Reviewerly/exHarmony huggingface dataset:
MSWOA: A Mixed-Strategy-Based Improved Whale Optimization Algorithm for Multilevel Thresholding Image Segmentation

Multilevel thresholding image segmentation is one of the most widely used segmentation methods in the field of image segmentation. This paper proposes a multilevel thresholding image segmentation technique based on an improved whale optimization algorithm. The WOA has been applied to many complex optimization problems because of its excellent performance; however, it easily falls into local optimization. Therefore, firstly, a mixed-strategy-based improved whale optimization algorithm (MSWOA) is proposed using the k-point initialization algorithm, the nonlinear convergence factor, and the adaptive weight coefficient to improve the optimization ability of the algorithm. Then, the MSWOA is combined with the Otsu method and Kapur entropy to search for the optimal thresholds for multilevel thresholding gray image segmentation, respectively. The results of algorithm performance evaluation experiments on benchmark functions demonstrate that the MSWOA has higher search accuracy and faster convergence speed than other comparative algorithms and that it can effectively jump out of the local optimum. In addition, the image segmentation experimental results on benchmark images show that the MSWOA–Kapur image segmentation technique can effectively and accurately search multilevel thresholds.
Enhancing software engineering education in higher education institutions through cloud-based learning tools: methodological and practical perspectives

Objective: This study aims to develop a comprehensive methodology for integrating cloud-based learning tools into software engineering education for students in higher education institutions (HEIs).Research tasks: The research encompasses exploring the psychological and pedagogical aspects of software engineering education in HEIs, investigating the influence of cloud technologies on higher education, designing a robust model for implementing a methodological system that incorporates cloud-based learning tools, and conducting an experimental implementation of the proposed model.Research focus: The research focuses on the teaching and learning process of software engineering disciplines among students in HEIs, specifically emphasizing the utilization of cloud-based learning tools.Research methods: The research methodology involves analyzing relevant scholarly publications, dissertations, conference proceedings, and seminars, along with studying the experiences of educators through surveys and questionnaires. Additionally, the research incorporates practical experimentation with cloud-based learning tools.Research findings: The study highlights the crucial need to prioritize fundamental knowledge acquisition in software engineering education, which can be facilitated by the mobility features inherent in cloud-based learning tools. Furthermore, it reveals a significant correlation between emerging trends in information technology and the development of methodological teaching systems. The analysis underscores the transformative impact of cloud technologies on learning tools and other key components of the methodological teaching system. The research successfully implements an experimental model that integrates cloud-based learning tools into the methodological system.Conclusions and recommendations: The findings demonstrate that cloud technologies profoundly influence the technological aspects of the methodological system while simultaneously shaping the objectives and curriculum of IT professionals. The developed methodological system, which incorporates cloud technologies, plays a pivotal role in fostering the foundational education of IT professionals.
Nested Block Based Double Self-Embedding Fragile Image Watermarking With Super-Resolution Recovery

Self-recovery capability is challenging in developing fragile watermarking alongside authentication and tamper localization. In certain situations, authentication and tamper localization alone may not be sufficient. Information about the original condition of the damaged area is essential, particularly in forensic image analysis applications. This study proposes a nested block-based self-embedding method for fragile image watermarking. The data embedded in the cover image includes authentication and watermark bits based on the advanced least significant bit (LSB) method. The watermark bits are generated from the cover images, while the authentication bit is obtained through the SHA-512 operation. Authentication bit embedding is performed using a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2 \times 2$ </tex-math></inline-formula> block-based Morton pattern. Meanwhile, the watermark bit is embedded twice in different locations within <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4 \times 2$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2 \times 4$ </tex-math></inline-formula> blocks, alternating with the Morton pattern inside the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> block using the Zigzag pattern. This approach better preserves the watermark information when tampering occurs over a large area. However, embedding the watermark twice requires more space, necessitating a resize operation before embedding. During the recovery stage, the resized watermark is enhanced using the Feature Super-resolution CNN (FSRCNN) technique. The proposed method demonstrates good imperceptibility quality, with a PSNR value exceeding 40dB and an SSIM exceeding 0.98. Moreover, the proposed method effectively detects and localizes tampering, achieving a True Positive Rate (TPR) of over 95% and a False Positive Rate (FPR) of less than 1% for a watermarked image with 50% tampering. Additionally, the proposed method exhibits outstanding recovery capabilities, resulting in a PSNR of more than 36dB for images with 50% tampering.
Investigating the Effectiveness of Novel Support Vector Neural Network for Anomaly Detection in Digital Forensics Data

