Reviewerly/exHarmony huggingface dataset:
DLP-GAN: learning to draw modern Chinese landscape photos with generative adversarial network

Chinese landscape painting has a unique and artistic style, and its drawing technique is highly abstract in both the use of color and the realistic representation of objects. Previous methods focus on transferring from modern photos to ancient ink paintings. However, little attention has been paid to translating landscape paintings into modern photos. To solve such problems, in this paper, we (1) propose DLP-GAN (Draw Modern Chinese Landscape Photos with Generative Adversarial Network), an unsupervised cross-domain image translation framework with a novel asymmetric cycle mapping, and (2) introduce a generator based on a dense-fusion module to match different translation directions. Moreover, a dual-consistency loss is proposed to balance the realism and abstraction of model painting. In this way, our model can draw landscape photos and sketches in the modern sense. Finally, based on our collection of modern landscape and sketch datasets, we compare the images generated by our model with other benchmarks. Extensive experiments including user studies show that our model outperforms state-of-the-art methods.
NARRATIVE AS A MEANS OF TRANSFORMING TRAUMATIC EXPERIENCES

Abstract: This publication discusses the concept of “narrative therapy”, analyzes various studies and works devoted to the study of the narrative approach in practice. The advantages of using the narrative method in psychological activity are formulated, and the disadvantages that a specialist may face when using this method in his/her work are described.
Radar Emitter Signal Intra-Pulse Modulation Open Set Recognition Based on Deep Neural Network

Radar emitter signal intra-pulse modulation recognition is important for modern electronic reconnaissance systems to analyze target radar systems. In the actual environment, the intra-pulse modulations of the sampled radar emitter signals contain not only the known types in the library but also the unknown types. Therefore, the existing recognition methods, which are based on a closed set, cannot recognize the unknown samples. In order to solve this problem, in this paper, we proposed a method for radar emitter signal intra-pulse modulation open set recognition. The proposed method could classify the known modulations and identify the unknown modulation by using an original deep neural network-based recognition model trained on a closed set, estimating the signal-to-noise ratio, and calculating the reconstruction loss by an encoder–decoder model. For a given sample, the original deep neural network-based recognition model will label it as a certain known class temporarily. By estimating the SNR of the sample and calculating the reconstruction loss by inputting the sample to the corresponding encoder–decoder model related to the temporary predicted known class, whether the sample belongs to the predicted temporary known class or the unknown class will be confirmed. Experiments were conducted with five different openness conditions. The experimental results indicate that the proposed method has good performance on radar emitter signal intra-pulse modulation open set recognition.
Self-supervised Low-Light Image Enhancement via Histogram Equalization Prior

Deep learning-based methods for low-light image enhancement have achieved remarkable success. However, the requirement of enormous paired real data limits the generality of these models. Although there have been a few attempts in training low-light image enhancement model in the self-supervised manner with only low-light images, these approaches suffer from inefficient prior information or improper brightness. In this paper, we present a novel self-supervised method named HEPNet to train an effective low-light image enhancement model with only low-light images. Our method drives the self-supervised learning of the network through an effective image prior termed histogram equalization prior (HEP). This prior is a feature space information of the histogram equalized images. It is based on an interesting observation that the feature maps of histogram equalized images and the reference images are similar. Specifically, we utilize a mapping function to generate the histogram equalization prior, and then integrate it into the model through a spatial feature transform (SFT) layer. Guided by the histogram equalization prior, our method can recover finer details in real-world low-light scenarios. Extensive experiments demonstrate that our method performs favorably against the state-of-the-art unsupervised low-light image enhancement algorithms and even matches the state-of-the-art supervised algorithms.
Development of a Biology E-Module Based on Google Sites on Circulatory System and Digestive System Material

Technological developments have a big influence on the learning process. Various positive influences can be seen with technological developments, one of which is electronic learning media. The existence of electronic-based media has a positive impact on learning because of the various advantages it has. In reality in the field, there are still many biology teachers who do not use electronic learning media in the learning process. Learning media that does not utilize technology tends not to attract students' attention due to its monotonous nature. Based on these reasons, researchers developed electronic learning media in the form of e-modules based on Google sites. E-module is able to visualize and clarify various abstract concepts. E-modules can help teachers to facilitate students in learning and support the learning process. The development of a Google Sites-based e-module was created using the Plomp model which consists of 3 stages, namely the initial investigation stage (preliminary research), the development or prototyping phase (development or prototyping phase), and the assessment phase (assessment phase). The results of the e-module development show that the overall validation value of the Google Sites-based e-module is 86.40% (very valid) from experts. Furthermore, the practicality assessment given by the teacher towards the Google Sites-based e-module showed an average result of 96.25% (very practical).
Q-TrHDRI: A Qurey-Based Transformer for High Dynamic Range Imaging with Dynamic Scenes

