Reviewerly/exHarmony huggingface dataset:
Development of a Process to Enhance the Reimbursement Efficiency with OCR and Ontology for Financial Documents

This paper presents the development of the process to increase the efficiency of checking the financial documents for reimbursement by applying optical character recognition (OCR) and ontology. The users are able to take a photo of the receipt through the mobile phone camera after making the payment. Immediately, the system will check the completeness and correctness of the information on documents according to the standard accounting principles for disbursement which originally had to take more than a week to submit evidence and document disbursements accounting, where our processes can shorten the time frame starting from the occurrence of the transaction until the creation of disbursement documents can be reduced by more than 50%.
Adaptive dense pyramid network for object detection in UAV imagery

Object detection in Unmanned Aerial Vehicle (UAV) imagery has a wide variety of applications in both military and civilian fields. As UAV images are usually captured from flexible perspectives, with multiple altitudes, containing objects in various sizes and scales, such characteristics bring huge challenges to the object detection task. To address this issue, we develop a novel Adaptive Dense Pyramid Network (ADPN) that integrates congested scene analysis, aiming to incorporate the object distribution information into the object detection workflow without introducing additional annotations. We also design a creative Pyramid Density Module (PDM) for adaptive density prediction. The proposed ADPN incorporates the PDM and Object Detection Module (ODM) in a parallel manner to facilitate the feature alignment between density information and instance recognition. Our method can be applied to existing detection algorithms, and experimental results demonstrate the effectiveness and robustness of ADPN, and ADPN is able to improve detection accuracy on challenging datasets.
Deep linear graph attention model for attributed graph clustering

Attributed graph clustering is an important task for grouping the nodes in a graph. In recent years, the algorithms based on graph convolutional networks (GCN) have achieved promising performance. However, almost all existing methods ignore that the nonlinearity between two consecutive GCN layers is unnecessary for improving the performance of attributed graph clustering, and may even harm the efficiency of the model. In this paper, we propose a novel deep linear graph attention model for attributed graph clustering (DLGAMC), which consists of an attention-based aggregation module and a similarity preserve module. Specifically, we simply exploit cosine similarity to construct the attention for aggregation, which does not need to learn extra attention parameters. It is worth noting that the attention we designed not only explicitly considers the similarity of attribute information, but also implicitly takes into account the local graph structure. To select the proper order of aggregation, we propose an adaptive strategy to evaluate the smoothness of node representations, where intra-cluster distance and inter-cluster distance are the key indicators in this process. To learn node representations for clustering, we design a similarity preserve module to preserve local similarity and global dissimilarity of the smooth features obtained by multiple aggregations, which is different from the ideas in reconstruction-based methods. Finally, k-means is performed on the learned representations to obtain the cluster partition. Experiments on several datasets show that our algorithm achieves great performance in attributed graph clustering tasks.
A constrained cooperative adaptive multi-population differential evolutionary algorithm for economic load dispatch problems

Many engineering optimization problems are characterized by large scale and complex constraints. High optimization efficiency and reliable constraint handling are two major challenges. The traditional optimization methods hard to obtain practical and feasible solutions in reasonable time. To get better solutions and enhance the global search capability, a constrained cooperative adaptive multi-population differential evolutionary (CCAM-PDE) algorithm is proposed in this paper. The main contributions of this paper are in three aspects. First, a hyperspace dynamic constraint handling region between feasible region and infeasible region is proposed. Second, according to the feasible rate of population, a “one to one” or “one to many” subpopulation generation scheme is adopted for improving the global searching ability. Third, the selection operation of differential evolution algorithm is replaced by the elimination mechanism through the constraint handling technology. Eight economic load dispatch problems and CEC2017 Benchmark test functions are used to testify the performance of the CCAM-PDE algorithm. The experimental results shown that the CCAM-PDE algorithm has a strong constraint-handling efficiency and better global searching ability, its search accuracy and the speed of convergence against the other state-of-the-art algorithms. • Study an adaptive multi-population strategy. • Design the hyperspace dynamic region (HDR) strategy. • A cooperative constraint handling method is proposed based on the penalty function method and stochastic ranking.
Theoretical model for nonlinear spin-wave transient processes in active-ring oscillators with variable gain and its application for magnonic reservoir computing

