Reviewerly/exHarmony huggingface dataset:
IRUNet for medical image segmentation

In recent years, deep learning has been widely used to segment medical images and assist physicians in better diagnosis and treatment of diseases. Anthrax is a serious infectious disease that has a worldwide distribution. One of the most important ways to diagnose this disease is the microscopic examination of slides containing tissue samples of patients. The state-of-the-art models for segmentation of the slide images are based on deep neural networks and have encoder-decoder architecture, such as fully convolutional network, UNet, and their variants. Skip connections play a key role in such models. However, in many of these models, the skip connections only aggregate features related to the same scales of the encoder and decoder sections, which degrades the quality of the segmentation. We propose an improved UNet-based architecture to segment microscopic images of patient tissue samples. The proposed model, called IRUNet, takes the advantage of inception and residual blocks in the skip connections and combines multi-scale features in order to extract better features for segmentation. Also, to extract powerful representations in the encoder section, several convolutional networks have been used as the backbone and their effect on the segmentation results has been investigated. The experimental results show that despite many challenges in the field of microscopic image analysis such as high image resolution, different contrasts, image artifacts, object crowding, and overlapping, IRUNet has better performance on medical image segmentation compared to the state-of-the-art models. It achieves the precision of 92.8%, the recall rate of 93%, and the Dice score of 92.9% which are outstanding results.
Fault anomaly detection of synchronous machine winding based on isolation forest and impulse frequency response analysis

Synchronous machine is one of the critical power generation parts in the power system. Its stable operation ensures people's normal economic activities. Winding is an essential component of a synchronous machine, and the winding fault is a common fault type. The reliable and efficient fault diagnosis of synchronous machine winding is of great significance to ensure the stability of the power system. Therefore, this paper proposes an anomaly detection method of synchronous machine winding fault based on isolation forest (IF) and impulse frequency response analysis (IFRA). Firstly, the basic principle of the anomaly detection method is introduced, and mathematical-statistical indicators of IFRA signatures used are then explained. Besides, the experimental verification is carried out on a 5 kW synchronous machine, and the performance of the anomaly detection method for winding fault is compared with other conventional methods. The experimental results show that the proposed method is feasible and effective, and the generalization ability of the data is strong. The comparative experimental results show that the proposed method is superior to the existing conventional supervised learning method. It has a shorter calculation time and higher accuracy, with stronger robustness, which is more suitable for the actual data structure.
Applications of Blockchain Technology in the Finance and Banking Industry Beyond Digital Currencies

Blockchain and cryptocurrency have almost become synonymous. Cryptocurrency is arguably one of the most sensational financial innovations of the 21st century. The current study claims that blockchain technology is not limited to the application of digital currencies in finance and banking; there are wide applications of blockchain technology in the given field. Blockchain uses the unique properties enabling decentralized, secured, transparent, and temper-proof financial transactions that have the potential to revolutionize the financial services industry. Given such a stance, the chapter outlines the application of blockchain technology in the finance arena beyond the digital currency. In this chapter, the authors provide the 10 applications of blockchain technology in the financial services industry implementing the blockchain technology and revolutionizing the finance and banking industry. The chapter also highlights the hurdles to application of blockchain technology in the finance and banking industry.
Heterogeneous star graph attention network for product attributes prediction

The target of product attributes prediction is to complete the characteristics set for defining a particular product. Most of the existing methods treat the product attributes prediction as a Named-Entity Recognition (NER) problem from the products' affiliated data, such as product title and introduction. However, in a large number of industrial applications of Alibaba, we found that the existing methods are good at concrete attributes extraction (e.g., color, size) but short of abstract attributes extraction (e.g., applicable event). Moreover, these abstract attributes are usually not easy to extract from the products' affiliated data. In this paper, we propose a novel heterogeneous star graph attention network called "SAN", which incorporates the advantages of multiple information in the e-commerce scene to predict the abstract attributes of products. Specifically, we model the customer interactive behaviors, product title and concrete attributes of a product as a star graph. Then, we extract the node features, node types and graph structure information from the heterogeneous star graph network which consists of n star graphs. By leveraging the parallel multiple attention mechanism, SAN can aggregate features and learn weights of nodes for product representation and abstract attribute prediction. Extensive experimental results of a real-world e-commerce dataset have demonstrated that SAN outperforms state-of-the-art methods significantly for product attributes prediction. These series of solutions are already planned for use in the applications of Alibaba.
Efficient separable reversible data hiding algorithm for compressed 3D mesh models