As criminal activity increasingly relies on digital devices, the field of digital forensics plays a vital role in identifying and investigating criminals. In this paper, we addressed the problem of anomaly detection in digital forensics data. Our objective was to propose an effective approach for identifying suspicious patterns and activities that could indicate criminal behavior. To achieve this, we introduce a novel method called the Novel Support Vector Neural Network (NSVNN). We evaluated the performance of the NSVNN by conducting experiments on a real-world dataset of digital forensics data. The dataset consisted of various features related to network activity, system logs, and file metadata. Through our experiments, we compared the NSVNN with several existing anomaly detection algorithms, including Support Vector Machines (SVM) and neural networks. We measured and analyzed the performance of each algorithm in terms of the accuracy, precision, recall, and F1-score. Furthermore, we provide insights into the specific features that contribute significantly to the detection of anomalies. Our results demonstrated that the NSVNN method outperformed the existing algorithms in terms of anomaly detection accuracy. We also highlight the interpretability of the NSVNN model by analyzing the feature importance and providing insights into the decision-making process. Overall, our research contributes to the field of digital forensics by proposing a novel approach, the NSVNN, for anomaly detection. We emphasize the importance of both performance evaluation and model interpretability in this context, providing practical insights for identifying criminal behavior in digital forensics investigations.
HFPNet: Super Feature Aggregation Pyramid Network for Maritime Remote Sensing Small-Object Detection

In recent years, with the development of the maritime industry, computer vision tasks based on optical remote sensing images have gained increasing attention in the field of marine remote sensing. However, in complex marine meteorological environments, traditional detection methods often suffer from high rates of missed detections and false alarms for small targets. To address this issue, this paper proposes a Super Feature Pyramid Network (HFPNet) for detecting marine remote sensing objects, which includes a Feature Enhancement Module (FEM) and a Multi-Scale Feature Aggregation Module (MFM). The FEM can highlight features of small targets and weaken complex background features in detection; the MFM can share high-level and low-level features to better fuse multi-scale feature information. Additionally, due to the lack of marine remote sensing object datasets, this paper constructs a Marine Target Detection Dataset (MTDS) containing six types of marine objects. To address the issue of imbalanced positive and negative samples in the dataset, this paper designs a network loss function to accelerate network convergence and improve the accuracy of small target detection. Compared with other models, HFPNet achieved the highest <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$mAP_{0.5}$</tex-math></inline-formula> of 97.48% and 95.46% on the self-built dataset MTDS and the public dataset NWPU-VHR-10, respectively, after environmental enhancement. At the same time, it also achieved the fastest FPS on the test set. Finally, this paper discusses the influence of attention mechanisms and post-processing methods on HFPNet and obtains the best small target detection model.
Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspection

Abstract Neural sequence generation models are known to “hallucinate”, by producing outputs that are unrelated to the source text. These hallucinations are potentially harmful, yet it remains unclear in what conditions they arise and how to mitigate their impact. In this work, we first identify internal model symptoms of hallucinations by analyzing the relative token contributions to the generation in contrastive hallucinated vs. non-hallucinated outputs generated via source perturbations. We then show that these symptoms are reliable indicators of natural hallucinations, by using them to design a lightweight hallucination detector which outperforms both model-free baselines and strong classifiers based on quality estimation or large pre-trained models on manually annotated English-Chinese and German-English translation test beds.
EDET: Entity Descriptor Encoder of Transformer for Multi-Modal Knowledge Graph in Scene Parsing

In scene parsing, the model is required to be able to process complex multi-modal data such as images and contexts in real scenes, and discover their implicit connections from objects existing in the scene. As a storage method that contains entity information and the relationship between entities, a knowledge graph can well express objects and the semantic relationship between objects in the scene. In this paper, a new multi-phase process was proposed to solve scene parsing tasks; first, a knowledge graph was used to align the multi-modal information and then the graph-based model generates results. We also designed an experiment of feature engineering’s validation for a deep-learning model to preliminarily verify the effectiveness of this method. Hence, we proposed a knowledge representation method named Entity Descriptor Encoder of Transformer (EDET), which uses both the entity itself and its internal attributes for knowledge representation. This method can be embedded into the transformer structure to solve multi-modal scene parsing tasks. EDET can aggregate the multi-modal attributes of entities, and the results in the scene graph generation and image captioning tasks prove that EDET has excellent performance in multi-modal fields. Finally, the proposed method was applied to the industrial scene, which confirmed the viability of our method.
Graph Convolution Network over Dependency Structure Improve Knowledge Base Question Answering

