Miracle-dz/UniFAR-FTUs huggingface dataset:
Convolutional Neural Network based Eye Recognition from Distantly Acquired Face Images for Human Identification

Eye image recognition from a face image acquired at a distant is a promising physical biometric technique to use for human identification. This contemporary field of research depends on image preprocessing, feature extraction, and reliable classification techniques. In this work, we separate eye images from an image of the entire face of a subject and then extract features from these eye images utilizing a convolutional neural network (CNN) model. In general, CNN models convolve images in different layers to extract effective features and then use the softmax function to produce a probability output in the final layer. In our approach, we use CNN features and a kernel extreme learning machine (KELM) classifier instead of softmax to modify the original CNN model. The modified CNN-KELM model has been verified using the publicly available CASIA.v4 distance image database. The experimental results demonstrate that our proposed approach obtains a satisfactory recognition result when compared with several current state-of-the-art human identification approaches.
Deep Multimodal Reconstruction of Retinal Images Using Paired or Unpaired Data

This paper explores the application of deep learning-based methods for the multimodal reconstruction of fluorescein angiography from retinography. The objective of this multimodal reconstruction is not only to estimate an invasive modality from a non-invasive one, but also to apply the learned models for transfer learning or domain adaption.Deep neural networks have demonstrated to be successful at learning the mapping between complementary image domains, using both paired or unpaired data. The paired data allows taking advantage of the rich information that is available from the pixelwise correspondence of paired images. However, this requires the pre-registration of the multimodal image pairs. In the case of the retinal images, the multimodal registration is a challenging task that may fail in complex scenarios, such as severe pathological cases or low quality samples. In contrast, the use of generative adversarial networks allows learning the mapping between image domains using unpaired data. This avoids the preregistration of the images and allows including all the available data for training.In this work, we analyze both paired and unpaired deep learning-based approaches for the multimodal reconstruction of retinal images. The objective is to understand the implications of each alternative and the considerations for their future usage. For that purpose, we perform several experiments with the focus on producing a fair comparison between paired and unpaired approaches.
Diting: An Author Disambiguation Method Based on Network Representation Learning

It is important to disambiguate names among persons in many scenarios. In this work, we propose an unsupervised method Diting and a semi-supervised method Diting++ for author disambiguation. In Diting, we learn a low-dimensional vector to represent each paper in networks, which are formed by connecting papers with multiple types of relationship (such as co-author). During representation learning, we focus on maximizing the gap between positive edges and negative edges. Further, we propose a clustering algorithm which associates papers to their real-life authors. To make full use of the authorship information, which is easy to obtain from the authors’ homepages, we design Diting++ to improve the performance for name disambiguation. Diting++ uses the authorship information listed on the authors’ homepages to construct label networks and uses a network representation learning method to learn paper representations based on label networks and other networks. Further, Diting++ uses a semi-supervised clustering method to partition learned paper representations into disjoint groups. Each group belongs to a distinct author. By making use of the label information, the clustering method partitions papers written by the same author in the same group, whereas papers written by different authors locate in different groups. Through extensive experiments, we show that our methods are significantly better than the state-of-the-art author disambiguation methods.
Distantly Supervised Relation Extraction through a Trade-off Mechanism

Distantly supervised relation extraction can label large amounts of unstructured text without human annotations for training. However, distant supervision inevitably accompanies with the wrong labeling problem, which can deteriorate the performance of relation extraction. What’s more, the entity-pair information, which can enrich instance information, is still underutilized. In the light of these issues, we propose TMNN, a novel Neural Network framework with a Trade-off Mechanism, which combines the feature of text and entity pair on the sentence level to predict relations. Our proposed trade-off mechanism is a probability generation module to dynamically adjust the weights of text and corresponding entity pair for each sentence. Experimental results on a widely used dataset show that the proposed method reduces the noisy labels and achieves substantial improvement over the state-of-the-art methods.
Dimension Estimation and Topological Manifold Learning