A typical problem examined in most of the earlier reversible data hiding methods is the vast amount of information to be transmitted towards the recipient end, which significantly limits the applicability of these methods. Hence, the need of data compression is required before transmission. An efficient reversible data hiding algorithm for 3D mesh models in the compressed domain is proposed to ensure secret message privacy and security. The main contribution of proposed algorithm is to hide secret message in 3D mesh model in such a way that mesh vertices and its corresponding topology remained as it is while allowing the receiver to correctly extract secret message and perfectly reconstruct the cover 3D mesh model in a reversible manner. For traversing 3D mesh vertices, a novel mesh traversal algorithm is proposed where the traversal order of stego-mesh is not affected by vertex reordering attack because it is based on the computation of the shortest distance between neighbor vertices regardless of their reference indexes. Experimental study reveals that the proposed method has more embedding rate with comparing image quality than the existing methods. The average PSNR value is 96.40 dB for an embedding rate of 6.94 bits per vertex(bpv) for all test mesh models, demonstrating that the method can give good quality stego mesh models even at high payload also.
Malicious URL Detection using NLP, Machine Learning and FLASK

A URL created to attack with spam or fraud is known as a malicious/phishing URL. Viruses are downloaded into the system if the user clicks such URLs. Malicious URLs can lead to phishing and spam. With phishing, user credentials, valuable information is compromised. So, it is important to identify safe links and malicious links. Cyber-attacks are attempting with the origin of malicious URLs Phishers are manipulating their cyber attacking techniques rapidly. Machine Learning is a field of study where a system learns from previous experience and reacts to future events. Machine Learning methods are useful for resolving security applications. In this paper, authors proposed machine learning oriented solution for detecting malicious websites. For experiments, a Kaggle dataset with a large number of URLs (above 5, 00000 URLs) is used. We applied three techniques for text feature extraction count vectorizer, hashing vectorizer-IDF vectorizer, and later build a phishing website detection model with four ML classifiers Logistic Regression, K-NN, Decision Tree, Random Forest. The ML model with hash vectorizer and random forest achieved 97.5% accuracy. We also created a web app using Flask for detecting the entered URL is malicious or not.
Pocketable Labs for Everyone: Synchronized Multi-Sensor Data Streaming and Recording on Smartphones with the Lab Streaming Layer

The streaming and recording of smartphone sensor signals is desirable for mHealth, telemedicine, environmental monitoring and other applications. Time series data gathered in these fields typically benefit from the time-synchronized integration of different sensor signals. However, solutions required for this synchronization are mostly available for stationary setups. We hope to contribute to the important emerging field of portable data acquisition by presenting open-source Android applications both for the synchronized streaming (Send-a) and recording (Record-a) of multiple sensor data streams. We validate the applications in terms of functionality, flexibility and precision in fully mobile setups and in hybrid setups combining mobile and desktop hardware. Our results show that the fully mobile solution is equivalent to well-established desktop versions. With the streaming application Send-a and the recording application Record-a, purely smartphone-based setups for mobile research and personal health settings can be realized on off-the-shelf Android devices.
BAANet: Learning Bi-directional Adaptive Attention Gates for Multispectral Pedestrian Detection

Thermal infrared (TIR) image has proven effectiveness in providing temperature cues to the RGB features for multispectral pedestrian detection. Most existing methods directly inject the TIR modality into the RGB-based framework or simply ensemble the results of two modalities. This, however, could lead to inferior detection performance, as the RGB and TIR features generally have modality-specific noise, which might worsen the features along with the propagation of the network. Therefore, this work proposes an effective and efficient cross-modality fusion module called Bi-directional Adaptive Attention Gate (BAA-Gate). Based on the attention mechanism, the BAA-Gate is devised to distill the informative features and recalibrate the representations asymptotically. Concretely, a bi-direction multi-stage fusion strategy is adopted to progressively optimize features of two modalities and retain their specificity during the propagation. Moreover, an adaptive interaction of BAA-Gate is introduced by the illumination-based weighting strategy to adaptively adjust the recalibrating and aggregating strength in the BAA-Gate and enhance the robustness towards illumination changes. Considerable experiments on the challenging KAIST dataset demonstrate the superior performance of our method with satisfactory speed.
Generic network for domain adaptation based on self-supervised learning and deep clustering