In the absence of well-exposed contents in images, high dynamic range image (HDRI) provides an attractive option that fuses stacked low dynamic range (LDR) images into an HDR image. Existing HDRI methods utilized convolutional neural networks (CNNs) to model local correlations, which can perform well on LDR images with static scenes, but always failed on dynamic scenes where large motions exist. Here we focus on the dynamic scenarios in HDRI, and propose a Query-based Transformer framework, called Q-TrHDRI. To avoid ghosting artifacts induced by moving content fusion, Q-TrHDRI uses Transformer instead of CNNs for feature enhancement and fusion, allowing global interactions across different LDR images. To further improve performance, we investigate comprehensively different strategies of transformers and propose a query-attention scheme for finding related contents across LDR images and a linear fusion scheme for skillfully borrowing complementary contents from LDR images. All these efforts make Q-TrHDRI a simple yet solid transformer-based HDRI baseline. The thorough experiments also validate the effectiveness of the proposed Q-TrHDRI, where it achieves superior performances over state-of-the-art methods on various challenging datasets.
DiffusionTracker: Targets Denoising Based on Diffusion Model for Visual Tracking

The problem of background clutter (BC) is caused by distractors in the background that resemble the target’s appearance, thereby reducing the precision of visual trackers. We consider these similar distractors as noise and formulate a denoising task to solve the visual tracking problem. We propose a target denoising method based on a diffusion model for visual tracking, referred to as DiffusionTracker, which introduces the diffusion model to distinguish between targets and noise (distractors). Specifically, we introduce a reverse diffusion model to eliminate noisy distractors from the proposal candidates generated by the Siamese tracking backbone. To handle the difficulty that distractors do not strictly conform to a Gaussian distribution, we incorporate Spatial-Temporal Weighting (STW) to integrate spatial correlation and noise decay time information, mitigating the impact of noise distribution on denoising effectiveness. Experimental results demonstrate the effectiveness of the proposed method, with DiffusionTracker achieving a precision of 64.0% on BC sequences and a success rate of 63.8% on BC sequences from the LaSOT test datasets, representing improvements of 11.7% and 10.2% respectively over state-of-the-art trackers. Furthermore, our proposed method can be seamlessly integrated as a plug-and-play module with cutting-edge tracking algorithms, significantly improving the success rate for tracking task in background clutter scenarios.
Cognitive Inherent SLR Enabled Survey for Software Defect Prediction

Introduction: Any software is created to help automate manual processes most of the time. It is expected from the developed software that it should perform the tasks it is supposed to do. Methods: More formally, it should work in a deterministic manner. Further, it should be capable of knowing if any provided input is not in the required format. Correctness of the software is inherent virtue that it should possess. Any remaining bug during the development phase would hamper the application's correctness and impact the software's quality assurance. Software defect prediction is the research area that helps the developer to know bug-prone areas of the developed software. Results: Datasets are used using data mining, machine learning, and deep learning techniques to achieve study. A systematic literature survey is presented for the selected studies of software defect prediction. Conclusion: Using a grading mechanism, we calculated each study's grade based on its compliance with the research validation question. After every level, we have selected 54 studies to include in this study.
THE ROLE OF CYBER SITUATIONAL AWARENESS OF HUMANS IN SOCIAL ENGINEERING CYBER ATTACKS ON THE MARITIME DOMAIN

Through technological advancements, the expanding proportion of maritime transportation on a global scale is becoming faster, more automated, more digital, and ultimately more cyber-space. In particular, the Industrial Revolution 4.0 has brought real-time digital integration of stakeholders in the maritime industry, both on land and at sea, into cyberspace. However, the scope of life and property protection at sea has expanded with the participation of the cyber environment as well as the physical environment. The human factor plays a leading role in ensuring the security of both the physical and cyber environment. In parallel, the main target of hackers who try to gain profit by violating the security environment is the person who does not have sufficient situational awareness of cyber security and can be called the weakest link in the chain. In this study, as main goal, the role of the situational awareness of the employees in the past cyber-attacks on the maritime industry was examined, and a perspective on the measures to be taken was presented. To achieve this research goal, the study utilized the snowballing technique to access literature, which helped uncover additional relevant resources not initially detected. This was followed by a systematic analysis of the collected literature. An analysis of attacks conducted since 2010 revealed that 76% of them utilized social engineering methods, such as phishing, malware, and ransomware. These attackers appear to exploit the maritime industry's insufficient cybersecurity awareness among its employees and the lack of a comprehensive understanding of cybersecurity within the industry.
Trajectory Tracking Control of a Mobile Robot using Neural Networks

