizations of items are neglected for fine-grained item relationships. We design a unified Bayesian framework MRLR to learn user and item embeddings from a multi-level item organization, thus benefiting from RL as well as achieving the goal of personalized ranking. Extensive validation on real-world datasets shows that MRLR consistently outperforms state-of-the-art algorithms.

A REVIEW ON STATE-OF-THE-ART FACE RECOGNITION APPROACHES

Fractals ◽  
2017 ◽  
Vol 25 (02) ◽  
pp. 1750025 ◽  
Author(s):  
ZAHID MAHMOOD ◽  
NAZEER MUHAMMAD ◽  
NARGIS BIBI ◽  
TAUSEEF ALI
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Recognition Rate ◽  
Research Problem ◽  
Critical Discussion ◽  
The Past ◽  
Open Research ◽  
Comprehensive Survey ◽  
Facial Images ◽  
Enormous Number

Automatic Face Recognition (FR) presents a challenging task in the field of pattern recognition and despite the huge research in the past several decades; it still remains an open research problem. This is primarily due to the variability in the facial images, such as non-uniform illuminations, low resolution, occlusion, and/or variation in poses. Due to its non-intrusive nature, the FR is an attractive biometric modality and has gained a lot of attention in the biometric research community. Driven by the enormous number of potential application domains, many algorithms have been proposed for the FR. This paper presents an overview of the state-of-the-art FR algorithms, focusing their performances on publicly available databases. We highlight the conditions of the image databases with regard to the recognition rate of each approach. This is useful as a quick research overview and for practitioners as well to choose an algorithm for their specified FR application. To provide a comprehensive survey, the paper divides the FR algorithms into three categories: (1) intensity-based, (2) video-based, and (3) 3D based FR algorithms. In each category, the most commonly used algorithms and their performance is reported on standard face databases and a brief critical discussion is carried out.

scholarly journals Context Modulated Dynamic Networks for Actor and Action Video Segmentation with Language Queries

2020 ◽  
Vol 34 (07) ◽  
pp. 12152-12159
Author(s):  
Hao Wang ◽  
Cheng Deng ◽  
Fan Ma ◽  
Yi Yang
Keyword(s):  
Video Segmentation ◽  
State Of The Art ◽  
Dynamic Networks ◽  
Specific Region ◽  
Visual Features ◽  
Convolutional Network ◽  
Fine Grained ◽  
Convolutional Networks ◽  
Benchmark Datasets ◽  
Context Features

Actor and action video segmentation with language queries aims to segment out the expression referred objects in the video. This process requires comprehensive language reasoning and fine-grained video understanding. Previous methods mainly leverage dynamic convolutional networks to match visual and semantic representations. However, the dynamic convolution neglects spatial context when processing each region in the frame and is thus challenging to segment similar objects in the complex scenarios. To address such limitation, we construct a context modulated dynamic convolutional network. Specifically, we propose a context modulated dynamic convolutional operation in the proposed framework. The kernels for the specific region are generated from both language sentences and surrounding context features. Moreover, we devise a temporal encoder to incorporate motions into the visual features to further match the query descriptions. Extensive experiments on two benchmark datasets, Actor-Action Dataset Sentences (A2D Sentences) and J-HMDB Sentences, demonstrate that our proposed approach notably outperforms state-of-the-art methods.

scholarly journals Mucko: Multi-Layer Cross-Modal Knowledge Reasoning for Fact-based Visual Question Answering

2020 ◽  
Author(s):  
Zihao Zhu ◽  
Jing Yu ◽  
Yujing Wang ◽  
Yajing Sun ◽  
Yue Hu ◽  
...  
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Relevant Information ◽  
Convolutional Network ◽  
Graph Representations ◽  
Fine Grained ◽  
Visual Question Answering ◽  
Knowledge Reasoning ◽  
Final Answer ◽  
The Given