A theoretical model for numerical simulation of the nonlinear spin-wave transient processes in magnonic active-ring oscillators (MAROs) with variable gain has been developed. The model employs the formalism of the Landau–Ginzburg equation to describe the nonlinear propagation of spectrally narrow magnonic wave packets in a magnetic film. We show that the model allows one to simulate ring's operation above the self-generation threshold as a magnonic physical reservoir computer for which the control of ring gain is employed as a method of data input into the physical reservoir. Performance of the reservoir computer was evaluated by carrying out numerical simulations using the developed model. To this end, we simulated the completion of the short-term memory and the parity-check tasks by the model. We found that the simulation results are in good agreement with experimental data. This evidences that the constructed model can be used for investigating physics underlying the performance of the MARO as a physical reservoir computer and for reservoir optimization with the final goal of maximizing reservoir performance.
YOLO-Based Deep Learning Design for In-Cabin Monitoring System with Fisheye-Lens Camera

To exploit an image-based in-cabin monitoring system for driving behavior and in-vehicle occupants detections to improve driving safety, in this paper, by installing a fisheye-lens camera at the in-car roof center and by using RGB-format images as inputs, the YOLO-based deep learning models, including YOLOv3-tiny, YOLOv3-tiny-3I, YOLO-fastest, YOLO-fastest-xl, and YOLO-fastest-three scales, are studied to be candidate detectors. The proposed in-cabin monitoring design can detect the normal and distracted driving cases and in-vehicle occupants including back seat passengers and pet dogs. The experimental results show that the YOLO-fastest-three scales model performs the best metrics for F1-Score and mAP, which are 95.89&#x0025; and 97.16&#x0025;, respectively. The YOLO-fastest-xl model has the best metric for false negative rate (FNR), which is 2.63&#x0025;. By the software realization, the proposed design executes up to 30 frames per second (FPS) with the GPU-based embedded device.
Deep Learning-Based Complete Coverage Path Planning With Re-Joint and Obstacle Fusion Paradigm

With the introduction of autonomy into the precision agriculture process, environmental exploration, disaster response, and other fields, one of the global demands is to navigate autonomous vehicles to completely cover entire unknown environments. In the previous complete coverage path planning (CCPP) research, however, autonomous vehicles need to consider mapping, obstacle avoidance, and route planning simultaneously during operating in the workspace, which results in an extremely complicated and computationally expensive navigation system. In this study, a new framework is developed in light of a hierarchical manner with the obtained environmental information and gradually solving navigation problems layer by layer, consisting of environmental mapping, path generation, CCPP, and dynamic obstacle avoidance. The first layer based on satellite images utilizes a deep learning method to generate the CCPP trajectory through the position of the autonomous vehicle. In the second layer, an obstacle fusion paradigm in the map is developed based on the unmanned aerial vehicle (UAV) onboard sensors. A nature-inspired algorithm is adopted for obstacle avoidance and CCPP re-joint. Equipped with the onboard LIDAR equipment, autonomous vehicles, in the third layer, dynamically avoid moving obstacles. Simulated experiments validate the effectiveness and robustness of the proposed framework.
Ensemble transfer attack targeting text classification systems

Deep neural networks provide good performance for image recognition, speech recognition, text recognition, and pattern recognition. However, such networks are vulnerable to attack by adversarial examples. Adversarial examples are created by adding a small amount of noise to an original sample in such a way that no problem is perceptible to humans yet the sample will be incorrectly classified by a classification model. Adversarial examples have been studied mainly in the context of images, but research has expanded to include the text domain. In the textual context, an adversarial example is a sample of text in which certain important words have been changed so that the sample will be misclassified by a model even though to humans it is the same as the original text in terms of meaning and grammar. However, studies of black box attacks using text adversarial examples are sparse. In this paper, we propose the ensemble transfer textfooler method. This method performs a black box attack on an unknown model after generating an ensemble adversarial example that simultaneously deceives several models. Experiments were conducted using a movie review dataset and with TensorFlow as the machine learning library. The experimental results show that the proposed method has an attack success rate of 71.64%, in contrast to the 19.01%, 24.29%, and 44.96% attack success rate for the conventional transfer attacks using an adversarial example generated to deceive a WordCNN, WordLSTM, and BERT model.
Detection of hyperpartisan news articles using natural language processing technique

Yellow journalism has increased the spread of hyperpartisan news on the internet. It is very difficult for online news article readers to distinguish hyperpartisan news articles from mainstream news articles. There is a need for an automated model that can detect hyperpartisan news on the internet and tag them as hyperpartisan so that it is very easy for readers to avoid that news. A hyperpartisan news detection article was developed by using three different natural language processing techniques named BERT, ELMo, and Word2vec. This research used the bi-article dataset published at SEMEVAL-2019. The ELMo word embeddings which are trained on a Random forest classifier has got an accuracy of 0.88, which is much better than other state of art models. The BERT and Word2vec models have got the same accuracy of 0.83. This research tried different sentence input lengths to BERT and proved that BERT can extract context from local words. Evidenced from the described ML models, this study will assist the governments, news' readers, and other political stakeholders to detect any hyperpartisan news, and also helps policy to track, and regulate, misinformation about the political parties and their leaders.
CiteRead: Integrating Localized Citation Contexts into Scientific Paper Reading