This paper presents a novel soft computing-based machine learning technique designed to enhance the trajectory tracking capabilities of mobile robots through the application of neural networks. The goal of this approach is to enhance the accuracy and overall performance of trajectory tracking without the need for manual gain recalibration, which is a tedious and time-consuming task for the designer when setting up the robot. This improvement is achieved by creating a kinematic controller based on neural networks, which are constructed using the kinematic model of the robot. In the initial phase, the controller requires gains defined by the designer. Subsequently, during the application phase, the backpropagation algorithm is used to dynamically adjust the gains of the neural network, aiming to minimize the closed-loop error. One of the key innovations introduced by this controller is the potential for automatic online gain tuning, thereby eliminating the need for a pre-learning phase, typically required by traditional neural controllers. To validate the effectiveness of this approach, the results are systematically analyzed and compared against those obtained using a conventional kinematic controller. Performance metrics reveal the improved precision in trajectory tracking achieved by the controller, with reduced effort, highlighting the performance enhancements in different trajectories. Doi: 10.28991/ESJ-2023-07-06-01 Full Text: PDF
Classification Of Earthquake Observation Stations Using Multiple Input Convolutional Neural Network Method

Abstract Analysis of the data quality of earthquake observation stations becomes very important as quality control. However, determining the quality of earthquake observation stations is done manually by analyzing several existing parameters by an expert. This study proposes a deep learning method approach to classify the quality of earthquake observation stations based on the expert’s ability to analyze the quality of earthquake observation station data. The method used is Multiple Input Convolutional Neural Network (MICNN). The proposed MICNN architecture development is a combination of previous research. The dataset used in this study results from recording 411 seismometers for 12 months with a data set containing three horizontal components (East and North) and the vertical component (Z) with a recording length of 30 days. Each component is transformed from the time domain into the frequency domain form to obtain a spectrogram image used as input in the MICNN model. In addition, each component has a CNN block that functions as an extraction feature on the spectrogram image. Classification in this study used three classes: Classified, Usable, and Unusable. Tests and experiments on the model were carried out to obtain the best accuracy by tuning hyperparameters for model validation and the accuracy of this MICNN model was 99%.
A State Graph-Based Improved Framework for Monkey GUI Testing for EDA Desktop Applications

Monkey testing is one of the autonomous Graphical User Interface (GUI) automation testing techniques. Due to the complex GUI of Electronic Design Automation (EDA) software applications, applying Monkey Testing in a timely manner is very challenging. By making use of the GUI regressions knowledge about the application we can have a faster and improved Monkey testing approach. Yet directing the improved Monkey flow to have better coverage needs enhancements so this paper addresses this by building a state graph while running to guide traversing new states using image processing and weighted random selection technique. A state is represented as an image and the improved Monkey testing flow is guided to avoid already hit windows and actions. The paper proves that the state graph-based Monkey testing approach reaches higher coverage than the improved Monkey testing approach.
Enhancing Local Descriptor Networks for Few-Shot Image Classification

Deep learning models rely on a large amounts of annotated data, and the performance of the model is unsatisfactory when the labeled samples are scarce. Existing few-shot learning methods based on local descriptors fail to consider correlation and usefulness between local descriptors. At the same time, due to the limited perceptual fields, it cannot make full use of the global information generated by distant pixels, leading to the problem of extracting features that are highly similar due to the same background, which in turn makes the model misclassify. In order to solve these problems, Fusioning Local and Global features Deep Nearest Neighbor Neural Network is proposed, which is gather relevant features through clusters of local descriptors and synthesize local and global information to learn more discriminative features, so as to further improve the classification performance of few-shot data. The experimental results in this paper show that the method shows superiority on miniImagNet, tieredImageNet and CUB-200-2011 datasets.
Unsupervised Domain Adaptation Methods for Causal Correlation

Unsupervised domain adaptation methods enhance the ability of models to generalize by minimizing the distributional disparities between the source and target domains through the transfer of knowledge from different domains and tasks, utilizing both labeled and unlabeled data. However, a challenge arises due to the presence of pseudo-correlations between certain “confounders” and category labels, leading to a decrease in model prediction accuracy. To tackle this issue, this paper introduces the Causal Correlation Reweighting Module (CCRM). The CCRM samples features related to the labels and features independent of the labels, evaluates their independence, applies reweighting to the label-related features, and reduces the pseudo-correlation between the label-independent features and category labels. This approach results in improved model generalization performance and enhances the model's ability to handle unsupervised domain adaptation tasks. The proposed method is validated on widely-used domain adaptive datasets such as Digits, Office-Home and VisDA-2017, demonstrating higher accuracy compared to previous domain adaptation models.
Certifiably Robust Out-Of-Distribution Detection Based on Prediction Uncertainy Weighting