Fact-based Visual Question Answering (FVQA) requires external knowledge beyond the visible content to answer questions about an image. This ability is challenging but indispensable to achieve general VQA. One limitation of existing FVQA solutions is that they jointly embed all kinds of information without fine-grained selection, which introduces unexpected noises for reasoning the final answer. How to capture the question-oriented and information-complementary evidence remains a key challenge to solve the problem. In this paper, we depict an image by a multi-modal heterogeneous graph, which contains multiple layers of information corresponding to the visual, semantic and factual features. On top of the multi-layer graph representations, we propose a modality-aware heterogeneous graph convolutional network to capture evidence from different layers that is most relevant to the given question. Specifically, the intra-modal graph convolution selects evidence from each modality and cross-modal graph convolution aggregates relevant information across different graph layers. By stacking this process multiple times, our model performs iterative reasoning across three modalities and predicts the optimal answer by analyzing all question-oriented evidence. We achieve a new state-of-the-art performance on the FVQA task and demonstrate the effectiveness and interpretability of our model with extensive experiments.

scholarly journals Online Signature Verification: State of the art

2005 ◽  
Vol 4 (2) ◽  
pp. 664-678 ◽  
Author(s):  
Ibrahim El-Henawy ◽  
Magdy Rashad ◽  
Omima Nomir ◽  
Kareem Ahmed
Keyword(s):  
State Of The Art ◽  
User Authentication ◽  
Cost Effective ◽  
The State ◽  
Signature Verification ◽  
Stroke Length ◽  
Online Signature ◽  
Online Signature Verification ◽  
Dynamic Signature ◽  
The Relationship

online or Dynamic signature verification (DSV) is one of the most acceptable, intuitive, fast and cost effective tool for user authentication. DSV uses some dynamics like speed, pressure, directions, stroke length and pen-ups/pen-downs to verify the signer's identity. The state of the art in DSV is presented in this paper. several approaches for DSV are compared and the most influential techniques in this field are highlighted. We concentrate on the relationship between the verification approach used (the nature of the classifier) and the type of features that are used to represent the signature.

Behavioral Genetics: Concepts for Research and Practice in Language Development and Disorders

1995 ◽  
Vol 38 (5) ◽  
pp. 1126-1142 ◽  
Author(s):  
Jeffrey W. Gilger
Keyword(s):  
Language Development ◽  
Behavioral Genetics ◽  
State Of The Art ◽  
Genetic Research ◽  
Great Promise ◽  
Behavioral Genetic ◽  
Fine Grained ◽  
Future Goals ◽  
Current State ◽  
Research Designs

This paper is an introduction to behavioral genetics for researchers and practioners in language development and disorders. The specific aims are to illustrate some essential concepts and to show how behavioral genetic research can be applied to the language sciences. Past genetic research on language-related traits has tended to focus on simple etiology (i.e., the heritability or familiality of language skills). The current state of the art, however, suggests that great promise lies in addressing more complex questions through behavioral genetic paradigms. In terms of future goals it is suggested that: (a) more behavioral genetic work of all types should be done—including replications and expansions of preliminary studies already in print; (b) work should focus on fine-grained, theory-based phenotypes with research designs that can address complex questions in language development; and (c) work in this area should utilize a variety of samples and methods (e.g., twin and family samples, heritability and segregation analyses, linkage and association tests, etc.).

The Influence of Template Ageing on a Dynamic Signature Verification System

2014 ◽  
Vol 24 ◽  
pp. 47-52
Author(s):  
Joanna Putz-Leszczynska
Keyword(s):  
Potential Risk ◽  
Signature Verification ◽  
Verification System ◽  
Verification Systems ◽  
Dynamic Signature

This paper addresses template ageing in automatic signature verification systems. Handwritten signatures are a behavioral biometric sensitive to the passage of time. The experiments in this paper utilized a database that contains signature realizations captured in three sessions. The last session was captured seven years after the first one. The results presented in this paper show a potential risk of using an automatic handwriting verification system without including template ageing Purchase Article for $10 

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