Knowledge base question answering (KBQA) can be divided into two types according to the type of complexity: questions with constraints and questions with multiple hops of relationships. Previous work on knowledge base question answering have mostly focused on entities and relations. In a multihop question, it is insufficient to focus solely on topic entities and their relations since the relation between words also contains some important information. In addition, because the question contains constraints or multiple relationships, the information is difficult to capture, or the constraints are missed. In this paper, we applied a dependency structure to questions that capture relation information (e.g., constraint) between the words in question through a graph convolution network. The captured relation information is integrated into the question for re-encoding, and the information is used to generate and rank query graphs. Compared with existing sequence models and query graph generation models, our approach achieves a 0.8–3% improvement on two benchmark datasets.
Fusion of Multi-Modal Features to Enhance Dense Video Caption

Dense video caption is a task that aims to help computers analyze the content of a video by generating abstract captions for a sequence of video frames. However, most of the existing methods only use visual features in the video and ignore the audio features that are also essential for understanding the video. In this paper, we propose a fusion model that combines the Transformer framework to integrate both visual and audio features in the video for captioning. We use multi-head attention to deal with the variations in sequence lengths between the models involved in our approach. We also introduce a Common Pool to store the generated features and align them with the time steps, thus filtering the information and eliminating redundancy based on the confidence scores. Moreover, we use LSTM as a decoder to generate the description sentences, which reduces the memory size of the entire network. Experiments show that our method is competitive on the ActivityNet Captions dataset.
The classification method based on evolutionary algorithm for high‐dimensional imbalanced missing data

Abstract High dimensional imbalanced missing data classification is a challenging and complex problem that traditional algorithms struggle to solve effectively. To address this issue, a novel method is proposed, named the hybrid classification approach based on particle swarm optimization (HCPSO). HCPSO integrates ideas of feature selection, resampling and imputation, and breaks particles down into three parts. These parts represent the feature values, probabilities of resampling, and probabilities of imputation approaches, respectively. Moreover, HCPSO employs particle swarm optimization to optimize these parameters simultaneously to take advantage of these methods. Six types of algorithms, eleven datasets, and four performance indicators are used to evaluate our method. The results demonstrate a significant improvement in HCPSO's performance, with an average improvement of 13.02%, 18.95%, 20.25%, and 28.63% for accuracy, F1, AUC, and Gmean, respectively, compared to all other methods. Furthermore, the experiments also demonstrate the robustness of HCPSO.
An Optimized Approach to Translate Technical Patents from English to Japanese Using Machine Translation Models

This paper addresses the challenges associated with machine translation of patents from English to Japanese. This translation poses unique difficulties due to their legal nature, distinguishing them from general Japanese-to-English translation. Furthermore, the complexities inherent in the Japanese language add an additional layer of intricacy to the development of effective translation models within this specific domain. Our approach encompasses a range of essential steps, including preprocessing, data preparation, expert feedback acquisition, and linguistic analysis. These steps collectively contribute to the enhancement of machine learning model performance. The experimental results, presented in this study, evaluate three prominent alternatives considered for the final step of the transformer model. Through our methodology, which incorporates a modified version of NLP-Model-III, we achieved outstanding performance for the given problem, attaining an impressive BLEU score of 46.8. Furthermore, significant improvements of up to three points on the BLEU score were observed through hyperparameter fine-tuning. This research also involved the development of a novel dataset consisting of meticulously collected patent document data. The findings of this study provide valuable insights and contribute to the advancement of Japanese patent translation methodologies.
A fast and robust hippocampal subfields segmentation: HSF revealing lifespan volumetric dynamics