When reading a scholarly paper, scientists oftentimes wish to understand how follow-on work has built on or engages with what they are reading. While a paper itself can only discuss prior work, some scientific search engines can provide a list of all subsequent citing papers; unfortunately, they are undifferentiated and disconnected from the contents of the original reference paper. In this work, we introduce a novel paper reading experience that integrates relevant information about follow-on work directly into a paper, allowing readers to learn about newer papers and see how a paper is discussed by its citing papers in the context of the reference paper. We built a tool, called CiteRead, that implements the following three contributions: 1) automated techniques for selecting important citing papers, building on results from a formative study we conducted, 2) an automated process for localizing commentary provided by citing papers to a place in the reference paper, and 3) an interactive experience that allows readers to seamlessly alternate between the reference paper and information from citing papers (e.g., citation sentences), placed in the margins. Based on a user study with 12 scientists, we found that in comparison to having just a list of citing papers and their citation sentences, the use of CiteRead while reading allows for better comprehension and retention of information about follow-on work.
Using Augmented and Virtual Reality (AR/VR) to Support Safe Navigation on Inland and Coastal Water Zones

The aim of this research is to propose a new solution to assist sailors in safe navigation on inland shallow waters by using Augmented and Virtual Reality. Despite continuous progress in the methodology of displaying bathymetric data and 3D models of the bottoms, there is still a lack of solutions promoting these data and their widespread use. Most existing products present navigation content on 2D/3D maps onscreen. Augmented Reality (AR) technology revolutionises the way digital content is displayed. This paper presents the solution for the use of AR on inland and coastal waterways to increase the safety of sailing and other activities on the water (diving, fishing, etc.). The real-time capability of AR in the proposed mobile application also allows other users to be observed on the water in limited visibility and even at night. The architecture and the prototype Mobile Augmented Reality (MAR) applications are presented. The required AR, including the preparation methodology supported by the Virtual Reality Geographic Information System (VRGIS), is also shown. The prototype’s performance has been validated in water navigation, specifically for exemplary lakes of Warmia and Mazury in Poland. The performed tests showed the great usefulness of AR in the field of content presentation during the navigation process.
PhotoelastNet: a deep convolutional neural network for evaluating the stress field by using a single color photoelasticity image

Quantifying the stress field induced into a piece when it is loaded is important for engineering areas since it allows the possibility to characterize mechanical behaviors and fails caused by stress. For this task, digital photoelasticity has been highlighted by its visual capability of representing the stress information through images with isochromatic fringe patterns. Unfortunately, demodulating such fringes remains a complicated process that, in some cases, depends on several acquisitions, e.g., pixel-by-pixel comparisons, dynamic conditions of load applications, inconsistence corrections, dependence of users, fringe unwrapping processes, etc. Under these drawbacks and taking advantage of the power results reported on deep learning, such as the fringe unwrapping process, this paper develops a deep convolutional neural network for recovering the stress field wrapped into color fringe patterns acquired through digital photoelasticity studies. Our model relies on an untrained convolutional neural network to accurately demodulate the stress maps by inputting only one single photoelasticity image. We demonstrate that the proposed method faithfully recovers the stress field of complex fringe distributions on simulated images with an averaged performance of 92.41% according to the SSIM metric. With this, experimental cases of a disk and ring under compression were evaluated, achieving an averaged performance of 85% in the SSIM metric. These results, on the one hand, are in concordance with new tendencies in the optic community to deal with complicated problems through machine-learning strategies; on the other hand, it creates a new perspective in digital photoelasticity toward demodulating the stress field for a wider quantity of fringe distributions by requiring one single acquisition.
A Task Decomposing and Cell Comparing Method for Cervical Lesion Cell Detection