The application of machine learning in safety-critical systems requires a reliable assessment of uncertainy. However, deep neural networks are known to produce highly overconfident predictions on out-of-distribution (OOD) data. Even if trained to be non-confident on OOD data one can still adversarially manipulate OOD data so that the classifier again assigns high confidence to the manipulated data. In this paper, we improve the existing guaranteed OOD detection GOOD, and propose Certifiably Robust Out-of-distribution Detection based on Prediction Uncertainy Weighting (PUWOOD). Due to the loose boundary of GOOD, it is obviously not robust enough to deal with adversarial OOD close to the boundary of the decision. Therefore, the evaluation and weighting based on prediction uncertainty enable the model to achieve importance ranking in the face of different samples, and make it focus on key samples. Through experiments, we achieve the detection on adversarial OOD data while maintaining good performance on the original classification task and detecting clean data, which proves that our method has better performance in both robustness and accuracy.
Second-order Scalable Complex System Modeling Framework and Parallelization Dynamics Method

This paper establishes a concept of the expansion capability order of complex system model from the perspective of structuralism, synthesizes the extension level from zero to second order to graphically represent the model architecture, and gives a parallel computing method for the “domain” with second-order scalability, as well as the corresponding estimation method and model of the upper bound of update time overhead.
Functional Pattern-Related Anomaly Detection Approach Collaborating Binary Segmentation with Finite State Machine

The process control-oriented threat, which can exploit OT (Operational Technology) vulnerabilities to forcibly insert abnormal control commands or status information, has become one of the most devastating cyber attacks in industrial automation control. To effectively detect this threat, this paper proposes one functional pattern-related anomaly detection approach, which skillfully collaborates the BinSeg (Binary Segmentation) algorithm with FSM (Finite State Machine) to identify anomalies between measuring data and control data. By detecting the change points of measuring data, the BinSeg algorithm is introduced to generate some initial sequence segments, which can be further classified and merged into different functional patterns due to their backward difference means and lengths. After analyzing the pattern association according to the Bayesian network, one functional state transition model based on FSM, which accurately describes the whole control and monitoring process, is constructed as one feasible detection engine. Finally, we use the typical SWaT (Secure Water Treatment) dataset to evaluate the proposed approach, and the experimental results show that: for one thing, compared with other change-point detection approaches, the BinSeg algorithm can be more suitable for the optimal sequence segmentation of measuring data due to its highest detection accuracy and least consuming time; for another, the proposed approach exhibits relatively excellent detection ability, because the average detection precision, recall rate and F1-score to identify 10 different attacks can reach 0.872, 0.982 and 0.896, respectively.
From Social Media to Ballot Box: Leveraging Location-Aware Sentiment Analysis for Election Predictions

Predicting election outcomes is a crucial undertaking, and various methods are employed for this purpose, such as traditional opinion polling, and social media analysis. However, traditional polling approaches often struggle to capture the intricate nuances of voter sentiment at local levels, resulting in a limited depth of analysis and understanding. In light of this challenge, this study focuses on predicting elections at the state/regional level along with the country level, intending to offer a comprehensive analysis and deeper insights into the electoral process. To achieve this, the study introduces the Location-Based Election Prediction Model (LEPM), which utilizes social media data, specifically Twitter, and integrates location-aware sentiment analysis techniques at both the state/region and country levels. LEPM predicts the support and opposing strength of each political party/candidate. To determine the location of users/voters who have not disclosed their location information in tweets, the model utilizes a Voter Location Detection (VotLocaDetect) approach, which leverages recent tweets/posts. The sentiment analysis techniques employed in this study include rule-based sentiment analysis, Valence Aware Dictionary and Sentiment Reasoner (VADER) as well as transformers-based sentiment analysis such as Bidirectional Encoder Representations from Transformers (BERT), BERTweet, and Election based BERT (ElecBERT). This study uses the 2020 United States (US) Presidential Election as a case study. By applying the LEPM model to the election, the study demonstrates its ability to accurately predict outcomes in forty-one states, achieving an 0.84 accuracy rate at the state level. Moreover, at the country level, the LEPM model outperforms traditional polling results. With a low Mean Absolute Error (MAE) of 0.87, the model exhibits more precise predictions and serves as a successful alternative to conventional polls and other methodologies. Leveraging the extensive social media data, the LEPM model provides nuanced insights into voter behavior, enabling policymakers to make informed decisions and facilitating in-depth analyses of elections. The study emphasizes the importance of using social media data for reliable election prediction and offers implications for enhancing prediction accuracy and understanding voter sentiment and behavior.