Reviewerly/exHarmony huggingface dataset:
A Study on the Formation Control Algorithm of Multi-USVs According to COLREGs

In this paper, we propose a formation control algorithm for multi-USVs according to COLREGs. First, we applied the Dynamic Window Approach algorithm that can reflect the kinematic characteristics for the path movement of USVs. Then, we propose a virtual structure-based virtual leader-follower method that applies the advantages of leader-follower and virtual structure methods among conventional formation control algorithms for stability. Next, we proposed a collision avoidance algorithm according to all COLREGs when encountering an opposing ship by adding COLREGs situational conditions to the virtual leader, and finally confirmed the feasibility of the proposed method through simulation.
A Study on the Classification of Military Airplanes in Neighboring Countries Using Deep Learning and Various Data Augmentation Techniques

The analysis of foreign aircraft appearing suddenly in air defense identification zones requires a lot of cost and time. This study aims to develop a pre-trained model that can identify neighboring military aircraft based on aircraft photographs available on the web and present a model that can determine which aircraft corresponds to based on aerial photographs taken by allies. The advantages of this model are to reduce the cost and time required for model classification by proposing a pre-trained model and to improve the performance of the classifier by data augmentation of edge-detected images, cropping, flipping and so on.
Visual Analysis of Multisource Heterogeneous Data Based on Improved DPCA Algorithm

This paper proposes a multiview collaborative visual analysis system of network security based on a DPCA (clustering by fast search and find of density peaks) clustering algorithm called DPCANETVis, with network security analysis requirements for multisource heterogeneous data. Firstly, the system proposes an improved DPCA clustering algorithm based on the hierarchical relationship of mail sending and receiving to achieve the purpose of accurate classification. Secondly, a three-layer visual layout is designed to display relevant information such as data hierarchy, node relationship, behavior model, and other relevant information, by mixing a variety of interactive visual analysis methods such as tree diagram, word cloud, line diagram, subject river, and parallel coordinate. Lastly, based on the exploration of events, all suspicious nodes and their abnormal behaviors can be displayed in the system. Finally, the prototype system is used to analyze the network security log data set provided by the ChinaVis 2018, and the feasibility of the multilevel interactive visual analysis method for network security is verified through many experiments and discussions.
An End-to-End Scene Text Detector with Dynamic Attention

Detecting the arbitrarily oriented text in natural images is a challenging task in multimedia due to variations in text curvatures, orientations, and aspect ratios of natural scenes. Most previous scene text detectors often fail to locate the text instances which have a peculiar shape (an extreme aspect ratio) precisely. In this paper, we propose a dynamic end-to-end framework (DEF) which includes a convolution-based dynamic encoder (CDE) with various attention types to generate a deformable and dynamic view for multi-oriented text instances and curve ones. Different from previous methods that apply time-consuming post-processing steps like NMS, our method uses a Transformer-based decoder (TD) with a bipartite matching loss to model the relationship of corresponding queries and ground truths. As a result, by leveraging such a well-designed architecture, the receptive field will not be limited to a fixed shape, and a combination of global attention and local features provides a better representation for texts in natural scenes. We conduct extensive experiments qualitatively and quantitatively on several popular datasets. Experimental results show that the proposed method achieves superior performance compared with several state-of-the-art scene text detectors.
Enhancing the Robustness of Deep Learning Based Fingerprinting to Improve Deepfake Attribution

Artificial Fingerprinting (AF or the so-called digital watermarking) is a technique that can be used to conduct Deepfake attribution by ensuring media authenticity. However, AF does not prioritize its robustness to certain kinds of distortions, making the embedded watermarks vulnerable to some standard image processing operations. Insufficient robustness reduces the practicality of digital watermarking techniques. To address this issue, we propose an enhanced distortion agnostic artificial fingerprinting (EDA-AF) framework which introduces a novel noise layer consisting of an attack booster followed by a convolutional network-based attacker. The attacker simulates various distortions by exploiting adversarial learning with AF for distortion agnostic robustness. Meanwhile, due to the modeling limitation of the convolutional network, we also employ the attack booster to apply a set of differentiable image distortions which cannot be well simulated by the attacker. Extensive experimental results show that the proposed approach improves the quality of the extracted fingerprints. EDA-AF can improve the bitwise accuracy by up to 36%, which takes another step forward on the road of Deepfake attribution.
Robust Learning with Adversarial Perturbations and Label Noise