Domain adaptation methods train a model to find similar feature representations between a source and target domain. Recent methods leverage self-supervised learning to discover the analogous representations of the two domains. However, prior self-supervised methods have three significant drawbacks: (1) leveraging pretext tasks that are susceptible to learning low-level representations, (2) aligning the two domains using adversarial loss without considering if the extracted features are low-level representations, (3) the models are not flexible to accommodate various proportions of target labels, i.e., they assume target labels are always available. This paper presents a Generic Domain Adaptation Network (GDAN) to address these issues. First, we introduce a criterion based on instance discrimination to select appropriate pretext tasks to learn high-level domain invariant representations. Then, we propose a semantic neighbor cluster to align the two domain features. The semantic neighbor cluster implements a clustering technique in a feature embedding space to form clusters according to high-level semantic similarities. Finally, we present a weighted target loss function to balance the model weights according to the target labels. This loss function makes GDAN flexible for semi-supervised scenarios, i.e., partly labeled target data. We evaluate the proposed methods on four domain adaptation benchmark datasets. The experiment findings show that the proposed methods align the two domains well and achieve competitive results.
Dynamically corrected gates from geometric space curves

Abstract Quantum information technologies demand highly accurate control over quantum systems. Achieving this requires control techniques that perform well despite the presence of decohering noise and other adverse effects. Here, we review a general technique for designing control fields that dynamically correct errors while performing operations using a close relationship between quantum evolution and geometric space curves. This approach provides access to the global solution space of control fields that accomplish a given task, facilitating the design of experimentally feasible gate operations for a wide variety of applications.
Robust State Estimation With Maximum Correntropy Rotating Geometric Unscented Kalman Filter

The geometric unscented Kalman filter (GUF) is an effective method for state estimation due to its advantages of high precision, reasonable efficiency, and good stability. In order to further improve the filtering performance of GUF, multiple rotation matrices are first designed by optimizing the rotation angles of their corresponding sampling points with maximum likelihood estimation, leading to a novel sampling strategy, namely, rotating geometric unscented sampling (RGUS). Then, applying RGUS to the GUF framework generates a rotating geometric unscented Kalman filter (RGUF). Since observable measurements are generally corrupted by non-Gaussian noise, RGUF may suffer from performance degradation due to the used minimum mean square error (MMSE) criterion. To improve the robustness of RGUF against non-Gaussian noises, we propose a novel maximum correntropy rotating geometric unscented Kalman filter (MCRGUF) using the maximum correntropy criterion (MCC). Finally, a Cramér–Rao lower bound (CRLB) of MCRGUF is introduced as a performance indicator. Simulations on three examples validate the high filtering accuracy and strong robustness of MCRGUF in the presence of non-Gaussian noise.
GERNERMED: An open German medical NER model

Recent advancements in natural language processing (NLP) have been achieved by the use of increasingly complex neural networks.In clinical context, NLP is a key technique to access highly relevant information from unstructured texts such as clinical notes.We evaluate the feasibility of training our neural model GERNERMED on annotated German training data generated by automated translation from a public English dataset.The work guides other researchers about the use of machine-translation methods for dataset acquisition.Due to the public origin of the dataset, our trained software can be used by fellow researchers without any legal access restrictions.
Triple Generative Adversarial Networks

We propose a unified game-theoretical framework to perform classification and conditional image generation given limited supervision. It is formulated as a three-player minimax game consisting of a generator, a classifier and a discriminator, and therefore is referred to as Triple Generative Adversarial Network (Triple-GAN). The generator and the classifier characterize the conditional distributions between images and labels to perform conditional generation and classification, respectively. The discriminator solely focuses on identifying fake image-label pairs. Theoretically, the three-player formulation guarantees consistency. Namely, under a nonparametric assumption, the unique equilibrium of the game is that the distributions characterized by the generator and the classifier converge to the data distribution. As a byproduct of the three-player formulation, Triple-GAN is flexible to incorporate different semi-supervised classifiers and GAN architectures. We evaluate Triple-GAN in two challenging settings, namely, semi-supervised learning and the extremely low data regime. In both settings, Triple-GAN can achieve excellent classification results and generate meaningful samples in a specific class simultaneously. In particular, using a commonly adopted 13-layer CNN classifier, Triple-GAN outperforms extensive semi-supervised learning methods substantially on several benchmarks no matter data augmentation is applied or not.
Self-gated rectified linear unit for performance improvement of deep neural networks