Automatic detection of cervical lesion cells or cell clumps using cervical cytology images is critical to computer-aided diagnosis (CAD) for accurate, objective, and efficient cervical cancer screening. Recently, many methods based on modern object detectors were proposed and showed great potential for automatic cervical lesion detection. Although effective, several issues still hinder further performance improvement of such known methods, such as large appearance variances between single-cell and multi-cell lesion regions, neglecting normal cells, and visual similarity among abnormal cells. To tackle these issues, we propose a new task decomposing and cell comparing network, called TDCC-Net, for cervical lesion cell detection. Specifically, our task decomposing scheme decomposes the original detection task into two subtasks and models them separately, which aims to learn more efficient and useful feature representations for specific cell structures and then improve the detection performance of the original task. Our cell comparing scheme imitates clinical diagnosis of experts and performs cell comparison with a dynamic comparing module (normal-abnormal cells comparing) and an instance contrastive loss (abnormal-abnormal cells comparing). Comprehensive experiments on a large cervical cytology image dataset confirm the superiority of our method over state-of-the-art methods.
Clustering Matters: Sphere Feature for Fully Unsupervised Person Re-identification

In person re-identification (Re-ID) , the data annotation cost of supervised learning, is huge and it cannot adapt well to complex situations. Therefore, compared with supervised deep learning methods, unsupervised methods are more in line with actual needs. In unsupervised learning, a key to solving Re-ID is to find a standard that can effectively distinguish the difference (distance) between the features of images belonging to different pedestrian identities. However, there are some differences in the images captured by different cameras (such as brightness, angle, etc.). It is well known that the training of neural networks is mainly based on the distance between features, while in unsupervised learning, especially in unsupervised learning methods based on hierarchical clustering, the distance between features plays a more important role in the clustering phase. We improve the accuracy of a deep learning method based on hierarchical clustering under fully unsupervised conditions, starting from both feature and distance metrics. First, we propose to use spherical features, by normalizing the images in the feature space, to weaken the structural differences (length) between features, while saving the feature differences (direction) between different identities. Then, we use the sum of squared errors (SSE) as a regularization term to balance different cluster states. We evaluate our method on four large-scale Re-ID datasets, and experiments show that our method achieves better results than the state-of-the-art unsupervised methods.
A Density-Based Random Forest for Imbalanced Data Classification

Many machine learning problem domains, such as the detection of fraud, spam, outliers, and anomalies, tend to involve inherently imbalanced class distributions of samples. However, most classification algorithms assume equivalent sample sizes for each class. Therefore, imbalanced classification datasets pose a significant challenge in prediction modeling. Herein, we propose a density-based random forest algorithm (DBRF) to improve the prediction performance, especially for minority classes. DBRF is designed to recognize boundary samples as the most difficult to classify and then use a density-based method to augment them. Subsequently, two different random forest classifiers were constructed to model the augmented boundary samples and the original dataset dependently, and the final output was determined using a bagging technique. A real-world material classification dataset and 33 open public imbalanced datasets were used to evaluate the performance of DBRF. On the 34 datasets, DBRF could achieve improvements of 2–15% over random forest in terms of the F1-measure and G-mean. The experimental results proved the ability of DBRF to solve the problem of classifying objects located on the class boundary, including objects of minority classes, by taking into account the density of objects in space.
A Crypto-Steganography Approach for Hiding Ransomware within HEVC Streams in Android IoT Devices

Steganography is a vital security approach that hides any secret content within ordinary data, such as multimedia. This hiding aims to achieve the confidentiality of the IoT secret data; whether it is benign or malicious (e.g., ransomware) and for defensive or offensive purposes. This paper introduces a hybrid crypto-steganography approach for ransomware hiding within high-resolution video frames. This proposed approach is based on hybridizing an AES (advanced encryption standard) algorithm and LSB (least significant bit) steganography process. Initially, AES encrypts the secret Android ransomware data, and then LSB embeds it based on random selection criteria for the cover video pixels. This research examined broad objective and subjective quality assessment metrics to evaluate the performance of the proposed hybrid approach. We used different sizes of ransomware samples and different resolutions of HEVC (high-efficiency video coding) frames to conduct simulation experiments and comparison studies. The assessment results prove the superior efficiency of the introduced hybrid crypto-steganography approach compared to other existing steganography approaches in terms of (a) achieving the integrity of the secret ransomware data, (b) ensuring higher imperceptibility of stego video frames, (3) introducing a multi-level security approach using the AES encryption in addition to the LSB steganography, (4) performing randomness embedding based on RPS (random pixel selection) for concealing secret ransomware bits, (5) succeeding in fully extracting the ransomware data at the receiver side, (6) obtaining strong subjective and objective qualities for all tested evaluation metrics, (7) embedding different sizes of secret data at the same time within the video frame, and finally (8) passing the security scanning tests of 70 antivirus engines without detecting the existence of the embedded ransomware.