The hippocampal subfields, pivotal to episodic memory, are distinct both in terms of cyto- and myeloarchitectony. Studying the structure of hippocampal subfields in vivo is crucial to understand volumetric trajectories across the lifespan, from the emergence of episodic memory during early childhood to memory impairments found in older adults. However, segmenting hippocampal subfields on conventional MRI sequences is challenging because of their small size. Furthermore, there is to date no unified segmentation protocol for the hippocampal subfields, which limits comparisons between studies. Therefore, we introduced a novel segmentation tool called HSF short for hippocampal segmentation factory, which leverages an end-to-end deep learning pipeline. First, we validated HSF against currently used tools (ASHS, HIPS, and HippUnfold). Then, we used HSF on 3,750 subjects from the HCP development, young adults, and aging datasets to study the effect of age and sex on hippocampal subfields volumes. Firstly, we showed HSF to be closer to manual segmentation than other currently used tools (p < 0.001), regarding the Dice Coefficient, Hausdorff Distance, and Volumetric Similarity. Then, we showed differential maturation and aging across subfields, with the dentate gyrus being the most affected by age. We also found faster growth and decay in men than in women for most hippocampal subfields. Thus, while we introduced a new, fast and robust end-to-end segmentation tool, our neuroanatomical results concerning the lifespan trajectories of the hippocampal subfields reconcile previous conflicting results.
Inverse Kinematics of Robot Manipulator Based on BODE-CS Algorithm

Differential evolution is a popular algorithm for solving global optimization problems. When tested, it has reportedly outperformed both robotic problems and benchmarks. However, it may have issues with local optima or premature convergence. In this paper, we present a novel BODE-CS (Bidirectional Opposite Differential Evolution–Cuckoo Search) algorithm to solve the inverse kinematics problem of a six-DOF EOD (Explosive Ordnance Disposal) robot manipulator. The hybrid algorithm was based on the differential evolution algorithm and Cuckoo Search algorithm. To avoid any local optimum and accelerate the convergence of the swarm, various strategies were introduced. Firstly, a forward-kinematics model was established, and the objective function was formulated according to the structural characteristics of the robot manipulator. Secondly, a Halton sequence and an opposite search strategy were used to initialize the individuals in the swarm. Thirdly, the optimization algorithms applied to the swarm were dynamically allocated to the Differential Evolution algorithm or the Cuckoo algorithm. Fourthly, a composite differential algorithm, which consisted of a dynamically opposite differential strategy, a bidirectional search strategy, and two other typically used differential strategies were introduced to maintain the diversity of the swarm. Finally, two adaptive parameters were introduced to optimize the amplification factor F and cross-over probability Cr. To verify the performance of the BODE-CS algorithm, two different tasks were tested. The experimental results of the simulation showed that the BODE-CS algorithm had high accuracy and a fast convergence rate, which met the requirements of an inverse solution for the manipulator.
Network adaptation for color image semantic segmentation

Abstract Image analysis using deep learning has made significant progress in the last few decades, and the importance of pre‐processing input images has become evident. However, adapting a network structure suitable for input images has not been considered. In this study, a simple network adaptation method for color image analysis is described. The method is illustrated using semantic segmentation, which mainly takes a color image as input. The method is inspired by chrominance subsampling, which is a practical method for image and video analysis. The human visual system is less sensitive to color differences than it is to brightness, and based on this phenomenon, it is possible to improve existing networks by providing less resolution to chroma information than luminance information in the network encoder design by applying the group convolution concept. The proposed method helps to achieve improved results without changing the complexity of the baseline network model, and is especially helpful in applications with limited resources, such as autonomous driving, augmented reality. Experiments were performed on a combination of datasets (i.e. CamVid, Cityscapes and KITTI‐360) and networks (i.e. ENet, ERFNet, Deeplabv3plus with mobilenetv2). The results show that the method improves the performance of existing network structures without increasing the number of parameters.
Quantifying Visual Pollution from Urban Air Mobility

Unmanned aerial vehicles (UAVs) can bring many benefits, particularly in emergency response and disaster management. However, they also induce negative effects, such as noise and visual pollution, risk, and integrity concerns. In this work, we study visual pollution, developing a quantitative measure that can calculate the visual pollution from one or multiple UAVs. First, the Analytic Hierarchy Process was utilized in an expert workshop to find and rank factors relevant to visual pollution. Then an image-based questionnaire targeted at the general public was used to find relations between the factors. The results show that the two main factors causing visual pollution are the number of UAVs and the distance between a UAV and the observer. They also show that while a UAV used for emergency medical services is as polluting as any other UAV, it is easier to tolerate this pollution. Based on the questionnaire results, two visual pollution functions were developed that can be used when carrying out path planning for one or multiple UAVs. When combining this function with other existing measures for noise pollution, and ground and air risk, it is possible to find paths that will give as little negative impact as possible from urban air mobility.