This technical paper proposes an activation function, self-gated rectified linear unit (SGReLU), to achieve high classification accuracy, low loss, and low computational time. Vanishing gradient problem, dying ReLU, noise vulnerability are also resolved in our proposed SGReLU function. SGReLU's performance is evaluated on MNIST, Fashion-MNIST, and Imagenet datasets and compared with seven highly effective activation functions. We obtained that the proposed SGReLU outperformed other activation functions in most cases in VGG16, Inception v3, and ResNet50. In VGG16 and Inception v3, it achieved an accuracy of 90.87% and 95.01%, respectively, exceeding other functions with the second-fastest computing time in these networks.
Optimal spraying task assignment problem in crop protection with multi-UAV systems and its order irrelevant enumeration solution

Creating a team of crop protection unmanned aerial vehicles (UAVs) for a scheduled spraying task over fields is a method that seeks to overcome the various limitations of using individual UAVs. However, given some of the issues with pesticide spraying operations involving multiple UAVs (multi-UAVs), such as differences in hardware, irregular field borders, spray drift, and operator proficiency, assigning spraying sub-tasks to multi-UAVs during crop protection is more important and more complex than for other homogeneous multi-agent systems in common use. In this study, a mathematical model of a heterogeneous multi-UAV crop protection system is established. Based on this model, an optimal spraying task assignment problem (OSTAP) for multi-UAVs used in crop protection is proposed. To solve this problem, it is converted to an equivalent combinatorial problem. The order irrelevant enumeration solution (OIES) algorithm is proposed where time and space complexities are more efficiently implemented on a horizontal comparison among OIES, equal task assignment (ETA), sequential task assignment (STA), discrete particle swarm optimisation (DPSO), and a full permutation algorithm. Numerical simulation with a six-UAV system showed that OIES could solve OSTAP in a finite time using C++ in the Visual Studio environment and performed efficiently and accurately. Finally, with the proposed OIES, an OSTAP application software was developed as a visualisation of an OSTAP solution. With this application, a spraying test with the five-UAV system was carried out to determine the effectiveness of OIES in an actual pesticide spraying operation. Test results showed that OIES performs best with these algorithms, which takes only 51.3% of the operating time taken by ETA and it requires fewer flights.
An overview of the activation functions used in deep learning algorithms

In deep learning models, the inputs to the network are processed using activation functions to generate the output corresponding to these inputs. Deep learning models are of particular importance in analyzing big data with numerous parameters and forecasting and are useful for image processing, natural language processing, object recognition, and financial forecasting. Also, in deep learning algorithms, activation functions have been developed by taking into account features such as performing the learning process in a healthy way, preventing excessive learning, increasing the accuracy performance, and reducing the computational cost. In this study, we present an overview of common and current activation functions used in deep learning algorithms. In the study, fixed and trainable activation functions are introduced. As fixed activation functions, sigmoid, hyperbolic tangent, ReLU, softplus and swish, and as trainable activation functions, LReLU, ELU, SELU and RSigELU are introduced.
AccLoc: Anchor-Free and two-stage detector for accurate object localization

Current anchor-free object detectors have obtained detection performances comparable to those of anchor-based object detectors while avoiding the weaknesses of anchor designs. However, two challenges limit the localization performance. First, such anchor-free detectors have one stage that predicts the classification and localization results directly. A large regression space reduces the localization performance of such methods. Second, most of the existing detectors extract features which are ineffective for accurate localization. In this paper, for the first challenge, we propose two-stage networks to predict regression results stage by stage, thereby reducing the scope of the prediction space. For the second challenge, we design two novel modules with the aim of extracting effective features for accurate localization. Experimental results validate that each module in our approach is effective and validate that our approach has better object localization performance than previous related and advanced methods.
MC-OCR Challenge 2021: Deep Learning Approach for Vietnamese Receipts OCR

Receipts OCR has made a significant improvement on accounting, which has attracted much attention of the research community in the field of computer vision as well as natural language processing. In this paper, we solve the problem of extracting pieces of information on Vietnamese receipts including seller, address, timestamp, and total cost. We divided this into two problems: detecting locations of information and using an OCR model to recognize texts. In this paper, we propose a pipeline that employs Faster R-CNN as an information location detector and training a Transformer model for text recognition. Through experiments, we achieved CER 32.19%, which is 9.65% higher than previous method CRNN, while pointing out the remaining statements and challenges of this problem.