Despite great success achieved, deep learning methods are vulnerable to noise in the training dataset, including adversarial perturbations and annotation noise. These harmful factors significantly influence the learning process of deep models, leading to less confident models. However, existing methods have not yet studied this practical and challenging issue.
Disentangled Image Attribute Editing in Latent Space via Mask-Based Retention Loss

We propose an image attribute editing method with the mask-based retention loss. Although conventional image attribute editing methods can edit a particular attribute, they cannot retain non-editing attributes including unknown attributes before and after editing, which causes unexpected changes in the edited images. We solve this problem by dividing the pre- and post-edited images into the editing and non-editing regions and increasing the image similarity in the non-editing regions. In this paper, we introduce the novel mask-based retention loss to retain the non-editing regions. To compute the mask-based retention loss, we divide the images into the editing and non-editing regions by using a binary mask generated from the difference between the pre- and post-edited images. Experimental results show that our proposed method is qualitatively and quantitatively superior to state-of-the-art methods.
Multi-Scale Channel Transformer Network for Single Image Deraining

Single image deraining is a very challenging task, as it requires not only restoring the spatial details and high contextual structures of the images, but also removing multiple layers of rain with varying degrees of blurring and resolutions. Recently, due to the powerful modeling capability of long-dependency, transformer-based models have manifested superior performance for high-level vision tasks, and have begun to be applied for low-level vision tasks such as various image restoration applications. However, its computational complexity increases quadratically with spatial resolutions, making it impossible to apply it to high-resolution images. In this study, we propose a novel Channel Transformer, which performs self-attention in the channel direction instead of the spatial direction. Specifically, we first incorporate multiple channel transformer blocks into a multi-scale architecture to extract multi-scale contexts and exploit channel long-dependence, and then learn a coarse estimation of the rain-free image. Finally, an original-resolution CNN-based module is employed to refine the coarse estimation via leveraging the previously learned multi-scale contexts. Experiments on several benchmark datasets demonstrate its superiority over the state-of-the-art methods.
Zero-Shot Font Style Transfer with a Differentiable Renderer

Recently, a large-scale language-image multi-modal model, CLIP, has been used to realize language-based image translation in a zero-shot manner without training. In this study, we attempted to generate language-based decorative fonts for font images using CLIP. By the existing image style transfer methods using CLIP, stylized font images are usually only surrounded by decorations, and the characters themselves do not change significantly. On the other hand, in this study, we use CLIP and vector graphics image representation using a differentiable renderer to achieve a style transfer of text images that matches the input text. The experimental results show that the proposed method transfers the style of font images to match the given texts. In addition to text images, we confirmed that the proposed method was also able to transform the style of simple logo patterns based on the given texts.
Phymastichus-Hypothenemus-based Algorithm for Optimal Node Selection on Pinning Control of Complex Networks

In this article, a new optimized node selection algorithm is proposed for implementation of pinning control for complex networks. This algorithm is inspired on the biological control of berry borers by means of parasitoid agents. The algorithm optimizes node selection using a criterion to ensure searching asymptotic stability of the complex network. The objective is to determine the optimal selection of nodes for pinning control applications.
Development of Audiobook Interaction Design for People with Vision Impairment Based on User-Centered Design Approach

People with vision impairment have fewer sources of information than normal people because of the limitation to accessing information with their sight. As a result, this leads to an information gap. The use of audiobooks has become a way to lessen the gap. An audiobook is a recording of a book that is read aloud by the narrator. With the audiobook, a person can access the information contained in a book using his hearing. However, audiobooks have not been widely used and are still not common in developing countries, including Indonesia. This study proposes the development of an interaction design for an audiobook on the Android platform. The design is proposed using a User-Centered Design (UCD) approach that involves the predefined user in the development process. The design process begins with collecting data through questionnaires and interviews. From the data, we design two prototypes, i.e., a low-fidelity prototype and a high-fidelity prototype. These prototypes are tested on the predefined user to analyze the task completion rate, single ease question (SEQ), and system usability scale (SUS). The post-test questionnaire is additionally given to ensure the prototype has met the targeted usability and user experience goals, i.e., effective to use, easy to use, and helpful. Based on the evaluation of the test results, the proposed design has achieved the targeted usability and user experience goals.
Supplier classification by applying AutoML