We describe a framework which can be used to investigate the geometrical structure of datasets in high-dimensional spaces. Understanding these geometrical properties is essential in machine learning in general. An application which has received much attention recently is the investigation of adversarial examples which can be easily identified by humans but which are misleading neural networks. We will argue that these examples can be understood (and consequently avoided) by an investigation of the dimension and geometry of the classification hypersurface of the underlying neural network as a manifold using the tools introduced in this paper.We argue that a key concept in the analysis is the dimension of a manifold. Many machine learning methods use dimension reduction techniques to understand or classify the input data. We point out different definitions of a dimension of a manifold. We use tools from the theory of topological manifolds to introduce local, or intrinsic and global dimensions. The local dimension describes the local similarity between the dataset and a Euclidean space. The global dimension is the lowest dimension of a Euclidean space into which the dataset can be embedded. For a data space with a Riemannian structure the isometric embedding dimension is the lowest dimensional space into which the dataset can be embedded under the condition that the metric tensor is preserved between the two spaces.We introduce a framework for dimension estimation and topological manifold learning based on the measure ratio method to estimate the dimensions and structure of the data manifold. As an illustration we use images of handwritten digits and points of a Klein-bottle embedded in a five-dimensional space. We compare the results obtained by the measure ratio method with the well-known local-principal component analysis estimation.
TCoD: A Traveling Companion Discovery Method Based on Clustering and Association Analysis

Widely used mobile locating equipment, like phones, generates extensive spatio-temporal data every day. Since the data implicitly reflects behavior characteristics of moving objects, many applications focus on trajectory data mining while traveling companion discovery is one of the most fundamental techniques in these areas. Previous work based on time snapshot slicing have yielded some success on finding companions in regularly and frequently sampled trajectory data. However, it does not work well when data is sparse. This situation commonly appears in real world because of equipment failure or manual intervention. In this paper, we propose a novel Traveling Companion Discovery (TCoD) method that can discovery travelling companions even when time gaps between sample data are more than hours. TCoD combines density clustering and association analysis, while density clustering mine potential sets from perspective of location and association analysis identify related objects in potential sets. The evaluation on two real trajectory data sets shows that TCoD effectively overwhelms previous work with satisfying discovery of long-term companion patterns in sparse trajectory data.
Optimal control tuning of a VSC converter for microgrid applications

A methodology for optimally tune PI controls in voltage source converters (VSC) is presented. The parameters of the proportional and integral level zero and primary control of the VSC are considered by proposing an objective function based on the error estimation, from the desired and the measured outputs. The minimization of the objective function is performed using particle swarm optimization (PSO) technique. The proposed methodology and the test scenarios are implemented in MATLAB-Simulink for a grid-connected microgrid, which includes a renewable source, load, distribution line and disturbances in reference signals and in the microgrid. The results show the advantages of the proposed methodology considering the adequate performance of the proportional integral control of the converter.
Competitive STDP-based Feature Representation Learning for Sound Event Classification

Humans are good at discriminating environmental sounds and associating them with opportunities or dangers. While the deep learning approach to sound event classification (SEC) is achieving human parity, unsolved problems remain, instances include high computational cost, requirement of massive labeled training data, and question of biological plausibility. Motivated by the human auditory system, we propose a biologically plausible SEC system, which integrates the auditory front-end, population coding, competitive spike-timing-dependent plasticity (STDP) based feature representation learning and supervised temporal classification into a unified spiking neural network (SNN) system. The proposed SEC system achieves a classification accuracy on the RWCP database that is on par with other competitive baseline systems. Furthermore, the STDP-based feature representation learning shows low intra-class variability and high inter-class variability in our experiments, which is highly desirable for pattern classification tasks.
Self-Supervised Deep Learning for Retinal Vessel Segmentation Using Automatically Generated Labels from Multimodal Data

This paper presents a novel approach that allows training convolutional neural networks for retinal vessel segmentation without manually annotated labels. In order to learn how to segment the retinal vessels, convolutional neural networks are typically trained with a set of pixel-level labels annotated by a clinical expert. This annotation is a tedious and error-prone task that limits the number of available training samples.To alleviate this problem, we propose the use of unlabeled multimodal data for learning about the retinal vasculature. Instead of using manually annotated labels, the networks learn to segment the retinal vessels from a complementary image modality where the vasculature is already highlighted. In this complementary modality, a vessel map can be easily constructed with simple image processing techniques. Then, a convolutional neural network is trained to learn the cross-modal mapping from the original modality to the automatically derived vessel maps. Using this strategy, the supervisory signal for training is automatically obtained from the unlabeled multimodal data. Thus, the number of training samples can be increased without any human annotation effort.Several experiments were conducted to evaluate the performance of the networks that were trained with the automatically derived labels, obtaining competitive results for retinal vessel segmentation in relevant public datasets. Furthermore, the results are promising towards including the presented approach in semi-supervised methods.
Non-Cooperative Interference Control for Random D2D Communications with Minority Game