The interest of this research referred to the possibility of detecting patterns to identify the best supplier in a citrus exporter under a classification model that would provide a high degree of confidence. This work aims to verify the validity of Automated Machine Learning (AutoML) employing automatic model selection and hyperparameter optimization method applied in suppliers classification. These results can be used as support for decision-makers to evaluate and select the best of their alternatives. 786 historical records of suppliers were analyzed. The historical records were separated into datasets classified by season (high, low) and production area (north, center, and south). The sampling criteria implemented on the datasets were: cross-validation, percentage split (66%), and representative sample. The classification was evaluated by employing confusion matrix and performance indicators for each dataset according to the sampling criterion chosen. Through AutoML, the following algorithms: Vote, Random Forest, Attribute-Selected-Classifier, Bayes Network, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), and Naive Bayes found the best percentage of sensitivity (89.44, 70.79, 87.50, 88.59, 76.62, 90.13) for each dataset respectively under the highest performance sampling criterion. The AutoML approach helped discriminate Persian lemon suppliers based on their sourcing history, providing support for decision-making under acceptable percentages in reliability indicators.
Identifying Agile Roles in Software Engineering Projects using Repository and Work-Tracking Data

Software development at corporate scale is a collaborative effort requiring the involvement of people with different responsibilities. In agile software engineering approaches like SCRUM, responsibilities are divided along two main axes. Team membership sets the scope of a group of engineers, defining the parts of the software or types of challenges the individual approaches. Roles define the contribution of the individual within the team. As team and role data are not necessarily defined in the analyzed repository and work-tracking systems, their prediction from available data is beneficial for the understanding of processes and the providing of stakeholder-focused insights based on data. In this paper, we show that data gathered from software repositories and work-tracking systems can be used to predict the role of persons within these systems using machine learning, achieving an accuracy of around 83 percent. We also show how the process definition, specifically the work item hierarchy of the analyzed system is reflected in the analyzed data.
Natural Language Interface to Database (NLIDB) for Interrogative Sentences with Temporal Elements in Indonesian Language

Natural Language Interface to Database (NLIDB) is a solution that allows users to retrieve data from databases in natural language. Various studies were conducted to translate Indonesian sentences into database queries, but these studies still have weaknesses. In this study, we propose the translation of interrogative sentences in the temporal database accompanied by the addition of several components that can improve the ability of NLIDB. The translation process is carried out by first masking the input sentence and decomposing the sentence into a collection of syntax trees. Then, the existing parsing tree is selected and the best parsing tree will be selected. Then, the process is continued with the analysis of the query components in the interrogative sentences. The results of the analysis will be translated into parts of database query and parts of database query will be rearranged into database queries. This query will be executed and the result can be evaluated by the user and the user can provide feedback according to the system output. The built NLIDB system can translate interrogative sentences for what, who, when, where, how, if, and how many questions with the types of queries that can be handled are current state, time-slice, sequenced, and non-sequenced.
Stages of Progression Classification of Alzheimer's Disease Using Deep Transfer Learning Models with Over-Sampling

Alzheimer's disease is a chronic neurodegenerative disease that affected patients loss of memory, ability of thinking, reading, and cognitive decline. The Alzheimer's disease divided stages of progression into four general stages on the basis of their symptoms. The early diagnosis helps to slow down the disease and reduce the costs of treatment. Since Alzheimer's disease has four stages of progression, the classification problems are those where a stage must be predicted in the case of an unequal number of instances of each class. This study has proposed the classification the stages of progression of Alzheimer's disease using four transfer learning models such as VGG19, Xception, ResNet50, and MobileNetV2. The proposed models classify Alzheimer's disease into four stage of progression. The models gained an accuracy level of VGG19, Xception, ResNet50, and MobileNetV2 model of 77.73%, 82.46%, 76.28% and 79.29%, respectively. By considering the F1 score, the Xception, VGG19, and ResNet50, and MobileNetV2 models gave the high score of 0.7995, 0.8870, 0.8305, and 0.5993, respectively. Therefore, the VGG19 model is the best model by considering the F1 score that means the VGG19 model is the best model in overall performance. Finally, this study measures the AUC value that indicates the ability to classify between classes. The results show that AUC value of MobileNetV2, Xception, ResNet50, and VGG19 are 0.9290, 0.9539, 0.7937, and 0.8037, respectively. Therefore, the Xception model is the best model that has capable of distinguishing the stages of progression of the Alzheimer's disease.
Experimental Validation of Nonlinear Optimization Frameworks for Solving Bundle Adjustment in Structure from Motion