In this paper, we present a minority game approach for the interference coordination in the distributed random device-to-device (D2D) communications cellular network. The proximity D2D pairs could access the shared channel and the transmissions generate interference to each other. As the number of devices increases, the sum rate does not necessarily increase accordingly, but could saturate in the high SNR region due to the interferences. Consequently, the energy efficiency of the system degrades as more devices access to the same channel without much gain in sum rate. In this paper, a non-cooperative minority game method is formulated for the distributed D2D communications network to determine the activity strategy of each device. The approach seeks to balance between the activities of devices signal transmission and the corresponding inter-device interferences. The overall energy efficiency of the system can be improved. The simulation results show that the system energy efficiency is improved with minority game based activity control. Furthermore, with distributed power control, near Pareto optimality energy efficiency is achievable.
Author Disambiguation through Adversarial Network Representation Learning

Many persons share with the same name. Distinguishing different persons with the same name is important but challenging. Albeit much work has been proposed for author disambiguation, most of them do not adequately consider the heterogeneous relationships among authors and papers. In our work, ambiguous names and their related information, such as papers, conferences, titles, abstracts, etc., are constructed into a heterogeneous network which consists of different edge types. To fully incorporate all the information of the constructed network, we use Generative Adversarial Networks (GAN) to learn the network representation of the heterogeneous network. Although GAN has been used in many fields such as image generation, it hasn’t been used to obtain representations for the heterogeneous network. As far as we know, our work is the first work which use adversarial training to learn heterogeneous network representation. After the representations are learned, they are partitioned into different groups each representing distinct authors. After extensive experiments on three major author disambiguation datasets, we demonstrate that our method outperforms several state-of-the-art baselines in author disambiguation problem.
A Method based on Convolutional Neural Networks for Fingerprint Segmentation

In forensic science, the resolution of crimes is associated with the identification of those involved. In the civil context, the security of automated processes depends on the identification of authorized people. In this sense, fingerprint-based recognition techniques stand out. A fundamental stage is the calculation of the degree of similarity between the samples presented, so the task of identifying a region of interest (ROI), excluding noisy regions, can improve the precision and reduce the computational cost. In this aspect, this work presents a technique of segmentation of the region of interest based on convolutional neural networks (CNN) without pre-processing steps. The new approach was evaluated in two different architectures from state of the art, presenting similarity indexes Distance of Hausdorff (5.92), Dice coefficient (97.28%) and Jaccard Similarity (96.77%) superior to the classic methods. The error rate (3.22%) was better than five segmentation techniques from state of the art and showed better results than another deep learning approach, presenting promising results to identify the region of interest with potential for application in systems based on biometric identification.
A UL-NOMA system providing low E2E latency

The 5G (5th generation mobile communications systems) will not only realize high speed and high capacity communication but also support the Internet of Things (IoT). To support IoT, it is important that a base station (BS) of 5G is able to deal with massive uplink signals transmitted from IoT devices. In addition, some IoT devices used for automatic operation require low latency communication. Therefore, low latency communication is also important to support IoT. We have developed techniques based on non-orthogonal multiple access in uplink (UL-NOMA) in order to realize both massive device connectivity and low latency. Although we could not measure the delay time between an UE transmitting uplink data and receiving downlink data (E2E latency) because of no downlink signal in our previous system, we developed a downlink system and an application to measure E2E latency, and measured E2E latency at this time. We conducted the experiment using hardware to evaluate the E2E latency. Under the condition of Rician and Nakagami fading with $K$ factor = 20 dB, we confirm that the probability that our system can achieve an E2E latency of 20 ms or less is about 90% when two UEs send the uplink signals. In the case of three UEs, the probability is about 50%.
Automatic Image Annotation based on Co-Training

To learn a well-performed image annotation model, a large number of labeled samples are usually required. Although the unlabeled samples are readily available and abundant, it’s a difficult task for humans to annotate large amounts of images manually. In this paper, we propose a novel semi-supervised approach based on co-training algorithm for automatic image annotation, which can utilize the labeled data and unlabeled data for the system simultaneously. Firstly, two different classifiers, namely the CNN (convolutional neural network) and the LDA-SVM, are constructed by all the labeled data. These two classifiers are independently represented as different feature views. Then, the most confident data with relevant pseudo-labels are chosen and amalgamated with the whole labeled dataset. After that, the two classifiers are retrained with the new labeled dataset until a stop condition is reached. In each iteration process, the unlabeled samples are labeled by high confidence pseudo-labels that are estimated by an adaptive weighted fusion method. Finally, we conduct experiments on two datasets, namely, IAPR TC-2 and NUS-WIDE, and measure the performance of the model with standard criteria, including precision, recall, F-measure, N+ and mAP. The experimental results show that our approach has superior annotation performance and outperforms many state-of-the-art automatic image annotation approaches.
Improving Visual Road Condition Assessment by Extensive Experiments on the Extended GAPs Dataset

Aging public roads need frequent inspections in order to guarantee their permanent availability. In many countries, this includes the standardized visual assessment of millions of images. Due to the lack of sophisticated approaches, often, the evaluation is done manually and therefore requires excessive manual labor. GAPs is the most extensive publicly available dataset that provides standardized, high-quality images for training deep neural networks for pavement distress detection. We further enlarge this dataset and provide refined annotations. By conducting extensive experiments on the GAPs dataset, we improve the performance of automated visual road condition assessment. We evaluate the performance gain of several modern neural network architectures and advanced training techniques.
Robust Multi-objective Optimization with Less Computational Effort

Aiming at efficiently and effectively solving robust multi-objective optimization problems and enhancing the uncertainty handling ability of evolutionary algorithms, we design a robust multi-objective optimization framework by fully exploiting the population-based nature of evolutionary algorithms. The goal of our research includes 1) obtaining a group of robust optimal solutions which can balance the optimality and robustness in the maximum degree, as well as 2) reducing the computational resource consumed in the evolutionary process. First, in multi-objective optimization, besides the obtained global Pareto optimal solutions, all visited solutions in the evolutionary process are organized in both decision space and objective space. Then, based on the exploration of multi-objective optimization, the robust optimization effectively searches for the robust region. Afterward, a part of regions are picked up among all robust regions so that these selected regions together construct a robust optimal front which achieves the best convergence and diversity performance. Under this framework, we provide two approaches, one involves sampling process while the other does not. Experimental results on a group of benchmark functions demonstrate the superiority of both design in terms of both solutions' quality under the disturbance and computational efficiency in solving robust multi-objective optimization problems.
Adversarial Imitation Learning via Random Search

Developing agents that can perform challenging complex tasks is the goal of reinforcement learning. The model-free reinforcement learning has been considered as a feasible solution. However, the state of the art research has been to develop increasingly complicated techniques. This increasing complexity makes the reconstruction difficult. Furthermore, the problem of reward dependency is still exists. As a result, research on imitation learning, which learns policy from a demonstration of experts, has begun to attract attention. Imitation learning directly learns policy based on data on the behavior of the experts without the explicit reward signal provided by the environment. However, imitation learning tries to optimize policies based on deep reinforcement learning such as trust region policy optimization. As a result, deep reinforcement learning based imitation learning also poses a crisis of reproducibility. The issue of complex model-free model has received considerable critical attention. A derivative-free optimization based reinforcement learning and the simplification on policies obtain competitive performance on the dynamic complex tasks. The simplified policies and derivative free methods make algorithm be simple. The reconfiguration of research demo becomes easy. In this paper, we propose an imitation learning method that takes advantage of the derivative-free optimization with simple linear policies. The proposed method performs simple random search in the parameter space of policies and shows computational efficiency. Experiments in this paper show that the proposed model, without a direct reward signal from the environment, obtains competitive performance on the MuJoCo locomotion tasks.
Graph Convolutional Networks with Structural Attention Model for Aspect Based Sentiment Analysis

With the amount of user-generated information on the Web, identifying the sentiment polarity of the given aspect provides more complete and in-depth results for businesses and customers. Aspect based sentiment analysis has gained increasing attention in decade years, but it remains a daunting task. Recently, approaches based on recurrent neural networks and convolutional neural networks have shown competitive results in this field. However, they don’t take fully account of the entire text structure and the relation between words in a given document. In this paper, we propose a novel neural network method to address this problem, in which the text is treated as a graph and the aspect is the specific area of the graph. For the first time, we apply graph convolutional neural networks and structural attention model to aspect based sentiment analysis. Experiments on public-available datasets demonstrate the efficiency and effectiveness of our model.