Structure from Motion (SfM) is a proficient technique for 3D reconstruction from multiple views. However, its potentials could only be seen after the development of computer and digital photograph especially in the developing countries. Nowadays, SfM is applied in many applicative scenarios ranging from earth observation to heritage documentation. An important task in SfM that has been solved just recently in practice is bundle adjustment, which is accomplished through nonlinear least-squares optimization based on Levenberg–Marquardt (LM) algorithm. The aim of this paper is to present an experimental validation of the most popular open-source optimization frameworks, which implanted LM algorithm for solving bundle adjustment in SfM. For this purpose, the comparison among their performance is conducted by using bundle adjustment in the large benchmark.
A survey of approaches in Deep Learning techniques for the detection and classification of mammography abnormalities

Mammography is currently the most widely used laboratory study for the early detection of precursor abnormalities of breast cancer, which is one of the main causes of mortality worldwide since this disease generates the appearance of large volumes of cancerous cells in the breast.Thus, Computer-aided detection or diagnosis (CADe and CADx) can help the specialist improve the accuracy of the diagnosis, providing the patient with a better treatment and disease management assessment. Unfortunately, there are several circumstances in which it is possible to obtain an erroneous diagnosis, such as subjectivity on the part of the radiologist due to the size or morphology of the anomalies detected, as well as the fact that mammograms may include embedded noise.For this reason, this manuscript seeks to provide the reader with a more general overview of how this issue has been addressed in recent years (2016 onwards) through the compilation of various scientific research focused on the detection and classification of breast cancer precursor anomalies based on the implementation of artificial intelligence techniques, where it is possible to visualize the performance of each of the proposed models through the implementation of various metrics such as F1 Score, AUC, accuracy, etc. Thus, these studies have been obtained from recognized multidisciplinary scientific sites in the world such as Nature, Springer, IEEE Xplorer, and PubMed, among others.
Integrating Fresnel diffraction, multi-phase retrieval, and hyperchaos mapping for color image encryption

This study proposes a color image encryption method combining Fresnel diffraction, multi-phase retrieval, and hyperchaotic mapping in two stages. First, the color image is converted into an amplitude-type computer-generated hologram (CGH) that overlaps in the frequency domain and separates in the spatial domain. The Gerchberg-Saxton (GS) multi-phase retrieval algorithm encrypts the CGH into a meaningless ciphertext in the second stage, scrambling/diffusing it further using four random sequences generated from the Lorenz hyperchaotic system. The experiment on public color images shows that the decrypted color images are highly consistent with plaintext images. This method's key has a large space (approximately 10∧301) and a small volume. Slight changes to the plaintext completely alter the ciphertext, and its pixel values are statistically independent, thus securing the proposed method against chosen-plaintext attacks and chosen-ciphertext attacks.
Sequential methods and algorithms

Abstract The research aims to investigate the application and applicability of sequential methods and algorithms. The content of the concept of sequentiality is disclosed. Its polysemy and application in programming are shown. The notions of logical algorithm and sequential algorithm are introduced. The difference between sequential and logical algorithms is shown. A comparison with deterministic algorithms is given. The principles of logical algorithms and sequential algorithms are described. Common is a step-by-step calculation. However, the link of sequential algorithms is more complex and represents a multigraph. Similarities and differences between sequential algorithms and computational networks are shown. Sequential algorithms can be considered as integrated technological systems. The research shows that the application of sequential algorithms allows solving a larger number of problems that logical program algorithms do not solve. The research gives criteria for the applicability of logical and sequential algorithms
Performance Analysis of Reinforcement Learning Techniques for Augmented Experience Training Using Generative Adversarial Networks

This paper aims at analyzing the performance of reinforcement learning (RL) agents when trained in environments created by a generative adversarial network (GAN). This is a first step towards the greater goal of developing fast-learning and robust RL agents by leveraging the power of GANs for environment generation. The RL techniques that we tested were exact Q-learning, approximate Q-learning, approximate SARSA and a heuristic agent. The task for the agents was to learn how to play the game Super Mario Bros (SMB). This analysis will be helpful in suggesting which RL techniques are best suited for augmented experience training (with synthetic environments). This would further help in establishing a reinforcement learning framework using the agents that can learn faster by bringing a greater variety in environment exploration.
Data Marketplaces: A Solution for Personal Data Control and Ownership?

Data sharing is critical for advancing research, commercializing technologies, and informing people. However, data owners have been reluctant to share data due to concerns about data control, access, and a lack of secure data storage solutions. Frequent data breaches have also led to a lack of user trust in businesses. Despite widespread recognition of user concerns, few steps have been taken to empower users. While there are models for decentralized data sharing, the question of how to incentivize these structures to enable data sharing at scale is largely unexplored. This work discusses the potential of blockchain-based data marketplaces to empower users by giving them more control over their data and bringing transparency to the data-driven ecosystem.